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Digital Health
Number of Followers: 10  Open Access journalISSN (Print) 2055-2076 - ISSN (Online) 2055-2076 Published by Sage Publications  [1176 journals]
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- Evaluation of the accuracy and quality of ChatGPT-4 responses for
hyperparathyroidism patients discussed at multidisciplinary endocrinology
meetings
Authors: Işılay Taşkaldıran, Çağatay Emir Önder, Püren Gökbulut, Gönül Koç, Şerife Mehlika Kuşkonmaz
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
PurposeChat Generative Pre-trained Transformer (ChatGPT) is now utilized in various fields of healthcare in order to obtain answers to questions related to healthcare-related problems and to evaluate available information. Primary hyperparathyroidism is a common endocrine disorder. We aimed to evaluate the accuracy and quality of ChatGPT's responses to questions specific to hyperparathyroidism cases discussed at multidisciplinary endocrinology meetings.MethodsChatGPT-4 was asked to respond to 10 hyperparathyroidism cases evaluated at multidisciplinary endocrinology meetings. The accuracy, completeness, and quality of the responses were scored independently by two endocrinologists. Accuracy and completeness were evaluated on the Likert scale, and quality was evaluated on the global quality scale (GQS).ResultsNo misleading information was detected in the responses. In terms of diagnosis, the mean accuracy scores (ranging from 1 to 5) were 4.9 ± 0.1 and the mean completeness scores (ranging from 1 to 3) were 3.0. In the responses given in terms of further examination, the mean accuracy and completeness scores were 4.8 ± 0.13 and 2.6 ± 0.16, respectively. The mean accuracy and completeness scores for treatment recommendations were 4.9 ± 0.1 and 2.4 ± 0.16, respectively. The GQS evaluation result was 80% high quality and 20% medium quality.ConclusionIn this study, the accuracy and quality rates of ChatGPT-4 were generally high in responding to questions as to hyperparathyroidism patients. It can be concluded that artificial intelligence may serve as a valuable tool in healthcare. However, the limitations and risks of ChatGPT should also be evaluated.
Citation: DIGITAL HEALTH
PubDate: 2024-08-28T02:42:58Z
DOI: 10.1177/20552076241278692
Issue No: Vol. 10 (2024)
- Maintaining healthy lifestyle through fitness app use: A parallel
mediation model from a nationwide survey
Authors: Min Zhang, Xiaojing Li
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveAdolescents face various health challenges due to academic pressures and sedentary lifestyles. Establishing healthy habits during this critical period is essential for long-term well-being. With the widespread use of fitness apps, understanding their impact on adolescent health behaviors and the underlying mechanisms is crucial. Guided by social support theory and social comparison theory, this study examined the influence of WeRun, a fitness app within WeChat, on adolescents’ adoption of healthy lifestyles. It investigated the correlation between WeRun usage and healthy behaviors, as well as the underlying mechanisms driving this relationship.MethodsA cross-sectional survey was conducted across 31 provinces and metropolitans in China, utilizing a random cluster sampling approach targeting high school and freshman students aged 15–24 (N = 1312). A parallel mediation model was employed to test the hypotheses.ResultsThe analysis showed that WeRun use positively predicted both social support and social comparison. Meanwhile, both social support and social comparison were positively associated with healthy lifestyles. Additionally, WeRun use could not directly predict healthy lifestyles. However, WeRun use indirectly predicted healthy lifestyles via social support and social comparison.ConclusionsThe study's findings revealed the pivotal roles of social support and social comparison as mediating variables in the relationship between adolescents’ WeRun usage and adoption of healthy lifestyles. The results contributed to the current comprehension of the mechanisms linking app utilization to health-promoting behaviors. Furthermore, it provided valuable insights for promoting adolescent health and informed improved design strategies for fitness apps.
Citation: DIGITAL HEALTH
PubDate: 2024-08-28T02:42:38Z
DOI: 10.1177/20552076241277483
Issue No: Vol. 10 (2024)
- Personal goal setting eHealth component associated with improved weight
loss at 6 months: A mixed methods secondary analysis
Authors: Lex Hurley, Brooke T Nezami, Christopher Sciamanna, Deborah F Tate
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveGoal setting is a behavior change technique associated with improved change in outcomes. Digital (eHealth, mHealth) behavior change interventions often prescribe all goals with no opportunity for participants to create and track their own; thus, little is known about the types of goals participants create for themselves and their impacts on behavioral outcomes. This analysis describes the goals created by participants using an optional personal goal-setting component and evaluates the association between participant goal creation and weight loss in an eHealth adult weight loss intervention.MethodsThis represents a mixed methods QUANT-qual design to understand the types of goals users create for themselves and their impacts on behavior change outcomes. Qualitative codes were applied for the topic, behavior/outcome focus, adherence to SMART criteria, and repetition with count summaries. Quantitative analyses applied regression modeling to determine if the number of goals set was associated with the 6-month weight change, controlling for covariates.ResultsParticipants (n = 363) set an average of 23.4 goals (SD = 22.7) over 6 months. Those who reached at least 5% weight loss set significantly more goals than those who lost between 1% and 4.99% or who lost
Citation: DIGITAL HEALTH
PubDate: 2024-08-28T02:41:59Z
DOI: 10.1177/20552076241277351
Issue No: Vol. 10 (2024)
- Mobile health technologies in the prevention and management of
hypertension: A scoping review
Authors: Abdulhammed Opeyemi Babatunde, Deborah Abisola Ogundijo, Abdul-Gafar Olayemi Afolayan, Olutola Vivian Awosiku, Zainab Opeyemi Aderohunmu, Mayowa Sefiu Oguntade, Uthman Hassan Alao, Abdulrahman Ololade Oseni, Abdulqudus Abimbola Akintola, Olanrewaju Adams Amusat
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
IntroductionAn estimated one billion people globally are currently suffering from hypertension. Prevention and management of hypertension are suboptimal especially in low- and middle-income countries leading to increased complications and deaths. With increased mobile phone coverage globally, this study aims to review mobile health technologies used for the prevention and management of hypertension.MethodsWe conducted a literature search on electronic databases using identified keywords involving “hypertension”, “mobile health technology” and their synonyms. Snowballing technique was also used. Papers were screened at two levels by independent reviewers. The targets were studies published in peer-reviewed journals reporting mobile health interventions for hypertension prevention and management. Only primary research studies published in English from January 2017 to April 2024 were included. Google Forms were used to extract the data along with other characteristics, and selected articles were categorised into: mobile application, web-based solutions, and Short Message Service (SMS) and other offline solutions.ResultThe search yielded 184 articles, and 44 studies were included in the review. Most (n = 26) were randomised control trials. Twenty-two studies (22) focused only on mobile applications solutions, 12 on SMS and other offline mHealth, 5 web-based solutions, and 5 combined more than one type of mobile health technology. The United States of America had the majority of studies (n = 17), with 6 studies from other American countries, 11 from Asia and nine from Europe, while only one from Africa. A total of 36 studies reported that mobile health technology significantly improved hypertension care through reduced blood pressure, improved adherence to follow-up visits and medications, and lifestyle changes. SMS and offline mHealth strategies have also demonstrated effectiveness in promoting self-management and reducing racial disparities in hypertension care.ConclusionMobile health technology has the potential to play a significant role in the prevention and management of hypertension. However, there is a need for mobile health solutions for hypertension prevention and management in African countries and other developing countries. Integrating mHealth into primary healthcare delivery would also go a long way in strengthening patient care and reducing the burden on healthcare systems.
Citation: DIGITAL HEALTH
PubDate: 2024-08-28T02:41:29Z
DOI: 10.1177/20552076241277172
Issue No: Vol. 10 (2024)
- Navigating the crossroads of aging, caregiving and technology: Insights
from a southern Spain about video-based technology in the care context
Authors: Tamara Mujirishvili, Julio Cabrero-Garćıa, Francisco Fló rez-Revuelta, Miguel Richart-Mart´ınez
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveAs the world faces an aging population, the complexities of care management become increasingly pronounced. While technological solutions hold promise in addressing the dynamic demands of care, many nuances are to be considered in the design and implementation of active and assisted living technologies (AAL) for older adult care. This qualitative study, set in southern Spain, is positioned at the crossroads of healthcare challenges, as seen by the different actors involved in the care process and the technological solutions developed in response to these challenges. By investigating the complex landscape of caregiving and by examining the experiences and challenges faced by caregivers, healthcare professionals, and older adults, we aim to guide the development of vision-based AAL technologies that are responsive to the genuine needs of older adults and those requiring care.MethodsA qualitative research methodology was used in the study. In total15 in-depth interviews and five focus groups were conducted with a diverse group of stakeholders involved in the process of care provision and reception.ResultsWhile the results demonstrate that there is a readiness for technological solutions, concerns over privacy and trust highlight the need for a carefully integrated, human-centric approach to technology in caregiving.ConclusionThis research serves as a compass, guiding future discussions on the intersection of aging, technology, and care, with the ultimate goal of transforming caregiving into a collaborative and enriching journey for all stakeholders involved.
Citation: DIGITAL HEALTH
PubDate: 2024-08-28T02:40:50Z
DOI: 10.1177/20552076241271856
Issue No: Vol. 10 (2024)
- Digital health tools in nephrology: A comparative analysis of AI and
professional opinions via online polls
Authors: Justin H Pham, Charat Thongprayoon, Supawadee Suppadungsuk, Jing Miao, Iasmina M Craici, Wisit Cheungpasitporn
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundProfessional opinion polling has become a popular means of seeking advice for complex nephrology questions in the #AskRenal community on X. ChatGPT is a large language model with remarkable problem-solving capabilities, but its ability to provide solutions for real-world clinical scenarios remains unproven. This study seeks to evaluate how closely ChatGPT's responses align with current prevailing medical opinions in nephrology.MethodsNephrology polls from X were submitted to ChatGPT-4, which generated answers without prior knowledge of the poll outcomes. Its responses were compared to the poll results (inter-rater) and a second set of responses given after a one-week interval (intra-rater) using Cohen's kappa statistic (κ). Subgroup analysis was performed based on question subject matter.ResultsOur analysis comprised two rounds of testing ChatGPT on 271 nephrology-related questions. In the first round, ChatGPT's responses agreed with poll results for 163 of the 271 questions (60.2%; κ = 0.42, 95% CI: 0.38–0.46). In the second round, conducted to assess reproducibility, agreement improved slightly to 171 out of 271 questions (63.1%; κ = 0.46, 95% CI: 0.42–0.50). Comparison of ChatGPT's responses between the two rounds demonstrated high internal consistency, with agreement in 245 out of 271 responses (90.4%; κ = 0.86, 95% CI: 0.82–0.90). Subgroup analysis revealed stronger performance in the combined areas of homeostasis, nephrolithiasis, and pharmacology (κ = 0.53, 95% CI: 0.47–0.59 in both rounds), compared to other nephrology subfields.ConclusionChatGPT-4 demonstrates modest capability in replicating prevailing professional opinion in nephrology polls overall, with varying performance levels between question topics and excellent internal consistency. This study provides insights into the potential and limitations of using ChatGPT in medical decision making.
Citation: DIGITAL HEALTH
PubDate: 2024-08-28T01:42:03Z
DOI: 10.1177/20552076241277458
Issue No: Vol. 10 (2024)
- Comparison and use of explainable machine learning-based survival models
for heart failure patients
Authors: Tao Shi, Jianping Yang, Ningli Zhang, Wei Rong, Lusha Gao, Ping Xia, Jie Zou, Na Zhu, Fazhi Yang, Lixing Chen
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveExplainable machine learning (XAI) was introduced in this study to improve the interpretability, explainability and transparency of the modelling results. The survex package in R was used to interpret and compare two survival models – the Cox proportional hazards regression (coxph) model and the random survival forest (rfsrc) model – and to estimate overall survival (OS) and its determinants in heart failure (HF) patients using these models.MethodsWe selected 1159 HF patients hospitalised at the First Affiliated Hospital of Kunming Medical University. First, the performance of the two models was investigated using the C-index, the integrated C/D AUC, and the integrated Brier score. Second, a global explanation of the whole cohort was carried out using the time-dependent variable importance and the partial dependence survival profile. Finally, the SurvSHAP(t) and SurvLIME plots and the ceteris paribus survival profile were used to obtain a local explanation for each patient.ResultsBy comparing the C-index, the C/D AUC, and the Brier score, this study showed that the model performance of rfsrc was better than coxph. The global explanation of the whole cohort suggests that the C-reactive protein, lg BNP (brain natriuretic peptide), estimated glomerular filtration rate, albumin, age and blood chloride were significant unfavourable predictors of OS in HF patients in both the cxoph and the rfsrc models. By including individual patients in the model, we can provide a local explanation for each patient, which guides the clinician in individualising the patient's treatment.ConclusionBy comparison, we conclude that the model performance of rfsrc is better than that of coxph. These two predictive models, which address not only the whole population but also selected patients, can help clinicians personalise the treatment of each HF patient according to his or her specific situation.
Citation: DIGITAL HEALTH
PubDate: 2024-08-26T06:37:18Z
DOI: 10.1177/20552076241277027
Issue No: Vol. 10 (2024)
- Investigation of nurses’ general attitudes toward artificial
intelligence and their perceptions of ChatGPT usage and influencing
factors
Authors: Gülsüm Zekiye Tuncer, Metin Tuncer
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
PurposeThis study aimed to investigate professional nurses’ general attitudes toward artificial intelligence, their knowledge and perceptions of ChatGPT usage, and the influencing factors.MethodsThe population of the research consists of nurses who follow a social media platform account in Turkey. The sample of the study consisted of 288 nurses who participated in the study between December 2023 and March 2024. Data were collected through an account on a social media platform via Google Forms using the Information Identification Questionnaire for ChatGPT and Artificial Intelligence Programs and the General Attitudes to Artificial Intelligence Scale (GAAIS).ResultsThe mean scores obtained from the overall GAAIS and its Positive Attitudes subscale from the participants in this study were 67.54 ± 13.14 and 41.89 ± 11.24, respectively. Of the participants, 48.3% knew about ChatGPT and artificial intelligence programs. Of the participants, 27.8% used ChatGPT and artificial intelligence programs. Their scores for the Positive Attitude subscale were higher than were the scores of those who did not use such programs. Of the participants, 84.4% thought that nurses should be made aware of ChatGPT and artificial intelligence programs, 67% thought that the use of these programs would contribute to nurses’ professional development, 42.4% thought that the use of these programs would not reduce nurses’ workload, and 58.3% thought that the use of these programs would positively affect patient care.ConclusionIn this study, it can be said that nurses in Turkey have positive attitudes toward integrating ChatGPT and AI programs to improve patient outcomes and add them to nursing practices.Implications for nursing practiceThe present study in which nurses’ attitudes toward the implementation of ChatGPT and artificial intelligence programs were investigated is expected to provide information for healthcare institutions, policy makers and artificial intelligence developers on the integration of ChatGPT and artificial intelligence into nursing practice. It is necessary to create environments that use AI technologies that reduce the nursing workload of nurses in the clinical area and positively affect the quality of patient care.
Citation: DIGITAL HEALTH
PubDate: 2024-08-26T06:36:38Z
DOI: 10.1177/20552076241277025
Issue No: Vol. 10 (2024)
- Behavioral marker-based predictive modeling of functional status for older
adults with subjective cognitive decline and mild cognitive impairment:
Study protocol
Authors: Bada Kang, Jinkyoung Ma, Innhee Jeong, Seolah Yoon, Jennifer Ivy Kim, Seok-jae Heo, Sarah Soyeon Oh
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThis study describes a research protocol for a behavioral marker-based predictive model that examines the functional status of older adults with subjective cognitive decline and mild cognitive impairment.MethodsA total of 130 older adults aged ≥65 years with subjective cognitive decline or mild cognitive impairment will be recruited from the Dementia Relief Centers or the Community Service Centers. Data on behavioral and psychosocial markers (e.g. physical activity, mobility, sleep/wake patterns, social interaction, and mild behavioral impairment) will be collected using passive wearable actigraphy, in-person questionnaires, and smartphone-based ecological momentary assessments. Two follow-up assessments will be performed at 12 and 24 months after baseline. Mixed-effect machine learning models: MErf, MEgbm, MEmod, and MEctree, and standard machine learning models without random effects [random forest, gradient boosting machine] will be employed in our analyses to predict functional status over time.ResultsThe results of this study will be fundamental for developing tailored digital interventions that apply deep learning techniques to behavioral data to predict, identify, and aid in the management of functional decline in older adults with subjective cognitive decline and mild cognitive impairment. These older adults are considered the optimal target population for preventive interventions and will benefit from such tailored strategies.ConclusionsOur study will contribute to the development of self-care interventions that utilize behavioral data and machine learning techniques to provide automated analyses of the functional decline of older adults who are at risk for dementia.
Citation: DIGITAL HEALTH
PubDate: 2024-08-26T06:35:38Z
DOI: 10.1177/20552076241269555
Issue No: Vol. 10 (2024)
- Evaluation of the automated dispensing cabinets users’ level of
satisfaction and the influencing factors in Al-Ahsa hospitals
Authors: Manar K. Alomair, Lama S. Alabduladheem, Marwah A. Almajed, Amjad A. Alobaid, Maged E. Mohamed, Abdulaziz O. Alsultan, Nancy S. Younis
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
Automated dispensing cabinets (ADCs) are decentralized, computer-controlled systems used to store, distribute, and track medications at the point of care in the wards.ObjectiveThe objective of the current study is to evaluate how healthcare practitioners are satisfied with ADCs and scrutinize some influencing factors that could affect this satisfaction.MaterialA cross-sectional survey study was designed and distributed online to healthcare providers in Al-hasa hospitals.ResultsA total of 166 participants. Regarding the frequency and pattern of ADC use, around 79.5% used ADC and 85.4% were informed about using ADC on a daily basis. As for the level of satisfaction with ADC, an exact 81.9% gave a high rate for overall satisfaction, 81.3% were highly satisfied with the system's accuracy, and 74.7% were highly satisfied with the time it takes to complete the task. Regarding usability of the system, 69.8% thought it was easy whereas 36.8% agreed that the time required for reloading medication is longer than before ADC. Furthermore, 79.5% agreed that ADC allowed them to accomplish their job safely, and 67.4% agreed that it improved their productivity. Regarding challenges, 74.7% agreed that all drawer types assure safe access and removal of medications, and 18.7% agreed that there is a significant potential for loss of data.ConclusionThis study investigated healthcare staff's perceptions and satisfaction with ADCs in Al-hasa hospitals. The healthcare participants were mostly highly satisfied with the use of the ADCs which translated into better patient care and improved patient safety as well as higher productivity.
Citation: DIGITAL HEALTH
PubDate: 2024-08-26T06:34:21Z
DOI: 10.1177/20552076241264641
Issue No: Vol. 10 (2024)
- Parkwood's VIP4SCI platform: A virtual e-health self-management solution
for persons with spinal cord injury across the care continuum
Authors: Luxshmi Nageswaran, Charlie Giurleo, Merna Seliman, Heather K Askes, Zeina Abu-Jurji, B Catherine Craven, Anna Kras-Dupuis, Julie Watson, Dalton L Wolfe
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveParkwood VIP4SCI platform is a virtual e-health solution adapted from a version created for Spinal Cord Injury Ontario (SCIO) that focused on self-management skill development for persons with spinal cord injury (SCI) transitioning between stages of care, in partnership with caregivers and clinicians. This evaluation of the platform informs the usability and feasibility of a model to facilitate service care aims postrehabilitation.Design Participants: Inpatients and outpatients admitted to the SCI Rehabilitation Program (n = 31), and a mix of interdisciplinary clinicians on the Rehabilitation Team (n = 20). Caregivers participated at the discretion of the patient.Interventions: Inpatients were randomized into two groups (Platform or Standard Care (i.e., delayed access)). Outpatients were given access at enrollment. Pre–post assessments were completed using surveys, and platform analytics were collected. Weekly check-ins were introduced to increase engagement. Focus groups were held with a subset of participants near study completion.ResultsVIP4SCI was viewed as usable and feasible. Platform satisfaction assessed on a −3 to +3 scale ranged from +0.9 to 2.5, demonstrating positive agreement. Self-efficacy related to self-management ranged from 5.4 to 7.6 out of 10. The educational resource hub was identified as the most beneficial feature. Lack of clinician uptake was a barrier to integration into day-to-day practice.ConclusionsPlatform usage was low among all groups despite the perceived need for facilitating care coordination with consistent and intentional self-management programming. Despite the lack of uptake, partly due to challenges associated with the pandemic, conclusions on platform features and barriers to implementation will help to inform future programming.
Citation: DIGITAL HEALTH
PubDate: 2024-08-23T07:24:14Z
DOI: 10.1177/20552076241272618
Issue No: Vol. 10 (2024)
- Data management plan and REDCap mobile data capture for a multi-country
Household Air Pollution Intervention Network (HAPIN) trial
Authors: Shirin Jabbarzadeh, Lindsay M Jaacks, Amy Lovvorn, Yunyun Chen, Jiantong Wang, Lisa Elon, Azhar Nizam, Vigneswari Aravindalochanan, Jean de Dieu Ntivuguruzwa, Kendra N Willams, Alexander Ramirez, Michael A Johnson, Ajay Pillarisetti, Thangavel Gurusamy, Ghislaine Rosa, Anaité Diaz-Artiga, Juan C Romero, Kalpana Balakrishnan, William Checkley, Jennifer L Peel, Thomas F Clasen, Lance A Waller
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundHousehold air pollution (HAP) is a leading environmental risk factor accounting for about 1.6 million premature deaths mainly in low- and middle-income countries (LMICs). However, no multicounty randomized controlled trials have assessed the effect of liquefied petroleum gas (LPG) stove intervention on HAP and maternal and child health outcomes. The Household Air Pollution Intervention Network (HAPIN) was the first to assess this by implementing a common protocol in four LMICs.ObjectiveThis manuscript describes the implementation of the HAPIN data management protocol via Research Electronic Data Capture (REDCap) used to collect over 50 million data points in more than 4000 variables from 80 case report forms (CRFs).MethodsWe recruited 800 pregnant women in each study country (Guatemala, India, Peru, and Rwanda) who used biomass fuels in their households. Households were randomly assigned to receive LPG stoves and 18 months of free LPG supply (intervention) or to continue using biomass fuels (control). Households were followed for 18 months and assessed for primary health outcomes: low birth weight, severe pneumonia, and stunting. The HAPIN Data Management Core (DMC) implemented identical REDCap projects for each study site using shared variable names and timelines in local languages. Field staff collected data offline using tablets on the REDCap Mobile Application.ResultsUtilizing the REDCap application allowed the HAPIN DMC to collect and store data securely, access data (near real-time), create reports, perform quality control, update questionnaires, and provide timely feedback to local data management teams. Additional REDCap functionalities (e.g. scheduling, data validation, and barcode scanning) supported the study.ConclusionsWhile the HAPIN trial experienced some challenges, REDCap effectively met HAPIN study goals, including quality data collection and timely reporting and analysis on this important global health trial, and supported more than 40 peer-reviewed scientific publications to date.
Citation: DIGITAL HEALTH
PubDate: 2024-08-22T06:59:50Z
DOI: 10.1177/20552076241274217
Issue No: Vol. 10 (2024)
- Potential role of hybrid weight management intervention: A scoping review
Authors: Khang Jin Cheah, Zahara Abdul Manaf, Arimi Fitri Mat Ludin, Nurul Huda Razalli
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundDigital health has been widely used in delivering healthcare, presenting emerging opportunities to overcome barriers to effective obesity care. One strategy suggested for addressing obesity involves a hybrid weight management intervention that incorporates digital health. This scoping review aimed to map existing evidence regarding hybrid weight management intervention.MethodsPubMed, Scopus, Cochrane Library, and the Web of Science electronic databases were searched for studies published between January 1, 2012 and May 16, 2023, with language restricted to English. The focus was on controlled trials in which a hybrid weight management intervention was used in the intervention among overweight or obese adults. The scoping review framework followed Arksey and O’Malley's guidelines and the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISM-P).ResultsFull-text article review in the screening stage resulted in a total of 10 articles being included for narrative synthesis. Almost two-third of the articles originated from the United States (60%), followed by Europe and Australia, each accounting for 20%. The most common hybrid weight management intervention type was the combination of face-to-face and telehealth (i.e. phone call/text messaging) (40%), closely followed by a combination email intervention (30%) and mHealth apps intervention (30%). Most of the face-to-face dietary interventions were delivered as a group counseling (80%), while some were conducted as individual counseling (20%). Most studies observed a positive effect of the hybrid weight management intervention on body weight (weight lost 3.9–8.2 kg), body mass index (decreased 0.58 kg/m2), waist circumference (decreased 2.25 cm), and physical activity level compared to standard care. Findings suggest a direct association between hybrid weight management interventions and weight loss. The weight loss ranged from 3.9 to 8.2 kg, with some evidence indicating a significant weight loss of 5% from baseline. There is a need to explore stakeholders’ telehealth perspective to optimize the delivery of hybrid weight management interventions, thereby maximizing greatest benefits for weight management.
Citation: DIGITAL HEALTH
PubDate: 2024-08-22T06:31:46Z
DOI: 10.1177/20552076241258366
Issue No: Vol. 10 (2024)
- Toward reliable diabetes prediction: Innovations in data engineering and
machine learning applications
Authors: Md. Alamin Talukder, Md. Manowarul Islam, Md Ashraf Uddin, Mohsin Kazi, Majdi Khalid, Arnisha Akhter, Mohammad Ali Moni
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveDiabetes is a metabolic disorder that causes the risk of stroke, heart disease, kidney failure, and other long-term complications because diabetes generates excess sugar in the blood. Machine learning (ML) models can aid in diagnosing diabetes at the primary stage. So, we need an efficient ML model to diagnose diabetes accurately.MethodsIn this paper, an effective data preprocessing pipeline has been implemented to process the data and random oversampling to balance the data, handling the imbalance distributions of the observational data more sophisticatedly. We used four different diabetes datasets to conduct our experiments. Several ML algorithms were used to determine the best models to predict diabetes faultlessly.ResultsThe performance analysis demonstrates that among all ML algorithms, random forest surpasses the current works with an accuracy rate of 86% and 98.48% for Dataset 1 and Dataset 2; extreme gradient boosting and decision tree surpass with an accuracy rate of 99.27% and 100% for Dataset 3 and Dataset 4, respectively. Our proposal can increase accuracy by 12.15% compared to the model without preprocessing.ConclusionsThis excellent research finding indicates that the proposed models might be employed to produce more accurate diabetes predictions to supplement current preventative interventions to reduce the incidence of diabetes and its associated costs.
Citation: DIGITAL HEALTH
PubDate: 2024-08-21T09:13:31Z
DOI: 10.1177/20552076241271867
Issue No: Vol. 10 (2024)
- Erratum to “Proposal for A Set of Standards and Indicators for JCI, SKS,
and HIMSS EMRAM Quality Assessment Models”
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
Citation: DIGITAL HEALTH
PubDate: 2024-08-21T07:10:48Z
DOI: 10.1177/20552076241277985
Issue No: Vol. 10 (2024)
- The Fully Understanding Eating and Lifestyle Behaviors (FUEL) trial:
Protocol for a cohort study harnessing digital health tools to phenotype
dietary non-adherence behaviors during lifestyle intervention
Authors: Stephanie P. Goldstein, Kevin M. Mwenda, Adam W. Hoover, Olivia Shenkle, Richard N. Jones, John Graham Thomas
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveLifestyle intervention can produce clinically significant weight loss and reduced disease risk/severity for many individuals with overweight/obesity. Dietary lapses, instances of non-adherence to the recommended dietary goal(s) in lifestyle intervention, are associated with less weight loss and higher energy intake. There are distinct “types” of dietary lapse (e.g., eating an off-plan food, eating a larger portion), and behavioral, psychosocial, and contextual mechanisms may differ across dietary lapse types. Some lapse types also appear to impact weight more than others. Elucidating clear lapse types thus has potential for understanding and improving adherence to lifestyle intervention.MethodsThis 18-month observational cohort study will use real-time digital assessment tools within a multi-level factor analysis framework to uncover “lapse phenotypes” and understand their impact on clinical outcomes. Adults with overweight/obesity (n = 150) will participate in a 12-month online lifestyle intervention and 6-month weight loss maintenance period. Participants will complete 14-day lapse phenotyping assessment periods at baseline, 3, 6, 12, and 18 months in which smartphone surveys, wearable devices, and geolocation will assess dietary lapses and relevant phenotyping characteristics. Energy intake (via 24-h dietary recall) and weight will be collected at each assessment period.ResultsThis trial is ongoing; data collection began on 31 October 2022 and is scheduled to complete by February 2027.ConclusionResults will inform novel precision tools to improve dietary adherence in lifestyle intervention, and support updated theoretical models of adherence behavior. Additionally, these phenotyping methods can likely be leveraged to better understand non-adherence to other health behavior interventions.Trial RegistrationThis study was prospectively registered https://clinicaltrials.gov/study/NCT05562427
Citation: DIGITAL HEALTH
PubDate: 2024-08-21T07:08:10Z
DOI: 10.1177/20552076241271783
Issue No: Vol. 10 (2024)
- Lightweight convolutional neural network (CNN) model for obesity early
detection using thermal images
Authors: Hendrik Leo, Khairun Saddami, Roslidar, Rusdha Muharar, Khairul Munadi, Fitri Arnia
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThe presence of a lightweight convolutional neural network (CNN) model with a high-accuracy rate and low complexity can be useful in building an early obesity detection system, especially on mobile-based applications. The previous works of the CNN model for obesity detection were focused on the accuracy performances without considering the complexity size. In this study, we aim to build a new lightweight CNN model that can accurately classify normal and obese thermograms with low complexity sizes.MethodsThe DenseNet201 CNN architectures were modified by replacing the standard convolution layers with multiple depthwise and pointwise convolution layers from the MobileNet architectures. Then, the depth network of the dense block was reduced to determine which depths were the most comparable to obtain minimum validation losses. The proposed model then was compared with state-of-the-art DenseNet and MobileNet CNN models in terms of classification performances, and complexity size, which is measured in model size and computation cost.ResultsThe results of the testing experiment show that the proposed model has achieved an accuracy of 81.54% with a model size of 1.44 megabyte (MB). This accuracy was comparable to that of DenseNet, which was 83.08%. However, DenseNet’s model size was 71.77 MB. On the other hand, the proposed model’s accuracy was higher than that of MobileNetV2, which was 79.23%, with a computation cost of 0.69 billion floating-point operations per second (GFLOPS), which approximated that of MobileNetV2, which was 0.59 GFLOPS.ConclusionsThe proposed model inherited the feature-extracting ability from the DenseNet201 architecture while keeping the lightweight complexity characteristic of the MobileNet architecture.
Citation: DIGITAL HEALTH
PubDate: 2024-08-20T09:04:24Z
DOI: 10.1177/20552076241271639
Issue No: Vol. 10 (2024)
- Corrigendum to “AFEX-Net: Adaptive feature extraction convolutional
neural network for classifying computerized tomography images”
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
Citation: DIGITAL HEALTH
PubDate: 2024-08-20T06:49:20Z
DOI: 10.1177/20552076241276671
Issue No: Vol. 10 (2024)
- Machine learning-based prognostic model for in-hospital mortality of
aortic dissection: Insights from an intensive care medicine perspective
Authors: Jiahao Lei, Zhuojing Zhang, Yixuan Li, Zhaoyu Wu, Hongji Pu, Zhijue Xu, Xinrui Yang, Jiateng Hu, Guang Liu, Peng Qiu, Tao Chen, Xinwu Lu
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveAortic dissection (AD) is a severe emergency with high morbidity and mortality, necessitating strict monitoring and management. This retrospective study aimed to identify prognostic factors and establish predictive models for in-hospital mortality among AD patients in the intensive care unit (ICU).MethodsWe retrieved ICU admission records of AD patients from the Medical Information Mart for Intensive Care (MIMIC)-IV critical care data set and the eICU Collaborative Research Database. Functional data analysis was further applied to estimate continuous vital sign processes, and variables associated with in-hospital mortality were identified through univariate analyses. Subsequently, we employed multivariable logistic regression and machine learning techniques, including simple decision tree, random forest (RF), and eXtreme Gradient Boosting (XGBoost) to develop prognostic models for in-hospital mortality.ResultsGiven 643 ICU admissions from MIMIC-IV and 501 admissions from eICU, 29 and 28 prognostic factors were identified from each database through univariate analyses, respectively. For prognostic model construction, 507 MIMIC-IV admissions were divided into 406 (80%) for training and 101 (20%) for internal validation, and 87 eICU admissions were included as an external validation group. Of the four models tested, the RF consistently exhibited the best performance among different variable subsets, boasting area under the receiver operating characteristic curves of 0.870 and 0.850. The models highlighted the mean 24-h fluid intake as the most potent prognostic factor.ConclusionsThe current prognostic models effectively forecasted in-hospital mortality among AD patients, and they pinpointed noteworthy prognostic factors, including initial blood pressure upon ICU admission and mean 24-h fluid intake.
Citation: DIGITAL HEALTH
PubDate: 2024-08-20T06:48:49Z
DOI: 10.1177/20552076241269450
Issue No: Vol. 10 (2024)
- Enhancing health care through medical cognitive virtual agents
Authors: Sushruta Mishra, Pamela Chaudhury, Hrudaya Kumar Tripathy, Kshira Sagar Sahoo, NZ Jhanjhi, Asma Abbas Hassan Elnour, Abdelzahir Abdelmaboud
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThe modern era of cognitive intelligence in clinical space has led to the rise of ‘Medical Cognitive Virtual Agents’ (MCVAs) which are labeled as intelligent virtual assistants interacting with users in a context-sensitive and ambient manner. They aim to augment users' cognitive capabilities thereby helping both patients and medical experts in providing personalized healthcare like remote health tracking, emergency healthcare and robotic diagnosis of critical illness, among others. The objective of this study is to explore the technical aspects of MCVA and their relevance in modern healthcare.MethodsIn this study, a comprehensive and interpretable analysis of MCVAs are presented and their impacts are discussed. A novel system framework prototype based on artificial intelligence for MCVA is presented. Architectural workflow of potential applications of functionalities of MCVAs are detailed. A novel MCVA relevance survey analysis was undertaken during March-April 2023 at Bhubaneswar, Odisha, India to understand the current position of MCVA in society.ResultsOutcome of the survey delivered constructive results. Majority of people associated with healthcare showed their inclination towards MCVA. The curiosity for MCVA in Urban zone was more than in rural areas. Also, elderly citizens preferred using MCVA more as compared to youths. Medical decision support emerged as the most preferred application of MCVA.ConclusionThe article established and validated the relevance of MCVA in modern healthcare. The study showed that MCVA is likely to grow in future and can prove to be an effective assistance to medical experts in coming days.
Citation: DIGITAL HEALTH
PubDate: 2024-08-20T06:48:03Z
DOI: 10.1177/20552076241256732
Issue No: Vol. 10 (2024)
- Telemedicine in civil protection: A controlled simulation study for the
analysis of patient care
Authors: Anna Müller, Simon Kraus, Robert Arimond, Janosch Kunczik, Rolf Rossaint, Michael Czaplik, Andreas Follmann
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectivesMore and more disasters are occurring and there will be an increasing shortage of physicians in the future. Telemedicine could be a solution here to offer medical care despite the lack of physicians in the area of operation. This study analyzes whether telemedicine for lower-qualified paramedics is comparable to conventional disaster medicine.MethodsA simulation study was conducted in which one conventional and two telemedicine groups cared for thermal-traumatically injured in a stressful situation. The telemedicine was conducted on tablets with integrated vital sign monitoring or on smartphones. The physician engagement time, the number of physician contacts, the time for relevant measures and the usage behavior of telemedicine were examined between these groups.ResultsOne telemedicine group showed significantly fewer patient contacts. This can be attributed to the ongoing consultation in the intervention group with more contacts. There are no significant differences in the relevant measures.ConclusionTelemedicine is comparable to conventional disaster medicine in civil protection. Due to potential technical failures, it should primarily be used to compensate for the lack of physicians, and training should focus on an exit-strategy in case of a failure of the telemedicine.
Citation: DIGITAL HEALTH
PubDate: 2024-08-19T06:08:57Z
DOI: 10.1177/20552076241272662
Issue No: Vol. 10 (2024)
- Paving initial forecasting COVID-19 spread capabilities by nonexperts: A
case study
Authors: Idan Roth, Arthur Yosef
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThe COVID-19 outbreak compelled countries to take swift actions across various domains amidst substantial uncertainties. In Israel, significant COVID-19-related efforts were assigned to the Israeli Home Front Command (HFC). HFC faced the challenge of anticipating adequate resources to efficiently and timely manage its numerous assignments despite the absence of a COVID-19 spread forecast. This paper describes the initiative of a group of motivated, though nonexpert, people to provide the needed COVID-19 rate of spread of the epidemic forecasts.MethodsTo address this challenge, the Planning Chamber, reporting to the HFC Medical Commander, undertook the task of mapping HFC healthcare challenges and resource requirements. The nonexpert team continuously collected public COVID-19-related data published by the Israeli Ministry of Health (MoH) of verified cases, light cases, mild cases, serious condition cases, life-support cases, and deaths, and despite lacking expertise in statistics and healthcare and having no sophisticated statistical packages, generated forecasts using Microsoft® Excel.ResultsThe analysis methods and applications successfully demonstrated the desired outcome of the lockdown by showing a transition from exponential to polynomial growth in the spread of the virus. These forecasting activities enabled decision-makers to manage resources effectively, supporting the HFC's operations during the pandemic.ConclusionsNonexpert forecasting may become a necessity and be beneficial, and similar analysis efforts can be easily replicated in future events. However, they are inherently short-lived and should persist only until knowledge centers can bridge the expertise gap. It is crucial to identify major events, such as lockdowns, during forecasting due to their potential impact on spread rates. Despite the expertise gap, the Planning Chamber's approach provided valuable resource management insights for HFC's COVID-19 response.
Citation: DIGITAL HEALTH
PubDate: 2024-08-19T06:07:43Z
DOI: 10.1177/20552076241272565
Issue No: Vol. 10 (2024)
- Evaluation of the gamified application KIJANI to promote physical activity
in children and adolescents: A multimethod study
Authors: Laura Willinger, Florian Schweizer, Birgit Böhm, Daniel A Scheller, Stephan Jonas, Renate Oberhoffer-Fritz, Jan Müller, Lara Marie Reimer
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveDigital approaches have the potential to make activity promotion attractive and age-appropriate for children and adolescents. KIJANI is a mobile application aiming to increase physical activity (PA) in youth via gamification and augmented reality. This study investigates the user experience with KIJANI through a multimethod approach.ApproachesKIJANI is based on the concept that virtual coins can be earned through PA, for example, in the form of collected step counts. With these coins, blocks can be bought, which can be used to create virtual buildings and landscapes and integrate these into the player's real-world environment via augmented reality. To evaluate the user experience, participants played KIJANI in groups of three for 25 min. Afterwards KIJANI was evaluated qualitatively with one-on-one semi-structured interviews as well as quantitatively with standardized questionnaires.ResultsOverall, 22 participants (12.6 ± 1.7 years, 6 girls) were included in the study. The overall game concept and realization were well received by the target group. Study participants did have various and creative ideas for the further development of KIJANI. The majority (n = 16) thought that using KIJANI would increase their PA level. User experience based on the UEQ scale was (mean ± SD): attractiveness (1.78 ± 1.82), perspicuity (2.15 ± 0.680), efficiency (0.67 ± 1.25), dependability, (1.21 ± 0.93), stimulation (1.24 ± 1.78), and novelty (1.27 ± 1.34).ConclusionWith these insights, a further step has been taken in the participatory development of KIJANI. Apps like KIJANI appear to be suitable for PA promotion in children and adolescents.
Citation: DIGITAL HEALTH
PubDate: 2024-08-19T06:07:03Z
DOI: 10.1177/20552076241271861
Issue No: Vol. 10 (2024)
- Improving acute kidney injury alerts in tertiary care by linking primary
care data: An observational cohort using routine care data
Authors: Huibert-Jan Joosse, Wouter Tiel Groenestege, Robin WM Vernooij, Mark CH De Groot, Imo E Hoefer, Wouter W van Solinge, Maarten B Kok, Saskia Haitjema
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveAcute kidney injury (AKI) is easily missed and underdiagnosed in routine clinical care. Timely AKI management is important to decrease morbidity and mortality risks. We recently implemented an AKI e-alert at the University Medical Center Utrecht, comparing plasma creatinine concentrations with historical creatinine baselines, thereby identifying patients with AKI. This alert is limited to data from tertiary care, and primary care data can increase diagnostic accuracy for AKI. We assessed the added value of linking primary care data to tertiary care data, in terms of timely diagnosis or excluding AKI.MethodsWith plasma creatinine tests for 84,984 emergency department (ED) visits, we applied the Kidney Disease Improving Global Outcome guidelines in both tertiary care-only data and linked data and compared AKI cases.ResultsUsing linked data, the presence of AKI could be evaluated in an additional 7886 ED visits. Sex- and age-stratified analyses identified the largest added value for women (an increase of 4095 possible diagnoses) and patients ≥60 years (an increase of 5190 possible diagnoses). We observed 398 additional visits where AKI was diagnosed, as well as 185 cases where AKI could be excluded. We observed no overall decrease in time between baseline and AKI diagnosis (28.4 days vs. 28.0 days). For cases where AKI was diagnosed in both data sets, we observed a decrease of 2.8 days after linkage, indicating a timelier diagnosis of AKI.ConclusionsCombining primary and tertiary care data improves AKI diagnostic accuracy in routine clinical care and enables timelier AKI diagnosis.
Citation: DIGITAL HEALTH
PubDate: 2024-08-19T06:06:27Z
DOI: 10.1177/20552076241271767
Issue No: Vol. 10 (2024)
- Predicting dementia in Parkinson's disease on a small tabular dataset
using hybrid LightGBM–TabPFN and SHAP
Authors: Vinh Quang Tran, Haewon Byeon
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThis study aims to create a robust and interpretable method for predicting dementia in Parkinson's disease (PD), especially in resource-limited settings. The model aims to be accurate even with small datasets and missing values, ultimately promoting its use in clinical practice to benefit patients and medical professionals.MethodsOur study introduces LightGBM–TabPFN, a novel hybrid model for predicting dementia conversion in PD. Combining LightGBM's strength in handling missing values with TabPFN's ability to exploit small datasets, LightGBM–TabPFN outperforms seven existing methods, achieving outstanding accuracy and interpretability thanks to SHAP analysis. This analysis leverages data from 242 PD patients across 17 variables.ResultsOur LightGBM–TabPFN model significantly outperformed seven existing methods. Achieving an accuracy of 0.9592 and an area under the ROC curve of 0.9737.ConclusionsThe interpretable LightGBM–TabPFN with SHAP signifies a significant advancement in predictive modeling for neurodegenerative diseases. This study not only improves dementia prediction in PD but also provides clinical professionals with insights into model predictions, offering opportunities for application in clinical settings.
Citation: DIGITAL HEALTH
PubDate: 2024-08-16T12:58:24Z
DOI: 10.1177/20552076241272585
Issue No: Vol. 10 (2024)
- Connected health services: Health professionals’ role as seen by parents
of a child with inflammatory bowel disease
Authors: Aline Christen, Franzisca Domeisen Benedetti, Daniela Händler-Schuster
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveConnected health services will change the scope of health professionals’ roles. It is unclear how parents of a child with inflammatory bowel disease perceive the role of health professionals in relation to these services and what their experiences and needs are. The purpose of this study is to highlight parents’ experiences with this role. Furthermore, it aims to outline the fundamental needs that parents have regarding this role, in order to promote audience-specific access to these services and derive overarching action measures.MethodsFourteen parents of children with inflammatory bowel disease from seven different clinics in Switzerland were recruited. Between August 2022 and February 2023, these parents were interviewed in semi-structured interviews. The interviews were analyzed using a structured qualitative content analysis.ResultsFive main categories were identified, with few parents having prior experience with the role of health professionals in this area. Parents saw health professionals in the role of gatekeepers, transferers of knowledge and in a supporting function for these services. From the parents’ perspective, health professionals should recognize the limitations of these services and use them as a complement to standard treatment.ConclusionThe role of health professionals in relation to connected health services needs to be adapted from the parents’ perspective. To meet the needs of parents, health professionals must have access to these services. In addition to health professionals’ personal engagement with these services, institutional and policy changes, as well as research on role development from the perspective of other stakeholders are needed.
Citation: DIGITAL HEALTH
PubDate: 2024-08-16T12:57:44Z
DOI: 10.1177/20552076241271772
Issue No: Vol. 10 (2024)
- Evaluating cognitive performance: Traditional methods vs. ChatGPT
Authors: Xiao Fei, Ying Tang, Jianan Zhang, Zhongkai Zhou, Ikuo Yamamoto, Yi Zhang
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundNLP models like ChatGPT promise to revolutionize text-based content delivery, particularly in medicine. Yet, doubts remain about ChatGPT's ability to reliably support evaluations of cognitive performance, warranting further investigation into its accuracy and comprehensiveness in this area.MethodA cohort of 60 cognitively normal individuals and 30 stroke survivors underwent a comprehensive evaluation, covering memory, numerical processing, verbal fluency, and abstract thinking. Healthcare professionals and NLP models GPT-3.5 and GPT-4 conducted evaluations following established standards. Scores were compared, and efforts were made to refine scoring protocols and interaction methods to enhance ChatGPT's potential in these evaluations.ResultWithin the cohort of healthy participants, the utilization of GPT-3.5 revealed significant disparities in memory evaluation compared to both physician-led assessments and those conducted utilizing GPT-4 (P
Citation: DIGITAL HEALTH
PubDate: 2024-08-16T12:57:14Z
DOI: 10.1177/20552076241264639
Issue No: Vol. 10 (2024)
- Sustaining telehealth in pediatric diabetology beyond COVID-19: How to set
the tone
Authors: Roberto Franceschi, Gianluca Tornese
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
In the post-COVID-19 era, telehealth experience and knowledge must be structured to deliver high-quality care. Type 1 diabetes is a chronic disease that lends itself to being a model for telehealth diffusion, especially in the pediatric setting where the use of cloud-connected technologies is widespread. Here, we present “how to set the tone” and manage a telemedicine session according to our experiences and those reported in the literature, according to the health professional perspective. A practical workflow on how healthcare professionals can structure a virtual diabetes clinic is reported, as well as critical issues related to limits in physical examination, communication registers, relationships, and visit settings. A proactive virtual visit model could be feasible, stratifying patients according to continuous glucose monitoring metrics, and personalized interventions can be provided to each patient. Analysis of benefits and hassles due to telehealth for each patient has to be considered, as well as their personal perspective, expectations, and reported barriers, mainly related to connection issues and digital literacy.
Citation: DIGITAL HEALTH
PubDate: 2024-08-16T12:56:33Z
DOI: 10.1177/20552076241249272
Issue No: Vol. 10 (2024)
- A hybrid approach for automatic segmentation and classification to detect
tuberculosis
Authors: Muzammil Khan, Abnash Zaman, Sarwar Shah Khan, Muhammad Arshad
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveTuberculosis (TB) remains a significant global infectious disease, posing a considerable health threat, particularly in resource-constrained regions. Due to diverse datasets, radiologists face challenges in accurately diagnosing TB using X-ray images. This study aims to propose an innovative approach leveraging image processing techniques to enhance TB diagnostic accuracy within the automatic segmentation and classification (AuSC) framework for healthcare.MethodsThe AuSC of detection of TB (AuSC-DTB) framework comprises several steps: image preprocessing involving resizing and median filtering, segmentation using the random walker algorithm, and feature extraction utilizing local binary pattern and histogram of gradient descriptors. The extracted features are then classified using the support vector machine classifier to distinguish between healthy and infected chest X-ray images. The effectiveness of the proposed technique was evaluated using four distinct datasets, such as Japanese Society of Radiological Technology (JSRT), Montgomery, National Library of Medicine (NLM), and Shenzhen.ResultsExperimental results demonstrate promising outcomes, with accuracy rates of 94%, 95%, 95%, and 93% achieved for JSRT, Montgomery, NLM, and Shenzhen datasets, respectively. Comparative analysis against recent studies indicates superior performance of the proposed hybrid approach.ConclusionsThe presented hybrid approach within the AuSC framework showcases improved diagnostic accuracy for TB detection from diverse X-ray image datasets. Furthermore, this methodology holds promise for generalizing other diseases diagnosed through X-ray imaging. It can be adapted with computed tomography scans and magnetic resonance imaging images, extending its applicability in healthcare diagnostics.
Citation: DIGITAL HEALTH
PubDate: 2024-08-14T10:35:19Z
DOI: 10.1177/20552076241271869
Issue No: Vol. 10 (2024)
- Digital health literacy and associated factors among health professionals
during the outbreak of corona virus pandemic in Ethiopia: A systematic
review and meta-analysis
Authors: Mulugeta Desalegn Kasaye, Natnael Kebede, Mulugeta Hayelom Kalayou, Shimels Derso Kebede, Asressie Molla
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
IntroductionThe COVID-19 pandemic had a significant impact on healthcare delivery worldwide. Digital tools emerged as a preferred solution for maintaining healthcare services during this crisis. This study aimed to assess the magnitude of digital health literacy among healthcare professionals in Ethiopia in 2020–2023.MethodsA systematic review and meta-analysis were conducted following the Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) guidelines. Articles published from 2020 to 2023 were reviewed using various electronic databases such as Medline, PubMed, and Cochrane Library, CINAHL, HINARI, Science Direct, Google Scholar, and Global Health. Meta-analysis was performed using STATA 17, and publication bias and heterogeneity were assessed.ResultsSix studies involving a total of 2739 participants were included in the analysis. The pooled level of high digital health literacy among health professionals in Ethiopia during the pandemic was found to be 56.0% (95% CI: 55, 58). Several factors were identified as significant contributors to high digital health literacy, including internet use (AOR = 2.72, 95% CI: 1.86, 3.98), perceived ease of use (AOR = 2.79, 95% CI: 1.83, 4.25), favorable attitude (AOR = 2.49, 95% CI: 1.61, 3.85), perceived usefulness (AOR = 2.29, 95% CI: 1.65, 3.18), information-communication-technology training (AOR = 6.09, 95% CI: 1.83, 24.27), and educational level (AOR = 3.60, 95% CI: 2.96, 4.37).Conclusion and recommendationThe study findings revealed a moderate level of high digital health literacy among Ethiopian health professionals. Factors such as internet use, favorable attitude, and information-communication-technology training were associated with high-level digital health literacy. To enhance digital health literacy, it is crucial to provide timely training and improve internet access for healthcare professionals. Additionally, promoting the perception of digital tools as useful and supporting evidence-based decision-making can further improve digital health literacy. Comprehensive information-communication-technology training programs should be implemented to equip healthcare professionals with necessary skills to effectively combat outbreaks like the COVID-19 pandemic.
Citation: DIGITAL HEALTH
PubDate: 2024-08-14T10:32:49Z
DOI: 10.1177/20552076241271799
Issue No: Vol. 10 (2024)
- Ortho-digital dynamics: Exploration of advancing digital health
technologies in musculoskeletal disease management
Authors: Zulipikaer Maimaiti, Zhuo Li, Zhiyuan Li, Jun Fu, Chi Xu, Jiying Chen, Wei Chai, Liang Liu
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundMusculoskeletal (MSK) disorders, affecting billions of people worldwide, pose significant challenges to the healthcare system and require effective management models. The rapid development of digital healthcare technologies (DHTs) has revolutionized the healthcare industry. DHT-based interventions have shown promising clinical benefits in managing MSK disorders, alleviating pain, and improving functional impairment. There is, however, no bibliometric analysis of the overall trends on this topic.MethodsWe extracted all relevant publications from the Web of Science Core Collection (WoSCC) database until April 30, 2023. We performed bibliometric analysis and visualization using CiteSpace, VOSviewer, and R software. Annual trends of publications, countries/regions distributions, funding agencies, institutions, co-cited journals, author contributions, references, core journals, and keywords and research hotspots were analyzed.ResultsA total of 6810 papers were enrolled in this study. Publications have increased drastically from 16 in 1995 to 1198 in 2022, with 4067 articles published in the last five years. In all, 53 countries contributed with publications to this research area. The United States, the United Kingdom, and China were the most productive countries. Harvard University was the most contributing institution. Regarding keywords, research focuses include artificial intelligence, deep learning, machine learning, telemedicine, rehabilitation, and robotics.ConclusionThe COVID-19 pandemic has further accelerated the adoption of DHTs, highlighting the need for remote care options. The analysis reveals the positive impact of DHTs on improving physician productivity, enhancing patient care and quality of life, reducing healthcare expenditures, and predicting outcomes. DHTs are a hot topic of research not only in the clinical field but also in the multidisciplinary intersection of rehabilitation, nursing, education, social and economic fields. The analysis identifies four promising hotspots in the integration of DHTs in MSK pain management, biomechanics assessment, MSK diagnosis and prediction, and robotics and tele-rehabilitation in arthroplasty care.
Citation: DIGITAL HEALTH
PubDate: 2024-08-14T10:32:20Z
DOI: 10.1177/20552076241269613
Issue No: Vol. 10 (2024)
- ChatGPT v4 outperforming v3.5 on cancer treatment recommendations in
quality, clinical guideline, and expert opinion concordance
Authors: Chung-You Tsai, Pai-Yu Cheng, Juinn-Horng Deng, Fu-Shan Jaw, Shyi-Chun Yii
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectivesTo assess the quality and alignment of ChatGPT's cancer treatment recommendations (RECs) with National Comprehensive Cancer Network (NCCN) guidelines and expert opinions.MethodsThree urologists performed quantitative and qualitative assessments in October 2023 analyzing responses from ChatGPT-4 and ChatGPT-3.5 to 108 prostate, kidney, and bladder cancer prompts using two zero-shot prompt templates. Performance evaluation involved calculating five ratios: expert-approved/expert-disagreed and NCCN-aligned RECs against total ChatGPT RECs plus coverage and adherence rates to NCCN. Experts rated the response's quality on a 1-5 scale considering correctness, comprehensiveness, specificity, and appropriateness.ResultsChatGPT-4 outperformed ChatGPT-3.5 in prostate cancer inquiries, with an average word count of 317.3 versus 124.4 (p
Citation: DIGITAL HEALTH
PubDate: 2024-08-14T10:31:03Z
DOI: 10.1177/20552076241269538
Issue No: Vol. 10 (2024)
- Effectiveness of utilizing step-monitoring devices to prevent and treat
obesity in children and adolescents: A systematic review and meta-analysis
Authors: Wentao Wang, Hongfang Ruan, Yi Shen, Jing Cheng, Wei Sun, Cong Huang
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundChildren and adolescents who are overweight and obese represent a growing public health issue. The use of step-monitoring devices as an intervention tool may be a simple, cost-effective, and easily replicable solution for addressing obesity in children and adolescents. No prior systematic reviews have evaluated the effectiveness of utilizing step-monitoring devices as an intervention method for obesity in children and adolescents.MethodsPrevious studies on using step-monitoring devices to prevent and treat obesity in children and adolescents were identified in the following databases: Web of Science, EMBASE, PubMed, Cochrane Library, SPORTDiscus, and SCOPUS. The search period for each database ranged from the year of their inception to 8 March 2023 (updated in June 2024). Meta-analyses were performed for mean differences (MDs) in body mass index (BMI), BMI z-score (BMI-Z), body fat, waist circumference, and body weight.ResultsFrom 12,907 relevant records, 23 studies were included in this meta-analysis. The included studies were mainly at low risk of bias, except for blinding. Step-monitoring device-based interventions had significant effects in reducing BMI-Z (MD −0.06; 95% CI −0.10 to −0.02), body fat (MD −0.95%; 95% CI −1.35 to −0.54), and body weight (MD −1.23 kg; 95% CI −2.36 to −0.10). However, there was no significant effect on BMI (MD −0.16 kg/m2; 95% CI −0.55 to 0.22) and waist circumference (MD −0.33 cm; 95% CI −1.23 to 0.58). Subgroup analyses indicated that participants who were overweight or obese showed greater intervention effects on BMI and BMI-Z compared to participants with normal weight. The programs with an intervention duration of ≤6 months presented a greater intervention effect on BMI-Z than those with an intervention duration of more than 6 months. The programs that established goals had a greater intervention effect on body fat than those that did not.ConclusionsStep-monitoring devices may be an effective and generalizable intervention tool for the prevention and treatment of obesity in children and adolescents. Future studies should further explore how to set step goals and the duration of interventions to achieve better intervention effects.
Citation: DIGITAL HEALTH
PubDate: 2024-08-14T02:12:00Z
DOI: 10.1177/20552076241272589
Issue No: Vol. 10 (2024)
- Living with my diabetes – introducing eHealth into daily practices of
patients with type 2 diabetes mellitus
Authors: Catharina M van Leersum, Kornelia E Konrad, Marloes Bults, Marjolein EM den Ouden
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveDiabetes patients can draw on an increasing number of eHealth apps to support them in the self-management of their disease. While studies so far have focused on patients with type 1 diabetes, we explored how patients with type 2 diabetes mellitus (T2DM) integrate eHealth apps into their practices aimed at managing and coping with the disease, which aspects were considered particularly valuable and which challenges users encountered.MethodsSemi-structured interviews and focus group sessions were conducted to explore how patients cope with T2DM in their daily lives and their attitude towards eHealth. In a further step, four eHealth apps were tested by patients and their expectations and experiences studied by way of qualitative interviews and focus groups.ResultsThe analysis showed that the study participants valued in particular the possibility to use eHealth apps to sense and gain a better understanding of their own body, to learn about specific responses of their body to nutrition and physical activity, and to support changes in daily routines and lifestyle. Key challenges encountered related to difficulties in interpreting the data, matching the data to other bodily sensations, getting overly occupied with the disease and difficulties in integrating the apps into personal, family, and care practices.ConclusionUnder certain conditions, eHealth can play an important role for patients in developing a nuanced, personal understanding of their body and coping with T2DM. A prerequisite is that eHealth needs to be fitted into the specific practices of users, and patients desire a strong role by their care professionals in providing support in interpretation of data.
Citation: DIGITAL HEALTH
PubDate: 2024-08-14T02:11:09Z
DOI: 10.1177/20552076241257052
Issue No: Vol. 10 (2024)
- Comparing remote versus in-person assessment of learning skills in
children with specific learning disabilities
Authors: Valentina Lampis, Chiara Dondena, Chiara Mauri, Martina Villa, Antonio Salandi, Massimo Molteni, Chiara Cantiani, Sara Mascheretti
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundInteractive telemedicine applications have been progressively introduced in the assessment of cognitive and literacy skills. However, there is still a lack of research focusing on the validity of this methodology for the neuropsychological assessment of children with Specific Learning Disorder (SLD).MethodsSeventy-nine children including 40 typically developing children (18 males, age 11.5 ± 1.06) and 39 children with SLD (24 males, age 12.3 ± 1.28) were recruited. Each participant underwent the same neuropsychological battery assessing reading accuracy, speed, and comprehension, writing, numerical processing, computation, and semantic numerical sense, twice (once during an in-person session (I) and once during a remote (R) home-based videoconference session). Four groups were subsequently defined based on the administration order. Repeated-measure-ANOVAs with assessment type (R vs. I testing) as within-subject factor and diagnosis (SLD vs. TR) and administration order (R-I vs. I-R) as between-subject factors, and between-group t-tests comparing the two assessment types within each time of administration, were run.ResultsNo differences emerged between I and R assessments of reading accuracy and speed, numerical processing, and computation; on the contrary, potential biases against R assessment emerged when evaluating skills in writing, reading comprehension, and semantic numerical sense. However, regardless of the assessment type, the scores obtained with I and R assessments within the same administration time point overlapped.DiscussionThese results partially support the validity and reliability of the assessment of children's learning skills via a remote home-based videoconferencing system. Implementing telemedicine as an assessment tool may increase timely access to primary health care and to support research activity.
Citation: DIGITAL HEALTH
PubDate: 2024-08-14T02:08:48Z
DOI: 10.1177/20552076241254453
Issue No: Vol. 10 (2024)
- Precision public health, the key for future outbreak management: A scoping
review
Authors: Ellappa Ghanthan Rajendran, Farizah Mohd Hairi, Rama Krishna Supramaniam, Tengku Amatullah Madeehah T Mohd
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundPrecision Public Health (PPH) is a newly emerging field in public health medicine. The application of various types of data allows PPH to deliver more tailored interventions to a specific population within a specific timeframe. However, the application of PPH possesses several challenges and limitations that need to be addressed.ObjectiveWe aim to provide evidence of the various use of PPH in outbreak management, the types of data that could be used in PPH application, and the limitations and barriers in the application of the PPH approach.Methods and analysisArticles were searched in PubMed, Web of Science, and Science Direct. Our selection of articles was based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) for Scoping Review guidelines. The outcome of the evidence assessment was presented in narrative format instead of quantitative.ResultsA total of 27 articles were included in the scoping review. Most of the articles (74.1%) focused on PPH applications in performing disease surveillance and signal detection. Furthermore, the data type mostly used in the studies was surveillance (51.9%), environment (44.4), and Internet query data. Most of the articles emphasized data quality and availability (81.5%) as the main barriers in PPH applications followed by data integration and interoperability (29.6%).ConclusionsPPH applications in outbreak management utilize a wide range of data sources and analytical techniques to enhance disease surveillance, investigation, modeling, and prediction. By leveraging these tools and approaches, PPH contributes to more effective and efficient outbreak management, ultimately reducing the burden of infectious diseases on populations. The limitation and challenges in the application of PPH approaches in outbreak management emphasize the need to strengthen the surveillance systems, promote data sharing and collaboration among relevant stakeholders, and standardize data collection methods while upholding privacy and ethical principles.
Citation: DIGITAL HEALTH
PubDate: 2024-08-13T06:14:32Z
DOI: 10.1177/20552076241256877
Issue No: Vol. 10 (2024)
- The impact of complementary feeding education for mothers using mobile
phone applications on the anthropometric indices of Iranian infants
Authors: Fariba Mousavi Ezmareh, Zahra Bostani Khalesi, Fatemeh Jafarzadeh Kenarsari, Saman Maroufizadeh
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundMaternal education is often linked to improved awareness. This study aimed to determine the impact of complementary feeding education for mothers using mobile phone applications on the anthropometric indices of Iranian infants.MethodsThis quasi-experiment study involved 86 eligible women divided into two groups—intervention (n = 43) and control (n = 43)—using a multistage sampling method. A researcher-designed questionnaire collected demographic data from parents and infants. Education was delivered through a mobile phone application. Infant anthropometric indices (weight-for-age, length-for-age, and weight-for-length) were measured before and 3 months after the intervention. Statistical analysis included independent t-tests, paired t-tests, chi-square tests (or Cochran–Armitage tests), and analysis of covariance.ResultsThe mean weight-for-age Z-scores of the infants in the intervention group were significantly different before (0.07 ± 0.52) and after the intervention (0.37 ± 0.53) (p
Citation: DIGITAL HEALTH
PubDate: 2024-08-12T06:38:43Z
DOI: 10.1177/20552076241272558
Issue No: Vol. 10 (2024)
- Exploring the feasibility of remote administration of speech audiometry: A
comparative study of conventional and digital methods
Authors: Chen Yuan
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThis study investigated the effectiveness of remote administration of speech audiometry, an essential tool for diagnosing hearing loss and determining its severity. Utilizing two software tools for remote testing, the research aimed to compare these digital methods with traditional, in-person speech audiometry to evaluate their feasibility and accuracy.DesignParticipants underwent the Cantonese Hearing in Noise Test (CHINT) under three listening conditions—quiet, noise from the front, and noise from the right side—using three different administration methods: the conventional in-person approach, video conferencing software, and remote access software.Study SampleFifty-six Cantonese-speaking adults residing in Hong Kong participated in this study.ResultsAnalysis revealed no significant differences in CHINT scores among the three administration methods, indicating the potential for remote administration to yield results comparable to those of conventional methods.ConclusionsThe findings supported the feasibility of remote speech audiometry using the investigated digital tools. This study paved the way for the wider adoption of tele-audiology practices, particularly in situations where in-person assessments are not possible.
Citation: DIGITAL HEALTH
PubDate: 2024-08-12T06:38:16Z
DOI: 10.1177/20552076241271834
Issue No: Vol. 10 (2024)
- Using inpatient telehealth for family engagement: A mixed methods study of
perceptions from patients, families, and care team providers
Authors: Jennifer L. Rosenthal, Jacob Williams, Keegan F. Bowers, Sarah C. Haynes, Lori Kennedy
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundThe Inpatient Telehealth Program permits family to remotely communicate with the patient and care team through secure, live video. We aimed to assess the implementation of this program for family engagement from the perspectives of patients, families, and providers.MethodsWe used a convergent mixed methods design. The quantitative component was a cross-sectional analysis of surveys assessing patient, family, and provider experience. The qualitative component used thematic analysis of patient, family, and provider interviews plus survey free text responses. We performed memo-writing and coding. We developed hypotheses about relationships among categories and identified analytic themes. We used data transformation and narrative discussion to report the integrated findings.ResultsSurveys from 214 individuals (33 patients, 145 family, 36 providers) were evaluated. Mean (standard deviation) experience ratings (1-poor, 5-best) were 4.0 (1.5) for patients, 4.6 (0.8) for family, and 4.0 (1.4) for providers. We received 134 free text responses and conducted 21 interviews. Three themes emerged: (1) inpatient telehealth enhanced patient and family experience through strengthened relationships and increased support; (2) inpatient telehealth enhanced patient care through improved information sharing and engagement; (3) low awareness of the program limited adoption. Quantitative and qualitative data aligned in that participants perceived inpatient telehealth to be valuable; however, surveys revealed that patients and providers have relatively lower satisfaction with the program.ConclusionInpatient telehealth for family engagement was perceived to improve family-centeredness of care. Future work is needed to overcome implementation challenges and to increase awareness of this resource among patients and families.
Citation: DIGITAL HEALTH
PubDate: 2024-08-10T11:57:00Z
DOI: 10.1177/20552076241267374
Issue No: Vol. 10 (2024)
- Secondary use of patient data within decentralized studies using the
example of rare diseases in Germany: A data scientist's exploration of
process and lessons learned
Authors: Michele Zoch, Christian Gierschner, Anne-Katrin Andreeff, Elisa Henke, Martin Sedlmayr, Gabriele Müller, Jenny Tippmann, Helge Hebestreit, Daniela Choukair, Georg F. Hoffmann, Fleur Fritz-Kebede, Nicole Toepfner, Reinhard Berner, Stephanie Biergans, Raphael Verbücheln, Jannik Schaaf, Julia Fleck, Felix Nikolaus Wirth, Josef Schepers, Fabian Prasser
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveUnlocking the potential of routine medical data for clinical research requires the analysis of data from multiple healthcare institutions. However, according to German data protection regulations, data can often not leave the individual institutions and decentralized approaches are needed. Decentralized studies face challenges regarding coordination, technical infrastructure, interoperability and regulatory compliance. Rare diseases are an important prototype research focus for decentralized data analyses, as patients are rare by definition and adequate cohort sizes can only be reached if data from multiple sites is combined.MethodsWithin the project “Collaboration on Rare Diseases”, decentralized studies focusing on four rare diseases (cystic fibrosis, phenylketonuria, Kawasaki disease, multisystem inflammatory syndrome in children) were conducted at 17 German university hospitals. Therefore, a data management process for decentralized studies was developed by an interdisciplinary team of experts from medicine, public health and data science. Along the process, lessons learned were formulated and discussed.ResultsThe process consists of eight steps and includes sub-processes for the definition of medical use cases, script development and data management. The lessons learned include on the one hand the organization and administration of the studies (collaboration of experts, use of standardized forms and publication of project information), and on the other hand the development of scripts and analysis (dependency on the database, use of standards and open source tools, feedback loops, anonymization).ConclusionsThis work captures central challenges and describes possible solutions and can hence serve as a solid basis for the implementation and conduction of similar decentralized studies.
Citation: DIGITAL HEALTH
PubDate: 2024-08-10T11:31:23Z
DOI: 10.1177/20552076241265219
Issue No: Vol. 10 (2024)
- Impact of coordination mechanisms based on information and communication
technologies on cross-level clinical coordination: A scoping review
Authors: Daniela Campaz-Landazábal, Ingrid Vargas, María-Luisa Vázquez
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundCoordination mechanisms based on information and communication technologies (ICTs) are gaining attention, especially since the pandemic, due to their potential to improve communication between health professionals. However, their impact on cross-level clinical coordination remains unclear. The aim is to synthesize the evidence on the impact of ICT-based coordination mechanisms on clinical coordination between primary care and secondary care (SC) doctors and to identify knowledge gaps.MethodsA scoping review was conducted by searching for original articles in six electronic databases and a manual search, with no restrictions regarding time, area, or methodology. Titles and abstracts were screened. Full texts of the selected articles were reviewed and analysed to assess the impact of each mechanism, according to the cross-level clinical coordination conceptual framework.ResultsOf the 6555 articles identified, 30 met the inclusion criteria. All had been conducted in high-income countries, most (n = 26) evaluated the impact of a single mechanism – asynchronous electronic consultations via electronic health records (EHR) – and were limited in terms of design and types and dimensions of cross-level clinical coordination analysed. The evaluation of electronic consultations showed positive impacts on the appropriateness of referrals and accessibility to SC, yet the qualitative studies also highlighted potential risks. Studies on other mechanisms were scarce (shared EHR, email consultations) or non-existent (videoconferencing, mobile applications).ConclusionsEvidence of the impact of ICT-based mechanisms on clinical coordination between levels is limited. Rigorous evaluations are needed to inform policies and strategies for improving coordination between healthcare levels, thus contributing to high-quality, efficient healthcare.
Citation: DIGITAL HEALTH
PubDate: 2024-08-09T08:19:20Z
DOI: 10.1177/20552076241271854
Issue No: Vol. 10 (2024)
- Evaluating ChatGPT responses to frequently asked patient questions
regarding periprosthetic joint infection after total hip and knee
arthroplasty
Authors: Xiaojun Hu, Marcel Niemann, Arne Kienzle, Karl Braun, David Alexander Back, Clemens Gwinner, Nora Renz, Ulrich Stoeckle, Andrej Trampuz, Sebastian Meller
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundPatients access relevant information concerning their orthopaedic surgery resources through multiple information channels before presenting for clinical treatment. Recently, artificial intelligence (AI)-powered chatbots have become another source of information for patients. The currently developed AI chat technology ChatGPT (OpenAI LP) is an application for such purposes and it has been rapidly gaining popularity, including for patient education. This study sought to evaluate whether ChatGPT can correctly answer frequently asked questions (FAQ) regarding periprosthetic joint infection (PJI).MethodsTwelve FAQs about PJI after hip and knee arthroplasty were identified from the websites of fifteen international clinical expert centres. ChatGPT was confronted with these questions and its responses were analysed for their accuracy using an evidence-based approach by a multidisciplinary team. Responses were categorised in four groups: (1) Excellent response that did not require additional improvement; (2) Satisfactory responses that required a small amount of improvement; (3) Satisfactory responses that required moderate improvement; and (4) Unsatisfactory responses that required a large amount of improvement.ResultsFrom the analysis of the responses given by the chatbot, no reply received an ‘unsatisfactory’ rating; one did not require any correction; and the majority of the responses required low (7 out of 12) or moderate (4 out of 12) clarification. Although a few responses required minimal clarification, the chatbot responses were generally unbiased and evidence-based, even when asked controversial questions.ConclusionsThe AI-chatbot ChatGPT was able to effectively answer the FAQs of patients seeking information around PJI diagnosis and treatment. The given information was also written in a manner that can be assumed to be understandable by patients. The chatbot could be a valuable clinical tool for patient education and understanding around PJI treatment in the future. Further studies should evaluate its use and acceptance by patients with PJI.
Citation: DIGITAL HEALTH
PubDate: 2024-08-09T07:54:00Z
DOI: 10.1177/20552076241272620
Issue No: Vol. 10 (2024)
- What explains trust in online mental health therapy provision platforms'
An online descriptive survey
Authors: Elizaveta Fomicheva, Jasmin Jyrgalbek, Oxana Mikhaylova
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundCurrently, there is an increased interest in providing mental health care through digital devices and services, and the demand for these services is growing.ObjectiveIn this study, we considered the phenomenon of trust in online consultations, and the factors affecting this trust, within a Russian context.MethodsAn online survey was conducted using Google Forms in May 2023 and the data were analyzed using SPSS. All the participants were students from Moscow universities aged from 18 to 35 years. The final sample consisted of 203 students, of which 154 (75.9%) were women, 44 (21.7%) were men, and five (2.5%) preferred not to specify their gender.ResultsWe found that students had a high level of trust, which depended on personal factors, such as experience, socio-economic status, and age, and contextual factors, such as geographical and temporal independence, price of the session, availability of recommendations, popularity of the platform, and the level of technical equipment.
Citation: DIGITAL HEALTH
PubDate: 2024-08-08T08:16:01Z
DOI: 10.1177/20552076241272616
Issue No: Vol. 10 (2024)
- How to establish digital health ecosystems from the perspective of health
service-organizations: A taxonomy developed based on expert interviews
conducted as modified Delphi approach
Authors: Robin Huettemann, Benedict Sevov, Sven Meister, Leonard Fehring
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveDigital health ecosystems may be the next revolution in improving citizens’ well-being, health delivery, data management, and health system processes, but solutions have not yet been broadly established. Reasons could be that health service-organizations have misaligned interests or lack capabilities. This study investigates reasons from a multi-health-service-organization perspective, differentiating between payers, insurers, healthcare providers, and innovators, detailing the expected value-adds, preferred participation roles, and required capabilities including a rating assessment.MethodsFindings are based on a taxonomy development methodology, which combines a literature review with semi-structured qualitative expert interviews, conducted using a modified Delphi approach. Interviews were thematically analysed.ResultsIn total, 21 experts across the four health service-organization groups were interviewed. The capability taxonomy includes a total of 16 capabilities, categorized in three themes: ‘Health market’, ‘organizational’, and ‘technology and informatic’. Providers expect a value-add from strengthening their health process economics through efficiency gains but reveal the largest capability gaps, especially in ‘interoperability’ and ‘platform’. Innovators’ ‘technology and informatic’ capabilities complement well with those of payers for the ‘health market’.ConclusionsWe present a health service-organization-specific three-stage approach for establishing digital health ecosystems. Payers and insurers should address their ‘technology and informatic’ capability gaps, using technical enablers or forming new entities to reduce dependencies from legacy information technology systems. Innovators should clarify their monetization models and create positive awareness for their services, possibly entering the market directly. Providers must address interoperability issues and may require incentives to encourage their participation. Findings suggest governmental policymakers to prioritize three health policy initiatives.
Citation: DIGITAL HEALTH
PubDate: 2024-08-08T08:15:41Z
DOI: 10.1177/20552076241271890
Issue No: Vol. 10 (2024)
- Development and acceptability of a gestational diabetes mellitus
prevention system (Better pregnancy) based on a user-centered approach: A
clinical feasibility study
Authors: Beibei Duan, Zheyi Zhou, Mengdi Liu, Zhe Liu, Qianghuizi Zhang, Leyang Liu, Cunhao Ma, Baohua Gou, Weiwei Liu
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundGestational diabetes mellitus (GDM) can increase the risk of adverse outcomes for both mothers and infants. Preventive interventions can effectively assist pregnant women suffering from GDM. At present, pregnant women are unaware of the importance of preventing GDM, and they possess a low level of self-management ability. Recently, mHealth technology has been used worldwide. Therefore, developing a mobile health app for GDM prevention could potentially help pregnant women reduce the risk of GDM.ObjectiveTo design and develop a mobile application, evaluate its acceptance, and understand the users’using experience and suggestions, thus providing a valid tool to assist pregnant women at risk of GDM in enhancing their self-management ability and preventing GDM.MethodsAn evidence-based GDM prevent app (Better pregnancy) was developed using user-centered design methods, following the health belief model, and incorporating GDM risk prediction. A convenient sampling method was employed from June to August 2022 to select 102 pregnant women at risk of GDM for the pilot study. After a week, the app's acceptability was evaluated using an application acceptance questionnaire, and we updated the app based on the feedback from the women. We used SPSS 26.0 for data analysis.ResultsThe application offers various functionalities, including GDM risk prediction, health management plan, behavior management, health information, personalized guidance and consultation, peer support, family support, and other functions. In total, 102 pregnant women consented to participate in the study, achieving a retention rate of 98%; however, 2% (n = 2) withdrew. The Better pregnancy app's average acceptability score is 4.07 out of 5. Additionally, participants offered several suggestions aimed at enhancing the application.ConclusionsThe Better pregnancy app developed in this study can serve as an auxiliary management tool for the prevention of GDM, providing a foundation for subsequent randomized controlled trials.
Citation: DIGITAL HEALTH
PubDate: 2024-08-08T08:14:51Z
DOI: 10.1177/20552076241266056
Issue No: Vol. 10 (2024)
- Re-hospitalization factors and economic characteristics of urinary tract
infected patients using machine learning
Authors: Yul Hee Lee, Young Seo Baik, Young Jae Kim, Hye Jin Shi, Jong Youn Moon, Kwang Gi Kim
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveUrinary tract infection is one of the most prevalent bacterial infectious diseases in outpatient treatment, and 50–80% of women experience it more than once, with a recurrence rate of 40–50% within a year; consequently, preventing re-hospitalization of patients is critical. However, in the field of urology, no research on the analysis of the re-hospitalization status for urinary tract infections using machine learning algorithms has been reported to date. Therefore, this study uses various machine learning algorithms to analyze the clinical and nonclinical factors related to patients who were re-hospitalized within 30 days of urinary tract infection.MethodsData were collected from 497 patients re-hospitalized for urinary tract infections within 30 days and 496 patients who did not require re-hospitalization. The re-hospitalization factors were analyzed using four machine learning algorithms: gradient boosting classifier, random forest, naive Bayes, and logistic regression.ResultsThe best-performing gradient boosting classifier identified respiratory rate, days of hospitalization, albumin, diastolic blood pressure, blood urea nitrogen, body mass index, systolic blood pressure, body temperature, total bilirubin, and pulse as the top-10 factors that affect re-hospitalization because of urinary tract infections. The 993 patients whose data were collected were divided into risk groups based on these factors, and the re-hospitalization rate, days of hospitalization, and medical expenses were observed to decrease from the high- to low-risk group.ConclusionsThis study showed new possibilities in analyzing the status of urinary tract infection-related re-hospitalization using machine learning. Identifying factors affecting re-hospitalization and incorporating preventable and reinforcement-based treatment programs can aid in reducing the re-hospitalization rate and average number of days of hospitalization, thereby reducing medical expenses.
Citation: DIGITAL HEALTH
PubDate: 2024-08-08T07:58:11Z
DOI: 10.1177/20552076241272697
Issue No: Vol. 10 (2024)
- Diagnostic value of a deep learning-based hyoid bone tracking model for
aspiration in patients with post-stroke dysphagia
Authors: Yeong Hwan Ryu, Ji Hyun Kim, Dohhyung Kim, Seo Young Kim, Seong Jae Lee
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveHyoid bone movement is potentially related to aspiration risk in post-stroke dysphagia (PSD) patients but is difficult to assess quantitatively. This study aimed to measure the distance of hyoid bone movement more efficiently and accurately using a deep learning model and determine the clinical usefulness of the model in PSD patients.MethodsThis study included 85 patients with PSD within 6 months from onset. Patients were grouped into an aspiration group (n = 35) and a non-aspiration group (n = 50) according to the results of a videofluoroscopic swallowing study. Hyoid bone movement was tracked using a deep learning model constructed with the BiFPN-U-Net(T) architecture. The maximum distance of hyoid bone movement was measured horizontally (Hmax), vertically (Vmax), and diagonally (Dmax).ResultsCompared with the non-aspiration group, the aspiration group showed significant decreases in hyoid bone movement in all directions. The area under the curve of Vmax was highest at 0.715 with a sensitivity of 0.680 and specificity of 0.743. The Vmax cutoff value for predicting aspiration risk was 1.61 cm. The success of oral feeding at the time of discharge was significantly more frequent when hyoid movement was equal to or larger than the cutoff value although no significant relationship was found between hyoid movement and other clinical characteristics.ConclusionHyoid bone movement of PSD patients can be measured quantitatively and efficiently using a deep learning model. Deep learning model-based analysis of hyoid bone movement seems to be useful for predicting aspiration risk and the possibility of resuming oral feeding.
Citation: DIGITAL HEALTH
PubDate: 2024-08-08T07:57:46Z
DOI: 10.1177/20552076241271778
Issue No: Vol. 10 (2024)
- Predicting obstructive sleep apnea hypopnea syndrome using
three-dimensional optical devices: A systematic review
Authors: Beibei Chen, Rongkai Cao, Danni Song, Piaopiao Qiu, Chongshan Liao, Yongming Li
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
PurposeAs a global health concern, the diagnosis of obstructive sleep apnea hypopnea syndrome (OSAHS), characterized by partial reductions and complete pauses in ventilation, has garnered significant scientific and public attention. With the advancement of digital technology, the utilization of three-dimensional (3D) optical devices demonstrates unparalleled potential in diagnosing OSAHS. This study aimed to review the current literature to assess the accuracy of 3D optical devices in identifying the prevalence and severity of OSAHS.MethodsA systematic literature search was conducted in the Web of Science, Scopus, PubMed/MEDLINE, and Cochrane Library databases for English studies published up to April 2024. Peer-reviewed researches assessing the diagnostic utility of 3D optical devices for OSAHS were included. The Quality Assessment of Diagnostic Accuracy Studies-2 (QUADAS-2) guideline was employed to appraise the risk of bias.ResultsThe search yielded 3216 results, with 10 articles meeting the inclusion criteria for this study. Selected studies utilized structured light scanners, stereophotogrammetry, and red, green, blue-depth (RGB-D) cameras. Stereophotogrammetry-based 3D optical devices exhibited promising potential in OSAHS prediction.ConclusionsThe utilization of 3D optical devices holds considerable promise for OSAHS diagnosis, offering potential improvements in accuracy, cost reduction, and time efficiency. However, further clinical data are essential to assist clinicians in the early detection of OSAHS using 3D optical devices.
Citation: DIGITAL HEALTH
PubDate: 2024-08-07T10:36:46Z
DOI: 10.1177/20552076241271749
Issue No: Vol. 10 (2024)
- Effects of a walking program using the “WalkON” mobile app
among college students
Authors: Yun-Su Kim
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThis study implemented a 12-week walking program using the mobile app WalkON among college students and evaluated its effects.MethodsThis study used a quasi-experimental, non-equivalent control group, pre/post design. The WalkON program was conducted from September to December 2022, involving 50 participants in the experimental group and 52 participants in the control group.ResultsSignificant between-group differences were observed as follows. Sleep quality evaluation score (i.e. higher scores mean poorer sleep quality) decreased more significantly in the experimental group (vs. control group; 1.40 vs. 0.34 points, respectively; p = .027). Anxiety decreased more in the experimental group than in the control group (2.08 vs. 1.75 points, respectively; p = .020). Regarding the mean score of the “health responsibility” domain of health-promoting behaviors, the experimental group saw an increase of 0.25 points compared to 0.15 in the control group (p = .005). The self-efficacy level increased by an average of 0.34 points in the experimental group and 0.03 points in the control group (p = .046).ConclusionsThe WalkON program has the potential to positively influence physical activity engagement and health-promoting behaviors in college students. It could be an effective strategy for promoting the physical and mental health of college students lacking engagement in physical activity. This study is significant in that it provides evidence of a mobile app-based program potentially useful to encourage college students to increase their regular physical activity through walking exercises, something especially pertinent in the new norm of contactless environments post-COVID-19.
Citation: DIGITAL HEALTH
PubDate: 2024-08-07T10:36:09Z
DOI: 10.1177/20552076241269463
Issue No: Vol. 10 (2024)
- Rhythmic musical intervention improves response time of memory tests: A
pilot study on the application of a digital cognitive assessment
Authors: Ziyu Hao, Joshua YS Tran, Baker KK Bat, Karen KL Yiu, Joyce YC Chan, Kelvin KF Tsoi
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundMemory complaints are the early symptoms of cognitive impairment, and they usually bring anxiety about cognitive deterioration among the elderly population. Musical interventions were demonstrated to relieve dementia symptoms. This pilot study investigated the potential benefits of rhythmic musical intervention, African drumming, on cognitive function and mood status with traditional and digital assessments for elderly participants.MethodParticipants were recruited through social media. The musical intervention was arranged by drumming instructors certified by the Hong Kong Association of African Drumming. Participants joined regular training classes with eight lessons, which covered rhythmic clapping and drumming, power control, and overall performance with songs. The inclusion criteria included the following: (1) age over 50; (2) self-reported complaints of memory loss; (3) the ability to use digital devices, such as a smartphone; and (4) can understand the content of questionnaires and follow the intervention schedules. Those with hearing impairment, failure to use Chinese, and active psychosis or dementia were excluded. Cognitive function was measured by the Hong Kong version of the Montreal Cognitive Assessment (HK-MoCA) and a digital platform, ScreenMat. Anxiety and depression levels were assessed by the State–Trait Anxiety Inventory (STAI) and Geriatric Depression Scale (GDS-15). All assessments were performed before and after the drumming classes. The outcomes were compared using the Wilcoxon signed rank test with 0.05 as the significance level.ResultTwenty-two participants joined this study with an attendance rate of 90%. The overall cognitive function of the participants was good with an average score of 27 for HK-MoCA. After eight sessions of African drum intervention, the cognitive function did not show a significant improvement, but the response time of answering the digital cognitive questions was significantly faster than before the intervention (−39.9 s, p = 0.03). The response time for the short-term memory function was most significantly reduced (−13.5 s, p = 0.017). The anxiety and depression scores (i.e. STAI and GDS) also significantly improved (p
Citation: DIGITAL HEALTH
PubDate: 2024-08-07T02:12:17Z
DOI: 10.1177/20552076241271875
Issue No: Vol. 10 (2024)
- Automated machine learning models for nonalcoholic fatty liver disease
assessed by controlled attenuation parameter from the NHANES 2017–2020
Authors: Lihe Liu, Jiaxi Lin, Lu Liu, Jingwen Gao, Guoting Xu, Minyue Yin, Xiaolin Liu, Airong Wu, Jinzhou Zhu
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundNonalcoholic fatty liver disease (NAFLD) is recognized as one of the most common chronic liver diseases worldwide. This study aims to assess the efficacy of automated machine learning (AutoML) in the identification of NAFLD using a population-based cross-sectional database.MethodsAll data, including laboratory examinations, anthropometric measurements, and demographic variables, were obtained from the National Health and Nutrition Examination Survey (NHANES). NAFLD was defined by controlled attenuation parameter (CAP) in liver transient ultrasound elastography. The least absolute shrinkage and selection operator (LASSO) regression analysis was employed for feature selection. Six algorithms were utilized on the H2O-automated machine learning platform: Gradient Boosting Machine (GBM), Distributed Random Forest (DRF), Extremely Randomized Trees (XRT), Generalized Linear Model (GLM), eXtreme Gradient Boosting (XGBoost), and Deep Learning (DL). These algorithms were selected for their diverse strengths, including their ability to handle complex, non-linear relationships, provide high predictive accuracy, and ensure interpretability. The models were evaluated by area under receiver operating characteristic curves (AUC) and interpreted by the calibration curve, the decision curve analysis, variable importance plot, SHapley Additive exPlanation plot, partial dependence plots, and local interpretable model agnostic explanation plot.ResultsA total of 4177 participants (non-NAFLD 3167 vs NAFLD 1010) were included to develop and validate the AutoML models. The model developed by XGBoost performed better than other models in AutoML, achieving an AUC of 0.859, an accuracy of 0.795, a sensitivity of 0.773, and a specificity of 0.802 on the validation set.ConclusionsWe developed an XGBoost model to better evaluate the presence of NAFLD. Based on the XGBoost model, we created an R Shiny web-based application named Shiny NAFLD (http://39.101.122.171:3838/App2/). This application demonstrates the potential of AutoML in clinical research and practice, offering a promising tool for the real-world identification of NAFLD.
Citation: DIGITAL HEALTH
PubDate: 2024-08-07T01:51:06Z
DOI: 10.1177/20552076241272535
Issue No: Vol. 10 (2024)
- Online medical consultation in China: Demand-side analysis of obese
patients’ preferences and willingness-to-pay for online obesity
consultations
Authors: Yaolin Hu, Jian Wang, Yuanyuan Gu, Stephen Nicholas, Elizabeth Maitland, Jianbo Zhou
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveWith obesity a major health concern and call on healthcare resources in China, we explored the preferences and willingness to pay (WTP) for obesity OMC, including the influencing factors behind WTP and preferences.MethodWe recruited 400 obese participants to undertake a discrete choice experiment (DCE) and the contingent value method (CVM) survey. We used CVM to measure obese participants’ WTP for one-click services (OCS) and used DCE to estimate obesity participants’ preferences and WTP for OMC with different attributes.ResultsObese participants were willing to pay more than RMB80 on average for OCS, and more than 50% of participants had a WTP over RMB50 and 5% had a WTP over RMB300, reflecting the strong willingness of Chinese obese patients to pay for OMC. Educational background, income, ethnicity, previous OMC experience and accessibility to offline hospitals with different levels impacted WTP. The relative importance score of attributes in descending order was cost, doctors’ hospital level, doctors’ level, online waiting time, consultation time and consultation form. Obese patients preferred lower cost, doctors from higher-level hospitals, doctors with higher expertise levels, shorter waiting time and consultation duration, and telephone consultation were preferred. 30-min waiting time, 15-min consultation duration and telephone consultation were the most economically efficient set we found.ConclusionTo maximize health resources, provincial tertiary and municipal hospitals face different paths to developing obesity OMC platforms. We encouraged young doctors to use OMC. OMC regulators should implement consumer protection policies to optimize OMC pricing and address potential ‘unfair’ pricing.
Citation: DIGITAL HEALTH
PubDate: 2024-08-07T01:50:11Z
DOI: 10.1177/20552076241272525
Issue No: Vol. 10 (2024)
- Validity of the Smombie Scale: Sensitivity and specificity in identifying
pedestrian risk group
Authors: Sumi Oh, Sunhee Park
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThe objective of this study was to determine the most effective cut-off point for the Smombie Scale and evaluate its ability to screen for pedestrian safety risks among young adults.MethodsData were obtained from an online sample of 396 Korean young adults aged 18–39 years. Latent profile analysis was used to distinguish the risk group as a reference measure for the Smombie Scale. Discriminative power was assessed using sensitivity, specificity, receiver operating characteristic (ROC) curves, and the area under the ROC curve. The cut-off points were estimated from the Youden index and the balanced score.ResultsThe latent profile analysis showed two different classes: “risk group” of 17.8% and “others.” Based on the latent profile analysis, sensitivity, and specificity analysis showed that an adequate cut-off point of 2.78 of five points or higher was associated with a high risk of distracted walking.ConclusionThe Smombie Scale is a good predictor of problematic smartphone use on the road and can be used as a screening tool for assessing risk levels among young adult pedestrians.
Citation: DIGITAL HEALTH
PubDate: 2024-08-07T01:48:49Z
DOI: 10.1177/20552076241271851
Issue No: Vol. 10 (2024)
- Interpretable prediction of acute respiratory infection disease among
under-five children in Ethiopia using ensemble machine learning and
Shapley additive explanations (SHAP)
Authors: Zinabu Bekele Tadese, Debela Tsegaye Hailu, Aschale Wubete Abebe, Shimels Derso Kebede, Agmasie Damtew Walle, Beminate Lemma Seifu, Teshome Demis Nimani
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundAlthough the prevalence of childhood illnesses has significantly decreased, acute respiratory infections continue to be the leading cause of death and disease among children in low- and middle-income countries. Seven percent of children under five experienced symptoms in the two weeks preceding the Ethiopian demographic and health survey. Hence, this study aimed to identify interpretable predicting factors of acute respiratory infection disease among under-five children in Ethiopia using machine learning analysis techniques.MethodsSecondary data analysis was performed using 2016 Ethiopian demographic and health survey data. Data were extracted using STATA and imported into Jupyter Notebook for further analysis. The presence of acute respiratory infection in a child under the age of 5 was the outcome variable, categorized as yes and no. Five ensemble boosting machine learning algorithms such as adaptive boosting (AdaBoost), extreme gradient boosting (XGBoost), Gradient Boost, CatBoost, and light gradient-boosting machine (LightGBM) were employed on a total sample of 10,641 children under the age of 5. The Shapley additive explanations technique was used to identify the important features and effects of each feature driving the prediction.ResultsThe XGBoost model achieved an accuracy of 79.3%, an F1 score of 78.4%, a recall of 78.3%, a precision of 81.7%, and a receiver operating curve area under the curve of 86.1% after model optimization. Child age (month), history of diarrhea, number of living children, duration of breastfeeding, and mother's occupation were the top predicting factors of acute respiratory infection among children under the age of 5 in Ethiopia.ConclusionThe XGBoost classifier was the best predictive model with improved performance, and predicting factors of acute respiratory infection were identified with the help of the Shapely additive explanation. The findings of this study can help policymakers and stakeholders understand the decision-making process for acute respiratory infection prevention among under-five children in Ethiopia.
Citation: DIGITAL HEALTH
PubDate: 2024-08-06T09:43:50Z
DOI: 10.1177/20552076241272739
Issue No: Vol. 10 (2024)
- Sociodemographic factors and health digital divide among urban residents:
Evidence from a population-based survey in China
Authors: Yanbin Yang, Chengyu Ma
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundThe deep integration of digital technology and healthcare services has propelled the healthcare system into the era of digital health. However, vulnerable populations in the field of information technology, they face challenges in benefiting from the digital dividends brought by digital health, leading to the emerging phenomenon of the “health digital divide.”MethodsThis study utilized the sample of 3547 urban from the 2021 Chinese Social Survey data for analysis. Models were constructed with digital access divide, digital usage divide, and digital outcome divide for urban residents, and structural equation modeling was implemented for analysis.ResultsThe impact β coefficients (95% CI) of urban residents’ digital access on the frequency of digital use, internet healthcare utilization, and patient experience were (β = 0.737, P
Citation: DIGITAL HEALTH
PubDate: 2024-08-06T09:32:09Z
DOI: 10.1177/20552076241271812
Issue No: Vol. 10 (2024)
- Exploring nurses’ awareness and attitudes toward artificial
intelligence: Implications for nursing practice
Authors: Majed Mowanes Alruwaili, Fuad H. Abuadas, Mohammad Alsadi, Abeer Nuwayfi Alruwaili, Osama Mohamed Elsayed Ramadan, Mostafa Shaban, Abdulellah Al Thobaity, Saad Muaidh Alkahtani, Rabie Adel El Arab
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
IntroductionWorldwide, healthcare systems aim to achieve the best possible quality of care at an affordable cost while ensuring broad access for all populations. The use of artificial intelligence (AI) in healthcare holds promise to address these challenges through the integration of real-world data-driven insights into patient care processes. This study aims to assess nurses’ awareness and attitudes toward AI-integrated tools used in clinical practice.MethodsA descriptive cross-sectional design captured nurses’ responses at three governmental hospitals in Saudi Arabia by using an online questionnaire administered over 4 months. The study involved 220 registered nurses with a minimum of one year of clinical experience, selected through a convenience sampling method. The online survey consisted of three sections: demographic information, an assessment of nurses’ AI knowledge, and the general attitudes toward the AI scale.ResultsNurses displayed “moderate” levels of awareness toward AI technology, with 70.9% having basic information about AI and only 58.2% (128 nurses) were considered “aware” of AI as they dealt with one of its healthcare applications. Nurses expressed openness to AI integration (M = 3.51) on one side, but also had some concerns about AI. Nurses expressed conservative attitudes toward AI, with significant differences observed based on gender (χ² = 4.67, p
Citation: DIGITAL HEALTH
PubDate: 2024-08-06T09:31:31Z
DOI: 10.1177/20552076241271803
Issue No: Vol. 10 (2024)
- Development of a patient-centered transition program for stroke survivors
and their informal caregivers, combining case-management and access to an
online information platform: A user-centered design approach
Authors: Marion Delvallée, Mathilde Marchal, Anne Termoz, Ouazna Habchi, Laurent Derex, Anne-Marie Schott, Julie Haesebaert
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundDuring the hospital-to-home transition, stroke survivors and their caregivers face a significant lack of support and information which impacts their psychosocial recovery. We aimed to co-design a program combining individual support by a trained case-manager (dedicated professional providing individual support) and an online information platform to address needs of stroke survivors and caregivers.MethodsA two-step methodology was used. The first step followed a “user-centered design” approach during four workshops with stroke survivors, caregivers, and healthcare professionals to develop the platform and define the case-manager profile. The second step was a usability test of the platform following a Think Aloud method with patients and caregivers. The workshops and interviews were analyzed following a qualitative thematic analysis. The analysis of Think Aloud interviews was based on User Experience Honeycomb framework by Morville.ResultsEight participants attended the workshops: two patients, two caregivers, three nurses, and a general practitioner. Activities, training, and skills of the case-manager were defined according to stroke survivors and caregivers needs. Name, graphics, navigation, and content of the platform were developed with the participants, a developer and a graphic designer. The usability of the platform was tested with 5 patients and 5 caregivers. The Think Aloud confirmed satisfaction with graphics and content but a need for improvement regarding the navigability. An update of the platform was conducted in order to answer the needs expressed by participants.ConclusionWe developed, with a participatory approach, a patient-centered transition program, which will be evaluated in a randomized controlled trial.
Citation: DIGITAL HEALTH
PubDate: 2024-08-06T08:15:48Z
DOI: 10.1177/20552076241272628
Issue No: Vol. 10 (2024)
- An overview of environmental risk factors for type 2 diabetes research
using network science tools
Authors: Xia Cao, Huixin Yu, Yu Quan, Jing Qin, Yuhong Zhao, Xiaochun Yang, Shanyan Gao
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveCurrent studies lack a comprehensive understanding of the environmental factors influencing type 2 diabetes, hindering an in-depth grasp of the overall etiology. To address this gap, we utilized network science tools to highlight research trends, knowledge structures, and intricate relationships among factors, offering a new perspective for a profound understanding of the etiology.MethodsThe Web of Science database was employed to retrieve documents relevant to environmental risk factors in type 2 diabetes from 2012 to 2024. Bibliometric analysis using Microsoft Excel and OriginPro provided a detailed scientific production profile, including articles, journals, countries, and authors. Co-occurrence analysis was employed to determine the collaboration state and knowledge structures, utilizing social network tools such as Gephi, Tableau, and R Studio. Additionally, theme evolutionary analysis was conducted using SciMAT to offer insights into research trends.ResultsThe publications and themes related to environmental factors in type 2 diabetes have consistently risen, shaping a well-established research domain. Lifestyle environmental factors, particularly diet and nutrition, stand out as the most represented and rapidly growing topics. Key focal hotspots include sedentary and digital behavior, PM2.5, ethnicity and socioeconomic status, traffic and greenspace, and depression. The theme evolutionary analysis revealed three distinct paths: (1) oxidative stress–air pollutants–PM2.5–air pollutants; (2) calcium–metabolic syndrome–cardiovascular disease; and (3) polychlorinated biphenyls (PCBs)–persistent organic pollutants (POPs)–obesity.ConclusionsDigital behavior signifies a novel approach for preventing and managing type 2 diabetes. The influence of PM2.5 and calcium on oxidative stress and abnormal vascular contraction is intricately linked to microvascular diabetes complications. The transition from PCBs and POPs to obesity underscores the disruption of endocrine function by chemicals, elevating the risk of diabetes. Future studies should explore the connections between environmental factors, microvascular complications, and long-term outcomes in diabetes.
Citation: DIGITAL HEALTH
PubDate: 2024-08-06T08:15:11Z
DOI: 10.1177/20552076241271722
Issue No: Vol. 10 (2024)
- Enhancing postoperative anticoagulation therapy with remote patient
monitoring: A pilot crossover trial study to evaluate portable
coagulometers and chatbots in cardiac surgery follow-up
Authors: Federico Guede-Fernández, Tiago Silva Pinto, Helena Semedo, Clara Vital, Pedro Coelho, Maria Eduarda Oliosi, Salomé Azevedo, Pedro Dias, Ana Londral
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectivePrior research has not assessed the value of remote patient monitoring (RPM) systems for patients undergoing anticoagulation therapy after cardiac surgery. This study aims to assess whether the clinical follow-up through RPM yields comparable outcomes with the standard protocol.MethodsA crossover trial assigned participants to SOC-RPM or RPM-SOC, starting with the standard of care (SOC) for the first 6 months after surgery and using RPM for the following 6 months, or vice-versa, respectively. During RPM, patients used the Coaguchek© to accurately measure International Normalized Ratio values and a mobile text-based chatbot to report PROs and adjust the therapeutic dosage. The study assessed patients’ and clinicians’ experience with RPM and compared direct costs.ResultsTwenty-seven patients participated. The median time in therapeutic range (TTR) levels during RPM were 72.2% and 50.6% for the SOC-RPM and RPM-SOC arms, respectively, and during SOC, they were 49.4% and 58.4% for SOC-RPM and RPM-SOC arms, respectively. Patients and the clinical team reported high trust and satisfaction with the proposed digital service. Statistically significant differences were only found in the cost of RPM in the RPM-SOC, which was higher than SOC in the SOC-RPM arm.ConclusionsPortable coagulometers and chatbots can enhance the remote management of patients undergoing anticoagulation therapy, improving patient experience. This presents a promising alternative to the current standard procedure. The results of this study seem to suggest that RPM may have a higher value when initiated after a SOC period rather than starting RPM immediately after surgery.Trial registration: ClinicalTrials.gov NCT06423521.
Citation: DIGITAL HEALTH
PubDate: 2024-08-06T07:10:14Z
DOI: 10.1177/20552076241269515
Issue No: Vol. 10 (2024)
- Implementation of a conversational, videoconferencing-based therapy group
for postpartum depression and anxiety symptoms: A pragmatic evaluation
Authors: Neesha Hussain-Shamsy, Lori Wasserman, Greer Slyfield Cook, Kaeli Macdonald, Keisha Greene, Lucy C. Barker, Juveria Zaheer, Geetha Mukerji, Simone N. Vigod, Emily Seto
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundGroup psychotherapy is an effective treatment for postpartum depressive and anxiety symptoms, and interpersonal connection and support through the group process can aid recovery. Little is known about the implication of the delivery of interpersonally oriented group therapy in this population through videoconferencing.ObjectiveTo pragmatically evaluate the implementation of a conversationally-oriented postpartum videoconferencing psychotherapy group for depression and anxiety within the clinical setting.MethodsOver 8 weeks, five to six patients and one therapist facilitator (closed group) meet weekly for 1 hour via a secure videoconferencing platform. We evaluated group adoption metrics for all postpartum videoconferencing psychotherapy groups offered during the evaluation period (October 2021–August 2022), and offered patients the opportunity to complete baseline and post-group quality improvement surveys to evaluate outcomes including acceptability (Satisfaction with Therapist and Therapy Scale-Revised, STTS-R), group process (Group Questionnaire, GQ), and effectiveness (Edinburgh Postnatal Depression Scale, EPDS).ResultsOf 153 patients (n = 26 groups), most (72.5%) attended>70% of group sessions. Of 137 patients (n = 24 groups) who were sent surveys, n = 50 (36.5%) completed both baseline and post-group surveys. Mean (SD) ratings were high for acceptability (STTS-R-therapy: 25.0/30 (3.1); STTS-R-therapist: 27.6/30 (2.3)) and group process with GQ ratings of 81.4/91 (7.8) (positive bond), 34.1/56 (3.8) (positive working relationship) and 23.5/63 (4.4) (negative relationship). Patients with probable depression (EPDS ≥ 13) significantly decreased from n = 23 (50%) to n = 19 (41.3%, p
Citation: DIGITAL HEALTH
PubDate: 2024-08-05T08:31:31Z
DOI: 10.1177/20552076241269630
Issue No: Vol. 10 (2024)
- Lived experience at the core: A classification system for risk-taking
behaviours in bipolar
Authors: Daisy Harvey, Paul Rayson, Fiona Lobban, Jasper Palmier-Claus, Steven Jones
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveClinical observations suggest that individuals with a diagnosis of bipolar face difficulties regulating emotions and impairments to their cognitive processing, which can contribute to high-risk behaviours. However, there are few studies which explore the types of risk-taking behaviour that manifest in reality and evidence suggests that there is currently not enough support for the management of these behaviours. This study examined the types of risk-taking behaviours described by people who live with bipolar and their access to support for these behaviours.MethodsSemi-structured interviews were conducted with n = 18 participants with a lived experience of bipolar and n = 5 healthcare professionals. The interviews comprised open-ended questions and a Likert-item questionnaire. The responses to the interview questions were analysed using content analysis and corpus linguistic methods to develop a classification system of risk-taking behaviours. The Likert-item questionnaire was analysed statistically and insights from the questionnaire were incorporated into the classification system.ResultsOur classification system includes 39 reported risk-taking behaviours which we manually inferred into six domains of risk-taking. Corpus linguistic and qualitative analysis of the interview data demonstrate that people need more support for risk-taking behaviours and that aside from suicide, self-harm and excessive spending, many behaviours are not routinely monitored.ConclusionThis study shows that people living with bipolar report the need for improved access to psychologically informed care, and that a standardised classification system or risk-taking questionnaire could act as a useful elicitation tool for guiding conversations around risk-taking to ensure that opportunities for intervention are not missed. We have also presented a novel methodological framework which demonstrates the utility of computational linguistic methods for the analysis of health research data.
Citation: DIGITAL HEALTH
PubDate: 2024-08-05T08:30:31Z
DOI: 10.1177/20552076241269580
Issue No: Vol. 10 (2024)
- A real-time interactive restoration system for intraoral digital videos
using segment anything model
Authors: Yongjia Wu, Li Zeng, Yaya Hong, Xiaojun Li, Xuepeng Chen
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectivePoor conditions in the intraoral environment often lead to low-quality photos and videos, hindering further clinical diagnosis. To restore these digital records, this study proposes a real-time interactive restoration system using segment anything model.MethodsIntraoral digital videos, obtained from the vident-lab dataset through an intraoral camera, serve as the input for interactive restoration system. The initial phase employs an interactive segmentation module leveraging segment anything model. Subsequently, a real-time intraframe restoration module and a video enhancement module were designed. A series of ablation studies were systematically conducted to illustrate the superior design of interactive restoration system. Our quantitative evaluation criteria contain restoration quality, segmentation accuracy, and processing speed. Furthermore, the clinical applicability of the processed videos was evaluated by experts.ResultsExtensive experiments demonstrated its performance on segmentation with a mean intersection-over-union of 0.977. On video restoration, it leads to reliable performances with peak signal-to-noise ratio of 37.09 and structural similarity index measure of 0.961, respectively. More visualization results are shown on the https://yogurtsam.github.io/iveproject page.ConclusionInteractive restoration system demonstrates its potential to serve patients and dentists with reliable and controllable intraoral video restoration.
Citation: DIGITAL HEALTH
PubDate: 2024-08-05T08:29:41Z
DOI: 10.1177/20552076241269536
Issue No: Vol. 10 (2024)
- Perceived usefulness of digital self-tracking among people with multiple
sclerosis
Authors: Lasse Skovgaard, Josephine Lyngh Steenberg, Marie Lynning
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
Background and aimSelf-care technologies may support patients with multiple sclerosis (MS) in their everyday disease management by enabling self-monitoring of various health indicators, such as symptom levels and physical activity levels. The aim of this study was to assess the usefulness of tracking self-selected MS- and health-related measures via a digital self-tracking tool for people with MS (PwMS) over a period of six weeks.MethodsAn initial development phase was followed by a six-week testing phase with 58 test participants. The evaluation phase followed a sequential, exploratory mixed-methods design, consisting of 14 interviews with test participants during the testing phase, followed by a survey of all participants after the testing phase to confirm and elaborate on the interview findings. The interview data were analyzed through a five-step thematic analysis, and the survey data were analyzed descriptively.ResultsThe results of the mixed-methods study can be summarized in the following findings: (1) Use of the self-tracking tool assisted users in clarifying patterns regarding their symptoms, physical activity, sleep quality and emotional well-being. (2) Tracking physical activity and, to some extent, sleep had a motivational effect on participants in relation to increasing activity and/or changing habits. (3) Data quality/accuracy constitutes an important criterion for considering the self-tracking tool relevant. (4) The self-tracking tool may support dialogue between patients and healthcare professionals, and/or it may potentially play a role in peer-to-peer support.ConclusionThe results of the present study indicate that the self-tracking of symptoms, sleep, physical activity and other measures may contribute positively to everyday self-management among PwMS. Professional support in interpreting and acting upon the data should be considered.
Citation: DIGITAL HEALTH
PubDate: 2024-08-05T08:29:00Z
DOI: 10.1177/20552076241264389
Issue No: Vol. 10 (2024)
- Patient satisfaction with telemedicine for substance-related disorders
Authors: María Alejandra Farias, Manuel Badino, María Jose Fuster de Apocada
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
IntroductionTelemedicine has been shown to be an effective approach for people with substance-related disorders. Analyzing patient satisfaction with telemedicine is necessary for improving treatment outcomes. This study aims to assess patient satisfaction with telemedicine for substance-related disorders at the Centro Asistencial Córdoba in Argentina.MethodsA cross-sectional, descriptive, and correlational design was carried out. A patient satisfaction survey was created, consisting of eight questions and a quality-of-life question, which was administered to N = 115 patients.ResultsThe results showed that more than 90% agreed with the ease of use of virtual consultations, 82% felt they received the same level of care as if the consultation had been in person, 86% agreed with the adequacy of time utilized during the virtual session, and over 85% agreed to repeat their telemedicine treatment. Regarding the composite variable “users’ assessment of telemedicine,” we found an average of 17.41 ± 2.80. Concerning satisfaction with virtual care and the previous use of telemedicine, 95.7% were satisfied, and nearly 61.7% reported not having used virtual care previously. In terms of money and time saved, 93.9% saved money with virtual consultations, 66.1% saved more than two hours per week, 23.5% saved more than one hour per week, and 10.4% saved less than one hour per week.ConclusionsOverall, there is significant approval of telemedicine among users of substance-related disorders services. In particular, they were satisfied with the time employed, the benefits of saving time and money, and the ease of use of telemedicine; furthermore, they were positive about undergoing telemedicine treatment in the future.
Citation: DIGITAL HEALTH
PubDate: 2024-08-05T08:28:31Z
DOI: 10.1177/20552076241240974
Issue No: Vol. 10 (2024)
- Pre-pandemic preventable hospitalization is associated with increased
telemedicine use in safety-net settings
Authors: Chinedum O Ojinnaka, Sandra Yuh, Lora Nordstrom, Omolola E Adepoju, Marisa Domino
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
IntroductionThe COVID-19 pandemic necessitated a major expansion in telemedicine use. The continued use of telemedicine post-pandemic has the potential to enhance healthcare use for people at risk for sub-optimal healthcare access and utilization, such as patients with previous preventable hospitalization. This study analyzed the association between pre-pandemic preventable hospitalizations (PPHs) and telemedicine use during the pandemic.MethodsThis retrospective cohort study uses Medicaid administrative claims data (01/2018–06/2022) for patients of a large Federally Qualified Health Center in Arizona that implemented telemedicine in March 2020. Bivariate and multivariable generalized estimating equations were used to analyze the relationship between the outcome and predictor variables. We also analyze racial/ethnic and primary language disparities in telemedicine use among those with PPH and report the average predicted probability.ResultsThere was a statistically significant relationship between telemedicine use and PPH even after adjusting for comorbidity severity (OR:1.85; CI: 1.74, 1.96). Analyses restricted to those who had PPHs showed an seven-percentage point difference in the predicted probability of telemedicine use between non-Hispanic White individuals and Asian/Pacific Islanders, the group with the lowest probability of telemedicine use among our study sample.ConclusionTelemedicine is a unique tool that can be leveraged by interventions that aim to optimize healthcare use among those with a history of preventable hospitalizations. However, the lack of targeted interventions to identify and address barriers to telemedicine use among minoritized groups could limit the impact of such interventions and widen disparities.
Citation: DIGITAL HEALTH
PubDate: 2024-08-05T08:00:55Z
DOI: 10.1177/20552076241260515
Issue No: Vol. 10 (2024)
- Prediction of patient flow in the emergency department using explainable
artificial intelligence
Authors: Pedro A Moreno-Sánchez, Matti Aalto, Mark van Gils
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
IntroductionOvercrowding in emergency departments (ED) is a significant problem affecting patient outcomes, hospital length of stay, and staff job satisfaction. This issue often stems from unpredictable patient flow and suboptimal resource allocation.ObjectivesThis study aims to develop two machine learning (ML) models to assist in early and accurate resource allocation in EDs. The first model predicts patient admission at the time of triage, while the second predicts the specialty of care needed indicated by the initial ward transfer.MethodsThe study leverages the Medical Information Mart for Intensive Care (MIMIC-IV) database with 425,000 ED visits including basic vital signs, medications, presentation information, diagnoses, and demographic information about the patients. Ensemble tree classifiers are employed for model development, and model's explainability is assessed by investigating feature importance. The best model is selected based on the balance between performance and explainability. Features’ importances are calculated and presented using SHapley Additive exPlanations and models’ intrinsic feature importance.ResultsThe best-balanced admission prediction model in terms of classification performance and explainability achieved an accuracy of 0.775 and an area under the receiver operating curve (AUROC) of 0.779 by using eXtreme Gradient Boosting (XGBoost). The resource allocation prediction model, using a one-vs-rest approach, attained an AUROC of 0.783 again by using XGBoost. The models shared acuity and age in the three most important features, whereas admission ratio and gender were the additional features for admission prediction and resource prediction, respectively.ConclusionThe study successfully demonstrates the potential of ML models in predicting patient admission and required specialty care at the ED triage stage. While the admission prediction model shows moderate performance compared to existing studies, the resource prediction model exhibits superior performance compared to related works. The research highlights the importance of explainability in ML models, suggesting the need for further practical implementation to refine and validate these models in real-world settings.
Citation: DIGITAL HEALTH
PubDate: 2024-08-02T10:15:00Z
DOI: 10.1177/20552076241264194
Issue No: Vol. 10 (2024)
- Comparing virtual reality vs. augmented reality in promoting COVID-19
self-testing, vaccination, and preventive behaviors
Authors: Zhan Xu, Linda Dam
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveVirtual Reality (VR) and Augmented Reality (AR) are innovative technologies that can serve as effective tools for creating health interventions by altering psychological distance. Based on construal level theory and the reality-virtuality continuum, we designed, tested, and compared VR and AR campaigns to encourage proactive measures against COVID-19.Methods143 participants were randomly assigned to one of three messages: VR, AR, and a CDC video, and completed surveys before, immediately, and one week following message exposure.ResultsVR and AR increased preventive intentions and behaviors against COVID-19 both in the short and long run. VR was particularly effective as it also increased risk perceptions, more preventive intentions in the short term, and more preventive behaviors, including social distancing and mask wearing, in the long term. VR was more efficient than AR in enhancing risk perceptions and preventive intentions right after being exposed to the messages as well as promoting behaviors such as avoiding crowds, maintaining social distance from others, and wearing a mask in indoor public areas one week later. Moreover, among the three conditions, VR was the only intervention that generated actual behavior change after one week, which indicated potential long-term advantages of VR compared to other mediums. VR decreased social, spatial, and hypothetical distances to a greater degree than AR. VR was more effective than video. However, AR was not more persuasive than video.ConclusionsInsights gained from the findings extend beyond the pandemic phase, offering practical applications for employing VR and AR technologies in health campaigns.
Citation: DIGITAL HEALTH
PubDate: 2024-08-02T10:14:40Z
DOI: 10.1177/20552076241269587
Issue No: Vol. 10 (2024)
- Developing cue-behavior association for habit formation: A qualitative
study to explore the role of avatar in hypertension
Authors: Yujie Zhu, Yonghao Long, Lai Wei, Yaqi Zhang, Zhengtao Ma, Kun-Pyo Lee, Lie Zhang, Stephen J. Wang
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundElectronic health (eHealth) has been widely adopted in chronic disease management. Prior studies focused on time-based reminders as a cue to facilitate behavior change intentions, ignoring the development of automatic cue-behavior associations via other cue types.ObjectiveHence, this study utilized avatar appearance as a visual-based cue to help establish the automatic association between appearance transformation and health behavior to form habits without intention.MethodsTo better understand users’ attitudes and experiences toward applying changes in avatar appearance to develop cue-behavior associations for hypertensive patients. Fifteen participants were recruited in a 14-day experiment. After excluding one participant who dropped out of the experiment, others were randomly assigned to two groups. One group consisted of a visual-based cue (a virtual plant) and basic behavior change techniques (BCTs). The other group only included basic BCTs. Attitudes and experience outcomes were collected by interview, and qualitative data were analyzed using thematic analysis.Results57% of participants had been diagnosed with hypertension for more than five years, and more than 50% of participants have experience using mobile apps or wearables. 66% of participants did physical activity more than three times every week. The result shows that tailored time-based reminders, blood pressure monitoring, and daily dietary intake were the most attractive features. Additionally, hypertensive participants have positive attitudes toward avatar appearance as a visual-based cue to develop cue-behavior association, which enhances self-management motivation.ConclusionThis study proposes a visual-based cue design for habit formation and conducts a qualitative method to explore hypertensive patients’ perceptions. The findings offer insights from user's perspectives into hypertensive patients’ attitudes toward visual-based cues and perception of the connection between avatar appearance and health behavior for self-management. Subsequent discussions present eHealth design guidelines of habit formation from intention, automatic cue-behavior association, and self-management perspectives.
Citation: DIGITAL HEALTH
PubDate: 2024-08-02T10:14:20Z
DOI: 10.1177/20552076241265217
Issue No: Vol. 10 (2024)
- Erratum to “COVID-19 surveillance based on consumer wearable
devices”
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
Citation: DIGITAL HEALTH
PubDate: 2024-08-02T10:14:00Z
DOI: 10.1177/20552076241261219
Issue No: Vol. 10 (2024)
- Case management in a web-based cognitive and motor treatment for patients
with mild cognitive impairment: A study protocol of a multicentric,
randomized, two parallel arms controlled clinical trial
Authors: Alessia Gallucci, Cosimo Tuena, Francesca Bruni, Massimiliano Fedecostante, Lorena Rossi, Antonio Greco, Mauro Tettamanti, Fabrizia Lattanzio, Marco Stramba-Badiale, Fabrizio Giunco, Pietro Davide Trimarchi
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThe growing attention towards the psychosocial characteristics of mild cognitive impairment (MCI), a possible prodromal stage of dementia, contributed to the spread of patient-centered approaches to the care of age-related cognitive decline. Within these new care models, including Case-Management approaches, technology-based treatments showed to improve quality of life and psychosocial functioning of people with MCI. However, studies testing technologies to treat functional well-being of MCI patients are still few. Moreover, whether Case Manager supervised or unsupervised technological interventions lead to comparable results is so far unexplored. This study protocol aims to test the superiority of a 1-year physical and cognitive training treatment supervised by a Case Manager in improving the quality of life, functional decline, treatment adherence, cognitive impairment of MCI patients and the mental well-being of informal caregivers compared to the same yet unsupervised treatment.MethodsIn a multicentric, randomized, two parallel arms controlled clinical trial MCI patients ≥65 year-old will be randomized to receive a technology-based treatment either supervised or unsupervised by a Case Manager. Regardless of the study group, patients will perform cognitive and motor exercises with the monitoring of a specialist only during the first 6 weeks of intervention. Three and two assessment points will be considered during the intervention and follow-up period, respectively. The quality of life will be measured as primary outcome at 6 months after baseline.ConclusionsThe results of this study will provide an evidence base to use Case Management as an adjuvant approach for technology-based treatments of psychosocial characteristics of MCI.
Citation: DIGITAL HEALTH
PubDate: 2024-08-02T10:13:41Z
DOI: 10.1177/20552076241258768
Issue No: Vol. 10 (2024)
- Co-designing a new digital mental health platform, ‘Momentum’, with
caregivers of young people aged 7–17
Authors: Kristiana Ludlow, Sonja March, Jeremy K Russell, Brooke Ryan, Renee L Brown, Leanne Hides, Caroline Donovan, Susan H Spence, Joseph Saxby, Vanessa E Cobham
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
IntroductionDigital mental health interventions (DMHIs) can offer affordable, accessible and anonymous delivery of mental health treatment as an alternative or complement to face-to-face services. To enhance acceptability of, and treatment engagement with, youth DMHIs, they should be co-designed with young people and their caregivers. This study focuses on co-design with caregivers.ObjectiveTo explore caregivers’ perspectives, preferences, and ideas to inform the co-design of a digital youth mental health platform for anxiety and depression: ‘Momentum’.Methods and MeasuresSix group discussions were conducted with 16 caregivers of young people aged 7–17 years. In relation to Momentum, and DMHIs more broadly, participants’ views were sought on purpose and acceptability, access, assessments and feedback, information sharing, caregiver involvement and engagement. Data were thematically analysed using an inductive codebook approach, guided by template analysis.ResultsSeven themes were identified: (1) providing therapeutic and educational resources; (2) promoting shared and positive communication about mental health; (3) enhancing user experience and understanding; (4) facilitating personalisation and offering customisation of platform features; (5) encouraging end-user engagement through interactivity, incentives, relatability and attracting and maintaining attention; (6) enabling caregivers to provide support while promoting young people's independence; and (7) reducing concerns about, and enhancing trust in, DMHIs.ConclusionsParticipants expressed favourable views towards DMHIs and Momentum. They shared design ideas for a user-friendly, engaging, interactive, trustworthy, personalised and transparent platform that offered educational resources and mental health treatment. Two sets of recommendations were derived from the study findings: (1) recommendations for co-designing DMHIs with caregivers and (2) recommendations for the design of youth DMHIs.
Citation: DIGITAL HEALTH
PubDate: 2024-08-02T10:12:40Z
DOI: 10.1177/20552076241263693
Issue No: Vol. 10 (2024)
- An AI-based prognostic model for postoperative outcomes in non-cardiac
surgical patients utilizing TEE: A conceptual study
Authors: Yu Zhu, Renrui Liang, Cheng-Mao Zhou
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThe primary objective of this study was to assess the potential of artificial intelligence techniques, in conjunction with transthoracic echocardiography (TEE) examinations, to forecast postoperative mortality outcomes in patients undergoing moderate-to-high-risk noncardiac surgeries.MethodsThis is a second retrospective analysis using the BioStudies public database. This dataset includes data from two medical centers. We partitioned the dataset utilizing a 7:3 ratio. This model seamlessly integrated diverse algorithms, encompassing both machine learning and deep learning methodologies such as logistic regression, gradient boosting decision tree, XGBoost, LightGBM, CatBoost, linear support vector classification, multilayer perceptron classifier, Gaussian Naive Bayes, Adaboost, recurrent neural network, convolutional neural network, Bayesian neural network, and probabilistic Bayesian neural network. To thoroughly evaluate the model's performance, we employed multiple metrics, including the receiver operating characteristic curve, accuracy, precision, F1 score, recall, calibration curve, and clinical decision curve.ResultsThe present study included a total of 1453 patients. The Gbdt algorithm ranks the variable importance, and the top five important results are creatinine (Cr), creatinine exceeding twice the upper limit (Cr > 2), creatinine clearance, left ventricular end-diastolic internal diameter, and hemoglobin. Among these algorithms, only Gbdt algorithm yielded satisfactory results both in the training and test groups. In the training group, Gbdt had an area under the curve (AUC) value of 0.904, accuracy of 0.984, and precision of 1; In the testing group, Gbdt had an AUC value of 0.835, accuracy of 0.984, and precision of 0.5. However, the Gbdt algorithm demonstrated suboptimal performance in terms of recall rate and F1 score. Finally, we successfully developed an online intelligent prediction webpage that utilizes the Gbdt algorithm and TEE.ConclusionsGbdt represents an optimal approach for predicting postoperative mortality among patients undergoing non-cardiac surgery with moderate-to-high risk.
Citation: DIGITAL HEALTH
PubDate: 2024-08-02T10:12:20Z
DOI: 10.1177/20552076241261921
Issue No: Vol. 10 (2024)
- Relaxation training via tele-rehabilitation program in patients with
breast cancer receiving chemotherapy during COVID-19
Authors: Umut Bahçaci, Songül Atasavun Uysal, Esat Namal
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
IntroductionThe COVID-19 pandemic has highlighted the significance of tele-rehabilitation in granting access to physical therapy for breast cancer patients. To mitigate the adverse effects of chemotherapy, Jacobson's relaxation techniques can be performed from the comfort and safety of patients’ homes. The aim of this research was to investigate the effects of relaxation exercises delivered via tele-rehabilitation in breast cancer patients receiving chemotherapy.MethodA total of 64 patients, were randomly allocated to the exercise group (n = 33) and to the control group (n = 31). The exercise group performed supervised “Jacobson Progressive Relaxation” exercises in groups of up to eight participants, three times a week for 6 weeks via WhatsApp© meetings. The control group was provided with a simple relaxation exercise brochure. The primary outcome was the “pain” which was measured by “Brief Pain Inventory.” Fatigue, emotional state, quality of life, cognitive state, sleep quality, and kinesiophobia were considered as secondary outcomes. All measurements were made at the first appointment and after the intervention.ResultsThe study was finally completed with 52 participants, 26 in each group. The exercise group demonstrated a statistically significant improvement in pain (all subscales p
Citation: DIGITAL HEALTH
PubDate: 2024-08-02T10:12:01Z
DOI: 10.1177/20552076241261909
Issue No: Vol. 10 (2024)
- “How it is said”: Analyses of WhatsApp communications in a postpartum
depression preventive intervention
Authors: Olga Fernández, J Carola Pérez, Nicolle Alamo, Sofia Fernandez, Pamela Franco, Soledad Coo, M Ignacia García, Marcela Aravena
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
Introduction: “m-What Were We Thinking” (m-WWWT) is an m-health, intervention oriented to prevent symptoms of postpartum depression and anxiety in first-time mothers. Mothers receive psychoeducation and socioemotional support through written communication via text-messaging (i.e., WhatsApp) with the program counsellor. Although the use of m-health interventions targeting mental health has increased, the form/style of communication through text messages between participants and program counsellors has been understudied. Objective: The aim was to describe the formal structure of the communication (Basic Forms) and the communicational intentions (Communicative Intentions) used in the messages sent by the counsellor and to determine if these are related to the post-intervention outcomes. Methods: 438 text messages sent by the counsellor to 53 first-time mothers (M = 25.32 years, SD = 4.23) who participated in the m-WWWT intervention were analyzed. The Therapeutic Activity Coding System was used to capture the communication as a “Communicative Action”. Results: The study highlighted the counsellor's frequent use of the “assertion” communicative form (82%) and attuned communicative intentions (52%) and explored (39%) in her messages. The attractors of communication were “assert to attune” and “assert to explore”, indicating an empathetic and informative communication style. With respect to their relationship with the intervention outcome, only “assert to explore” messages were positively related to maternal self-efficacy increases. The number of messages was not associated with participants’ characteristics at baseline, except for educational level. Discussion: Our results show the relevance of combining the delivery of information with a communication style that allows the counsellor to connect with the specific needs and emotional tone of the participants.
Citation: DIGITAL HEALTH
PubDate: 2024-08-02T10:11:41Z
DOI: 10.1177/20552076241261894
Issue No: Vol. 10 (2024)
- Exploring the use of immersive virtual reality in adults with chronic
primary pain: A scoping review
Authors: Anfal Astek, Valerie Sparkes, Liba Sheeran
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundThe use of immersive virtual reality, wearing head mounted display, has recently increased for people with chronic pain, with no definitive conclusion of its efficacy on pain-related outcomes.ObjectiveTo map the available evidence on the use of immersive virtual reality as intervention for adults with chronic primary pain, illustrating gap in knowledge and direct future research.MethodsThe search was performed with a range of study designs, but only those written in English language. A search was created in CINAHL Plus, Medline, AMED, Embase, PsycINFO, ASSIA, Scopus, TripPro, CENTRAL and EmCare.ResultsThirty-two studies were included. Several chronic conditions were identified including chronic musculoskeletal pain and fibromyalgia. The immersive virtual reality mechanisms included distraction, physical exercises, mindfulness/biofeedback, graded exposure, hypnosis, neuromodulation, and multi-mechanisms, and all these mechanisms were associated with varied dose. The use of customised software, with wide range of head mounted displays were common in clinical setting with some degree of supervision. Motion sickness, head mounted display discomfort and technical issues affect the usability of immersive virtual reality leading to poor engagement and dropouts.ConclusionsThe use of immersive virtual reality for chronic primary pain is in early stages with lack of consensus regarding the mechanisms and associated dose. Future research needs to address the need of customisation, clinical usability of head mounted display as well as safety strategies to enhance the uptake of immersive virtual reality technology in healthcare practice.
Citation: DIGITAL HEALTH
PubDate: 2024-08-02T10:11:10Z
DOI: 10.1177/20552076241254456
Issue No: Vol. 10 (2024)
- Dance sport movement therapy in the metaverse: A new frontier for
alternative mental health therapies
Authors: Petar Radanliev
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
This paper investigates the integration of Dance Movement Therapy (DMT) within extended reality (XR) environments, exploring its potential as a non-pharmacological intervention for mental health conditions. The study employed a blend of qualitative evidence synthesis and analyses of primary quantitative data, focusing on the therapeutic implications of Dance in virtual, augmented and mixed realities. Data from wearables and sensors on movements, physiological responses and emotional feedback are analysed using AI/ML algorithms, including Random Forest, SVM, CNNs and RNNs. The research highlighted the importance of data privacy and ethical considerations, emphasising the need for securely storing metadata to ensure user trust and legal compliance. Findings underscored the potential of XR environments like the Metaverse in transforming mental health practices, offering efficient, engaging and effective therapeutic interventions. The study also introduced the novel concept of Physical Intensity Matching and the significance of personalised exercise selection. Despite its ground-breaking insights, the research acknowledged potential biases introduced by wearables and the challenges of ensuring data accuracy. This paper is a foundational exploration into the convergence of DMT, XR and AI, paving the way for future interdisciplinary research in mental health and technology.
Citation: DIGITAL HEALTH
PubDate: 2024-08-02T10:09:44Z
DOI: 10.1177/20552076241258273
Issue No: Vol. 10 (2024)
- Lifelog-based daily step counts, walking speed, and metabolically healthy
status
Authors: Ga-Young Lim, Eunkyo Park, Ji-Young Song, Ria Kwon, Jeonggyu Kang, Yoosun Cho, Se Young Jung, Yoosoo Chang, Seungho Ryu
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveOptimal metabolically healthy status is important to prevent various chronic diseases. This study investigated the association between lifelog-derived physical activity and metabolically healthy status.MethodsThis cross-sectional study included 51 Korean adults aged 30–40 years with no history of chronic diseases. Physical activity data were obtained by the International Physical Activity Questionnaire-Short Form (IPAQ-SF). Lifelog-derived physical activity was defined by step counts and walking speed for 1 week, as recorded by the Samsung Health application on both the Samsung Galaxy Fit2 and mobile phones. Participants without metabolic syndrome components were categorized as the metabolically healthy group (n = 31) and the remaining participants as the metabolically unhealthy group (n = 20). Prevalence ratios and 95% confidence intervals were estimated using Poisson regression models. The predictive ability of each physical activity measure was evaluated according to the area under the curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI) values.ResultsAmong the physical activity measures, lifelog-derived walking speed was significantly inversely associated with prevalent metabolically unhealthy status. The lifelog component model including walking speed, age, and sex had the highest AUC value for metabolically unhealthy status. Adding lifelog-derived step counts to the IPAQ-SF-derived metabolic equivalent (MET) model (including age, sex, and IPAQ-SF-METs) yielded 37% and 13% increases in the NRI and IDI values, respectively. Incorporating walking speed into the IPAQ-SF-derived MET model improved metabolically unhealthy status prediction by 42% and 21% in the NRI and IDI analyses, respectively.ConclusionsSlow walking speed derived from the lifelog was associated with a higher prevalence of metabolically unhealthy status. Lifelog-derived physical activity information may aid in identifying individuals with metabolic abnormalities.
Citation: DIGITAL HEALTH
PubDate: 2024-07-26T09:58:39Z
DOI: 10.1177/20552076241260921
Issue No: Vol. 10 (2024)
- Improving safety claims in digital health interventions using the digital
health assessment method
Authors: Stuart Harrison, Carsten Maple, Gregory Epiphaniou, Theodoros N Arvanitis
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveEstablish a relationship between digital health intervention (DHI) and health system challenges (HSCs), as defined by the World Health Organization; within the context of hazard identification (HazID), leading to safety claims. To improve the justification of safety of DHIs and provide a standardised approach to hazard assessment through common terminology, ontology and simplification of safety claims. Articulation of results, to provide guidance for health strategy and regulatory/standards-based compliance.MethodsWe categorise and analyse hazards using a qualitative HazID study. This method utilises a synergy between simplicity of DHI intended use and the interaction from a multidisciplinary team (technologists and health informaticians) in the hazard analysis of the subject under assessment as an influencing factor. Although there are other methodologies available for hazard assessment. We examine the hazards identified and associated failures to articulate the improvements in the quality of safety claims.ResultsApplying the method provides the hazard assessment and helps generate the assurance case. Justification of safety is made and elicits confidence in safety claim. Controls to hazards contribute to meeting the HSC.ConclusionsThis method of hazard assessment, analysis and the use of ontologies (DHI & HSC) improves the justification of safety claim and evidence articulation.
Citation: DIGITAL HEALTH
PubDate: 2024-07-25T11:14:19Z
DOI: 10.1177/20552076241258756
Issue No: Vol. 10 (2024)
- Development and usability evaluation of a culturally adapted stroke
prevention educational programme on WeChat apps
Authors: Cui Liu, Wan Ling Lee, Chin Hai Teo, Jin Hua Zhang, Mei Chan Chong
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundThe persistently high incidence of stroke in many nations is suggestive of an area for further improvement on existing strategies of primary stroke prevention. Although the era of digitalisation has led to the increasing use of mobile applications (apps) in healthcare, more studies are needed to determine the efficacy of apps in producing the desired health outcomes across different nations and cultures.ObjectiveTo describe the development and evaluate the usability of a mobile app in delivering a culturally adapted stroke prevention educational programme for middle-aged adults in the Republic of China.MethodsThe educational programme was developed in three phases. In Phase 1, the process involved analysing requirements and designing structured modules. Phase 2 concentrated on expert consultation and technical development to deliver the educational programme. Phase 3 included a usability trial and refinement of the educational program based on trial results.ResultsEducational content was derived from the Chinese Guidelines for the Prevention and Treatment of Stroke and the Dietary Guidelines for Residents. The WeChat platform was used to deliver the educational programme. Participants expressed satisfaction with the content, interface, and functions of the apps, indicating that the apps have good usability.ConclusionsThe development process of the Educational Programme was designed to maximise the culturally appropriate, and impact of lifestyle changes and stroke prevention. An app-based educational programme that has demonstrated good usability is a vital factor prior to deploying it in an intervention to evaluate its effects on health outcomes.
Citation: DIGITAL HEALTH
PubDate: 2024-07-25T11:13:38Z
DOI: 10.1177/20552076241263695
Issue No: Vol. 10 (2024)
- Cognitive-enhanced eHealth psychosocial stepped intervention for managing
breast cancer-related cognitive impairment: Protocol for a randomized
controlled trial
Authors: Maria Serra-Blasco, Arnau Souto-Sampera, Joan C. Medina, Aida Flix-Valle, Laura Ciria-Suarez, Alejandra Arizu-Onassis, Marina Ruiz-Romeo, Femke Jansen, Ana Rodríguez, Sonia Pernas, Cristian Ochoa-Arnedo
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
IntroductionBreast cancer often leads to cancer-related cognitive impairment (CRCI), which includes both objective and subjective cognitive deficits. While psychosocial interventions benefit quality of life and distress reduction, their impact on cognitive deficits is uncertain. This study evaluates the integration of a cognitive module into a digital psychosocial intervention for breast cancer patients.MethodsIn this randomized controlled trial (RCT), 88 recently diagnosed breast cancer (BC) patients will receive the ICOnnecta't program (control group) – a digital stepped intervention addressing a variety of psychosocial needs. The experimental group (n = 88) will receive ICOnnecta’t plus a cognitive module. Assessments at baseline, 3, 6, and 12 months will measure the interventions’ impact on cognition, emotional distress, medication adherence, quality of life, post-traumatic stress, work functioning and healthcare experience. Feasibility and cost-utility analyses will also be conducted.ResultsThe cognitive module includes three levels. The first level contains a cognitive screening using FACT-Cog Perceived Cognitive Impairment (PCI). Patients with PCI
Citation: DIGITAL HEALTH
PubDate: 2024-07-25T11:13:22Z
DOI: 10.1177/20552076241257082
Issue No: Vol. 10 (2024)
- Exploratory analysis of swallowing behaviour in community-dwelling older
adults using a wearable device: Differences by age and ingestant under
different task loads
Authors: Masashi Tsujimoto, Tomoko Hisajima, Saho Matsuda, Seiya Tanaka, Keisuke Suzuki, Tomoya Shimokakimoto, Yoshio Toyama
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveTo develop a new method of evaluating swallowing behaviour.MethodsSixty-nine healthy participants were divided into a younger (16 males and 16 females, mean age 39.09 ± 12.16 years) and older (18 males and 19 females, mean age 71.43 ± 5.50 years) group. The participants ingested water and yoghurt twice (directed and free swallowing) at rest and after performing simple daily life tasks (calculation and exercise). To measure swallowing frequency, we employed a smartphone-based, portable and neck-worn swallowing-sound-monitoring device. This device monitors swallowing behaviour continuously by collecting biological sounds from the neck without imposing behavioural restrictions. A neural network model of swallowing sound identification by deep learning was used for the subsequent evaluation. This device was used to obtain two types of saliva-swallowing sounds associated with different ingestants, at rest and after performing a stimulating task. Furthermore, we assessed the associated subjective psychological states.ResultsThe younger group showed a higher directed swallowing frequency (for both water and yoghurt) than the older group did. Regarding the type of ingestant, the swallowing frequency for yoghurt was higher during free swallowing in both the young and the older groups. ‘Feeling calm’ was reported significantly more often in the older group after swallowing yoghurt following exercise.ConclusionsSwallowing status in daily life was measured non-invasively using a wearable mobile device. It is important to consider the type of ingestant, daily living activities, and age when assessing swallowing.
Citation: DIGITAL HEALTH
PubDate: 2024-07-25T10:07:59Z
DOI: 10.1177/20552076241264640
Issue No: Vol. 10 (2024)
- Remote sensing mental health: A systematic review of factors essential to
clinical translation from validation research
Authors: Niranjan Bidargaddi, Richard Leibbrandt, Tamara L Paget, Johan Verjans, Jeffrey CL Looi, Jessica Lipschitz
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundMental illness remains a major global health challenge largely due to the absence of definitive biomarkers applicable to diagnostics and care processes. Although remote sensing technologies, embedded in devices such as smartphones and wearables, offer a promising avenue for improved mental health assessments, their clinical integration has been slow.ObjectiveThis scoping review, following preferred reporting items for systematic reviews and meta-analyses guidelines, explores validation studies of remote sensing in clinical mental health populations, aiming to identify critical factors for clinical translation.MethodsComprehensive searches were conducted in six databases. The analysis, using narrative synthesis, examined clinical and socio-demographic characteristics of the populations studied, sensing purposes, temporal considerations and reference mental health assessments used for validation.ResultsThe narrative synthesis of 50 included studies indicates that ten different sensor types have been studied for tracking and diagnosing mental illnesses, primarily focusing on physical activity and sleep patterns. There were many variations in the sensor methodologies used that may affect data quality and participant burden. Observation durations, and thus data resolution, varied by patient diagnosis. Currently, reference assessments predominantly rely on deficit focussed self-reports, and socio-demographic information is underreported, therefore representativeness of the general population is uncertain.ConclusionTo fully harness the potential of remote sensing in mental health, issues such as reliance on self-reported assessments, and lack of socio-demographic context pertaining to generalizability need to be addressed. Striking a balance between resolution, data quality, and participant burden whilst clearly reporting limitations, will ensure effective technology use. The scant reporting on participants’ socio-demographic data suggests a knowledge gap in understanding the effectiveness of passive sensing techniques in disadvantaged populations.
Citation: DIGITAL HEALTH
PubDate: 2024-07-25T10:07:40Z
DOI: 10.1177/20552076241260414
Issue No: Vol. 10 (2024)
- Digital mental health strategies used by young people in Aotearoa New
Zealand during the COVID-19 pandemic: ‘Just do it yourself, DIY’
Authors: Kerry Gibson, Susanna Trnka, Monique Jonas, Pikihuia Pomare, Shauney Thompson, Jemaima Tiatia-Siau, KDee Aimiti Ma'ia'i, Miriama Aoake, Thibaut Bouttier-Esprit, Imogen Spray, Sanchita Vyas
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveWith rising rates of mental health distress amongst youth during the COVID-19 pandemic, digital resources have been identified as a valuable tools for delivering support to young people. However, many of the websites and apps developed by professionals to support the youth do not take account of the importance young people place on exercising their own agency in managing their mental health. This article investigates how young people in Aotearoa New Zealand used digital resources to manage their mental health needs during the COVID-19 pandemic.MethodsThe study gathered information from semi-structured interviews with 34 young people aged 16–22 years. The data was analysed using reflexive thematic analysis.ResultsSix themes were identified including: searching for online information about mental health; evaluating digital mental health resources; controlling mood through online activity; looking for escape in the virtual world; staying connected online; and giving and receiving support.ConclusionYoung people's practices demonstrated their investment in their own agency, a general reluctance to engage with professional resources and recognition of the need to balance the risks and benefits of the informal strategies they preferred. Young people appeared sceptical of professionally-designed mental health resources and interventions and preferred to adapt and re-purpose the wide range of platforms and networks available in their informal digital worlds.
Citation: DIGITAL HEALTH
PubDate: 2024-07-25T09:57:44Z
DOI: 10.1177/20552076241260116
Issue No: Vol. 10 (2024)
- Challenges and conditions for successfully implementing and adopting the
telematics infrastructure in German outpatient healthcare: A qualitative
study applying the NASSS framework
Authors: Kim Nordmann, Stefanie Sauter, Marie-Christin Redlich, Patricia Möbius-Lerch, Michael Schaller, Florian Fischer
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundGermany's healthcare system provides high-quality, universal health coverage to almost all residents. However, a major challenge lies in the strong separation of healthcare structures, which hinders efficient interprofessional and intersectoral communication and collaboration. The mandatory nationwide implementation of the telematics infrastructure may offer a solution to enhance healthcare professionals’ communication and collaboration.ObjectiveOur study aims to elicit participants’ perceptions of and attitudes towards the implementation and usage of the telematics infrastructure in fostering interprofessional communication and collaboration between home-care nursing services and general practitioner practices.MethodsWe conducted interviews with seven members of general practitioner practices and 10 in home-care nursing services. Using thematic content analysis, we identified five themes, of which four along with 10 subthemes were integrated into Greenhalgh et al.'s ‘nonadoption, abandonment, scale-up, spread and sustainability’ framework.ResultsParticipants recognised the potential of digital technology to enhance interprofessional communication and collaboration. However, this potential largely depended on individual healthcare actors’ willingness to seek information, invest and adapt. Attitudes towards the telematics infrastructure varied widely from hopeful confidence to outright rejection. Home-care nursing services generally viewed the telematics infrastructure with optimism, while general practitioners expressed reservations, particularly due to technological disruptions, lack of user-friendliness, and organisational structures.ConclusionOur findings highlight the potential of digital technology to enhance interprofessional communication. Successful implementation of technological innovations, however, goes beyond technological aspects and involves social, political and organisational processes. Future implementation strategies for such innovations in healthcare should involve users early and ensure clear communication.
Citation: DIGITAL HEALTH
PubDate: 2024-07-25T09:57:14Z
DOI: 10.1177/20552076241259855
Issue No: Vol. 10 (2024)
- Experiences of rural clinicians accessing specialist support via
telehealth for trauma and emergency care in Queensland, Australia
Authors: Chiara Santomauro, Mia McLanders, Andrew Rae
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveTrauma and emergency patients presenting to rural facilities require time-critical treatment and management that is sometimes beyond the scope of clinicians in the facility. In Queensland, Australia's second largest state, telehealth infrastructure facilitates 24/7 communication between rural clinicians and tertiary-based critical care specialists. We sought to understand the current state of Queensland's emergency telehealth system from the perspective of direct end-users to inform future improvement efforts and resource allocation.MethodsSemi-structured interviews were conducted with 11 rural Queensland clinicians who use telehealth to access specialist support during critical presentations. Qualitative data were analysed in three inductive phases: immersion; a combination of process coding and in vivo coding; and focused coding.ResultsThe findings highlight that emergency telehealth support provides benefits beyond better patient care, as it fosters collegiality and alleviates professional isolation. Three key themes were identified: (a) strategies for overcoming challenges in providing trauma and emergency care in rural Queensland; (b) factors that affect perceptions of telehealth effectiveness; and (c) how support for rural trauma and emergency care can be improved. To provide context for the themes, a summary of scene-setting data is also provided.ConclusionsThere are both advantages and disadvantages for rural clinicians accessing telehealth specialist support for critical care. Although telehealth is seen as a vital service that supports rural clinicians and benefits patient care, the findings suggest that tools, systems and processes surrounding rural trauma and emergency care could benefit from streamlining, integration, and the introduction of fit-for-purpose technologies. Addressing limitations of efficiencies would improve support for rural clinicians and likely improve patient outcomes for rural communities.
Citation: DIGITAL HEALTH
PubDate: 2024-07-25T09:43:24Z
DOI: 10.1177/20552076241251950
Issue No: Vol. 10 (2024)
- Exploring the barriers and facilitators experienced by patients with heart
failure when using popular exergaming platforms for self-management—a
systematic review
Authors: Elham Ravani, Fai Ali, Sara Albuainain, Sara Flamarzi, Tuqa Dirar, Michelle O’Brien, Linzette Morris
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveTo systematically explore the barriers and facilitators experienced by patients with heart failure when using the most popular commercially-accessible digital exergaming platforms for self-management.MethodsA systematic literature search was conducted in: Embase, SCOPUS, PubMed, and ProQuest. Qualitative/mixed methods studies published in English between 2000 and 2024, including adults>18 years with heart failure using digital gaming platforms for exercise as self-management (i.e. Microsoft Xbox/Kinect, Sony PlayStation®, Nintendo™ Wii) were considered. Risk of bias was assessed using the Critical Appraisal Skills Program. The grounded theory method was used to extract, analyze, and synthesize the data.ResultsThree articles which qualitatively reported on the experiences of the patients with heart failure when using Nintendo™ Wii for self-management were included. Seventy-nine participants (male and female, age 32–81 years) were included in the studies. The three included studies were of high quality. Extracted qualitative data were grouped into subthemes, which were grouped into main themes, namely, Engagement, Apathy, Convenience, Health-related benefits and Gaming Platforms. The main themes were categorized into Barriers and Facilitators. Patients reported that gaming platforms were simple, easy to use, interesting, and motivating (Facilitators). Boredom while playing specific games, preferring other activities and technical issues were reported as Barriers.ConclusionThis review provides the first insights into the barriers and facilitators patients with heart failure experience when using popular digital exergaming platforms to participate in exercise programs in the self-management of their condition. However, few high-quality studies exist which limits the conclusions made and warrants further research in this area.
Citation: DIGITAL HEALTH
PubDate: 2024-07-25T09:43:07Z
DOI: 10.1177/20552076241249823
Issue No: Vol. 10 (2024)
- Stakeholder perspectives on ethical and trustworthy voice AI in health
care
Authors: Jean-Christophe Bélisle-Pipon, Maria Powell, Renee English, Marie-Françoise Malo, Vardit Ravitsky, Yael Bensoussan
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveVoice as a health biomarker using artificial intelligence (AI) is gaining momentum in research. The noninvasiveness of voice data collection through accessible technology (such as smartphones, telehealth, and ambient recordings) or within clinical contexts means voice AI may help address health disparities and promote the inclusion of marginalized communities. However, the development of AI-ready voice datasets free from bias and discrimination is a complex task. The objective of this study is to better understand the perspectives of engaged and interested stakeholders regarding ethical and trustworthy voice AI, to inform both further ethical inquiry and technology innovation.MethodsA questionnaire was administered to voice AI experts, clinicians, scholars, patients, trainees, and policy-makers who participated at the 2023 Voice AI Symposium organized by the Bridge2AI-Voice AI Consortium. The survey used a mix of Likert scale, ranking and open-ended questions. A total of 27 stakeholders participated in the study.ResultsThe main results of the study are the identification of priorities in terms of ethical issues, an initial definition of ethically sourced data for voice AI, insights into the use of synthetic voice data, and proposals for acting on the trustworthiness of voice AI. The study shows a diversity of perspectives and adds nuance to the planning and development of ethical and trustworthy voice AI.ConclusionsThis study represents the first stakeholder survey related to voice as a biomarker of health published to date. This study sheds light on the critical importance of ethics and trustworthiness in the development of voice AI technologies for health applications.
Citation: DIGITAL HEALTH
PubDate: 2024-07-23T11:15:22Z
DOI: 10.1177/20552076241260407
Issue No: Vol. 10 (2024)
- Effectiveness of self-reported management program of cancer patients
Authors: Cheolkyung Sin, Dong Yeop Lee, Hyeyeong Kim, Hyeon-Su Im, Su-Jin Koh, Dong Yoon Kang
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThis study aimed to analyze the effect of Smart Cancer Care program on the quality of life and ease of chemotherapy continuation in cancer patients and the effect of additional tele-management on frequency of use and satisfaction with the Smart Cancer Care program.Methods‘Smart Cancer Care’ is a mobile program that allows cancer patients undergoing chemotherapy to report symptoms of adverse events and receive remote management. In this study, patients were randomly assigned to three groups: Group A, who received only classical face-to-face management; Group B, who used the Smart Cancer Care program as addition; and Group C, who used the Smart Cancer Care program and received telephone management. After 12 weeks of follow-up, the effectiveness of using the Smart Cancer Care program was analyzed by examining the quality of life, ease of maintaining chemotherapy, and unplanned hospital visits in each group. The frequency of use and satisfaction with the Smart Cancer Care program were also analyzed.ResultsCancer patients who used the Smart Cancer Care program had 1.93-fold (1.15–3.25) higher overall quality of life than those who did not. This became 2.33-fold (1.34–4.04) higher when phone care was added. Patients with tele-management were significantly more likely to use the Smart Cancer Care program (odds ratio (OR) = 25.80; 95% confidence interval (CI), 11.28–58.97).ConclusionsA mobile self-reported management program has a positive effect on the quality of life of cancer patients undergoing chemotherapy. Tele-management is conducive to active and effective use of this program.
Citation: DIGITAL HEALTH
PubDate: 2024-07-23T11:15:03Z
DOI: 10.1177/20552076241253090
Issue No: Vol. 10 (2024)
- Construction and evaluation of acquired weakness nomogram model in
patients with mechanical ventilation in intensive care unit
Authors: Chen Lu, Jiang Wenjuan
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThe incidence of intensive care unit acquired weakness (ICU-AW) has shown an increasing trend with still a lack of effective treatment options. The early assessment of the risk of developing ICU-AW can provide patients with targeted interventions. This study aimed to determine the independent risk factors of ICU-AW in patients receiving mechanical ventilation (MV) and develop a nomogram and verify its predictive efficacy.MethodsThis observational study included patients receiving MV therapy in the ICU of our hospital between January 2020 and January 2023. They were divided into the ICU-AW and non-ICU-AW groups. The training cohort (n = 264) and the validation cohort (n = 143) were constructed. Multivariate logistic regression analyses were used to select the risk factors, and a nomogram model was established. Calibration, receiver operating characteristic (ROC), and decision curves were used to evaluate the effectiveness of the model.ResultsThe MV duration (OR = 1.24, 95%CI[1.11, 1.38]), APACHE II score (OR = 1.34, 95%CI[1.20, 1.50]), SOFA score (OR = 1.36, 95%CI[1.21, 1.53]), age (OR = 1.05, 95%CI[1.00, 1.10]), nerve blockers (OR = 3.26, 95%CI[1.34, 7.92]), and diabetes mellitus (OR = 3.12, 95%CI[1.10, 8.87]) were independent risk factors for ICU-AW. The nomogram had good predictive efficacy for both the training (area under the curve (AUC) = 0.950, 95%CI [0.93, 0.97]) and validation cohorts (AUC = 0.823, 95%CI [0.75, 0.89]).ConclusionThe MV duration, APACHE II, SOFA, age, use of nerve blockers, and diabetes mellitus are independent risk factors for ICU-AW. The nomogram model based on them had good predictive efficacy and may be clinically useful.
Citation: DIGITAL HEALTH
PubDate: 2024-07-23T10:23:19Z
DOI: 10.1177/20552076241261604
Issue No: Vol. 10 (2024)
- Association between fitness technology use and physical activity mediated
by communication behaviors on social media
Authors: Xia Zheng, Wenbo Li
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveFitness technologies, such as smartphone applications and wearable tracking devices, have gained widespread popularity. This study had two main objectives: 1) to examine whether fitness technology use is associated with increased physical activity (PA) levels and 2) to investigate whether communication behaviors on social media mediated the association between fitness technology use and PA.MethodsData were from the U.S. Health Information National Trends Survey 2022 (N = 6,252, weighted N = 258,418,467). Weighted linear regressions were conducted to examine the associations between fitness technology usage, physical activities, and communication behaviors on social media. Mediations were tested using PROCESS macro, a path-analysis based tool.ResultsControlling for demographic and other known influences on PA, the findings revealed that users of fitness technology reported higher levels of both moderate PA (β = .41, p
Citation: DIGITAL HEALTH
PubDate: 2024-07-21T04:03:23Z
DOI: 10.1177/20552076241266367
Issue No: Vol. 10 (2024)
- A 10-year retrospective cohort of diabetic patients in a large medical
institution: Utilizing multiple machine learning models for diabetic
kidney disease prediction
Authors: Guangpu Li, Jia Li, Fei Tian, Jingjing Ren, Zuishuang Guo, Shaokang Pan, Dongwei Liu, Jiayu Duan, Zhangsuo Liu
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveAs the prevalence of diabetes steadily increases, the burden of diabetic kidney disease (DKD) is also intensifying. In response, we have utilized a 10-year diabetes cohort from our medical center to train machine learning-based models for predicting DKD and interpreting relevant factors.MethodsEmploying a large dataset from 73,101 hospitalized type 2 diabetes patients at The First Affiliated Hospital of Zhengzhou University, we analyzed demographic and medication data. Machine learning models, including XGBoost, CatBoost, LightGBM, Random Forest, AdaBoost, GBDT (gradient boosting decision tree), and SGD (stochastic gradient descent), were trained on these data, focusing on interpretability by SHAP. SHAP explains the output of the models by assigning an importance value to each feature for a particular prediction, enabling a clear understanding of how individual features influence the prediction outcomes.ResultsThe XGBoost model achieved an area under the curve (AUC) of 0.95 and an area under the precision-recall curve (AUPR) of 0.76, while CatBoost recorded an AUC of 0.97 and an AUPR of 0.84. These results underscore the effectiveness of these models in predicting DKD in patients with type 2 diabetes.ConclusionsThis study provides a comprehensive approach for predicting DKD in patients with type 2 diabetes, employing machine learning techniques. The findings are crucial for the early detection and intervention of DKD, offering a roadmap for future research and healthcare strategies in diabetes management. Additionally, the presence of non-diabetic kidney diseases and diabetes with complications was identified as significant factors in the development of DKD.
Citation: DIGITAL HEALTH
PubDate: 2024-07-21T04:02:23Z
DOI: 10.1177/20552076241265220
Issue No: Vol. 10 (2024)
- Diagnostic performance of generative artificial intelligences for a series
of complex case reports
Authors: Takanobu Hirosawa, Yukinori Harada, Kazuya Mizuta, Tetsu Sakamoto, Kazuki Tokumasu, Taro Shimizu
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundDiagnostic performance of generative artificial intelligences (AIs) using large language models (LLMs) across comprehensive medical specialties is still unknown.ObjectiveWe aimed to evaluate the diagnostic performance of generative AIs using LLMs in complex case series across comprehensive medical fields.MethodsWe analyzed published case reports from the American Journal of Case Reports from January 2022 to March 2023. We excluded pediatric cases and those primarily focused on management. We utilized three generative AIs to generate the top 10 differential-diagnosis (DDx) lists from case descriptions: the fourth-generation chat generative pre-trained transformer (ChatGPT-4), Google Gemini (previously Bard), and LLM Meta AI 2 (LLaMA2) chatbot. Two independent physicians assessed the inclusion of the final diagnosis in the lists generated by the AIs.ResultsOut of 557 consecutive case reports, 392 were included. The inclusion rates of the final diagnosis within top 10 DDx lists were 86.7% (340/392) for ChatGPT-4, 68.6% (269/392) for Google Gemini, and 54.6% (214/392) for LLaMA2 chatbot. The top diagnoses matched the final diagnoses in 54.6% (214/392) for ChatGPT-4, 31.4% (123/392) for Google Gemini, and 23.0% (90/392) for LLaMA2 chatbot. ChatGPT-4 showed higher diagnostic accuracy than Google Gemini (P
Citation: DIGITAL HEALTH
PubDate: 2024-07-21T04:01:23Z
DOI: 10.1177/20552076241265215
Issue No: Vol. 10 (2024)
- Is Arabic online patient-centered information about dental extraction
trustworthy' An infodemiological study
Authors: Ahmad A. Othman, Muath S. Alassaf, Saad M. Hasubah, Mohammad Aljuhani, Ahmad M. Kabli, Mahmoud Alsulaimani
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundAssessment of the Arabic online patient-centered information is understudied. The study aims to assess the quality and readability of the Arabic web-based knowledge about dental extraction.MethodsThe first 100 Arabic websites focusing on dental extraction were gathered using popular terms from Google, Bing, and Yahoo searches. These sites were organized and their quality was assessed using three key standards: the Journal of the American Medical Association (JAMA) benchmark criteria, the DISCERN instrument, and the inclusion of the Health on the Net Foundation Code of Conduct (HON code) seal. Additionally, the ease of reading of these websites was evaluated through various online readability indexes.ResultsOut of 300 initially reviewed websites on dental extraction in Arabic, 80 met the eligibility criteria. Nonprofit organizations were most common (41.3%), followed by university/medical centers (36.3%), and commercial entities (21.3%). Government organizations were minimally represented (1.3%). All websites were medically oriented, with 60% offering Q&A sections. Quality assessment showed moderate scores on the DISCERN instrument, with no site reaching the highest score. JAMA benchmarks were poorly met, and none had the HON code seal. Readability was generally high, with most sites scoring favorably on readability scales.ConclusionsThe rapidly evolving online information about dental extraction lacks readability and quality and can spread misinformation. Creators should focus on clear, unbiased content using simple language for better public understanding.
Citation: DIGITAL HEALTH
PubDate: 2024-07-21T04:00:43Z
DOI: 10.1177/20552076241264390
Issue No: Vol. 10 (2024)
- Comparison between clinical and computerized methods for assessing
gingival pigmentation
Authors: Nik Madihah Nik Azis, Siti Nuramanina Abdul Shukor, Masfueh Razali, Hanis Yasreena Zakaria, Nur Zafira Zabarulla
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
Background and ObjectiveDigital computerized assessment can provide objective values for the measurement of gingival pigmentation. This study aims to compare the Commission Internationale de l’Eclairage Lab color space (CIELAB) values and the computerized intensity values (CIVs) from digital imaging with clinical evaluations using the Dummett–Gupta Oral Pigmentation Index (DOPI) for assessing gingival pigmentation in a multi-ethnic population.MethodologyDigital photographs of 188 participants were taken using standardized parameters. The buccal gingival pigmentation was evaluated using three methods (a) a clinical evaluation by two independent assessors using the DOPI, (b) the CIELAB values using the Adobe Photoshop® software (Version 23.1.1) and (c) the CIV calculated using the ImageJ software (Version 1.53k). A hierarchical clustering analysis was used to identify colour groups that clustered together. Agreement between the clinical and digital categorization of the pigmentation was carried out using weighted kappa analysis. Agreements between CIELAB and CIV were compared using intra-class correlation coefficient.ResultsThere was a statistically significant difference in the DOPI, the L*, a*, and b* coordinates, and the CIV between the different ethnic groups of the participants. Cluster analysis for the CIELAB and CIV both identified four clusters. The gingival pigmentation categorization using the L*, a*, and b* values moderately agreed with the clinical evaluation using the DOPI index while the categorization with the CIV was in slight agreement with the clinical evaluations.ConclusionThis study identified four clusters of gingival pigmentation in 188 multi-ethnic participants. The clusters, determined by CIELAB values, align with the clinical assessment of gingival pigmentation. Digital measurements derived from clinical photographs can serve as an effective means of pigmentation measurement in dental clinics.
Citation: DIGITAL HEALTH
PubDate: 2024-07-21T03:57:43Z
DOI: 10.1177/20552076241264154
Issue No: Vol. 10 (2024)
- Acceptability and usability of a WeChat-based intervention for depression
in China: A mixed-methods study
Authors: Yuxi Tan, Emily G Lattie, Hui Xiang, Hui Tang, Ziwei Teng, Yan Qiu, Jindong Chen, Haishan Wu
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundAs research on the use of mobile technology to deliver mental health support grows, the research from China is still very limited. How to design an acceptable and usable mobile mental health service model suitable for China's social and cultural environment remains to be studied.ObjectiveTo understand the acceptability and usability of a WeChat-based intervention among Chinese patients with depression, and to provide insights to promote future development of user-centered mobile mental health services design.MethodsThe research team developed a multi-theoretical intervention that includes seven modules: recovery lessons, recovery journal, coaching sessions, mindfulness, personalized support, regular assessments and feedback collection. Forty-two patients diagnosed with depressive disorder were recruited, with a mixed sample of patients who were using an antidepressant medication (n = 29) and patients who were not using an antidepressant medication (n = 13). A single-arm mixed-methods study was conducted to understand engagement, satisfaction, usability and potential clinical effectiveness of the intervention.ResultsThere was a retention rate of 83.33% — 22 participants who used an antidepressant medication and 13 participants who did not use an antidepressant medication completed the final assessments. The median (upper quartile–lower quartile) of the completed 60 recovery journals and 7 coaching sessions was 56 (59–46) and 6 (7–4) times, respectively. Participants' satisfaction regarding their recovery progress, and on perceived helpfulness on different modules were high. The overall score of the user version of the Mobile Application Rating Scale was 4.23 (SD 0.44, range 1–5), indicating high acceptability and usability. Qualitative feedback identified three key themes: an efficient access to professional help, a personalized source of social support, and a facilitator of cognitive and behavioral change.ConclusionsThis study demonstrated that a WeChat-based intervention for depression was acceptable, and has the potential to promote personal recovery. More studies are needed to understand the efficacy and implementation of this model in real world.
Citation: DIGITAL HEALTH
PubDate: 2024-07-21T03:45:23Z
DOI: 10.1177/20552076241262301
Issue No: Vol. 10 (2024)
- Acceptance of mobile application-based clinical guidelines among health
professionals in Northwestern Ethiopia: A mixed-methods study
Authors: Nebebe Demis Baykemagn, Araya Mesfin Nigatu, Berhanu Fikadie, Binyam Tilahun
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundGlobally, healthcare providers have faced significant difficulties in adhering to clinical guidelines. Applying mobile health systems is a crucial strategy for enhancing the dissemination and accessibility of clinical guidelines. This study aimed to assess the acceptance of mobile app-based primary healthcare clinical guidelines and associated factors among health professionals in central Gondar health centers.MethodsA cross-sectional study supplemented with qualitative data was conducted on 403 health workers. Data were collected using a pre-test structured printed questionnaire and entered into EpiData version 4.6. Analysis was conducted using Stata version 14, which included bivariable and multivariable logistic regression analyses. For qualitative data, thematic analysis was conducted using Open Code v.4.2.ResultsApproximately 28% (95% confidence interval (CI): 23%–32%) of health professionals had utilized mobile app-based clinical guidelines. The availability of IT support (adjusted odds ratio (AOR) = 3.51, 95% CI: 1.82–6.78), good knowledge (AOR = 3.46, 95% CI: 1.5–6.78), perceived usefulness (AOR = 2.21, 95% CI: 1.00–4.99), m-Health app exposure (AOR = 2.34, 95% CI: 1.2–4.50), and ease of use (AOR = 5.77, 95% CI: 2.50–13.32) were significantly associated with the acceptance of the mobile app-based clinical guideline. In qualitative data, lack of training and supervision and access to smartphones were barriers to acceptance of the mobile app-based clinical guideline.ConclusionIn summary, acceptance of the app is currently low. However, it can be increased by improving the availability of IT support in the workplace, offering training and supervision, and enhancing access to smartphones.
Citation: DIGITAL HEALTH
PubDate: 2024-07-21T03:44:21Z
DOI: 10.1177/20552076241261930
Issue No: Vol. 10 (2024)
- Epidemic exposure risk assessment in digital contact tracing: A fuzzy
logic approach
Authors: Mohsen Rashidian, Mohammad Reza Malek, Abolghasem Sadeghi-Niaraki, Soo-Mi Choi
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundBluetooth low energy (BLE)-based contact-tracing applications were widely used during the COVID-19 pandemic. However, the use of only the received signal strength feature for proximity calculations may not be adaptable to different virus variants or scalable for other potential epidemic diseases.ObjectiveThis study presents a novel framework in regard to evaluating and classifying personal exposure risk that considers both contact features, which include distance and length of contact, and environment features, which include crowd size and the number of recently infected cases in the environment. The framework utilizes a fuzzy expert system that is adaptable to different virus variants.MethodsThe proposed method was tested on two viruses with different close contact features, which used four membership functions and 256 fuzzy rule sets.ResultsThe proposed framework classified personal exposure risks into four classes, which include low, medium, high, and too high risk. The empirical results showed that the fuzzy logic-based approach reduced the number of false positive cases and demonstrated better accuracy and precision than the current BLE-only approaches.ConclusionsThe proposed framework provides a more practical and adaptable method in regard to assessing exposure risks in real-world scenarios. It has the potential to be scalable and adaptable to different virus variants and other potential epidemic diseases by considering both contact and environment features. These findings may be useful in order to develop more effective digital contact-tracing applications and policies.
Citation: DIGITAL HEALTH
PubDate: 2024-07-21T03:16:41Z
DOI: 10.1177/20552076241261929
Issue No: Vol. 10 (2024)
- A rapid review protocol of physiotherapy and occupational therapy
telerehabilitation to inform ethical and equity concerns
Authors: Mirella Veras, Jennifer Sigouin, Claudine Auger, Louis-Pierre Auger, Sara Ahmed, Zachary Boychuck, Sabrina Cavallo, Martine Lévesque, Stacey Lovo, William C Miller, Michelle Nelson, Nahid Norouzi-Gheidari, Jennifer O’Neil, Kadija Perreault, Reg Urbanowski, Lisa Sheehy, Hardeep Singh, Claude Vincent, Rosali Wang, Diana Zidarov, Anne Hudon, Dahlia Kairy
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundTelerehabilitation (TR) has emerged as a feasible and promising approach for delivering rehabilitation services remotely, utilizing technology to bridge the gap between healthcare providers and patients. As new modalities of virtual care and health technologies continue to emerge, it is crucial to stay informed about the growing landscape of virtual care to ensure that telehealth service delivery is ethical and equitable, and improves the quality of services and patient outcomes.ObjectiveThe primary objective of this article is to present the protocol of a rapid review to examine the equity-related aspects surrounding the implementation of TR. This includes a comprehensive analysis of the ethical dimensions and fairness concerns linked to this practice.MethodsA rapid review protocol was developed in accordance with Cochrane Rapid Reviews Methods Guidance. Medline and EMBASE databases were searched between January 2010 and March 2023. Study selection and data extraction will be conducted in two phases (Phase I) by two independent reviewers and subsequently (Phase II) by a single reviewer. Our study will utilize the PROGRESS-Plus and Equitable virtual rehabilitation in the metaverse era framework to identify dimensions where potential inequities may exist within TR interventions.ResultsThis rapid review is anticipated to enhance our knowledge of TR in the fields of physiotherapy and occupational therapy, with a specific focus on its influence on ethical and equitable practices and providing a foundation for informed decision-making and improved patient care.ConclusionThis rapid review will contribute to the advancement of our understanding of TR within physiotherapy and occupational therapy. Through synthesizing existing evidence, this study not only addresses current gaps in knowledge but also offers valuable insights for future research and clinical practice in TR services.
Citation: DIGITAL HEALTH
PubDate: 2024-07-21T03:04:14Z
DOI: 10.1177/20552076241260367
Issue No: Vol. 10 (2024)
- Quality controlled YouTube content intervention for enhancing health
literacy and health behavioural intention: A randomized controlled study
Authors: Yujin Park, Su Hwan Kim, Hyung-Jin Yoon
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundIndividuals increasingly turn to the Internet for health information, with YouTube being a prominent source. However, the quality and reliability of the health information vary widely, potentially affecting health literacy and behavioural intentions.MethodsTo analyse the impact of health information quality on health literacy and behavioural intention, we conducted a randomized controlled trial using a quality-controlled YouTube intervention. Health information quality on YouTube was evaluated using the Global Quality Score and DISCERN. We randomly allocated (1 : 1) to the intervention group to watch the highest quality-evaluated content and to the control group to watch the lowest quality-evaluated content. Health literacy and health behavioural intention were assessed before and after watching YouTube. The trial was set for two different topics: interpreting laboratory test results from health check-up and information about inflammatory bowel disease (IBD).ResultsFrom 8 April 2022 to 15 April 2022, 505 participants were randomly assigned to watch either high-quality content (intervention group, n = 255) or low-quality content (control group, n = 250). Health literacy significantly improved in the intervention group (28.1 before and 31.8 after; p
Citation: DIGITAL HEALTH
PubDate: 2024-06-18T06:38:40Z
DOI: 10.1177/20552076241263691
Issue No: Vol. 10 (2024)
- A text-based conversational agent for asthma support: Mixed-methods
feasibility study
Authors: Darren Cook, Dorian Peters, Laura Moradbakhti, Ting Su, Marco Da Re, Bjorn W. Schuller, Jennifer Quint, Ernie Wong, Rafael A. Calvo
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveMillions of people in the UK have asthma, yet 70% do not access basic care, leading to the largest number of asthma-related deaths in Europe. Chatbots may extend the reach of asthma support and provide a bridge to traditional healthcare. This study evaluates ‘Brisa’, a chatbot designed to improve asthma patients’ self-assessment and self-management.MethodsWe recruited 150 adults with an asthma diagnosis to test our chatbot. Participants were recruited over three waves through social media and a research recruitment platform. Eligible participants had access to ‘Brisa’ via a WhatsApp or website version for 28 days and completed entry and exit questionnaires to evaluate user experience and asthma control. Weekly symptom tracking, user interaction metrics, satisfaction measures, and qualitative feedback were utilised to evaluate the chatbot's usability and potential effectiveness, focusing on changes in asthma control and self-reported behavioural improvements.Results74% of participants engaged with ‘Brisa’ at least once. High task completion rates were observed: asthma attack risk assessment (86%), voice recording submission (83%) and asthma control tracking (95.5%). Post use, an 8% improvement in asthma control was reported. User satisfaction surveys indicated positive feedback on helpfulness (80%), privacy (87%), trustworthiness (80%) and functionality (84%) but highlighted a need for improved conversational depth and personalisation.ConclusionsThe study indicates that chatbots are effective for asthma support, demonstrated by the high usage of features like risk assessment and control tracking, as well as a statistically significant improvement in asthma control. However, lower satisfaction in conversational flexibility highlights rising expectations for chatbot fluency, influenced by advanced models like ChatGPT. Future health-focused chatbots must balance conversational capability with accuracy and safety to maintain engagement and effectiveness.
Citation: DIGITAL HEALTH
PubDate: 2024-06-17T09:09:11Z
DOI: 10.1177/20552076241258276
Issue No: Vol. 10 (2024)
- Japanese nurses’ confidence in their understanding of telenursing via
e-learning: A mixed-methods study
Authors: Tomoko Kamei, Aki Kawada, Hisako Kakai, Yuko Yamamoto, Yuki Nakayama, Haruhiko Mitsunaga, Naoki Nishimura
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveTelenursing e-learning courses have been shown to enhance nurses’ skills and knowledge; however, the subjective learning experience is unclear. In this study, we identified meta-inferences to quantitatively and qualitatively understand this experience, as well as the types of knowledge gained through an e-learning course and how they are linked to each other, in order to enhance nurses’ confidence in their understanding of telenursing.MethodsWe employed a single-arm intervention with a mixed-methods convergent parallel design. We converged participants’ self-reported pre- and post-course confidence scores with their reflections on the learning experience, which were reported qualitatively as improved or unimproved. A total of 143 Japanese nurses with a mean of 20 years of nursing experience participated in this study.ResultsAmong the participants, 72.7% demonstrated improved confidence in their understanding of telenursing after completing the e-learning course. The baseline confidence score was originally higher in the group that reported unimproved confidence (p
Citation: DIGITAL HEALTH
PubDate: 2024-06-17T09:08:32Z
DOI: 10.1177/20552076241257034
Issue No: Vol. 10 (2024)
- Fitbit's accuracy to measure short bouts of stepping and sedentary
behaviour: validation, sensitivity and specificity study
Authors: Julie Delobelle, Elien Lebuf, Delfien Van Dyck, Sofie Compernolle, Michael Janek, Femke De Backere, Tomas Vetrovsky
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThis study aims to assess the suitability of Fitbit devices for real-time physical activity (PA) and sedentary behaviour (SB) monitoring in the context of just-in-time adaptive interventions (JITAIs) and event-based ecological momentary assessment (EMA) studies.MethodsThirty-seven adults (18–65 years) and 32 older adults (65+) from Belgium and the Czech Republic wore four devices simultaneously for 3 days: two Fitbit models on the wrist, an ActiGraph GT3X+ at the hip and an ActivPAL at the thigh. Accuracy measures included mean (absolute) error and mean (absolute) percentage error. Concurrent validity was assessed using Lin's concordance correlation coefficient and Bland–Altman analyses. Fitbit's sensitivity and specificity for detecting stepping events across different thresholds and durations were calculated compared to ActiGraph, while ROC curve analyses identified optimal Fitbit thresholds for detecting sedentary events according to ActivPAL.ResultsFitbits demonstrated validity in measuring steps on a short time scale compared to ActiGraph. Except for stepping above 120 steps/min in older adults, both Fitbit models detected stepping bouts in adults and older adults with sensitivities and specificities exceeding 87% and 97%, respectively. Optimal cut-off values for identifying prolonged sitting bouts achieved sensitivities and specificities greater than 93% and 89%, respectively.ConclusionsThis study provides practical insights into using Fitbit devices in JITAIs and event-based EMA studies among adults and older adults. Fitbits’ reasonable accuracy in detecting short bouts of stepping and SB makes them suitable for triggering JITAI prompts or EMA questionnaires following a PA or SB event of interest.
Citation: DIGITAL HEALTH
PubDate: 2024-06-17T08:11:13Z
DOI: 10.1177/20552076241262710
Issue No: Vol. 10 (2024)
- Making it transparent: A worked example of articulating programme theory
for a digital health application using Intervention Mapping
Authors: Tamika A. Marcos, Rik Crutzen, Veronika Leitner, Jan D. Smeddinck, Eva-Maria Strumegger, Daniela Wurhofer, Stefan T. Kulnik
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveDigital health interventions for behaviour change are usually complex interventions, and intervention developers should ‘articulate programme theory’, that is, they should offer detailed descriptions of individual intervention components and their proposed mechanisms of action. However, such detailed descriptions often remain lacking. The objective of this work was to provide a conceptual case study with an applied example of ‘articulating programme theory’ for a newly developed digital health intervention.MethodsIntervention Mapping methodology was applied to arrive at a detailed description of programme theory for a newly developed digital health intervention that aims to support cardiac rehabilitation patients in establishing heart-healthy physical activity habits. Based on a Predisposing, Reinforcing, and Enabling Constructs in Educational Diagnosis and Evaluation (PRECEDE) logic model of the problem, a logic model of change was developed. The proposed mechanisms of action were visualised in an acyclic behaviour change diagram.ResultsProgramme theory for this digital health intervention includes 4 sub-behaviours of the main target behaviour (i.e. habitual heart-healthy physical activity), 8 personal determinants and 12 change objectives (i.e. changes needed at the determinant level to achieve the sub-behaviours). These are linked to 12 distinct features of the digital health intervention and 12 underlying behaviour change methods.ConclusionsThis case study offers a worked example of articulating programme theory for a digital health intervention using Intervention Mapping. Intervention developers and researchers may draw on this example to replicate the method, or to reflect on most suitable approaches for their own behaviour change interventions.
Citation: DIGITAL HEALTH
PubDate: 2024-06-17T03:17:29Z
DOI: 10.1177/20552076241260974
Issue No: Vol. 10 (2024)
- Nerva, a mobile application of gut-directed hypnotherapy for irritable
bowel syndrome: User characteristics, patterns of use, and predictors of
persistence
Authors: Lauren Simicich, Vanessa Muniz, Katherine Scheffrahn, Gary Elkins
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundHypnotic intervention for irritable bowel syndrome (IBS), or gut-directed hypnotherapy (GDH), is an effective treatment for improving IBS symptoms with minimal burden and risk in delivery to patients. The Nerva app, developed by Mindset Health, shows promise for dissemination and implementation of evidence-based GDH intervention for IBS.ObjectivesThis study aimed to describe the demographic and clinical characteristics of Nerva app users, examine usage patterns, and explore potential factors associated with Nerva app usage.MethodsA retrospective analysis of data was conducted of 14,898 individuals who downloaded and used the Nerva app between January 2022 and September 2022. Descriptive statistics and Chi-square tests of independence were calculated to examine demographic/clinical characteristics, usage patterns, and their associations to program persistence. Simple linear regression models were used for correlations of GI symptoms severity scores to user persistence.ResultsUsers predominantly identified as female (77.2%; n = 11,503) and had a mean age of 38.59 years old (SD = 13.38). Thirty-one percent of users persisted with the program, and a small statistically significant association was found between Nerva app program persistence and age, χ2 (6, N = 6745) = 164.82, p
Citation: DIGITAL HEALTH
PubDate: 2024-06-14T08:29:45Z
DOI: 10.1177/20552076241263257
Issue No: Vol. 10 (2024)
- Evaluation of health factors on artificial intelligence and the internet
of things-based older adults healthcare programmes
Authors: Jong In Kim, Gukbin Kim
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThis study evaluates Artificial intelligence and the Internet of Things-based older adults' healthcare programmes (AI·IoT-OAHPs), which offer non-face-to-face and face-to-face health management to older adults for health promotion.MethodsThe study involved 146 participants, adults over 60 who had registered in AI·IoT-OAHPs. This study assessed the health factors as the outcome of pre- and post-health screening and health management through AI·IoT-OAHPs for six months.ResultsPreand post-health screening and management through AI·IoT-OAHPs were evaluated as significant outcomes in 14 health factors. Notably, the benefits of post-cognitive function showed a twofold increase in older female adults through AI·IoT-OAHPs. Adults over 70 showed a fourfold increase in post-walking days, a threefold in post-dietary practice, and a twofold in post-cognitive function in the post-effects compared with pre via AI·IoT-OAHPs.ConclusionsAI·IoT-OAHPs seem to be an effective program in the realm of face-to-face and non-face-to-face AI·IoT-based older adults' healthcare initiatives in the era of COVID-19. Consequently, the study suggests that AI·IoT-OAHPs contribute to the upgrade in health promotion of older adults. In future studies, the effectiveness of AI·IoT-OAHPs can be evaluated as a continuous project every year in the short term and every two years in the long term.
Citation: DIGITAL HEALTH
PubDate: 2024-06-14T08:29:17Z
DOI: 10.1177/20552076241258663
Issue No: Vol. 10 (2024)
- My Health Coach: Community members’ perspectives on a mobile health tool
for adults with fetal alcohol spectrum disorders
Authors: Emily L Speybroeck, Christie Petrenko, Cristiano Tapparello, Katrina Griffin, Emily Hargrove, Myles Himmelreich, Anique Lutke, CJ Lutke, Maggie May, Shuo Zhang, Janna Looney, Carson Kautz-Turnbull, Madeline N Rockhold
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectivesFetal alcohol spectrum disorders (FASD) affect the health and development of people across the lifespan. Adults with FASD experience significant barriers to care. Accessible and scalable solutions are needed. In partnership with members of the International Adult Leadership Collaborative of FASD Changemakers, an international group of adults with FASD, we developed a mobile health (mHealth) application based on self-determination theory (SDT), called “My Health Coach,” to promote self-management and health advocacy.MethodsThis project follows an established user-centered design approach to app development and evaluation, allowing for feedback loops promoting iterative change. Research staff and ALC members co-led online focus groups (n = 26) and an online follow-up survey (n = 26) with adults with FASD to elicit feedback on completed design prototypes. Focus group transcriptions and surveys underwent systemic thematic and theoretical framework analysis.ResultsAnalyses show overall positive impressions of the My Health Coach app. Participants were enthusiastic about the proposed features and tools the app will provide. Discussions and free responses revealed SDT constructs (autonomy, competence, relatedness) are a strong fit with participants’ perceived outcomes shared in their evaluation of the prototype. Interesting recommendations were made for additional features that would further promote SDT constructs.ConclusionsThis project demonstrates advantages of community-engaged partnerships in FASD research. Adults with FASD have a strong interest in scalable mHealth tools and described the acceptability of our initial design. App features and tools promoted SDT constructs.
Citation: DIGITAL HEALTH
PubDate: 2024-06-14T07:36:54Z
DOI: 10.1177/20552076241261458
Issue No: Vol. 10 (2024)
- PaiNEd app. Assessing central sensitization in survivors of breast cancer:
A reliability study
Authors: Miguel Ángel Fernández-Gualda, Patrocinio Ariza-Vega, Noelia Galiano-Castillo, Isabel Tovar-Martín, Lucía Ortiz-Comino, Mario Lozano-Lozano, Carolina Fernández-Lao
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
IntroductionPain is a common adverse event in survivors of breast cancer (sBCs). As there is no gold standard to assess pain experience predominantly related to central sensitization (CS) symptoms, we designed the PaiNEd app, which includes an algorithm to report whether patients are under predominant CS pain mechanisms.ObjectiveWe aimed to assess the reliability of the PaiNEd app to estimate whether sBC pain experience is predominantly related to CS symptoms.MethodsAn observational, descriptive reliability design was employed to assess the inter- and intrarater reliability of the PaiNEd app. This app includes an algorithm that considers the number of painful body parts and some questionnaires related to pain, such as the Numeric Pain-Rating Scale, the Brief Pain Inventory, the Tampa Scale for Kinesiophobia, the Pain Catastrophizing Scale, and the Central Sensitization Inventory (CSI).ResultsA total of 21 sBCs with persistent pain were recruited. We observe a general trend of close agreement between the paper-based and app-based formats (ICCs ranged between 0.802 and 0.972; Cronbach's alpha ranged between 0.797 and 0.971). Test–retest reliabilities were moderate to excellent (ICCs ranged between 0.510 and 0.941; Cronbach's alpha ranged between 0.499 and 0.938). The agreement between the categorization of the CS algorithm and the CSI (cut-off point ≥ 40 for CS symptoms) was 95.24%.ConclusionThe PaiNEd app emerges as a robust tool for evaluating pain experience predominantly related to CS and pain-related symptoms in sBCs. Its demonstrated reliability not only bolsters its utility but also signifies its potential as a valuable asset for healthcare professionals engaged in pain education programs.
Citation: DIGITAL HEALTH
PubDate: 2024-06-14T07:36:16Z
DOI: 10.1177/20552076241260150
Issue No: Vol. 10 (2024)
- Tele-ECG improves diagnosis of acute coronary syndrome and ST-elevation
myocardial infarction in Côte d’Ivoire
Authors: K. F. Diby, A. Gnaba, P. Ouattara, G. Ayegnon, A. Coulibaly, G. Tro, S. A. Dakoi, F. Sall, A. Adoubi, K. E. N’guessan, S. F. Ehua, R. Ohannessian, T. Moulin
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
IntroductionThe emergence of cardiovascular risk factors in sub-Saharan Africa suggests an increasing incidence of acute coronary syndromes and STEMI. The aim of the study was to define the prevalence of STEMI and to describe the characteristics of patients diagnosed with STEMI within the tele-electrocardiogram (ECG) network in Côte d’Ivoire.MethodA retrospective study was conducted from January 2015 to August 2019. All adult patients managed by one of the six hospitals within the telemedicine network who benefited from a remote interpretation of their ECG by the cardiology department of Bouaké University Hospital were included. The main reason for ECG interpretation, patient and ECG characteristics, diagnosis, response time and treatment were described.ResultsA total of 5649 patients were included. The prevalence of STEMI was 0.7% (n = 44 cases) with a mean age of 58.6 ± 11.8 years and a M/F sex ratio of 1.93. Among STEMI patients, chest pain was the main reason for ECG testing (56.8%). Most ECGs were interpreted within 12 hours (72.8%). The anterior inter-ventricular artery location (59.1%, n = 26) was predominant. The Q wave of necrosis was absent in 18% (n = 8) of cases. All patients received double anti-platelet aggregation and 50% (n = 22) additional heparin therapy. No patient underwent primary angioplasty or thrombolysis, 65.9% (n = 29) were referred to the Bouaké Cardiology Department and 34.1% (n = 15) to the Abidjan Heart Institute. Scheduled angioplasty was performed in 20% (n = 3) of patients in Abidjan.ConclusionTele-ECG was an effective means of STEMI screening in Côte d’Ivoire. Systematic telethrombolysis of all patients diagnosed could improve their prognosis.
Citation: DIGITAL HEALTH
PubDate: 2024-06-14T06:59:56Z
DOI: 10.1177/20552076241262276
Issue No: Vol. 10 (2024)
- A gamified cognitive behavioral therapy for Arabs to reduce symptoms of
depression and anxiety: A case study research
Authors: Nourhan A. Amer, Samaa M. Shohieb, Waleed Eladrosy, Shilong Liu, Yunyoung Nam, Samir Abdelrazek
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundDepression and anxiety are prevalent mental health issues addressed by online cognitive behavioral therapy (CBT) via mobile applications. This study introduces Sokoon, a gamified CBT app tailored for Arabic individuals, focusing on alleviating depression and anxiety symptoms (DASDs).ObjectivesThe objectives of this study were to: Evaluate the effectiveness of Sokoon in reducing symptoms of depression and anxiety. Assess the usability of the intervention through user engagement and adherence to CBT skills.MethodsA single-group pre–post design evaluated Sokoon's impact on adults with DASDs. In consultation with psychiatrists, Sokoon integrates evidence-based skills such as relaxation, gratitude, behavioral activation, and cognitive restructuring, represented by planets. Its design incorporates Hexad theory and gamification, supported by a dynamic difficulty adjustment algorithm. The study involves 30 participants aged 18–35 (86.7% female), specifically those with mild to moderate depression and anxiety.ResultsBased on a sample of 30 participants, Sokoon, a smartphone-based intervention, significantly reduced symptoms of depression and anxiety (d = 2.7, d = 3.6, p
Citation: DIGITAL HEALTH
PubDate: 2024-06-14T01:06:33Z
DOI: 10.1177/20552076241263317
Issue No: Vol. 10 (2024)
- Effects of virtual distant viewing technology on preventing
nearwork-induced ocular parameter changes
Authors: Zhen Yi, Wang Ningli, Cao Kai, Huang Yan, Zhang Wei
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
PurposeThis study investigates whether virtual distant viewing technology can prevent nearwork-induced ocular parameter changes.MethodsTwenty-six volunteers read a textbook on one day and the same content on a virtual distant viewing display on another day based on a randomization sequence, with both reading sessions at 33 cm for 4 hours. Visual acuity, diopter, ocular biology, visual fatigue, and accommodative function before and after the nearwork, as well as the number of pages read, were recorded.ResultsAfter 4 hours of nearwork in the textbook group, the spherical equivalent refraction decreased from −3.13 ± 2.65 D to −3.32 ± 2.70 D (P
Citation: DIGITAL HEALTH
PubDate: 2024-06-13T06:38:23Z
DOI: 10.1177/20552076241259868
Issue No: Vol. 10 (2024)
- Use of voice features from smartphones for monitoring depressive
disorders: Scoping review
Authors: Jaeeun Shin, Sung Man Bae
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectThis review evaluates the use of smartphone-based voice data for predicting and monitoring depression.MethodsA scoping review was conducted, examining 14 studies from Medline, Scopus, and Web of Science (2010–2023) on voice data collection methods and the use of voice features for minitoring depression.ResultsVoice data, especially prosodic features like fundamental frequency and pitch, show promise for predicting depression, though their sole predictive power requires further validation. Integrating voice with multimodal sensor data has been shown to improve accuracy significantly.ConclusionSmartphone-based voice monitoring offers a promising, noninvasive, and cost-effective approach to depression management. The integration of machine learning with sensor data could significantly enhance mental health monitoring, necessitating further research and longitudinal studies for validation.
Citation: DIGITAL HEALTH
PubDate: 2024-06-13T06:37:23Z
DOI: 10.1177/20552076241261920
Issue No: Vol. 10 (2024)
- Assessment of left ventricular ejection fraction in artificial
intelligence based on left ventricular opacification
Authors: Ye Zhu, Zisang Zhang, Junqiang Ma, Yiwei Zhang, Shuangshuang Zhu, Manwei Liu, Ziming Zhang, Chun Wu, Chunyan Xu, Anjun Wu, Chenchen Sun, Xin Yang, Yonghuai Wang, Chunyan Ma, Jun Cheng, Dong Ni, Jing Wang, Mingxing Xie, Wufeng Xue, Li Zhang
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundLeft ventricular opacification (LVO) improves the accuracy of left ventricular ejection fraction (LVEF) by enhancing the visualization of the endocardium. Manual delineation of the endocardium by sonographers has observer variability. Artificial intelligence (AI) has the potential to improve the reproducibility of LVO to assess LVEF.ObjectivesThe aim was to develop an AI model and evaluate the feasibility and reproducibility of LVO in the assessment of LVEF.MethodsThis retrospective study included 1305 echocardiography of 797 patients who had LVO at the Department of Ultrasound Medicine, Union Hospital, Huazhong University of Science and Technology from 2013 to 2021. The AI model was developed by 5-fold cross validation. The validation datasets included 50 patients prospectively collected in our center and 42 patients retrospectively collected in the external institution. To evaluate the differences between LV function determined by AI and sonographers, the median absolute error (MAE), spearman correlation coefficient, and intraclass correlation coefficient (ICC) were calculated.ResultsIn LVO, the MAE of LVEF between AI and manual measurements was 2.6% in the development cohort, 2.5% in the internal validation cohort, and 2.7% in the external validation cohort. Compared with two-dimensional echocardiography (2DE), the left ventricular (LV) volumes and LVEF of LVO measured by AI correlated significantly with manual measurements. AI model provided excellent reliability for the LV parameters of LVO (ICC > 0.95).ConclusionsAI-assisted LVO enables more accurate identification of the LV endocardium and reduces observer variability, providing a more reliable way for assessing LV function.
Citation: DIGITAL HEALTH
PubDate: 2024-06-13T06:37:05Z
DOI: 10.1177/20552076241260557
Issue No: Vol. 10 (2024)
- Wearable biosensors for human health: A bibliometric analysis from 2007 to
2022
Authors: Nicolás Muñoz-Urtubia, Alejandro Vega-Muñoz, Carla Estrada-Muñoz, Guido Salazar-Sepúlveda, Nicolás Contreras-Barraza, Nicolás Salinas-Martínez, Paula Méndez-Celis, José Carmelo-Adsuar
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThis study aimed to determine the status of scientific production on biosensor usage for human health monitoring.MethodsWe used bibliometrics based on the data and metadata retrieved from the Web of Science between 2007 and 2022. Articles unrelated to health and medicine were excluded. The databases were processed using the VOSviewer software and auxiliary spreadsheets. Data extraction yielded 275 articles published in 161 journals, mainly concentrated on 13 journals and 881 keywords plus.ResultsThe keywords plus of high occurrences were estimated at 27, with seven to 30 occurrences. From the 1595 identified authors, 125 were consistently connected in the coauthorship network in the total set and were grouped into nine clusters. Using Lotka's law, we identified 24 prolific authors, and Hirsch index analysis revealed that 45 articles were cited more than 45 times. Crosses were identified between 17 articles in the Hirsch index and 17 prolific authors, highlighting the presence of a large set of prolific authors from various interconnected clusters, a triad, and a solitary prolific author.ConclusionAn exponential trend was observed in biosensor research for health monitoring, identifying areas of innovation, collaboration, and technological challenges that can guide future research on this topic.
Citation: DIGITAL HEALTH
PubDate: 2024-06-13T02:00:49Z
DOI: 10.1177/20552076241256876
Issue No: Vol. 10 (2024)
- Change in disease burden associated with influenza and air pollutants
during the COVID-19 pandemic in Hong Kong
Authors: Yanwen Liu, Xie Jingyu, Cai Cihan, Hilda Tsang, Shuya Lu, Daihai He, Lin Yang
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectivesThis study aimed to estimate the variation in disease burden associated with air pollutants and other respiratory viruses during the COVID-19 pandemic.MethodsWe adopted a machine learning approach to calculate the excess mortality attributable to air pollutants and influenza, during the pre-pandemic and pandemic period.ResultsIn the first 2 years of the COVID-19 pandemic, there were 8762 (95% confidence interval, 7503–9993), and 12,496 (11,718–13,332) excess all-cause deaths in Hong Kong. These figures correspond to 117.4 and 167.9 per 100,000 population, and 12.6% and 8.5% of total deaths in 2020 and 2021, respectively. Compared to the period before the pandemic, excess deaths from all-causes, cardiovascular and respiratory diseases, pneumonia and influenza attributable to influenza A and B significantly decreased in all age groups. However, excess deaths associated with ozone increased in all age-disease categories, while the relative change of nitrogen dioxide (NO2) and particular matters less than 10 µm (PM10) associated burden showed a varied pattern.ConclusionsA notable shift in disease burden attributable to influenza and air pollutants was observed in the pandemic period, suggesting that both direct and indirect impacts shall be considered when assessing the global and regional burden of the COVID-19 pandemic.
Citation: DIGITAL HEALTH
PubDate: 2024-06-12T05:58:30Z
DOI: 10.1177/20552076241261892
Issue No: Vol. 10 (2024)
- Summary of the best evidence on self-management support schemes for
patients with inflammatory bowel disease based on mobile health systems
Authors: Chenfei Ren, Yunxian Zhou, Qian Cai, Mi Zhou
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveSelf-management support services can improve patients’ self-management ability. This study summarized the best evidence on a self-management support scheme for patients with inflammatory bowel disease based on a mobile health system to accurately describe the current status of the field and provide recommendations for healthcare workers.MethodsTwo researchers retrieved studies from computer decision support systems, guideline websites, official association websites, and databases from the establishment of the database until October 2023. The quality of the included studies was independently evaluated by two authors using the Appraisal of Guidelines for Research and Evaluation Instrument II and the 2016 version of the corresponding evaluation standards of the Australian Joanna Briggs Institute Evidence-based Health Care Center. The classification of evidence and recommendation level adopted the 2014 version of the Australian Joanna Briggs Institute evidence pregrading and recommending level system.ResultsFifteen studies were included, comprising one guideline, two expert consensuses, four systematic reviews, four quasi-experimental studies, and four qualitative studies. The overall quality of the included studies was moderate to high. Thirty-six pieces of best evidence were compiled for seven elements, namely, mobile health system type and functional support; mobile health system application preparation; health information recording, uploading, and presentation; zoning management of diseases and early warning of the active period; support related to health education; healthcare support team formation and services; and virtual communities.ConclusionsOur study evaluated the quality of the included studies and summarized a self-management support scheme for patients with inflammatory bowel disease based on a mobile health system. The main scheme was divided into 7 parts and 36 items, which can be used as a reference for healthcare workers so that they can provide more comprehensive and scientific self-management support services for patients with inflammatory bowel disease through mobile health systems.
Citation: DIGITAL HEALTH
PubDate: 2024-06-11T07:31:58Z
DOI: 10.1177/20552076241261906
Issue No: Vol. 10 (2024)
- Promoting child and adolescent health through wearable technology: A
systematic review
Authors: Wei Zhang, Keying Xiong, Chengyan Zhu, Richard Evans, Lijuan Zhou, Christine Podrini
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundWearable technology is used in healthcare to monitor the health of individuals. This study presents an updated systematic literature review of the use of wearable technology in promoting child and adolescent health, accompanied by recommendations for future research.MethodsThis review focuses on studies involving children and adolescents aged between 2 and 18 years, regardless of their health condition or disabilities. Studies that were published from 2016 to 2024, and which met the inclusion criteria, were extracted from four academic databases (i.e. PubMed, Cochrane, Embase, and Web of Science) using the Preferred Reporting Items for Systematic Review and Meta-Analyses (PRISMA) protocol. Data on intervention purposes, interventions deployed, intervention duration, measurements, and the main outcomes of the studies were collected.ResultsA total of 53 studies involving 14,852 participants were reviewed. They focused on various aspects, including the ownership and use of wearable devices (n = 3), the feasibility (n = 22), effectiveness (n = 4), and adherence (n = 2) of intervention strategies, or a combination of multiple aspects (n = 22). Among the interventions deployed, Fitbit was the most frequently used, featuring in 26 studies, followed by ActiGraph (n = 11). In intervention studies, the majority of studies focused on pre-morbidity prevention (n = 26) and the treatment of illnesses (n = 20), with limited attention given to postoperative monitoring (n = 4).ConclusionsThe use of wearable technology by children and adolescents has proven to be both feasible and effective for health promotion. This systematic review summarizes existing research by exploring the use of wearable technology in promoting health across diverse youth populations, including healthy and unhealthy individuals. It examines health promotion at various stages of the disease continuum, including pre-disease prevention, in-disease treatment, and postoperative monitoring. Additionally, the review provides directions for future research.
Citation: DIGITAL HEALTH
PubDate: 2024-06-11T07:31:29Z
DOI: 10.1177/20552076241260507
Issue No: Vol. 10 (2024)
- Gamification preferences in nutrition apps: Toward healthier diets and
food choices
Authors: Michelle Berger, Carolin Jung
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundUnhealthy eating habits are costly and can lead to serious diseases such as obesity. Nutrition apps offer a promising approach to improving dietary behavior. Gamification elements (GEs) can motivate users to continue using nutrition apps by making them more enjoyable, which can lead to more positive behavioral changes regarding dietary choices. However, the effects of users’ preferences and individual characteristics on gamified systems are not yet understood. Current calls for research suggest that personalized gamified systems might lead to user satisfaction, continuous app use, and—ultimately—long-term improvements in diet.ObjectiveThe aim was to determine the most preferred GEs in nutrition apps and to define clusters of GEs preferences in terms of personality and socio-demographic characteristics.MethodsWe surveyed 308 people to measure their preferences regarding GEs in nutrition apps and applied best-worst scaling to determine the most preferred GEs. Furthermore, we used cluster analysis to identify different user clusters and described them in terms of personality and socio-demographic characteristics.ResultsWe determine that GEs most favored are goals, progress bars, and coupons. We revealed three distinct user clusters in terms of personality and socio-demographic characteristics. Based on the individual factors of openness and self-perception, we find that significant differences exist between the preferences for leaderboards and coupons.ConclusionWe contribute by shedding light on differences and similarities in GE preferences relating to specific contexts and individual factors, revealing the potential for individualized nutrition apps. Our findings will benefit individuals, app designers, and public health institutions.
Citation: DIGITAL HEALTH
PubDate: 2024-06-11T07:30:59Z
DOI: 10.1177/20552076241260482
Issue No: Vol. 10 (2024)
- Healthcare professionals’ perception on emergence of security threat
using digital health technologies in healthcare delivery
Authors: Jonathan Kissi, Godwin Azakpah, Nathan Kumasenu Mensah, Kennedy Dzamvivie, Twisty Ampofowaa Bosompem, Victor Adu Wireko, Grace Amoah-Anomah
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundThe advancement of digital health technologies (DHTs) in the healthcare industry has revolutionized healthcare by improving efficiency and accessibility. The shift from paper-based records to electronic health records (EHRs) as a result of modern technologies introduced various security threats, endangering patient data privacy and system reliability. This study explores intricate security threats among healthcare professionals affecting DHT utilization.MethodologyA structured questionnaire was designed to solicit for data from healthcare professionals on the existence of possible security threats, magnitude of physical and technical threats, and the extent to which the activities of staff and vendors affect the current DHTs being utilized in Cape Coast Teaching Hospital and Directorate of University Health Services at the University of Cape Coast. Purposive and convenience sampling techniques were employed to select healthcare professionals from various medical fields, and their views were captured for the study.ResultsFindings and data analyzed from the study revealed that technical threats, such as hacking, phishing, malware, and encryption weaknesses, pose more substantial dangers to DHTs compared to physical threats. The study explored viable strategies to prevent unauthorized access to DHTs and safeguard patient information. Encouraging measures, such as encryption, multi-factor authentication, regular security training, and periodic password changes, emerged as promising methods in addressing DHT security threats.ConclusionThe study emphasizes the necessity of robust security measures and regular updates to effectively counter the emerging security threats. It underscores the critical necessity for a comprehensive protocol to enhance DHT security, addressing physical, technical, and personnel-related threats.
Citation: DIGITAL HEALTH
PubDate: 2024-06-11T07:30:01Z
DOI: 10.1177/20552076241260385
Issue No: Vol. 10 (2024)
- Application of a mobile health data platform for public health
surveillance: A case study in stress monitoring and prediction
Authors: Pedro Elkind Velmovitsky, Paulo Alencar, Scott T Leatherdale, Donald Cowan, Plinio Pelegrini Morita
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundPublic health surveillance involves the collection, analysis and dissemination of data to improve population health. The main sources of data for public health decision-making are surveys, typically comprised of self-report which may be subject to biases, costs and delays. To complement subjective data, objective measures from sensors could potentially be used. Specifically, advancements in personal mobile and wearable technologies enable the collection of real-time and continuous health data.ObjectiveIn this context, the goal of this work is to apply a mobile health platform (MHP) that extracts health data from the Apple Health repository to collect data in daily-life scenarios and use it for the prediction of stress, a major public health issue.MethodsA pilot study was conducted with 45 participants over 2 weeks, using the MHP to collect stress-related data from Apple Health and perceived stress self-reports. Apple, Withings and Empatica devices were distributed to participants and collected a wide range of data, including heart rate, sleep, blood pressure, temperature, and weight. These were used to train random forests and support vector machines. The SMOTE technique was used to handle imbalanced datasets.ResultsAccuracy and f1-macro scores were in line with state-of-the-art models for stress prediction above 60% for the majority of analyses and samples analysed. Apple Watch sleep features were particularly good predictors, with most models with these data achieving results around 70%.ConclusionsA system such as the MHP could be used for public health data collection, complementing traditional self-reporting methods when possible. The data collected with the system was promising for monitoring and predicting stress in a population.
Citation: DIGITAL HEALTH
PubDate: 2024-06-08T07:15:42Z
DOI: 10.1177/20552076241249931
Issue No: Vol. 10 (2024)
- Testing the feasibility of mobile ecological momentary assessment for
symptom burden and management among metastatic cancer patients
Authors: Theodore M. Brasky, Alison M. Newton, Julie A. Stephens, Scott A. Strassels, Roberto M. Benzo, John L. Hays, Erin Stevens, Theodore L. Wagener, Donald Hedeker, Jessica L. Krok-Schoen
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundIndividuals who have metastatic cancer experience substantial physical and psychological distress (e.g., pain, depression, anxiety) from their disease and its treatment compared to patients with less advanced disease. As the burden of symptoms varies over time, ecological momentary assessment (EMA) may be used to better understand patients’ symptom trajectories, complimenting traditional longitudinal data collection methods. However, few have used EMA in patients with metastatic disease. The current study adds to the existing literature by exploring interrelated, common cancer-related symptoms of pain, anxiety, and depression and use of cannabis-based products, opioid medications, other (nonopioid) pain medications, and medications for anxiety or depression.MethodsAn eight-day prospective observational feasibility study was conducted among 50 patients with metastatic cancer recruited from seven solid cancer clinics at The Ohio State University Comprehensive Cancer Center. Participants completed a week of interval-contingent mobile EMA, administered daily at 9 a.m., 3 p.m., and 8 p.m., and a comprehensive interviewer-administered questionnaire on Day 8. Participants were queried on their symptom burden and management strategies (i.e., use of medications and cannabis). We considered EMA to be feasible if a priori retention (80%) and adherence goals (75%) were met.ResultsSeventy-nine percent of eligible patients contacted enrolled in the study (n = 50 of 63). Among those enrolled, 92% were retained through Day 8 and 80% completed>90% of EMAs, exceeding a priori objectives. Participants’ average pain, anxiety, and depressive symptoms across the week of EMA ranged from 1.7 to 1.8 (1 to 5 scale). Symptoms varied little by day or time of administration. On Day 8, significant proportions of participants reported past-week use of medications and cannabis for symptom management.ConclusionsParticipants exceeded a priori adherence and retention objectives, indicating that mobile EMA is feasible among metastatic cancer patients, addressing a gap in the existing literature and informing future research. Restricting eligibility to participants with a minimum cutoff of symptom burden may be warranted to increase observations of symptom variability and provide opportunities for future health interventions. Future research is needed to test the acceptability and quality of data over a longer study period in this patient population.
Citation: DIGITAL HEALTH
PubDate: 2024-06-08T06:18:41Z
DOI: 10.1177/20552076241261843
Issue No: Vol. 10 (2024)
- Digitally managing depression: A fully remote randomised attention-placebo
controlled trial
Authors: Aaron Kandola, Kyra Edwards, Marie AE Muller, Bettina Dührkoop, Bettina Hein, Joris Straatman, Joseph F Hayes
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundDepression is a common and disabling condition. Digital apps may augment or facilitate care, particularly in under-served populations. We tested the efficacy of juli, a digital self-management app for depression in a fully remote randomised controlled trial.MethodsA pragmatic randomised controlled trial that included participants aged> 18 who self-identified as having depression and scored> 5 on the Patient Health Questionnaire-8. Participants were randomly assigned (1:1) to receive juli for 8 weeks or a limited attention-placebo control app. Our primary outcome was the difference in Patient Health Questionnaire-8 scores at 8 weeks. Secondary outcomes were remission, minimal clinically important difference, worsening of depression, and health-related quality of life. Analyses were per-protocol (primary), and modified and full intention-to-treat (secondary). The trial was registered at ISRCTN (ISRCTN12329547).ResultsBetween May 2021 and January 2023, we randomised 908 participants. 662 completed the week 2 outcome assessment and were included in the modified intention-to-treat analysis, and 456 completed the week 8 outcome assessments (per-protocol). In the per-protocol analysis, the juli group had a greater reduction in Patient Health Questionnaire-8 score (10.78, standard deviation 6.26) than the control group (11.88, standard deviation 5.73) by week 8 (baseline adjusted β-coefficient −0.94, 95% CI: −1.87 to −0.22, p = 0.045). Achieving remission and a minimal clinically important difference was more likely in the juli group at 8 weeks (adjusted odds ratios 2.22, 95% CI: 1.45–3.39, p
Citation: DIGITAL HEALTH
PubDate: 2024-06-08T06:15:21Z
DOI: 10.1177/20552076241260409
Issue No: Vol. 10 (2024)
- A control system model of capability-opportunity-motivation and behaviour
(COM-B) framework for sedentary and physical activity behaviours
Authors: Reza Daryabeygi-Khotbehsara, David W. Dunstan, Sheikh Mohammed Shariful Islam, Ryan E. Rhodes, Sahar Hojjatinia, Mohamed Abdelrazek, Eric Hekler, Brittany Markides, Ralph Maddison
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveTheoretical frameworks are essential for understanding behaviour change, yet their current use is inadequate to capture the complexity of human behaviour such as physical activity. Real-time and big data analytics can assist in the development of more testable and dynamic models of current theories. To transform current behavioural theories into more dynamic models, it is recommended that researchers adopt principles such as control systems engineering. In this article, we aim to describe a control system model of capability-opportunity-motivation and behaviour (COM-B) framework for reducing sedentary behaviour (SB) and increasing physical activity (PA) in adults.MethodsThe COM-B model is explained in terms of control systems. Examples of effective behaviour change techniques (BCTs) (e.g. goal setting, problem-solving and social support) for reducing SB and increasing PA were mapped to the COM-B model for illustration.ResultA fluid analogy of the COM-B system is presented.ConclusionsThe proposed integrated model will enable empirical testing of individual behaviour change components (i.e. BCTs) and contribute to the optimisation of digital behaviour change interventions.
Citation: DIGITAL HEALTH
PubDate: 2024-06-08T06:13:21Z
DOI: 10.1177/20552076241255658
Issue No: Vol. 10 (2024)
- Supporting patients with a mental health diagnosis to use online services
in primary care. A qualitative interview study
Authors: Jo Parsons, Gary Abel, Carol Bryce, John Campbell, Jennifer Newbould, Emma Pitchforth, Stephanie Stockwell, Bethan Treadgold, Rachel Winder, Helen Atherton
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThe increase in reliance on online services for general practice has the potential to increase inequalities within some populations. Patients with a mental health condition are one such group. Digital facilitation is defined as a range of processes, procedures, and people, which seek to support NHS patients in using online services. This study aimed to examine the views and experiences of digital facilitation in primary care amongst patients living with a mental health condition.MethodsSemi-structured interviews were conducted with patients living with a mental health condition, recruited from general practices across England participating in the Di-Facto study. Thematic analysis was conducted on interview transcripts.ResultsInterviews were conducted with ten participants with a mental health condition, recruited from five general practices. Three themes were identified: (1) familiarity with online services; (2) experiences of those using online services; (3) the need for digital facilitation. The need for digital facilitation was identified in the registration for online services, and in trusting online services.ConclusionsOnline services offer convenience for patients, but registration for the use of such services remains a potential area of difficulty. Participants had difficulties with registering for online services and had concerns about trust in using them. Support offered by general practices in using online services needs to be varied and adaptable to meet the needs of individual patients.
Citation: DIGITAL HEALTH
PubDate: 2024-06-08T06:09:35Z
DOI: 10.1177/20552076241255637
Issue No: Vol. 10 (2024)
- Exploring how the design and provision of digital self-management
technology can improve the uptake by older adults with chronic kidney
disease, diabetes and dementia: A modified e-Delphi study
Authors: Louise Moody, Esme Wood, Abigail Needham, Andrew Booth, Wendy Tindale
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
Objectives: As development and introduction of digital self-management technologies continues to increase, the gap between those who can benefit, and those who cannot correspondingly widens. This research aimed to explore the use of digital self-management technology by older adults with three highly-prevalent long-term conditions (chronic kidney disease, diabetes and dementia), and build expert consensus across the conditions on changes needed to improve effective usage. Method: This qualitative research involved a modified e-Delphi Study. The Delphi panel was comprised of experts with personal, academic or clinical expertise related to one of the long-term conditions and/or the development and use of digital self-management technology. The e-Delphi involved a round of online semi-structured interviews followed by two rounds of a structured online survey. Results: Fourteen experts participated in the study, with eleven of the fourteen completing all three rounds. Analysis of the interviews (round 1 of the Delphi) led to 7 main themes and 29 sub-themes. These were translated into 26 statements that formed the basis of the online survey questions. In the first administration of the survey (round 2) 19 statements reached consensus. After the second administration a further 6 statements reach consensus. Conclusion: The findings reflect expert consensus on barriers to the use of digital self-management by older adults with 3 different, but inter-related conditions, and identify ways in which the design and provision of such technologies could be improved to facilitate more effective use. It is concluded that both the design and the provision of technologies should consider a combination of individual, condition-specific and age-related requirements. By building a consensus on issues and potential strategies common across the three conditions, we aim to inform future research and practice and facilitate effective self-management by older adults.
Citation: DIGITAL HEALTH
PubDate: 2024-06-08T06:06:41Z
DOI: 10.1177/20552076241247196
Issue No: Vol. 10 (2024)
- Patients’ acceptability of self-selected digital health services to
support diet and exercise among people with complex chronic conditions:
Mixed methods study
Authors: Amandine Barnett, Soraia de Camargo Catapan, Dev K Jegatheesan, Marguerite M Conley, Shelley E Keating, Hannah L Mayr, Lindsey Webb, Riley C C Brown, Jeff S Coombes, Graeme A Macdonald, Nicole M Isbel, Nicola W Burton, Katrina L Campbell, Ingrid J Hickman, Jaimon T Kelly
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThe acceptability of being offered a choice from a suite of digital health service options to support optimal diet and exercise behaviors in adults with complex chronic conditions was evaluated. This study sought to understand many areas of acceptability including satisfaction, ease of use, usefulness and user appropriateness and perceived effectiveness.MethodsThis mixed-methods study was embedded within a randomized-controlled feasibility trial providing digital health services managing diet and exercise for adults from specialist kidney and liver disease clinics. Post study surveys and semistructured interviews were used to determine patients’ acceptability of the trial interventions. Quantitative (surveys) and qualitative (surveys and interviews) results were merged using integrative analysis and mapped to each construct of the modified version of the Theoretical Framework of Acceptability.ResultsSeventeen interviews (intervention group) and 50 surveys (n = 24 intervention, n = 26 comparator) completed from a possible 67 participants were analyzed. In the intervention group, the survey results revealed high areas of acceptability for the digital health services including overall support received, ease of use, timely advice and feeling safe. The interviews also revealed high areas of acceptability including convenience, ability to adopt healthier behaviors and having regular interactions with health professionals. However, the interviews also revealed lower areas of acceptability as a result of absence of individualization, low digital literacy, and limitations from life circumstances.ConclusionsRecipients of digital health services that supported diet and exercise interventions found these useful, effective, and safe. Individualized care, technical support and patient confidence remain important to improve the acceptability of digital health service interventions.
Citation: DIGITAL HEALTH
PubDate: 2024-06-08T05:58:12Z
DOI: 10.1177/20552076241245278
Issue No: Vol. 10 (2024)
- Evaluating an interactive tool that reasons about quality of life to
support life planning by older people
Authors: Neil Maiden, Sophie Hide, James Lockerbie, Simone Stumpf, Juanita Hoe, Shashi Hirani
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectivesIn response to the lack of digital support for older people to plan their lives for quality of life, research was undertaken to co-design and then evaluate a new digital tool that combined interactive guidance for life planning with a computerised model of quality of life.MethodFirst, a workshop-based process for co-designing the SCAMPI tool with older people is reported. A first version of this tool was then evaluated over eight consecutive weeks by nine older people living in their own homes. Four of these people were living with Parkinson's disease, one with early-stage dementia, and four without any diagnosed chronic condition. Regular semi-structured interviews were undertaken with each individual older person and, where wanted, their life partner. A more in-depth exit interview was conducted at the end of the period of tool use. Themes arising from analyses of content from these interviews were combined with first-hand data collected from the tool's use to develop a description of how each older person used the tool over the 8 weeks.ResultsThe findings provided the first evidence that the co-designed tool, and in particular the computerised model, could offer some value to older people. Although some struggled to use the tool as it was designed, which led to limited uptake of the tool's suggestions, the older people reported factoring these suggestions into their longer-term planning, as health and/or circumstances might change.ConclusionsThe article contributes to the evolving discussion about how to deploy such digital technologies to support quality of life more effectively.
Citation: DIGITAL HEALTH
PubDate: 2024-06-07T08:36:14Z
DOI: 10.1177/20552076241255633
Issue No: Vol. 10 (2024)
- Assessing usability of intelligent guidance chatbots in Chinese hospitals:
Cross-sectional study
Authors: Yanni Yang, Siyang Liu, Ping Lei, Zhengwei Huang, Lu Liu, Yiting Tan
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThis study aimed to assessing usability of intelligent guidance chatbots (IGCs) in Chinese hospitals.MethodsA cross-sectional study based on expert survey was conducted between August to December 2023. The survey assessed the usability of chatbots in 590 Chinese hospitals. One-way ANOVA was used to analyze the impact of the number of functions, human-like characteristics, number of outpatients, and staff size on the usability of the IGCs.ResultsThe results indicate that there are 273 (46.27%) hospitals scoring above 45 points. In terms of function development, 581(98.47%) hospitals have set the number of functions between 1 and 5. Besides, 350 hospitals have excellent function implementation, accounting for 59.32%. In terms of the IGC's human-like characteristic, 220 hospitals have both an avatar and a nickname. Results of One-way ANOVA show that, the number of functions(F = 202.667, P
Citation: DIGITAL HEALTH
PubDate: 2024-06-07T04:58:51Z
DOI: 10.1177/20552076241260504
Issue No: Vol. 10 (2024)
- Effectiveness of an interprofessional assessment and management approach
for people with chronic low back disorders delivered via virtual care: A
randomized controlled trial pilot intervention
Authors: Stacey Lovo, Biaka Imeah, Nazmi Sari, Megan E O’Connell, Steve Milosavljevic, Adriana Angarita-Fonseca, Brenna Bath
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveVirtual care for chronic conditions has seen uptake due to COVID-19. Evaluation of virtual models is important to ensure evidence-based practice. There is a paucity of research in the use of virtual care for management of chronic back disorders. The objective of this study was to evaluate effectiveness of a team-based virtual care model for back disorder assessment where a physical therapist uses virtual care to join a nurse practitioner and patient in a rural Saskatchewan, Canada community.MethodsSixty-four rural adults with chronic back disorders were randomly allocated to receive either: (1) team-based virtual care (n = 24); (2) care from an urban physical therapist travelling to community (n = 20); or (3) care from a rural nurse practitioner (n = 20). The team-based care group involved a nurse practitioner located with a rural patient, and a physical therapist joining using virtual care. The physical therapist alone and the nurse practitioner alone groups received in-person assessments. Groups with a physical therapist involved had follow-up treatments by in-person physical therapy. Outcomes over six months included pain, disability, back beliefs, satisfaction, quality-adjusted health status and management-related costs.ResultsThere were no significant differences for pain, disability, back beliefs and satisfaction between groups. The average cost per patient for implementing in-person physical therapist assessment ($135) was higher compared with the team over virtual care ($118) and NP care ($59).ConclusionPrimary outcomes were not different by group. Physical therapist alone was more costly than other groups. Future research should include more participants, longer follow-up time and refined cost parameters.Trial RegistrationClinicalTrials.gov NCT02225535; https://clinicaltrials.gov/ct2/show/NCT02225535 (Archived by WebCite at http://www.webcitation.org/6lqLTCNF7).
Citation: DIGITAL HEALTH
PubDate: 2024-06-06T12:48:45Z
DOI: 10.1177/20552076241260569
Issue No: Vol. 10 (2024)
- E-therapists’ views on the acceptability and feasibility of an
internet-administered, guided, low-intensity cognitive behavioural therapy
intervention for parents of children treated for cancer: A qualitative
study
Authors: Christina Reuther, Johan Lundgren, Maria Gottvall, Johan Ljungberg, Joanne Woodford, Louise von Essen
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundChildhood cancer treatment completion can be a period of vulnerability for parents and is associated with mental health difficulties such as depression and anxiety. We developed an internet-administered, guided, low-intensity cognitive behavioural therapy-based self-help intervention (EJDeR) for parents delivered on the U-CARE-portal (Portal). The acceptability and feasibility of EJDeR and study procedures were examined using a single-arm feasibility trial (ENGAGE). Results indicated that EJDeR and ENGAGE study procedures are acceptable and feasible, however, a need for clinical and technical modifications to EJDeR and refinements to ENGAGE study procedures was identified.ObjectivesThis study aimed to explore the acceptability and feasibility of EJDeR and ENGAGE study procedures from the perspective of e-therapists to inform clinical and technical modifications to EJDeR and refinements to study procedures prior to progression to a superiority randomised controlled trial.MethodsWe conducted semi-structured interviews with 10 e-therapists. Data were analysed using manifest content analysis.ResultsWe identified three categories relating to the acceptability and feasibility of EJDeR: (a) Support to e-therapists (subcategories: Clinical supervision and Technical difficulties); (b) Guidance to parents (subcategories: Support protocols and Synchronous communication); and (c) Content (subcategories: Relevancy of the intervention and Pacing of the intervention). We identified four categories relating to the acceptability and feasibility of study procedures: (a) Recruitment and training of e-therapists (subcategories: Definition of the role and Training program); (b) Retention of parents (subcategories: Parent suitability and screening and Frequency of weekly Portal assessments); (c) Retention of e-therapists (subcategories: Administrative requirements and Communication with the research team); and (d) The Portal.ConclusionsEJDeR and study procedures were considered acceptable and feasible, however, clinical and technical modifications and refinements to study procedures were suggested to enhance acceptability and feasibility. Results may also inform implementation considerations for both EJDeR and other similar digital psychological interventions.Trial registration numberISRCTN 57233429
Citation: DIGITAL HEALTH
PubDate: 2024-06-06T12:48:25Z
DOI: 10.1177/20552076241260513
Issue No: Vol. 10 (2024)
- Navigating moral and ethical dilemmas in digital transformation processes
within healthcare organizations
Authors: Lior Naamati-Schneider, Mirit Arazi-Fadlon, Shir Daphna-Tekoah
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveHealthcare systems around the world face a turbulent and unstable global and local ecosystem that changes daily and impacts the healthcare organization and its workforce. This challenging environment, coupled with economic pressures, is forcing healthcare systems to change and adopt strategic and technological processes to adapt to change at all levels of the system (macro-holistic multi-systemic, mezzo-organizational, and micro-personal).MethodsIn this study, through 32 in-depth, semi-structured interviews with healthcare professionals working in public general hospitals in central Israel, we examined, mapped, and highlighted the conflicts and moral dilemmas they have faced in recent years, alongside the processes of strategic, technological, and digital changes that the healthcare system has undergone.ResultsThe findings from both a categorical–deductive approach and an inductive approach analysis reveals four main themes: innovation paradox, quality and treatment conflict, information and knowledge conflict, and personal needs and values. The themes and sub-themes are sorted across the three levels of the healthcare system.ConclusionsThese findings represent a wide range of conflicts and moral dilemmas that arise from the implementation of strategic change and digital transformation, adding to the already numerous ethical issues and moral dilemmas in healthcare and bioethics that are associated with three levels of the system. These challenges and moral conflicts can be barriers to implementing the necessary changes, as well as challenging individuals’ internal values, potentially leading to burnout and moral distress. Given the importance of this issue and the intensification of change processes over the next few years, it is up to the management and key stakeholders to implement these processes in a way that addresses the conflicts and challenges that health professionals face. Minimizing the level of challenges and moral distress in the health sector will be to the benefit of the system, its workers, and the patients it serves.
Citation: DIGITAL HEALTH
PubDate: 2024-06-06T12:48:05Z
DOI: 10.1177/20552076241260416
Issue No: Vol. 10 (2024)
- Exploring social media adoption for marketing purpose among healthcare
professionals in Gondar town, central Gondar zone: A facility-based
cross-sectional survey
Authors: Berhanemeskel Weldegerima Atsbeha, Mulugeta Negash Wodaje
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundSocial media has become an integral platform for global information exchange and business interactions, emerging as a crucial tool for promoting products and services in the digital age. Despite its worldwide significance, local businesses, especially in Ethiopia, lag behind in leveraging social media for healthcare marketing. The scarcity of studies on social media adoption among healthcare providers in Ethiopia highlights the imperative for comprehensive research.ObjectiveThis study, conducted in Gondar Town, focused on private healthcare professionals, aiming to identify the determinants of their behavioral intention and usage behavior in adopting social media marketing.MethodA facility-based cross-sectional survey involving 238 health professionals from private healthcare facilities in Gondar Town was conducted between March and June 2023. The study analyzed data using SPSS Version 26 and AMOS Structural Equation Modeling Version 23.ResultsAll participants reported using social media platforms, with Facebook, Telegram, and YouTube being popular choices. Performance expectancy, social influence, facilitation condition, and behavioral intention significantly influenced healthcare professionals’ adoption of social media marketing. Performance expectancy and social influence exhibited the strongest impact on behavioral intention, acting as mediators influencing usage behavior. However, effort expectancy did not significantly influence behavioral intention. Age, gender, experience, and voluntariness showed no significant moderating effects.ConclusionThis study contributes valuable insights into social media marketing adoption in the healthcare sector, emphasizing the critical role of various factors in shaping behavioral intention and usage behavior. The findings offer practical implications for private healthcare providers, policymakers, and marketers, guiding strategies to enhance patient communication and engagement through social media in Gondar Town.
Citation: DIGITAL HEALTH
PubDate: 2024-06-06T12:47:45Z
DOI: 10.1177/20552076241259872
Issue No: Vol. 10 (2024)
- Automated facial recognition system using deep learning for pain
assessment in adults with cerebral palsy
Authors: Álvaro Sabater-Gárriz, F Xavier Gaya-Morey, José María Buades-Rubio, Cristina Manresa-Yee, Pedro Montoya, Inmaculada Riquelme
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveAssessing pain in individuals with neurological conditions like cerebral palsy is challenging due to limited self-reporting and expression abilities. Current methods lack sensitivity and specificity, underlining the need for a reliable evaluation protocol. An automated facial recognition system could revolutionize pain assessment for such patients.The research focuses on two primary goals: developing a dataset of facial pain expressions for individuals with cerebral palsy and creating a deep learning-based automated system for pain assessment tailored to this group.MethodsThe study trained ten neural networks using three pain image databases and a newly curated CP-PAIN Dataset of 109 images from cerebral palsy patients, classified by experts using the Facial Action Coding System.ResultsThe InceptionV3 model demonstrated promising results, achieving 62.67% accuracy and a 61.12% F1 score on the CP-PAIN dataset. Explainable AI techniques confirmed the consistency of crucial features for pain identification across models.ConclusionThe study underscores the potential of deep learning in developing reliable pain detection systems using facial recognition for individuals with communication impairments due to neurological conditions. A more extensive and diverse dataset could further enhance the models’ sensitivity to subtle pain expressions in cerebral palsy patients and possibly extend to other complex neurological disorders. This research marks a significant step toward more empathetic and accurate pain management for vulnerable populations.
Citation: DIGITAL HEALTH
PubDate: 2024-06-06T12:47:14Z
DOI: 10.1177/20552076241259664
Issue No: Vol. 10 (2024)
- Patients’ needs and experiences of telerehabilitation after total hip
Authors: Wenzhong Zhang, Hong Ji, Yan Wu, Zhenzhen Xu, Jing Li, Qingxiang Sun, Chunlei Wang, Fengyi Zhao
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundThe number of patients undergoing joint replacement procedures is continuously increasing. Tele-equipment is progressively being employed for postrehabilitation of total hip and knee replacements. Gaining a comprehensive understanding of the experiences and requirements of patients undergoing total hip and knee arthroplasty who participate in telerehabilitation can contribute to the enhancement of telerehabilitation programs and the overall rehabilitation and care provided to this specific population.ObjectiveTo explore the needs and experiences of total hip and knee arthroplasty patients with telerehabilitation.DesignSystematic review and qualitative synthesis.MethodsElectronic databases PubMed, Web of Science, The Cochrane Library, Embase, CINAHL, Scopus, ProQuest, CNKI, Wanfang Data, VIP, and SinoMed were systematically searched for information on the needs and experiences of telerehabilitation for patients with total hip arthroplasty and total knee arthroplasty in qualitative studies. The search period was from the creation of the database to March 2024. Literature quality was assessed using the 2016 edition of the Australian Joanna Briggs Institute Centre for Evidence-Based Health Care Quality Assessment Criteria for Qualitative Research. A pooled integration approach was used to integrate the findings inductively.ResultsA total of 11 studies were included and 4 themes were identified: the desire to communicate and the need to acquire knowledge; accessible, high-quality rehabilitation services; positive psychological experiences; the dilemmas of participating in telerehabilitation.ConclusionsThis study's findings emphasize that the practical needs and challenges of total hip and knee arthroplasty patients’ participation in telerehabilitation should be continuously focused on, and the advantages of telerehabilitation should be continuously strengthened to guarantee the continuity of patients’ postoperative rehabilitation and to promote their postoperative recovery.
Citation: DIGITAL HEALTH
PubDate: 2024-06-06T12:46:47Z
DOI: 10.1177/20552076241256756
Issue No: Vol. 10 (2024)
- JCI, SKS için bir dizi standart ve gösterge önerisive HIMSS-EMRAM
kalite değerlendirme modelleri
Authors: Sinem Cece, İlker Köse
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThe Joint Commission International (JCI) and the Health Information Management System Society (HIMSS) are global accreditation groups for healthcare. JCI focuses on overall care quality, while HIMSS-Electronic Medical Record Adoption Model (EMRAM) looks at digital processes. Meanwhile, the Health Quality Standards (SKS) is Turkey's own system. It aligns with JCI and evaluates healthcare similarly. When a health institution wants to be accredited with one of these models, similar scope, process and criteria are repeatedly reviewed from different perspectives. However, it is not known whether the scope, process and criteria included in these models are related to the postmodern management approach (PMMA), which is one of today's business approaches. It is observed that today's businesses are dominated by PMMAs. Similarly, healthcare organizations are also influenced by PMMAs since they are qualified as businesses.YöntemlerThis study investigated the compatibility of the concepts, processes and criteria covered by SKS, JCI and HIMSS-EMRAM models with PMMAs. Using the Delphi technique, PMMAs were explained to subject-matter experts (SMEs) in the form of written texts delivered. SMEs evaluated whether the standards/criteria are compatible with PMMAs. During this evaluation, they examined whether the standard/criteria included in the relevant model are directly or indirectly indicative of these approaches. SMEs developed their standards/criteria for the approaches which no standards/criteria could be matched with. The binary pairwise comparison method was used to determine the weighted value of the proposed standards/criteria.SonuçlarSMEs proposed a total of 24 standards and 18 indicators for nine postmodern organizational management approaches.ConclusionThe literature presented a proposal for new standards and indicators. They would be unique. They would address how well these three models fit the PMMA.
Citation: DIGITAL HEALTH
PubDate: 2024-06-06T08:10:25Z
DOI: 10.1177/20552076241258455
Issue No: Vol. 10 (2024)
- Usability of an eHealth sleep education intervention for university
students
Authors: Lindsay Rosenberg, Gabrielle Rigney, Anastasija Jemcov, Derek van Voorst, Penny Corkum
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundIt has been estimated that more than one-third of university students suffer from insomnia. Few accessible eHealth sleep education programmes exist for university students and of the ones that do exist, fewer were developed using a user-centred approach, which allows for student input to be systematically collected and utilized to provide students with a programme that they consider to be easy to use and implement and to be effective. Better Nights, Better Days-Youth (BNBD-Youth) is a four-session eHealth sleep education programme designed for youth but previously only evaluated in younger adolescents (ages 14–18 years).AimsThe purpose of this study is to evaluate the usability of the BNBD-Youth programme with university students using Morville's User Experience Honeycomb framework to determine if this programme would meet the needs of university students and if so what modifications would be needed.MethodsCanadian undergraduate students (n = 46) completed the BNBD-Youth programme. Students completed online usability questionnaires based on the seven dimensions of Morville's User Experience Honeycomb (i.e. useful, usable, valuable, credible, desirable, accessible and findable) after each session and after completion of the programme. Open- and closed-ended questions were used to obtain both quantitative and qualitative responses.ResultsAverage quantitative ratings were positive across user experience dimensions, ranging from 3.43 to 4.46 (out of 5). Qualitative responses indicated overall positive experiences with the programme. The only constructive feedback that met the criteria for revising the programme was to include more interactive features in Session 4.ConclusionsThis study demonstrates that university students found BNBD-Youth to be a usable programme for older youth. Demonstrating usability is an essential step in developing a programme with a user-centred design that university students will want to use in the future. Once the BNBD-Youth programme is revised to create the BNBD-University (BNBD-Uni) programme, additional usability and effectiveness testing will be conducted.
Citation: DIGITAL HEALTH
PubDate: 2024-06-06T04:47:01Z
DOI: 10.1177/20552076241260480
Issue No: Vol. 10 (2024)
- Effects of virtual reality on anxiety and pain in adult patients
undergoing wound-closure procedures: A pilot randomized controlled trial
Authors: SY Ko, Eliza ML Wong, TL Ngan, HK Leung, Kennis TY Kwok, HF Tam, CC Chan
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundIn emergency departments, suturing is a typical procedure for closing lacerated wounds but is invasive and often causes anxiety and pain. Virtual reality (VR) intervention has been reported as a relaxing measure.ObjectiveThe study aims to examine the effects of VR intervention on anxiety, pain, physiological parameters, local anesthesia requirements and satisfaction in Chinese adult patients undergoing wound closure in emergency departments in Hong Kong.MethodsAdult patients who had lacerated wounds and were undergoing wound closure by suturing can communicate in Chinese and were hemodynamically stable were invited for this trial. Eighty patients were randomly assigned to the VR group, which received VR intervention and standard care, or to the control group, which received standard care only. The primary outcome was anxiety, and the secondary outcomes included pain, blood pressure, pulse rate, satisfactory with pain management, service satisfactory, and extra local analgesia requirement. Outcomes were conducted at baseline, during the procedure and 5 min after the procedure.ResultsThe VR group had a significantly greater reduction in anxiety (p
Citation: DIGITAL HEALTH
PubDate: 2024-06-06T04:46:23Z
DOI: 10.1177/20552076241250157
Issue No: Vol. 10 (2024)
- Factors influencing telemedicine adoption among physicians in the
Malaysian healthcare system: A revisit
Authors: Siow-Hooi Tan, Chee-Kuan Wong, Yee-Yann Yap, Siow-Kian Tan
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundThe Malaysian healthcare system is under strain due to an aging population, the rising prevalence of chronic diseases, and heavy workloads among physicians. As costs and requirements continue to rise, telemedicine is critical for bridging gaps in supply and demand. However, there are limited studies on telemedicine adoption among Malaysian physicians. Furthermore, the existing literature on telemedicine adoption does not contain a comprehensive framework that integrates the multidimensional social influence, and technological, clinical, and individual factors.ObjectiveThis research investigates the adoption of telemedicine among Malaysian physicians. It draws from the Technology Acceptance Model (TAM) and Kelman's Social Influence Theory.MethodsA survey was conducted among 230 participants in hospitals located in Kuala Lumpur and Selangor. The data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM).ResultsThe study identified crucial factors influencing telemedicine adoption, including informational influences, rewards, perceived ease of use, and usefulness. Meanwhile, the Importance-performance Map Analysis (IPMA) identified perceived ease of use as the most important factor for physicians, but the highest performance was patient’s records.ConclusionsThe proposed integrated model enhances the understanding of telemedicine adoption and highlights the differential effects of individual, technological, clinical, and multidimensional social influence factors from the physicians’ perspective. The findings can guide future studies and influence implementation strategies for telemedicine promotion in the Malaysian healthcare context. Hospitals should prioritize user-friendly technology and information provision, while telemedicine providers should enhance the accessibility of patient records to facilitate telemedicine adoption. Policymakers should consider supporting training programs that will boost physicians’ confidence in effectively utilizing telemedicine.
Citation: DIGITAL HEALTH
PubDate: 2024-06-06T01:09:08Z
DOI: 10.1177/20552076241257050
Issue No: Vol. 10 (2024)
- Your preoperative rehabilitation assistant: A study protocol for the
impact of a telemedicine-supported preoperative home rehabilitation
program on the prognosis of patients undergoing thoracoscopic surgery
Authors: Qihang Sun, Willis Wasonga Omindo, Wanjun Liu, Yan Huang, Ruijie Zhang, Yan Qian, Xianping Li, Ruixing Qiu, Shubin Zheng, Wei Ping, Ni Zhang
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundReduced cardiorespiratory fitness levels are associated with increased short-term complications after surgery, and potentially exert long-lasting effects on the postoperative lives, work and educational pursuits of patients. Currently, research suggests that lifestyle interventions, such as preoperative physical exercise undertaken by patients themselves, may improve patients’ cardiopulmonary fitness and reduce post-operative complications. This study aims to investigate the effectiveness and feasibility of a remote medical supervision model for prehabilitation exercise in patients undergoing thoracoscopic lung tumour resection surgery.Methods/DesignAll enrolled patients will participate in a 4-week pre-operative exercise intervention to improve their cardiorespiratory fitness. During this period, patients will wear wearable devices and exercise at home based on exercise prescriptions. The exercise prescription comprises aerobic exercise (three times a week or more), muscle strengthening exercise (twice a week or more), and respiratory muscle exercise (once a day). The primary aim is to investigate whether baseline VO2max could be improved following a 4-week preoperative exercise program. Secondary objectives include changes in forced expiratory volume in 1 s and forced vital capacity, degree of acceptance of the technology, quality of life, handgrip strength, postoperative complication rate and length of hospital stay.DiscussionThis study aims to evaluate the influence of preoperative prehabilitation exercises in a telemedicine active supervision mode in patients undergoing thoracoscopic lung tumour resection. As such, results of this trial might have some impact on future implementations of group- and home-based prehabilitation exercises in lung cancers.Trial registrationThis study was approved by the Medical Ethics Committee of Tongji Hospital Affiliated to Tongji Medical College of Huazhong University of Science and Technology (approval number: TJ-IRB20220564) with registration at ClinicalTrials.gov (identifier: NCT05608759).
Citation: DIGITAL HEALTH
PubDate: 2024-06-05T08:19:47Z
DOI: 10.1177/20552076241258362
Issue No: Vol. 10 (2024)
- Text mining analysis of scientific literature on digital intraoral
scanners in dentistry: Bibliometric analysis
Authors: Ravinder S Saini, Abdulkhaliq Ali F Alshadidi, Jaismeen Rakhra, Lujain Ibrahim N Aldosari, Saeed Awod Bin Hassan, Syed Altafuddin Quadri, V.N.V Madhav, Anna Avetisyan, Artak Heboyan
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThis study aimed to provide valuable insights into the current research status and gaps in digital intraoral scanner literature in dentistry.MethodologyScopus Search Query TITLE-ABS-KEY (intraoral AND scanners AND (dentistry OR digital AND dentistry)). The search query used in Scopus for the bibliometric analysis was “TITLE-ABS-KEY (intraoral AND scanners AND (dentistry OR digital AND dentistry)).” This query indicates that the analysis focused on documents in which the title, abstract, or keywords contained the terms “intraoral,” “scanners,” and either “dentistry” or “digital dentistry.”ResultsThe analysis covers a timespan from 1998 to 2023 and includes 331 documents sourced from 136 publications. The annual growth rate of research in this field is reported to be 15.9%, indicating a steady increase over time. Among the top sources, the “Journal of Esthetic and Restorative Dentistry” and the “Journal of Prosthetic Dentistry” have the highest number of articles, indicating their significance in the field. Some notable authors and their corresponding statistics include WÖSTMANN B, with 15 articles and a fractionalized value of 3.16, and SCHLENZ MA, with 14 articles and a fractionalized value of 2.91. The United States has the highest number of articles, indicating a significant presence in research publications. Germany closely follows this, demonstrating a notable contribution to the scientific community.ConclusionsThis bibliometric analysis of intraoral scanners used in dentistry provided valuable insights into the current state of research and scholarly publications in this field. This analysis sheds light on the trends, patterns, and advancements in the use of these scanners in dental practice.
Citation: DIGITAL HEALTH
PubDate: 2024-06-05T07:31:37Z
DOI: 10.1177/20552076241260837
Issue No: Vol. 10 (2024)
- Comparisons of three different modes of digital mirror therapy for
post-stroke rehabilitation: Preliminary results of randomized controlled
trial
Authors: Meng-Ta Lee, Chih-Chi Chen, Hsuan-Lun Lu, Yu-Wei Hsieh
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveTechnologically adapted mirror therapy shows promising results in improving motor function for stroke survivors. The treatment effects of a newly developed multi-mode stroke rehabilitation system offering multiple training modes in digital mirror therapy remain unknown. This study aimed to examine the effects of unilateral mirror visual feedback (MVF) with unimanual training (UM-UT), unilateral MVF with bimanual training (UM-BT), and bilateral MVF with bimanual training (BM-BT) on clinical outcomes in stroke survivors, compared to classical mirror therapy (CMT).MethodsThirty-five participants were randomly assigned to one of four groups receiving fifteen 60-minute training sessions for 3–4 weeks. The Fugl-Meyer Assessment for Upper Extremity (FMA-UE), Chedoke Arm and Hand Activity Inventory (CAHAI), Revised Nottingham Sensory Assessment (rNSA), Motor Activity Log (MAL), and EQ-5D-5L were administered at pre- and post-intervention and at 1-month follow-up.ResultsAfter intervention and follow-up, significant within-group treatment efficacies were found on most primary outcomes of the FMA-UE and CAHAI scores in all four groups. Significant within-group improvements in the secondary outcomes were found on the MAL and EQ-5D-5L index in the UM-BT group, and the rNSA tactile sensation and MAL quality of movement subscales in the BM-BT group. No significant between-group treatment efficacies were found.ConclusionsUM-UT, UM-BT, BM-BT, and CMT led to similar clinical effects on the FMA-UE and can be considered effective alternative interventions for post-stroke upper-limb motor rehabilitation. UM-BT and BM-BT showed within-group improvements in functional performance in the patients’ affected upper limbs in real-life activities.
Citation: DIGITAL HEALTH
PubDate: 2024-06-05T07:30:58Z
DOI: 10.1177/20552076241260536
Issue No: Vol. 10 (2024)
- TCS-Fall: Cross-individual fall detection system based on channel state
information and time-continuous stack method
Authors: Ziyu Zhou, Zhaoqing Liu, Yujie Liu, Yan Zhao, Jiarui Wang, Bowen Zhang, Youbing Xia, Xiao Zhang, Shuyan Li
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundFalls pose a serious health risk for the elderly, particular for those who are living alone. The utilization of WiFi-based fall detection, employing Channel State Information (CSI), emerges as a promising solution due to its non-intrusive nature and privacy preservation. Despite these advantages, the challenge lies in optimizing cross-individual performance for CSI-based methods.ObjectiveThis study aimed to develop a resilient real-time fall detection system across individuals utilizing CSI, named TCS-Fall. This method was designed to offer continuous monitoring of activities over an extended timeframe, ensuring accurate and prompt detection of falls.MethodsExtensive CSI data on 1800 falls and 2400 daily activities was collected from 20 volunteers. The grouped coefficient of variation of CSI amplitudes were utilized as input features. These features capture signal fluctuations and are input to a convolutional neural network classifier. Cross-individual performance was extensively evaluated using various train/test participant splits. Additionally, a user-friendly CSI data collection and detection tool was developed using PyQT. To achieve real-time performance, data parsing and pre-processing computations were optimized using Numba's just-in-time compilation.ResultsThe proposed TCS-Fall method achieved excellent performance in cross-individual fall detection. On the test set, AUC reached 0.999, no error warning ratio score reached 0. 955 and correct warning ratio score reached of 0.975 when trained with data from only two volunteers. Performance can be further improved to 1.00 when 10 volunteers were included in training data. The optimized data parsing/pre-processing achieved over 20× speedup compared to previous method. The PyQT tool parsed and detected the fall within 100 ms.ConclusionsTCS-Fall method enables excellent real-time cross-individual fall detection utilizing WiFi CSI, promising swift alerts and timely assistance to elderly. Additionally, the optimized data processing led to a significant speedup. These results highlight the potential of our approach in enhancing real-time fall detection systems.
Citation: DIGITAL HEALTH
PubDate: 2024-06-05T06:14:21Z
DOI: 10.1177/20552076241259047
Issue No: Vol. 10 (2024)
- When “virtual” works and when it doesn’t: A survey of physician and
patient experiences with virtual care during the COVID-19 pandemic
Authors: Jennifer M. Hensel, Jocelyne Lemoine, Shay-Lee Bolton, Essence Perera, Megan Arpin, Jitender Sareen, Mandana Modirrousta
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveTo assess the experience of virtual care among both patients and physicians across a range of clinical scenarios during the COVID-19 pandemic.MethodsA web-based survey was disseminated to patients and physicians through a variety of media and healthcare communications from May 2020 to July 2021. Demographic details and attitudes across a range of virtual care domains were collected. Quantitative responses were analyzed descriptively. Open-text responses were gathered to contrast when a virtual visit was superior or inferior to an in-person one, and a thematic content analysis was used.ResultsThere were 197 patients and 93 physician respondents, representing a range of demographic and practice characteristics. Patients noted several benefits of virtual care and felt it should continue to be available. Physicians felt they could do a lot of their care virtually. Common themes related to the superiority of virtual care were for “quick” visits, reviewing test results, chronic disease monitoring, and medication needs. Virtual care was less ideal when a physical exam was needed, and was not perceived as a good fit for an individual's cultural, language, or emotional needs. Certain conditions were identified as both ideal and non-ideal for the virtual format (e.g. mental healthcare).DiscussionCertain situations are more amenable to virtual care with personal preferences among both patients and physicians. Future priorities should ensure that virtual care is effective across the range of clinical situations in which it may be used and that both virtual and in-person options are equally available to those who want them.
Citation: DIGITAL HEALTH
PubDate: 2024-06-05T06:12:34Z
DOI: 10.1177/20552076241258390
Issue No: Vol. 10 (2024)
- Effectiveness of instant versus text messaging intervention on
antiretroviral therapy adherence among men who have sex with men living
with HIV
Authors: Kedi Jiao, Jing Ma, Yuxi Lin, Yijun Li, Yu Yan, Chunxiao Cheng, Wenwen Jia, Jing Meng, Lina Wang, Yanwen Cao, Zhonghui Zhao, Xuan Yang, Meizhen Liao, Dianmin Kang, Chunmei Wang, Wei Ma
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThis study aimed to compare the effectiveness of instant versus text messaging intervention (TMI) on antiretroviral therapy (ART) adherence among men who have sex with men (MSM) living with HIV.MethodsThis study was conducted in an infectious disease hospital of Jinan, China from October 2020 to June 2021, using non-randomized concurrent controlled design to compare the effectiveness of instant messaging intervention (IMI) versus TMI. The intervention strategies (health messaging, medication reminder, and peer education) and contents were consistent between the two groups, and the difference was service delivery method and type of information. The primary outcome was the proportion of achieving optimal ART adherence, defined as never missing any doses and delayed any doses more than 1 hour.ResultsA total of 217 participants (including 72 in TMI group and 145 in IMI group) were included in the study. The proportion of achieving optimal adherence was higher in IMI group than TMI group at the first follow-up (90.2% versus 77.6%, p = 0.021) and second follow-up (86.5% versus 76.6%, p = 0.083). The effect of IMI versus TMI on improving ART adherence was found not to be statistically significant (risk ratio (RR) = 1.93, 95% confidence interval (CI): 0.95–3.94) in complete-case analysis. However, when excluding participants who did not adhere to the interventions, a significant improvement was observed (RR = 2.77, 95%CI: 1.21–6.38). More participants in IMI group expressed highly rated satisfaction to the intervention services than those in TMI group (67.3% versus 50.0%).ConclusionsThe IMI demonstrated superior efficacy over TMI in improving ART adherence and satisfaction with intervention services. It is suggested that future digital health interventions targeting ART adherence should prioritize instant messaging with multimedia information in areas with Internet access.Trial registrationThe study was registered at the Chinese Clinical Trial Register (ChiCTR), with number [ChiCTR2000041282].
Citation: DIGITAL HEALTH
PubDate: 2024-06-05T06:02:55Z
DOI: 10.1177/20552076241257447
Issue No: Vol. 10 (2024)
- Focusing on experts: Expectations of healthcare professionals regarding
the use of telemedicine in intensive care units
Authors: Magdalena Eitenberger, Gernot Gerger, Sophie Klomfar, Marcus Alexander Gabriel, Maria Kletecka-Pulker, Eva Schaden, Atanas G Atanasov, Mathias Maleczek, Sabine Völkl-Kernstock, Elisabeth Klager
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectivesTelemedical applications are solutions to challenges in the healthcare system. However, it is unclear what intensive care unit healthcare professionals expect from such solutions. This study investigated the expectations and concerns of nurses and physicians when implementing telemedicine tools in intensive care units (tele-ICU).MethodsThe study was conducted in intensive care units in 2020 during the second wave of the COVID-19 pandemic. It used a mixed-methods approach targeted at physicians and nurses and involved 14 qualitative interviews and 63 quantitative questionnaires.ResultsThe qualitative and quantitative data showed that both nurses and physicians were willing to use tele-ICU. Nurses recognised the advantages of real-time access to expertise offered by tele-ICU, but feared this would reduce physicians’ on-site patient time. Physicians, in turn, were concerned that they would be expected to be continuously on call. The majority in both groups agreed that any tele-ICU solution must be simple to use and integrate easily into existing organisational structures, networks, and work routines. Additionally, COVID-19 significantly influenced expectations: those who reported having more personal health concerns during the pandemic were more predisposed to favour the use of tele-ICU.ConclusionsOverall, tele-ICU supports better care, but a successful implementation depends on its ease of use and context-sensitive approaches. Effectively integrating tele-ICU solutions into daily clinical routines requires input from nurses and physicians and their involvement in the implementation process from the outset, as well as consideration of existing organisational structures. Such measures will vastly increase the chance of acceptance and successful adoption of telemedical solutions in clinical practice.
Citation: DIGITAL HEALTH
PubDate: 2024-06-04T05:33:06Z
DOI: 10.1177/20552076241257042
Issue No: Vol. 10 (2024)
- Working (out) with fitness influencers – benefits for the fitness
influencer, user health, and the endorsed brand: Key factors and the role
of gender and brand familiarity
Authors: Julia Durau, Sandra Diehl, Ralf Terlutter
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveFitness influencers have become important digital health communicators with regard to influencing consumer health behaviours, such as physical activity. We investigate how working (out) with these new communicators can generate benefits for the fitness influencer, user health, and endorsed brands. Based on the source credibility, social identity theory, and gender congruity research, this paper aims to identify the main factors that affect users’ attitudes toward the fitness influencer (influencer benefit), users’ exercise intentions (user health benefit) and their purchase intentions (brand benefit), considering user and influencer gender.MethodsTwo studies were conducted, both with male and female influencers and users. In study 1 (n = 496), the fitness influencer endorsed an unfamiliar brand, and in study 2 (n = 529) a familiar brand was endorsed. To test the proposed models and to estimate the path coefficients, structural equation modelling was performed.ResultsKey influencing factors were identified in the two studies: For attitude toward the influencer, source credibility is the most important; for exercise intention, parasocial interaction and attitude toward the behaviour are crucial; and purchase intention is largely predicted by the brand attitude. The studies revealed gender-congruent and non-congruent tendencies for female and male respondents, and different effects of unfamiliar and familiar brands.ConclusionsWorking (out) with fitness influencers can improve user attitudes toward the influencer, increase users’ exercise intentions, and increase brand purchase intentions, for both unfamiliar and familiar brands. Some gender congruity and some gender incongruity effects exist. The paper discusses important implications for theory and practice.
Citation: DIGITAL HEALTH
PubDate: 2024-06-03T08:37:53Z
DOI: 10.1177/20552076241258393
Issue No: Vol. 10 (2024)
- Individualized predictions for clinical milestone in amyotrophic lateral
sclerosis: A multialgorithmic approach
Authors: Hyeon-Ji Oh, Won-Joon Lee, Jung-Joon Sung, Yoon-Ho Hong
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThe phenotypic heterogeneity and complex disease trajectory complicate the ability to predict specific clinical milestone for individual patients with amyotrophic lateral sclerosis (ALS). Here we developed individualized prediction models to estimate the time to the loss of autonomy in swallowing function.MethodsUtilizing the Pooled Resource Open-Access ALS Clinical Trials (PRO-ACT) database, we built three models of distinct time-to-event prediction algorithms: accelerated failure time (AFT), cox proportional hazard (COX) and random survival forest (RSF) for an individualized risk assessment of the swallowing milestone. The target variable was defined as the time to a decline in the ALSFRS-R swallowing item score to 1 or below, indicating a need for supplementary tube feeding.ResultsInternal cross-validation revealed the median concordance index (C-index) of 0.851 (IQR, 0.842–0.859) for AFT, 0.850 (0.841–0.859) for COX and 0.846 (0.839–0.854) for RSF, and all models demonstrated good distributional calibration with predicted and observed event probabilities closely matched across different time intervals. For external validation with a registry dataset with characteristics different from PRO-ACT, the discriminative power was replicated with comparable C-indices for all models, whereas the calibration revealed a left-skewed distribution suggesting a bias towards overestimation of event probabilities in real-world data. While all models were effective at stratifying patients, the results of RSF model, unlike AFT and COX, did not match well with the KM curves of the corresponding risk groups, supporting the importance of nuanced understanding of data structure and algorithmic properties.ConclusionOur models are implemented into a web application which could be applied to individualized counselling, management and clinical trial design for gastrostomy intervention. Further studies for model optimization will advance personalized care in patients with ALS.
Citation: DIGITAL HEALTH
PubDate: 2024-06-03T08:09:29Z
DOI: 10.1177/20552076241260120
Issue No: Vol. 10 (2024)
- Effectiveness of digital home rehabilitation and supervision for stroke
survivors: A systematic review and meta-analysis
Authors: Ann Marie Hestetun-Mandrup, Zheng An Toh, Hui Xian Oh, Hong-Gu He, Anne Catrine Trægde Martinsen, Minna Pikkarainen
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveStroke survivors often experience residual impairments and motor decline post-discharge. While digital home rehabilitation combined with supervision could be a promising approach for reducing human resources, increasing motor ability, and supporting rehabilitation persistence there is a lack of reviews synthesizing the effects. Thus, this systematic review and meta-analysis aimed to synthesize the effect of digital home rehabilitation and supervision in improving motor ability of upper limb, static balance, stroke-related quality of life, and self-reported arm function among stroke survivors.MethodsSix electronic databases, grey literature, ongoing studies, and reference lists were searched for relevant studies. Two investigators independently reviewed titles, abstracts, screened full texts for eligibility and performed data extraction. Meta-analysis of 13 independent studies were grouped into four separate meta-analyses. The Grading of Recommendations, Assessments, Development and Evaluations (GRADE) tool was used for evaluating the overall quality of the evidence.ResultsMeta-analyses showed no statistically significant difference between intervention (digital home rehabilitation) and control groups (home training/clinic-based) of all outcomes including motor ability of upper limb, static balance, stroke-related quality of life, and self-reported arm function. In the sub-group analysis digital home rehabilitation was associated with better quality of arm use (standardized mean difference = 0.68, 95% confidence interval: [0.27, 1.09], p = 0.001).ConclusionsThis result indicated that digital home rehabilitation has similar effects and could potentially replace home training or clinic-based services. This review highlights better-targeted digital motor interventions to examine the effects of interventions further. The quality of evidence was moderate to high in motor and self-reported arm outcomes, and low for balance and quality of life.
Citation: DIGITAL HEALTH
PubDate: 2024-06-03T08:08:30Z
DOI: 10.1177/20552076241256861
Issue No: Vol. 10 (2024)
- The evolution of health system planning and implementation of maternal
telehealth services during the COVID-19 Pandemic
Authors: Monisa Aijaz, Burcu Bozkurt, Arrianna Marie Planey, Dorothy Cilenti, Saif Khairat, Christopher M Shea
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundDifferential access to healthcare is associated with disparities in maternal outcomes. Telehealth is one approach for improving access to maternal services. However, little is known regarding how health systems leverage telehealth to close the access gap.ObjectiveThis study examines how health systems have approached decisions about using telehealth for maternal services before and during the COVID-19 public health emergency and what factors were considered.MethodsWe conducted semi-structured interviews with 15 health system leaders between July and October 2021 and June and August 2022. We used a rapid analysis followed by a content analysis approach.ResultsFive health systems did not provide maternal telehealth services before the PHE due to a lack of reimbursement. Two health systems provided limited services as research endeavors, and one had integrated telehealth into routine maternity care. During the PHE, all transitioned to telehealth, with the primary consideration being patient and staff safety. At the time of the interview, key considerations shifted to patient access, patient preferences, patient complexity, return on investment, and staff burnout. However, several barriers impacted telehealth use, including coverage of portable devices and connectivity. These issues were reported to be common among underinsured, low-income, and rural patients. Health systems with particularly advanced capabilities worked on approaches to fill access gaps for these patients.ConclusionSome health systems prioritized telehealth to improve access to high-quality maternal services for patients at the highest risk of adverse outcomes. However, policy and patient-level barriers to equitable implementation of these services persist.
Citation: DIGITAL HEALTH
PubDate: 2024-06-03T07:01:16Z
DOI: 10.1177/20552076241259858
Issue No: Vol. 10 (2024)
- The impact of automatic history-taking software on data quality in the
cardiology outpatient clinic: Retrospective observational study
Authors: Ismail Erden, Arda Sen, Ibrahim Emre Erden
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundHealthcare delivery now mandates shorter visits despite the need for more data entry, under-mining patient–provider interaction. Furthermore, enhancing access to the outcomes of prior tests and imaging conducted on the patient, along with accurately documenting medication history, will significantly elevate the quality of healthcare service delivery.ObjectiveTo enhance the efficiency of clinic visits, we have devised a patient–provider portal that systematically gathers symptom and clinical data from patients through a computer algorithm known as Automated Assessment of Cardiovascular Examination (AACE). We intended to assess the quality of computer-generated Electronic Health Records (EHRs) with those documented by physicians.MethodsWe conducted a cross-sectional study employing a paired-sample design, focusing on individuals seeking assessment for active cardiovascular symptoms at outpatient adult cardiovascular clinics. Participants initially completed the AACE, and subsequently, in the first protocol, patients were subjected to routine care without providing the AACE forms to examining physicians. In the second protocol, the AACE form was presented to the physician before the examination, and participants were subjected to routine care. We assessed the impact of AACE forms generated through computerized history-taking method on the examination, considering various clinical outcomes and satisfaction surveys.ResultsWe included non-randomized eligible patients who visited seven general cardiology outpatient clinics between September 18, 2023, and October 27, 2023. These clinics were staffed by the same physicians who were unaware of the content and details of the study. A total of 762 patients (394 patients in protocol 1 and 368 patients in protocol 2) were included in the study. The mean overall impression score for computer-generated EHRs was higher versus physician EHRs (4.2 vs. 2.6; p
Citation: DIGITAL HEALTH
PubDate: 2024-06-03T06:31:48Z
DOI: 10.1177/20552076241260155
Issue No: Vol. 10 (2024)
- Medical data market platforms and medical data trade demands of medical
device companies
Authors: Myung-Gwan Kim, Inho Lee, HyunWook Han, Hyeong Won Yu
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThe significance of big data is increasingly acknowledged across all sectors, including medicine. Moreover, the trend of data trading is on the rise, particularly in exchanging other data for medical data to rejuvenate the medical industry. This study aimed to discern the facilitating factors of healthcare data trade.MethodsWe assessed five medical data market platforms on October, 2022, based on three criteria: (a) clarity in articulating the data for sale; (b) transparency in specifying the data costs; and (c) explicit indication that payment grants data access. This helped identify the traded medical data types. Additionally, we anonymously surveyed 43 representatives from medical device companies about their demand for medical data trading, achieving a response rate of 66%.ResultsOf the medical data traded on these platforms, 93.34% was structured, while 5.66% was unstructured, indicating an imbalance. Although there was a higher demand for structured medical data, there was also interest in purchasing unstructured medical data.ConclusionUnstructured big data are crucial for medical device development, fueling the demand for trading such data. Many stakeholders view the data market as essential and are willing to procure medical data. Consequently, medical device companies will need methods to acquire unstructured medical data for developing innovative and enhanced medical devices.
Citation: DIGITAL HEALTH
PubDate: 2024-06-03T06:28:29Z
DOI: 10.1177/20552076241259871
Issue No: Vol. 10 (2024)
- Feasibility and acceptability of a novel digital therapeutic combining
behavioral and pharmacological treatment for opioid use disorder
Authors: Laura B. Monico, Megan Eastlick, Darcy Michero, Peyton Pielsticker, Suzette Glasner
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveDespite the worsening of the opioid epidemic, access to quality treatment for opioid use disorder (OUD) including buprenorphine remains a challenge. With the onset of the COVID-19 public health emergency, temporary regulatory changes and expanded reimbursement for telehealth services allowed for the rapid expansion of remote treatment for OUD and increased access to buprenorphine, but limited research exists to support this revolutionary shift in care delivery. This study evaluates the feasibility and acceptability of a novel digital therapeutic intervention for OUD combining buprenorphine and behavioral therapy.MethodsAdults (n = 27) with OUD received treatment with daily sublingual buprenorphine and psychosocial treatment delivered digitally via a smartphone app over 12 weeks. Participants were evaluated monthly for continued opioid use, medication adherence, anxiety and depression indicators, abstinence self-efficacy, craving, and overall well-being, as well as a one-time measure of treatment acceptability.ResultsParticipants reported increased opioid abstinence days from baseline (M = 8.2, SD = 8.6) to 12 weeks per 30 days (M = 24.9, SD = 10.1), t(20) = −6.5, p
Citation: DIGITAL HEALTH
PubDate: 2024-05-29T07:17:31Z
DOI: 10.1177/20552076241258400
Issue No: Vol. 10 (2024)
- Revolutionising healthcare with artificial intelligence: A bibliometric
analysis of 40 years of progress in health systems
Authors: Walayat Hussain, Mohamed Mabrok, Honghao Gao, Fethi A. Rabhi, Essam A. Rashed
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
The development of artificial intelligence (AI) has revolutionised the medical system, empowering healthcare professionals to analyse complex nonlinear big data and identify hidden patterns, facilitating well-informed decisions. Over the last decade, there has been a notable trend of research in AI, machine learning (ML), and their associated algorithms in health and medical systems. These approaches have transformed the healthcare system, enhancing efficiency, accuracy, personalised treatment, and decision-making. Recognising the importance and growing trend of research in the topic area, this paper presents a bibliometric analysis of AI in health and medical systems. The paper utilises the Web of Science (WoS) Core Collection database, considering documents published in the topic area for the last four decades. A total of 64,063 papers were identified from 1983 to 2022. The paper evaluates the bibliometric data from various perspectives, such as annual papers published, annual citations, highly cited papers, and most productive institutions, and countries. The paper visualises the relationship among various scientific actors by presenting bibliographic coupling and co-occurrences of the author's keywords. The analysis indicates that the field began its significant growth in the late 1970s and early 1980s, with significant growth since 2019. The most influential institutions are in the USA and China. The study also reveals that the scientific community's top keywords include ‘ML’, ‘Deep Learning’, and ‘Artificial Intelligence’.
Citation: DIGITAL HEALTH
PubDate: 2024-05-29T06:31:56Z
DOI: 10.1177/20552076241258757
Issue No: Vol. 10 (2024)
- Digital rights and mobile health in Southeast Asia: A scoping review
Authors: Adam Poulsen, Yun JC Song, Eduard Fosch-Villaronga, Haley M LaMonica, Olivia Iannelli, Mafruha Alam, Ian B Hickie
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveDigital technology has the potential to support or infringe upon human rights. The ubiquity of mobile technology in low- and middle-income countries (LMICs) presents an opportunity to leverage mobile health (mHealth) interventions to reach remote populations and enable them to exercise human rights. Yet, simultaneously, the proliferation of mHealth results in expanding sensitive datasets and data processing, which risks endangering rights. The promotion of digital health often centers on its role in enhancing rights and health equity, particularly in LMICs. However, the interplay between mHealth in LMICs and digital rights is underexplored. The objective of this scoping review is to bridge this gap and identify digital rights topics in the 2022 literature on mHealth in Southeast Asian LMICs. Furthermore, it aims to highlight the importance of patient empowerment and data protection in mHealth and related policies in LMICs.MethodsThis review follows Arksey and O’Malley's framework for scoping reviews. Search results are reported using the PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews) checklist. Frequency and content analyses were applied to summarize and interpret the data.ResultsThree key findings emerge from this review. First, the digital rights topics covered in the literature are sparse, sporadic, and unsystematic. Second, despite significant concerns surrounding data privacy in Southeast Asian LMICs, no article in this review explores challenges to data privacy. Third, all included articles state or allude to the role of mHealth in advancing the right to health.ConclusionsEngagement in digital rights topics in the literature on mHealth in Southeast Asian mHealth is limited and irregular. Researchers and practitioners lack guidance, collective understanding, and shared language to proactively examine and communicate digital rights topics in mHealth in LMIC research. A systematic method for engaging with digital rights in this context is required going forward.
Citation: DIGITAL HEALTH
PubDate: 2024-05-29T06:30:56Z
DOI: 10.1177/20552076241257058
Issue No: Vol. 10 (2024)
- Validation of gait analysis using smartphones: Reliability and validity
Authors: Shuai Tao, Hao Zhang, Liwen Kong, Yan Sun, Jie Zhao
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThis study aims to validate the reliability and validity of gait analysis using smartphones in a controlled environment.MethodsThirty healthy adults attached smartphones to the waist and thigh, while an inertial measurement unit was fixed at the shank as a reference device; each participant was asked to walk six gait cycles at self-selected low, normal, and high speeds. Thirty-five cerebral small vessel disease patients were recruited to attach the smartphone to the thigh, performing single-task (ST), cognitive dual-task (DT1), and physical dual-task walking (DT2) to obtain gait parameters.ResultsThe results from the healthy group indicate that, regardless of whether attached to the thigh or waist, the smartphones calculated gait parameters with good reliability (ICC2,1 > 0.75) across three different walking speeds. There were no significant differences in the gait parameters between the smartphone attached to the thigh and the IMU across all three walking speeds (P > 0.05). However, significant differences were observed between the smartphone at the waist and the IMU during the stance phase, swing phase, stance time, and stride length at high speeds (P 0.05). Patients demonstrated significant differences in the cadence, stride time, stance phase, swing phase, stance time, stride length, and walking speed between ST and DT1 (P
Citation: DIGITAL HEALTH
PubDate: 2024-05-29T06:30:16Z
DOI: 10.1177/20552076241257054
Issue No: Vol. 10 (2024)
- Investigating the effect of an online enhanced care program on the
emotional and physical wellbeing of patients discharged from hospital with
acute decompensated heart failure: Study protocol for a randomised
controlled trial: Enhanced care program for heart failure
Authors: Kristy Fakes, Breanne Hobden, Nick Zwar, Nick Collins, Christopher Oldmeadow, Francesco Paolucci, Allan Davies, Irosh Fernando, Michael McGee, Trent Williams, Cameron Robson, Robert Hungerford, Jia Ying Ooi, Aaron L Sverdlov, Rob Sanson-Fisher, Andrew J Boyle
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveDepression is highly prevalent and associated with increased hospitalisations and mortality among patients with heart failure (HF). This study will evaluate the effectiveness and cost-effectiveness of an online wellbeing program for patients discharged from hospital with acute decompensated heart failure (ADHF) in (i) improving emotional and physical wellbeing, and (ii) decreasing healthcare utilisation.MethodsTwo-arm randomised controlled trial. Eligible patients with ADHF will be recruited pre-discharge from two hospitals. Five hundred and seventy participants will be randomised to receive the intervention (online enhanced care program for HF: ‘Enhanced HF Care’) or usual care. Enhanced HF Care includes health education (11 micro-learning modules) and monitoring of depression and clinical outcomes via fortnightly/monthly surveys for 6 months, with participants offered tailored advice via video email and SMS. Cardiac nurses track real-time patient data from a dashboard and receive automated email alerts when patients report medium- or high-risk levels of depression or clinical symptoms, to action where needed. General practitioners also receive automated alerts if patients report medium- or high-risk survey responses and are encouraged to schedule a patient consultation.ResultsSixty-five participants enrolled to-date. Co-primary outcomes (‘Minnesota Living with Heart Failure Questionnaire’ Emotional and Physical subscales) and healthcare utilisation (secondary outcome) at 1- and 6-month post-recruitment will be compared between treatment arms using linear mixed effects regression models.ConclusionsThis study has the potential to reduce the burden of depression for patients with HF by prioritising urgent mental health needs and clinical symptoms while simultaneously empowering patients with self-care knowledge.Trial registrationThe trial was prospectively registered via the Australian New Zealand Clinical Trials Registry: ACTRN12622001289707. Issue date: 4 October 2022.
Citation: DIGITAL HEALTH
PubDate: 2024-05-29T06:29:36Z
DOI: 10.1177/20552076241256503
Issue No: Vol. 10 (2024)
- Understanding and comparing risk factors and subtypes in South Korean
adult and adolescent women's suicidal ideation or suicide attempt using
survey and social media data
Authors: Donghun Kim, Ting Jiang, Ji Hyun Baek, Sou Hyun Jang, Yongjun Zhu
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThis study aimed to investigate the similarities and differences in risk factors for suicide among adult and adolescent women in South Korea and identify subtypes of suicidal ideation or suicide attempt in each group.MethodsMultifaceted data were collected and analyzed by linking survey and social media data. Interpretable machine learning models were constructed to predict suicide risk and major risk factors were extracted by investigating their feature importance. Additionally, subtypes of suicidal adult and adolescent women were identified and explained using risk factors.ResultsThe risk factors for adult women were primarily related to mental disorders, while those for adolescent women were primarily related to interpersonal experiences and needs. Two subtypes of suicidal adult women were one with high psychiatric symptoms and mental disorders of them and/or their families and the other with excessive social media use and high online victimization. Two subtypes of suicidal adolescent women were one with high psychiatric symptoms, high ACEs, and high social connectedness, and the other with frequent social media use, high online sexual victimization, and high social assurance.ConclusionsThese findings enable a stratified and targeted understanding of suicide in women and help develop customized suicide prevention plans in South Korea.
Citation: DIGITAL HEALTH
PubDate: 2024-05-29T06:28:56Z
DOI: 10.1177/20552076241255660
Issue No: Vol. 10 (2024)
- NeuroRehabilitation OnLine: Description of a regional multidisciplinary
group telerehabilitation innovation for stroke and neurological conditions
Authors: Suzanne Ackerley, Neil Wilson, Paul Boland, Rosemary Peel, Louise Connell
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundProviding recommended amounts of rehabilitation for stroke and neurological patients is challenging. Telerehabilitation is viable for delivering rehabilitation and an acceptable adjunct to in-person therapy. NeuroRehabilitation OnLine (NROL) was developed as a pilot and subsequently operationalised as a regional innovation embedded across four National Health Service (NHS) Trusts.ObjectiveTo describe the NROL innovation to assist future implementation and replication efforts.MethodsThe Template for Intervention Description and Replication (TIDieR) checklist, with guidance from the TIDieR-Telehealth extension, was used to describe NROL. The description was developed collaboratively by clinical academics, therapists, managers and researchers. Updated Consolidated Framework for Implementation Research domains were used to describe the context in which the innovation was delivered.ResultsNROL delivers online group-based real-time neurorehabilitation with technology assistance. It incorporates multidisciplinary targeted therapy and peer support to complement existing therapy. Procedures, materials and structure are detailed to demonstrate how NROL is embedded within a healthcare system. NROL uses existing NHS therapy workforce alongside dedicated NROL roles, including an essential technology support role. Selection of NROL groups is dependent on patient needs. The NROL innovation is tailored over time in response to feedback. NROL described here is successfully integrated within a regional stroke and neurorehabilitation network, aligns with local and national strategies and capitalises on an existing clinical–academic partnership.ConclusionThis comprehensive description of a regional NROL innovation, and clarification of core components, should facilitate other healthcare settings to adapt and implement NROL for their context. Continuous evaluation alongside implementation will ensure maximal impact for neurorehabilitation.
Citation: DIGITAL HEALTH
PubDate: 2024-05-29T06:28:08Z
DOI: 10.1177/20552076241252263
Issue No: Vol. 10 (2024)
- Is information evaluated subjectively' Social media has changed the way
users search for medical information
Authors: Shiqi Yi, Yu Guo, Zixuan Lin, Cong Cao
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveIn recent years, social media platforms, such as TikTok and RedBook, have emerged as important channels through which users access and share medical information. Additionally, an increasing number of healthcare professionals have created social media accounts through which to disseminate medical knowledge. This paper explores why users obtain their medical information from social media and how the signals transmitted by social platforms affect use behaviours.MethodsWe combined the elaboration likelihood model and signal theories to construct a comprehensive model for this study. We used simple random sampling to investigate users’ behaviours related to social media usage. A total of 351 valid questionnaires were completed by people in Mainland China. The participants were enthusiastic about social media platforms and had searched for health-related information on social media in the past three months. We analysed the data using partial least squares structural equation modelling to investigate the influence of two pathways and two signals (objective and subjective judgement pathways and positive and negative signals) on social media use behaviours.ResultsWhen seeking medical information on social media, users tend to rely on subjective judgment rather than objective judgment, although both are influential. Furthermore, in the current era, in which marketing methods involving big data algorithms and artificial intelligence prevail, negative signals, such as information overload, have a more pronounced impact than positive signals.ConclusionsThis study demonstrates that the subjective judgment path has a greater impact on users than the objective judgment path. Platforms are encouraged to focus more on users’ emotional needs. The paper also discusses the negative impact of information overload on users, sounding an alarm for enterprises to control their use of homogeneous information resulting from the excessive use of big data algorithms.
Citation: DIGITAL HEALTH
PubDate: 2024-05-28T06:26:30Z
DOI: 10.1177/20552076241259039
Issue No: Vol. 10 (2024)
- Assessment of the online presence and regulatory compliance of dental
practice websites in France
Authors: Aude Pollet, Hadrien Diakonoff
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThe aim of this study was to investigate the online presence of French dental practices that have a professional website. Secondly, the degree of compliance of practices’ websites with the current regulatory framework was assessed.MethodsOpen data was used to identify a 5% random sample of private dental practices in France (n = 1370 facilities). Then, a manual search was made on Google to find the website for each practice. When found, the website was analyzed using criteria.ResultsOnly 25.9% of dental practices have a functional and referenced website, allowing 28.9% of the dentists in the sample to have an online presence. Disparities exist depending on the geographical location of the facility and its type. The assessment of website content uncovered a lack of compliance with the existing regulatory framework, exhibiting variations based on the type of facility and the person who designed the website. Hiring a specialized web design provider for dentists enhances compliance with laws and guidelines, though it doesn't guarantee complete adherence.ConclusionsThe online presence of dental practices and dentists in France is limited. Where they do exist, dental practice websites generally do not comply with legal requirements and guidelines for online communication. Efforts should be made to improve the information provided to patients.
Citation: DIGITAL HEALTH
PubDate: 2024-05-28T06:25:22Z
DOI: 10.1177/20552076241258143
Issue No: Vol. 10 (2024)
- Deep learning model for differentiating acute myeloid and lymphoblastic
leukemia in peripheral blood cell images via myeloblast and lymphoblast
classification
Authors: Sholhui Park, Young Hoon Park, Jungwon Huh, Seung Min Baik, Dong Jin Park
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveAcute leukemia (AL) is a life-threatening malignant disease that occurs in the bone marrow and blood, and is classified as either acute myeloid leukemia (AML) or acute lymphoblastic leukemia (ALL). Diagnosing AL warrants testing methods, such as flow cytometry, which require trained professionals, time, and money. We aimed to develop a model that can classify peripheral blood images of 12 cell types, including pathological cells associated with AL, using artificial intelligence.MethodsWe acquired 42,386 single-cell images of peripheral blood slides from 282 patients (82 with AML, 40 with ALL, and 160 with immature granulocytes).ResultsThe performance of EfficientNet-V2 (B2) using the original image size exhibited the greatest accuracy (accuracy, 0.8779; precision, 0.7221; recall, 0.7225; and F1 score, 0.7210). The next-best accuracy was achieved by EfficientNet-V1 (B1), with a 256 × 256 pixels image. F1 score was the greatest for EfficientNet-V1 (B1) with the original image size. EfficientNet-V1 (B1) and EfficientNet-V2 (B2) were used to develop an ensemble model, and the accuracy (0.8858) and F1 score (0.7361) were improved. The classification performance of the developed ensemble model for the 12 cell types was good, with an area under the receiver operating characteristic curve above 0.9, and F1 scores for myeloblasts and lymphoblasts of 0.8873 and 0.8006, respectively.ConclusionsThe performance of the developed ensemble model for the 12 cell classifications was satisfactory, particularly for myeloblasts and lymphoblasts. We believe that the application of our model will benefit healthcare settings where the rapid and accurate diagnosis of AL is difficult.
Citation: DIGITAL HEALTH
PubDate: 2024-05-28T06:24:50Z
DOI: 10.1177/20552076241258079
Issue No: Vol. 10 (2024)
- The advantages, disadvantages, threats, and opportunities of electronic
patient-reported outcome systems in cancer: A systematic review
Authors: Hosna Salmani, Somayeh Nasiri, Maryam Ahmadi
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveElectronic patient-reported outcome (ePRO) systems hold promise for revolutionizing communication between cancer patients and healthcare providers across various care settings. This systematic review explores the multifaceted landscape of ePROs in cancer care, encompassing their advantages, disadvantages, potential risks, and opportunities for improvement.MethodsIn our systematic review, we conducted a rigorous search in Scopus, Web of Science, and PubMed, employing comprehensive medical subject heading terms for ePRO and cancer, with no date limitations up to 2024. Studies were critically appraised and thematically analyzed based on inclusion and exclusion criteria, including considerations of advantages, disadvantages, opportunities, and threats.FindingsAnalyzing 85 articles revealed 69 themes categorized into four key areas. Advantages (n = 14) were dominated by themes like “improved quality of life and care.” Disadvantages (n = 26) included “limited access and technical issues.” Security concerns and lack of technical skills were prominent threats (n = 10). Opportunities (n = 19) highlighted advancements in symptom management and potential solutions for technical challenges.ConclusionThis review emphasizes the crucial role of continuous exploration, integration, and innovation in ePRO systems for optimizing patient outcomes in cancer care. Beyond traditional clinical settings, ePROs hold promise for applications in survivorship, palliative care, and remote monitoring. By addressing existing limitations and capitalizing on opportunities, ePROs can empower patients, enhance communication, and ultimately improve care delivery across the entire cancer care spectrum.
Citation: DIGITAL HEALTH
PubDate: 2024-05-28T06:24:10Z
DOI: 10.1177/20552076241257146
Issue No: Vol. 10 (2024)
- SVM-RLF-DNN: A DNN with reliefF and SVM for automatic identification of
COVID from chest X-ray and CT images
Authors: Sanjib Saha, Debashis Nandi
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
AimTo develop an advanced determination technology for detecting COVID-19 patterns from chest X-ray and CT-scan films with distinct applications of deep learning and machine learning methods.Methods and MaterialsThe newly enhanced proposed hybrid classification network (SVM-RLF-DNN) comprises of three phases: feature extraction, selection and classification. The in-depth features are extracted from a series of 3×3 convolution, 2×2 max polling operations followed by a flattened and fully connected layer of the deep neural network (DNN). ReLU activation function and Adam optimizer are used in the model. The ReliefF is an improved feature selection algorithm of Relief that uses Manhattan distance instead of Euclidean distance. Based on the significance of the feature, the ReliefF assigns weight to each extracted feature received from a fully connected layer. The weight to each feature is the average of k closest hits and misses in each class for a neighbouring instance pair in multiclass problems. The ReliefF eliminates lower-weight features by setting the node value to zero. The higher weights of the features are kept to obtain the feature selection. At the last layer of the neural network, the multiclass Support Vector Machine (SVM) is used to classify the patterns of COVID-19, viral pneumonia and healthy cases. The three classes with three binary SVM classifiers use linear kernel function for each binary SVM following a one-versus-all approach. The hinge loss function and L2-norm regularization are selected for more stable results. The proposed method is assessed on publicly available chest X-ray and CT-scan image databases from Kaggle and GitHub. The performance of the proposed classification model has comparable training, validation, and test accuracy, as well as sensitivity, specificity, and confusion matrix for quantitative evaluation on five-fold cross-validation.ResultsOur proposed network has achieved test accuracy of 98.48% and 95.34% on 2-class X-rays and CT. More importantly, the proposed model's test accuracy, sensitivity, and specificity are 87.9%, 86.32%, and 90.25% for 3-class classification (COVID-19, Pneumonia, Normal) on chest X-rays. The proposed model provides the test accuracy, sensitivity, and specificity of 95.34%, 94.12%, and 96.15% for 2-class classification (COVID-19, Non-COVID) on chest CT.ConclusionOur proposed classification network experimental results indicate competitiveness with existing neural networks. The proposed neural network assists clinicians in determining and surveilling the disease.
Citation: DIGITAL HEALTH
PubDate: 2024-05-28T06:23:21Z
DOI: 10.1177/20552076241257045
Issue No: Vol. 10 (2024)
- Advanced biomechanical analytics: Wearable technologies for precision
health monitoring in sports performance
Authors: Abdullah Alzahrani, Arif Ullah
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThis study investigated the impact of wearable technologies, particularly advanced biomechanical analytics and machine learning, on sports performance monitoring and intervention strategies within the realm of physiotherapy. The primary aims were to evaluate key performance metrics, individual athlete variations and the efficacy of machine learning-driven adaptive interventions.MethodsThe research employed an observational cross-sectional design, focusing on the collection and analysis of real-world biomechanical data from athletes engaged in sports physiotherapy. A representative sample of athletes from Bahawalpur participated, utilizing Dring Stadium as the primary data collection venue. Wearable devices, including inertial sensors (MPU6050, MPU9250), electromyography (EMG) sensors (MyoWare Muscle Sensor), pressure sensors (FlexiForce sensor) and haptic feedback sensors, were strategically chosen for their ability to capture diverse biomechanical parameters.ResultsKey performance metrics, such as heart rate (mean: 76.5 bpm, SD: 3.2, min: 72, max: 80), joint angles (mean: 112.3 degrees, SD: 6.8, min: 105, max: 120), muscle activation (mean: 43.2%, SD: 4.5, min: 38, max: 48) and stress and strain features (mean: [112.3 ], SD: [6.5 ]), were analyzed and presented in summary tables. Individual athlete analyses highlighted variations in performance metrics, emphasizing the need for personalized monitoring and intervention strategies. The impact of wearable technologies on athletic performance was quantified through a comparison of metrics recorded with and without sensors. Results consistently demonstrated improvements in monitored parameters, affirming the significance of wearable technologies.ConclusionsThe study suggests that wearable technologies, when combined with advanced biomechanical analytics and machine learning, can enhance athletic performance in sports physiotherapy. Real-time monitoring allows for precise intervention adjustments, demonstrating the potential of machine learning-driven adaptive interventions.
Citation: DIGITAL HEALTH
PubDate: 2024-05-28T06:21:50Z
DOI: 10.1177/20552076241256745
Issue No: Vol. 10 (2024)
- Associations between perceived usefulness and willingness to use smart
healthcare devices among Chinese older adults: The multiple mediating
effect of technology interactivity and technology anxiety
Authors: Sheng Sun, Lan Jiang, Yue Zhou
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThis study aims to explore the mediating roles of technological interactivity and technological anxiety in the relationship between perceived usefulness and the willingness to use a smart health device to provide insight into the decision-making process of older adults in relation to the adoption of smart devices.MethodsA cross-sectional survey was conducted in Jiangsu, China involving 552 older adults. The study utilized structural equation modeling (SEM) to analyze the relationship between the independent variable ‘perceived usefulness’ and the dependent variable ‘willingness to use.’ It also examined the multiple mediating effects of technological interactivity and technological anxiety between the independent and dependent variables.ResultsThe results indicate that the direct effect of perceived usefulness on willingness to use was insignificant. However, technological interactivity completely mediated the relationship between perceived usefulness and willingness to use. Additionally, technological interactivity and technological anxiety were found to have a serial mediating effect on the impact of perceived usefulness on willingness to use smart healthcare devices.ConclusionsThese findings suggest that increasing older adults’ intention to use smart healthcare devices requires not only raising awareness of their usefulness, but also addressing technological anxiety and enhancing the interactivity of these devices to improve the overall user experience.
Citation: DIGITAL HEALTH
PubDate: 2024-05-28T06:21:01Z
DOI: 10.1177/20552076241254194
Issue No: Vol. 10 (2024)
- Association between nutritional status assessed by a digital
self-administered tool (R+ dietitian) and clinicopathologic factors in
cancer patients: A comprehensive analysis
Authors: Jianmei Zhu, Silu Wang, Tenglong Li, Zhiwen Long, Chengyuan He, Ke Xie, Shan Huang
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveMalnutrition is prevalent among cancer patients, smartphone-based self-administered nutritional assessment tools offer a promising solution for effective nutritional screening. This study aims to retrospectively analyze the relationships between nutritional status evaluated by the digital tool (R+ Dietitian) and clinicopathologic factors of cancer patients.MethodsCancer patients who met the inclusion criteria were divided into two subgroups based on age, Nutritional Risk Screening-2002, Patient-Generated Subjective Global Assessment Short Form, body mass index, and hospital stays for comparison. Correlation and regression analysis were used to comprehensively assess the relationship between nutritional status and clinicopathologic factors.FindingsA total of 535 hospitalized cancer patients (58.32 ± 11.24 years old) were recruited. Patients identified with nutritional risk assessed by R+ Dietitian were significantly older, had lower body weight, lower body mass index, greater weight loss, and longer hospital stays (all of above, P
Citation: DIGITAL HEALTH
PubDate: 2024-05-28T06:00:22Z
DOI: 10.1177/20552076241255475
Issue No: Vol. 10 (2024)
- Development of a telemedicine virtual clinic system for remote, rural, and
underserved areas using user-centered design methods
Authors: Abby Blocker, Mohammed Ishaaq Datay, Joyce Mwangama, Bessie Malila
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
IntroductionVirtual clinics are an emerging form of telemedicine which can positively impact healthcare accessibility in remote, rural, and underserved areas. However, a virtual clinic system for these contexts must be designed appropriately. The user-centered design method can be employed to develop an appropriate virtual clinic.MethodsThe development of the virtual clinic used the user-centered design method. First, a situational analysis was conducted to understand the context of public primary healthcare facilities in South Africa. Literature review, observations, and interviews were conducted, which then informed system requirements. A virtual clinic system was then developed and its usability was evaluated with doctors and nurses in a lab setting using healthy participants acting as patients. Doctors and nurses completed system usability scale surveys and provided interview feedback.ResultsThe situational analysis revealed 10 key themes which were translated into a problem statement and 10 system requirements. A virtual clinic system was then developed based on these requirements. 5 doctors and 11 nurses were recruited to complete usability testing with the system. The system received an average of 80.6 scores (good to excellent) out of 100 on the system usability scale. Feedback from participants revealed key areas for improvement of the virtual clinic system, as well as opportunities for further implementation.ConclusionThe developed virtual clinic system demonstrated the application of the user-centered design method to telemedicine technologies for remote, rural, and underserved areas. The positive feedback received from the participants demonstrated the importance of the user-centered design method in developing technologies for enhancing service delivery in health systems. Further work will implement this system in real-world clinical settings.
Citation: DIGITAL HEALTH
PubDate: 2024-05-27T06:45:44Z
DOI: 10.1177/20552076241256752
Issue No: Vol. 10 (2024)
- Exploring the impact of computer game playing on cognitive function,
Alzheimer's disease risk, and brain-derived neurotrophic factor levels:
Basic evidence from Mendelian randomization
Authors: Jinlong Wu, Zhenwei Mao, Zhanbing Ren, Wanli Zang, Haodong Tian, Li Huang, Haowei Liu, Feiyang Liu, Li Peng
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
IntroductionThe potential positive impact of computer game playing on cognitive function and its potential role in reducing the risk of Alzheimer's disease (AD) has been suggested. However, current observational studies have certain limitations. We utilized Mendelian randomization (MR) alongside extensive genome-wide association study (GWAS) data to examine the relationship between computer game playing, cognitive function, risk of AD, and levels of brain-derived neurotrophic factor (BDNF).MethodsWe collected datasets on computer game playing, cognition function, risk of AD, and BDNF level from the IEU Open GWAS project. Causal effects were assessed using various MR methods, including inverse variance weighted (IVW), weighted median, MR-Egger, simple mode, and weighted mode. To ensure the accuracy of the results, sensitivity analyses were conducted.ResultsOur analysis revealed a significant association between computer game playing and cognitive function (β = 0.801, 95% CI: 0.351, 1.328, P = 0.001). There was no statistically significant association between computer game playing and either BDNF level or risk of AD (β = −0.112, 95%CI: −1.315, 1.091, P = 0.855; OR = 1.000, 95% CI: 1.004, 0.997, P = 0.891, respectively). We further confirmed the reliability of our evidence through the MR-Egger intercept test, MR-PRESSO global test, Cochran's Q test, and funnel plots.ConclusionThe results of our study indicate that engaging in computer game playing may confer a safeguarding influence on cognitive function. This underscores the potential advantages associated with computer gaming. Nevertheless, given the constraints inherent in our research, further investigation is warranted to substantiate our findings and delve into the underlying mechanisms.
Citation: DIGITAL HEALTH
PubDate: 2024-05-25T06:46:38Z
DOI: 10.1177/20552076241256519
Issue No: Vol. 10 (2024)
- Emerging trends and prospects in telerehabilitation for hip fracture:
Bibliometric and visualization study
Authors: Liqiong Wang, Xiangxiu Wang, Ruishi Zhang, Chengqi He
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundTelerehabilitation provide distant physical rehabilitation services and help overcome the barriers associated with face-to-face interventions for hip fractures. This study aims to analyse the progress of the application of telerehabilitation in hip fracture and reveal its research profile, hotspots and development trends.MethodsThe articles and reviews related to telerehabilitation in hip fracture were retrieved from 1992 to 2024. A bibliometric visualization and comparative analysis of countries, institutions, journals, authors, references and keywords were conducted using Java-based CiteSpace and VOSviewer.ResultsA total of 79 documents were obtained. Spain was the most academically influential country. The University of Granada was the most prolific institution. The author Ariza-vega P listed first in terms of publications and citations. Most publications were published in high academic impact journals. The major frontier keywords were “older adults,” “functional recovery,” “reliability,” “mortality,” “rehabilitation,” “mobility” and “quality of life.” The most popular keywords from 2020 to 2024 were “geriatric medicine,” “family caregiver” and “digital health.”ConclusionsThe historical and prospective perspective in telerehabilitation following hip fracture were presented. A primary focus in the early years was the impact of telerehabilitation on functional independence for hip fracture patients. Future outcomes are expected to include patient satisfaction, health-related quality of life and psychiatric symptoms. Exercise was also designed to eliminate travel burdens and strengthen self-efficacy, and improve the physical and psychosocial function of hip fracture patients. This work will provide a fundamental reference as well as a directional guide for future research.
Citation: DIGITAL HEALTH
PubDate: 2024-05-25T06:45:40Z
DOI: 10.1177/20552076241255465
Issue No: Vol. 10 (2024)
- Language adaptations of mental health interventions: User interaction
comparisons with an AI-enabled conversational agent (Wysa) in English and
Spanish
Authors: Dyuthi Nirupama Dinesh, M Namrata Rao, Chaitali Sinha
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundIn recent times, digital mental health interventions (DMHIs) have been proven to be efficacious; however, most are available only for English speakers, leaving limited options for non-English languages like Spanish. Research shows that mental health services in one's dominant language show better outcomes. Conversational agents (CAs) offer promise in supporting mental health in non-English populations. This study compared a culturally adapted version of an artificial intelligence (AI)-led mental health app, called Wysa, in Spanish and English.ObjectivesTo compare user engagement patterns on Wysa-Spanish and Wysa-English and to understand expressions of distress and preferred language in both versions of Wysa.MethodsWe adopted a cross-sectional retrospective exploratory design with mixed methods, analyzing users from 10 Spanish-speaking countries between 1 February and 1 August 2022. A quantitative sample A (n = 2767) was used for descriptive statistics, including user engagement metrics with a Wilcoxon test. A subset qualitative sample B (n = 338) was examined for word count differences based on valence, and a content analysis was conducted to examine idioms of distress.ResultsCompared to Wysa-English, Wysa-Spanish had more sessions (P
Citation: DIGITAL HEALTH
PubDate: 2024-05-24T08:17:25Z
DOI: 10.1177/20552076241255616
Issue No: Vol. 10 (2024)
- Predicting fracture risk for elderly osteoporosis patients by hybrid
machine learning model
Authors: Menghan Liu, Xin Wei, Xiaodong Xing, Yunlong Cheng, Zicheng Ma, Jiwu Ren, Xiaofeng Gao, Ajing Xu
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
Background and ObjectiveOsteoporotic fractures significantly impact individuals's quality of life and exert substantial pressure on the social pension system. This study aims to develop prediction models for osteoporotic fracture and uncover potential risk factors based on Electronic Health Records (EHR).MethodsData of patients with osteoporosis were extracted from the EHR of Xinhua Hospital (July 2012–October 2017). Demographic and clinical features were used to develop prediction models based on 12 independent machine learning (ML) algorithms and 3 hybrid ML models. To facilitate a nuanced interpretation of the results, a comprehensive importance score was conceived, incorporating various perspectives to effectively discern and mine critical features from the data.ResultsA total of 8530 patients with osteoporosis were included for analysis, of which 1090 cases (12.8%) were fracture patients. The hybrid model that synergistically combines the Support Vector Machine (SVM) and XGBoost algorithms demonstrated the best predictive performance in terms of accuracy and precision (above 90%) among all benchmark models. Blood Calcium, Alkaline phosphatase (ALP), C-reactive Protein (CRP), Apolipoprotein A/B ratio and High-density lipoprotein cholesterol (HDL-C) were statistically found to be associated with osteoporotic fracture.ConclusionsThe hybrid machine learning model can be a reliable tool for predicting the risk of fracture in patients with osteoporosis. It is expected to assist clinicians in identifying high-risk fracture patients and implementing early interventions.
Citation: DIGITAL HEALTH
PubDate: 2024-05-24T07:44:40Z
DOI: 10.1177/20552076241257456
Issue No: Vol. 10 (2024)
- The gamification and development of a chatbot to promote oral self-care by
adopting behavior change wheel for Taiwanese children
Authors: Wen-Jen Chang, Pei-Ching Chang, Yen-Hsiang Chang
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundOral health is closely related to general health and quality of life. School-aged children are at a critical stage for developing their self-care ability in oral health. Digital interventions can encourage and facilitate oral self-care in children.ObjectiveThis study aims to present the development of an educational chatbot for school-aged children to address their oral self-care and evaluate its usability.MethodsThe development and evaluation of the chatbot for oral self-care consisted of four stages: target behavior analysis, intervention design, system development, and the chatbot evaluation. The target behavior analysis identified barriers to children's engagement in oral self-care based on dentists’ clinical observations; hence, the requirements for achieving the desired behavior were categorized according to the capability-opportunity-motivation behavior model. Interventional functions were created following the behavior change wheel. A menu-driven chatbot was created and evaluated for usability as well as likeability.ResultsThe barriers and requirements for achieving good behavior in school-aged children's oral self-care were identified by the dental professionals. Intervention strategy incorporated specific functions enriched with gamification features to support school-aged children in developing their abilities for engaging in oral self-care. The intervention functions consist of capability establishment, motivation enhancement, and opportunity creation, which were designed to support children in their oral self-care practices. The designed chatbot was piloted with a convenient sample of 30 school-aged children and their accompanying parents at the pediatric dental clinic. The results indicated good usability, with a mean usability score of 79.91, and high likeability with a mean score of 4.32 out of 5 for the designed chatbot.ConclusionsThe educational chatbot incorporated a combination of clinical dentistry practice and guidelines, aiming to promote oral self-care behavior in school-aged children. The designed chatbot achieved high scores for its usability and user likability.
Citation: DIGITAL HEALTH
PubDate: 2024-05-24T07:43:54Z
DOI: 10.1177/20552076241256750
Issue No: Vol. 10 (2024)
- Economic evidence of clinical decision support systems in mental health: A
systematic literature review
Authors: Line Stien, Carolyn Clausen, Inna Feldman, Bennett Leventhal, Roman Koposov, Kaban Koochakpour, Øystein Nytrø, Odd Sverre Westbye, Dipendra Pant, Thomas Brox Røst, Norbert Skokauskas
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
Mental health conditions are among the highest disease burden on society, affecting approximately 20% of children and adolescents at any point in time, with depression and anxiety being the leading causes of disability globally. To improve treatment outcomes, healthcare organizations turned to clinical decision support systems (CDSSs) that offer patient-specific diagnoses and recommendations. However, the economic impact of CDSS is limited, especially in child and adolescent mental health. This systematic literature review examined the economic impacts of CDSS implemented in mental health services. We planned to follow PRISMA reporting guidelines and found only one paper to describe health and economic outcomes. A randomized, controlled trial of 336 participants found that 60% of the intervention group and 32% of the control group achieved symptom reduction, i.e. a 50% decrease as per the Symptom Checklist-90-Revised (SCL-90-R), a method to evaluate psychological problems and identify symptoms. Analysis of the incremental cost-effectiveness ratio found that for every 1% of patients with a successful treatment result, it added €57 per year. There are not enough studies to draw conclusions about the cost-effectiveness in a mental health context. More studies on economic evaluations of the viability of CDSS within mental healthcare have the potential to contribute to patients and the larger society.
Citation: DIGITAL HEALTH
PubDate: 2024-05-24T07:43:24Z
DOI: 10.1177/20552076241256511
Issue No: Vol. 10 (2024)
- Melanoma identification and classification model based on fine-tuned
convolutional neural network
Authors: Maram F Almufareh, Noshina Tariq, Mamoona Humayun, Farrukh Aslam Khan
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundBreakthroughs in skin cancer diagnostics have resulted from recent image recognition and Artificial Intelligence (AI) technology advancements. There has been growing recognition that skin cancer can be lethal to humans. For instance, melanoma is the most unpredictable and terrible form of skin cancer. Materials and MethodologyThis paper aims to support Internet of Medical Things (IoMT) applications by developing a robust image classification model for the early detection of melanoma, a deadly skin cancer. It presents a novel approach to melanoma detection using a Convolutional Neural Network (CNN)-based method that employs image classification techniques based on Deep Learning (DL). We analyze dermatoscopic images from publicly available datasets, including DermIS, DermQuest, DermIS&Quest, and ISIC2019. Our model applies convolutional and pooling layers to extract meaningful features, followed by fully connected layers for classification. ResultsThe proposed CNN model achieves high accuracy demonstrates the model’s effectiveness in distinguishing between malignant and benign skin lesions. We developed deep features and used transfer learning to improve the categorization accuracy of medical images. Soft-max classification layer and support vector machine have been used to assess the classification performance of deep features. The proposed model’s efficacy is rigorously evaluated using benchmark datasets: DermIS, DermQuest, and ISIC2019, having 621, 1233, and 25000 images, respectively. Its performance is compared to current best practices showing an average of 5% improved detection accuracy in DermIS, 6% improvement in DermQuest, and 0.81% in ISIC2019 datasets.ConclusionOur study showcases the potential of CNN in melanoma detection, contributing to early diagnosis and improved patient outcomes. The developed model proves its capability to aid dermatologists in accurate decision-making, paving the way for enhanced skin cancer diagnosis.
Citation: DIGITAL HEALTH
PubDate: 2024-05-24T07:42:47Z
DOI: 10.1177/20552076241253757
Issue No: Vol. 10 (2024)
- Machine-learning model for predicting depression in second-hand smokers in
cross-sectional data using the Korea National Health and Nutrition
Examination Survey
Authors: Na Hyun Kim, Myeongju Kim, Jong Soo Han, Hyoju Sohn, Bumjo Oh, Ji Won Lee, Sumin Ahn
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveDepression among non-smokers at risk of second-hand smoke (SHS) exposure has been a neglected public health concern despite their vulnerability. The objective of this study was to develop high-performance machine-learning (ML) models for the prediction of depression in non-smokers and to identify important predictors of depression for second-hand smokers.MethodsML algorithms were created using demographic and clinical data from the Korea National Health and Nutrition Examination Survey (KNHANES) participants from 2014, 2016, and 2018 (N = 11,463). The Patient Health Questionnaire was used to diagnose depression with a total score of 10 or higher. The final model was selected according to the area under the curve (AUC) or sensitivity. Shapley additive explanations (SHAP) were used to identify influential features.ResultsThe light gradient boosting machine (LGBM) with the highest positive predictive value (PPV; 0.646) was selected as the best model among the ML algorithms, whereas the support vector machine (SVM) had the highest AUC (0.900). The most influential factors identified using the LGBM were stress perception, followed by subjective health status and quality of life. Among the smoking-related features, urine cotinine levels were the most important, and no linear relationship existed between the smoking-related features and the values of SHAP.ConclusionsCompared with the previously developed ML models, our LGBM models achieved excellent and even superior performance in predicting depression among non-smokers at risk of SHS exposure, suggesting potential goals for depression-preventive interventions for non-smokers during public health crises.
Citation: DIGITAL HEALTH
PubDate: 2024-05-23T08:25:35Z
DOI: 10.1177/20552076241257046
Issue No: Vol. 10 (2024)
- Exploring the influence of transformer-based multimodal modeling on
clinicians’ diagnosis of skin diseases: A quantitative analysis
Authors: Yujiao Zhang, Yunfeng Hu, Ke Li, Xiangjun Pan, Xiaoling Mo, Hong Zhang
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectivesThe study aimed to propose a multimodal model that incorporates both macroscopic and microscopic images and analyze its influence on clinicians’ decision-making with different levels of experience.MethodsFirst, we constructed a multimodal dataset for five skin disorders. Next, we trained unimodal models on three different types of images and selected the best-performing models as the base learners. Then, we used a soft voting strategy to create the multimodal model. Finally, 12 clinicians were divided into three groups, with each group including one director dermatologist, one dermatologist-in-charge, one resident dermatologist, and one general practitioner. They were asked to diagnose the skin disorders in four unaided situations (macroscopic images only, dermatopathological images only, macroscopic and dermatopathological images, all images and metadata), and three aided situations (macroscopic images with model 1 aid, dermatopathological images with model 2&3 aid, all images with multimodal model 4 aid). The clinicians’ diagnosis accuracy and time for each diagnosis were recorded.ResultsAmong the trained models, the vision transformer (ViT) achieved the best performance, with accuracies of 0.8636, 0.9545, 0.9673, and AUCs of 0.9823, 0.9952, 0.9989 on the training set, respectively. However, on the external validation set, they only achieved accuracies of 0.70, 0.90, and 0.94, respectively. The multimodal model performed well compared to the unimodal models, achieving an accuracy of 0.98 on the external validation set. The results of logit regression analysis indicate that all models are helpful to clinicians in making diagnostic decisions [Odds Ratios (OR) > 1], while metadata does not provide assistance to clinicians (OR
Citation: DIGITAL HEALTH
PubDate: 2024-05-23T07:03:56Z
DOI: 10.1177/20552076241257087
Issue No: Vol. 10 (2024)
- Multiclassification of the symptom severity of social anxiety disorder
using digital phenotypes and feature representation learning
Authors: Hyoungshin Choi, Yesol Cho, Choongki Min, Kyungnam Kim, Eunji Kim, Seungmin Lee, Jae-Jin Kim
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveSocial anxiety disorder (SAD) is characterized by heightened sensitivity to social interactions or settings, which disrupts daily activities and social relationships. This study aimed to explore the feasibility of utilizing digital phenotypes for predicting the severity of these symptoms and to elucidate how the main predictive digital phenotypes differed depending on the symptom severity.MethodWe collected 511 behavioral and physiological data over 7 to 13 weeks from 27 SAD and 31 healthy individuals using smartphones and smartbands, from which we extracted 76 digital phenotype features. To reduce data dimensionality, we employed an autoencoder, an unsupervised machine learning model that transformed these features into low-dimensional latent representations. Symptom severity was assessed with three social anxiety-specific and nine additional psychological scales. For each symptom, we developed individual classifiers to predict the severity and applied integrated gradients to identify critical predictive features.ResultsClassifiers targeting social anxiety symptoms outperformed baseline accuracy, achieving mean accuracy and F1 scores of 87% (with both metrics in the range 84–90%). For secondary psychological symptoms, classifiers demonstrated mean accuracy and F1 scores of 85%. Application of integrated gradients revealed key digital phenotypes with substantial influence on the predictive models, differentiated by symptom types and levels of severity.ConclusionsLeveraging digital phenotypes through feature representation learning could effectively classify symptom severities in SAD. It identifies distinct digital phenotypes associated with the cognitive, emotional, and behavioral dimensions of SAD, thereby advancing the understanding of SAD. These findings underscore the potential utility of digital phenotypes in informing clinical management.
Citation: DIGITAL HEALTH
PubDate: 2024-05-23T06:58:33Z
DOI: 10.1177/20552076241256730
Issue No: Vol. 10 (2024)
- UK homecare providers’ views about, and experiences of,
digitalisation: A national survey
Authors: Jan Healey, Vanessa Davey, Jennifer Liddle, Gareth O’Rourke, Barbara Hanratty, Bryony Beresford
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveUsing digital systems to support the management and delivery of social care is a priority for UK governments. This study explored progress towards, and experiences of, digitalisation in the homecare sector and providers’ views on contributing client data to a national policy/research dataset.MethodsOver 150 UK homecare providers completed an on-line survey (October–December 2022). The survey was hosted on Qualtrics and comprised fixed- and free-text response questions. The recruited sample aligned with the profile of UK homecare providers in terms of use of digital systems, organisation type and size.ResultsAlmost all respondents (95.5%) were using digital systems, in part or exclusively, to support care delivery. However, many (42.7%) reported a desire to further digitalise or a dissatisfaction with existing systems. Findings highlight the time and work involved in choosing a a software system, with the decision regarded as relatively high risk. Over 50 different software systems were being used across the sample. Most respondents (72.5%) supported the creation of a national dataset on homecare users. However, support and recompense are likely to needed to secure buy-in from what is a predominantly private sector context.ConclusionsFindings suggest a complex and changing situation, with numerous different digital systems being used and the sector at different stages of digitalisation. The high-pressure, low margin context of UK homecare appeared to be exerting an influence on progress towards digitalisation. Evaluations of government strategies to stimulate and support digitalisation in this diverse and predominantly private sector context will be valuable.
Citation: DIGITAL HEALTH
PubDate: 2024-05-23T06:57:34Z
DOI: 10.1177/20552076241255477
Issue No: Vol. 10 (2024)
- Assessment of cognitive games to improve the quality of life of
Parkinson’s and Alzheimer’s patients
Authors: Álvaro Llorente, Alberto del Rio, Yusuf Can Semerci, Jorge Alfonso Kurano, David Jimenez, José Manuel Menéndez
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectivesThe core objectives of this study centre on enhancing the quality of life and well-being of individuals diagnosed with Parkinson’s and Alzheimer’s diseases. Our aim is to facilitate the monitoring of patient information, benefiting both caregivers and healthcare professionals.MethodsAs part of the PROCare4Life platform sensorial ecosystem, a web application with six engaging cognitive games focusing on developing cognitive training and stimulating brain activity are developed. A set of metrics calculated by the application feed machine learning predictive models to evaluate the cognitive status and evolution over time. Long-term analysis of the daily cognitive ability information is used to generate high-level outcomes and identify deviations for each patient from the multimodal fusion engine. And based on these results, a recommender system provides a set of personalized notifications.ResultsA 3-month pilot study that took place in five different countries shows the results obtained from 93 patients. An average of 22.4 games were completed per day and the recommender system generated a total of 260 game notifications, 37.7% of them were marked as read by the patients. The Cognitive State Score and the Deviations in Cognitive Abilities measurement, calculated by the multimodal fusion engine, when used in conjunction present a good overview of the patient’s current state and potential deviations.ConclusionThe cognitive games application was well-received by elderly individuals who took part in the study. This tool can be valuable for caregivers and healthcare providers in assessing the cognitive function of patients through engaging in cognitive games.
Citation: DIGITAL HEALTH
PubDate: 2024-05-23T05:56:53Z
DOI: 10.1177/20552076241254733
Issue No: Vol. 10 (2024)
- Evaluating the performance of an AI-powered VBAC prediction system within
a decision-aid birth choice platform for shared decision-making
Authors: Cherng Chia Yang, Ching Fu Wang, Wei Ming Lin, Shu Wen Chen, Hsiang Wei Hu
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundVaginal birth after cesarean (VBAC) is generally regarded as a safe and viable birthing option for most women with prior cesarean delivery. Nonetheless, concerns about heightened risks of adverse maternal and perinatal outcomes have often dissuaded women from considering VBAC. This study aimed to assess the performance of an artificial intelligence (AI)-powered VBAC prediction system integrated into a decision-aid birth choice platform for shared decision-making (SDM).Materials and MethodsEmploying a retrospective design, we collected medical records from a regional hospital in northern Taiwan from January 2019 to May 2023. To explore a suitable model for tabular data, we compared two prevailing modeling approaches: tree-based models and logistic regression models. We subjected the tree-based algorithm, CatBoost, to binary classification.ResultsForty pregnant women with 347 records were included. The CatBoost model demonstrated a robust performance, boasting an accuracy rate of 0.91 (95% confidence interval (CI): 0.86–0.94) and an area under the curve of 0.89 (95% CI: 0.86–0.93), surpassing both regression models and other boosting techniques. CatBoost captured the data characteristics on the significant impact of gravidity and the positive influence of previous vaginal birth, reinforcing established clinical guidelines, as substantiated by the SHapley Additive exPlanations analysis.ConclusionUsing AI techniques offers a more accurate assessment of VBAC risks, boosting women’s confidence in selecting VBAC as a viable birthing option. The seamless integration of AI prediction systems with SDM platforms holds a promising potential for enhancing the effectiveness of clinical applications in the domain of women's healthcare.
Citation: DIGITAL HEALTH
PubDate: 2024-05-22T12:26:48Z
DOI: 10.1177/20552076241257014
Issue No: Vol. 10 (2024)
- How do provider communication strategies predict online patient
satisfaction' A content analysis of online patient-provider communication
transcripts
Authors: Jian Raymond Rui, Jieqiong Guo, Keqing Yang
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveAs a result of the growing access to the Internet, online medical platforms have gained increased popularity in China. However, which strategies doctors should use to improve their online communication with patients remains understudied. Drawing upon the performance-evaluation-outcome (PEO) model, the present study seeks to develop a typology of patient-centered communication (PCC) strategies online and identify those strategies that can increase patient satisfaction.MethodsWe employed the data crawling technique to access text-based patient–provider transcripts through a large medical consultation platform in China and coded 9140 conversational turns of doctors.ResultsOur analysis revealed 15 PCC strategies that Chinese doctors often used online. In addition, several strategies were found to enhance patient satisfaction including information provision, making diagnosis, information appraisal, emotion expression, emotion recognition and support, in-depth discussion of medical treatments, providing coping strategies, and enabling self-management.ConclusionChinese patients may have developed multiple needs, which they expect to fulfill through their interactions with doctors. Technological affordances of online medical platforms may pressure doctors to adapt their communication strategies to patients’ needs. Our findings develop the PEO model from the perspective of patient-provider communication strategies and add a perspective centering on patients’ needs to the scholarship on patient satisfaction. In addition, these results provide practical implications on how to improve patient-provider communication online.
Citation: DIGITAL HEALTH
PubDate: 2024-05-22T12:26:08Z
DOI: 10.1177/20552076241255617
Issue No: Vol. 10 (2024)
- Toward interpretable and generalized mitosis detection in digital
pathology using deep learning
Authors: Hasan Farooq, Saira Saleem, Iffat Aleem, Ayesha Iftikhar, Umer Nisar Sheikh, Hammad Naveed
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThe mitotic activity index is an important prognostic factor in the diagnosis of cancer. The task of mitosis detection is difficult as the nuclei are microscopic in size and partially labeled, and there are many more non-mitotic nuclei compared to mitotic ones. In this paper, we highlight the challenges of current mitosis detection pipelines and propose a method to tackle these challenges.MethodsOur proposed methodology is inspired from recent research on deep learning and an extensive analysis on the dataset and training pipeline. We first used the MiDoG′22 dataset for training, validation, and testing. We then tested the methodology without fine-tuning on the TUPAC′16 dataset and on a real-time case from Shaukat Khanum Memorial Cancer Hospital and Research Centre.ResultsOur methodology has shown promising results both quantitatively and qualitatively. Quantitatively, our methodology achieved an F1-score of 0.87 on the MiDoG'22 dataset and an F1-score of 0.83 on the TUPAC dataset. Qualitatively, our methodology is generalizable and interpretable across various datasets and clinical settings.ConclusionIn this paper, we highlight the challenges of current mitosis detection pipelines and propose a method that can accurately predict mitotic nuclei. We illustrate the accuracy, generalizability, and interpretability of our approach across various datasets and clinical settings. Our methodology can speed up the adoption of computer-aided digital pathology in clinical settings.
Citation: DIGITAL HEALTH
PubDate: 2024-05-22T12:22:08Z
DOI: 10.1177/20552076241255471
Issue No: Vol. 10 (2024)
- Test–retest reliability and agreement of remote home-based functional
capacity self-administered assessments in community-dwelling, socially
isolated older adults
Authors: Rodrigo Villar, Thomas Beltrame, Gabriela Ferreira dos Santos, Anderson Saranz Zago, Danilo Sales Bocalini, Francisco Luciano Pontes Júnior
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectivesTo determine the test–retest reliability and agreement of home-based functional capacity self-administered assessments in socially isolated older adults.MethodsFourteen community-dwelling older adults (eight females, 67.9 ± 7.7 years) volunteered for this study. Before testing, participants were screened online for eligibility and received instructional videos explaining test set-up and execution. Participants underwent the 30-second sit-to-standing test, gait speed tests at the usual pace, and timed-up-and-go tests administered 4 weeks apart. For the 30-second sit-to-standing protocol, participants were instructed to repeatedly sit and stand from a chair (with a height of ∼ 43 cm and without armrests) for 30 s, with the number of repetitions recorded. In the gait speed test protocol, participants were instructed to walk at their usual and comfortable pace, with the time taken recorded (seconds). In the timed-up-and-go, participants stood up from a chair, walked as fast as possible for 3 m, circled a marked point, and returned to the chair to sit down, completing the test, with the score recorded (seconds). A trained researcher conducted the scoring virtually via synchronous video.Results30-second sit-to-standing, gait speed test, and timed-up-and-go showed excellent mean coefficient of variation values (2.0–4.9), small standard error of measurement (0.08–1.27), excellent intraclass coefficient (0.97–0.99), very strong correlations (0.97–0.99) and good agreement between the two days of testing.ConclusionSelf-administered functional capacity tests performed by older adults at home were reliable with good agreement. Healthcare professionals and older adults should take advantage of simple remote self-administered assessments performed at home to evaluate older adults’ health status.
Citation: DIGITAL HEALTH
PubDate: 2024-05-22T12:21:52Z
DOI: 10.1177/20552076241254904
Issue No: Vol. 10 (2024)
- Assessing the impact of message relevance and frequency on physical
activity change: A secondary data analysis from the random AIM trial
Authors: Jingchuan Wu, Deborah Brunke-Reese, Constantino M Lagoa, David E Conroy
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
Text messages are widely used to deliver intervention content; however, sending more intensive messages may not always improve behavioral outcomes. This study investigated whether message frequency was associated with daily physical activity, either by itself or in interaction with message content relevance. Healthy but insufficiently active young adults (aged 18–29 years) wore Fitbit activity trackers and received text messages for 180 days. Message frequencies varied daily at random, and messages were sent from three content libraries (40% Move More, 40% Sit Less, 20% Inspirational Quotes). Contrary to expectations, the results revealed a null association between total daily text message frequency and physical activity, both for daily step counts and moderate-to-vigorous physical activity (MVPA) duration. Additional analyses revealed that the daily frequency of messages with relevant content (i.e. Move More, Sit Less) was not associated with physical activity, but the daily frequency of messages with irrelevant content (i.e. Inspirational Quotes) was negatively associated with physical activity. We concluded that the effectiveness of text messages in promoting physical activity is impacted by the combination of content relevance and frequency, with frequent irrelevant messages potentially decreasing activity levels. This study suggests that irrelevant message frequency can negatively impact physical activity, highlighting the risks of delivering irrelevant content in digital health interventions.
Citation: DIGITAL HEALTH
PubDate: 2024-05-22T08:48:08Z
DOI: 10.1177/20552076241255656
Issue No: Vol. 10 (2024)
- Expert review of child and caregiver critiques of a therapeutic guided
imagery therapy mobile application targeting disorders of gut–brain
interaction in children
Authors: John M Hollier, Tiantá A Strickland, C Michael Fordis, Robert J Shulman, Debbe Thompson
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundA guided imagery therapy mobile application (GIT App) is a novel platform for treating children with disorders of gut–brain interaction (DGBI). Previous feedback from child/caregiver dyads suggested modifications for our App prototype. However, their feedback had the potential to affect the intervention's efficacy. Thus, we aimed to have their critiques vetted by relevant experts prior to further App refinement.ObjectiveCompare expert reviews of the GIT App with end-users’ (i.e., child/caregiver dyads’) feedback.MethodsThis mixed-methods study with experts included a hands-on App evaluation, a survey assessing usability, and focus groups comparing their perspectives with those previously provided by end-users.ResultsEight medical and technology experts were enrolled. Their average usability survey score of the GIT App was 69.0 ± 27.7, which was marginally above the 50th percentile. While the expert and end-user usability assessments were generally favorable, both groups agreed that the App's reminder notification feature location was not intuitive, detracting from its usability. Experts agreed with end-users that the App's aesthetics were acceptable and suggested increasing icon and font sizes. Like the end-users, the experts did not achieve consensus regarding the ideal session length or inclusion of background sounds and screen animations. Lastly, the experts agreed with end-users that gamification techniques (e.g., gift cards and virtual badges) would promote user engagement.ConclusionAn expert review of our therapeutic App revealed findings consistent with end-users and provided insight for modifying the interface and GIT sessions. Based on this experience, we recommend expert vetting of end-user suggestions as a routine checkpoint when developing therapeutic Apps.
Citation: DIGITAL HEALTH
PubDate: 2024-05-22T07:37:25Z
DOI: 10.1177/20552076241245376
Issue No: Vol. 10 (2024)
- Understanding citizens’ attitudes within user-centered digital health
ecosystems: A sequential mixed method methodology including a web-survey
Authors: Robin Huettemann, Benedict Sevov, Sven Meister, Leonard Fehring
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveTransitioning from digital health applications to digital health ecosystems, leveraging the advances in technologies and informatics, could be the next revolution in digital health. This includes offering centralized access to various health services and improving citizens’ well-being, delivery, clinical processes, and data management. However, a limited understanding of citizens may impede adaptation. Therefore, this study investigates citizens’ attitudes within digital health ecosystems, differentiated by their characteristics, to support health service-providers and governmental policymakers in establishing user-centered solutions.MethodsThis study follows a three-step sequential mixed method methodology: (1) a literature review. (2) Qualitative thematic analyses based on semi-structured qualitative interviews. (3) Quantitative analyses based on a web-survey (descriptive statistics, one-way analysis of variances, Tukey-honestly, and Cohen's d tests).ResultsN = 15 citizens were interviewed and n = 1289 responded to the web-survey, to our knowledge the largest survey on this topic. Citizens desire a more convenient management of health services and data (M = 5.2, SD = 1.59). Services with peer-to-peer interactions (M = 3.7, SD = 1.81) and lower involvement of health professionals (M = 3.8, SD = 1.75) are less demanded. Data protection is critical (M = 6.2, SD = 1.23). Public payers are mandated as orchestrators (M = 4.3, SD = 1.99), while private companies receive lower acceptance (M = 3.0, SD = 1.42).ConclusionsHealth service-providers could follow a three-staged approach to establish digital health ecosystems: (1) Increasing the convenience for citizens by enabling online management of health services and data. (2) Extending the citizen–healthcare provider partnership through online interactions. (3) Fostering preventative behaviors and quicker recovery by personalizing health services and interactions. Governmental policymakers should integrate an electronic health record.
Citation: DIGITAL HEALTH
PubDate: 2024-05-20T11:07:46Z
DOI: 10.1177/20552076241255929
Issue No: Vol. 10 (2024)
- Developing a digital mind body medicine supportive care intervention for
people with amyotrophic lateral sclerosis using stakeholder engagement and
design thinking
Authors: Claudia Canella, Carina Braun, Claudia M. Witt
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundAmyotrophic lateral sclerosis disease (ALS) is also called the disease of a thousand farewells. Consequently, it is important to offer supportive care interventions that can be applied continuously during the whole course of the disease. People with ALS are interested in complementary and integrative medicine. Due to ALS’ progressive nature, digital solutions might be most feasible and accessible for people with ALS in the long-term.ObjectivesIn our study, we explored with stakeholders which digital complementary and integrative medicine interventions and formats are considered as supportive for people with ALS, and which settings are needed by the people with ALS to incorporate the interventions in everyday life.MethodsWe used a participatory research approach and conducted a stakeholder engagement process, applying a design thinking process with qualitative research methods (interviews, workshops).ResultsDue to the unpredictable course of the disease on their loss of abilities, people with ALS welcome online settings because they are accessible and easy to implement in their daily life. Stakeholders considered the following implementation factors for a complementary and integrative medicine intervention as essential: short-term realization of planned interventions, short duration of interventions, and user-friendliness in terms of accessibility and applicability. Concerning the complementary and integrative medicine interventions, the people with ALS preferred mind body medicine interventions, such as breathing, mindfulness and relaxation exercises.ConclusionsShort-term treatment intervals and short online mind body medicine interventions align with the needs of people with ALS. The complementary and integrative medicine interventions as well as the digital infrastructure must meet the special accessibility and applicability needs of people with ALS.
Citation: DIGITAL HEALTH
PubDate: 2024-05-20T10:10:26Z
DOI: 10.1177/20552076241255928
Issue No: Vol. 10 (2024)
- User-centered development of an mHealth app for cardiovascular prevention
Authors: Lara Marie Reimer, Leon Nissen, Moritz von Scheidt, Benedikt Perl, Jens Wiehler, Sinann Al Najem, Florian P. Limbourg, Theodora Tacke, Angelina Müller, Stephan Jonas, Heribert Schunkert, Fabian Starnecker
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundMobile health (mHealth) apps can be used for cardiovascular disease (CVD) prevention. User-centered design, evidence-based content and user testing can be applied to ensure a high level of usability and adequate app access.ObjectiveTo develop and evaluate an mHealth app (HerzFit) for CVD prevention.MethodsHerzFit´s development included a user-centered design approach and guideline-based content creation based on the identified requirements of the target group. Beta testing and a preliminary usability evaluation of the HerzFit prototype were performed. For evaluation, German versions of the System Usability Scale (SUS) and the mHealth App Usability Questionnaire (GER-MAUQ) as well as free text feedback were applied.ResultsUser-centered design thinking led to the definition of four personas. Based on their requirements, HerzFit enables users to individually assess, monitor, and optimize their cardiovascular risk profile. Users are also provided with a variety of evidence-based information on CVD and their risk factors. The user interface and system design followed the identified functional requirements. Beta-testers provided feedback on the structure and functionality and rated the usability of HerzFit´s prototype as slightly above average both in SUS and GER-MAUQ rating. Participants positively noted the variety of functions and information presented in HerzFit, while negative feedback mostly concerned wearable synchronization.ConclusionsThe present study demonstrates the user-centered development of a guideline-based mHealth app for CVD prevention. Beta-testing and a preliminary usability study were used to further improve the HerzFit app until its official release.
Citation: DIGITAL HEALTH
PubDate: 2024-05-20T10:09:50Z
DOI: 10.1177/20552076241249269
Issue No: Vol. 10 (2024)
- Physical activity and sedentary behaviour of Bahraini people with type 2
diabetes: A cross-sectional study
Authors: Ebrahim Rajab, Pearl Wasif, Sally Doherty, Declan Gaynor, Hani Malik, Salim Fredericks, Amal Al-Qallaf, Rabab Almuqahwi, Wafa Alsharbati, Fiza Rashid-Doubell
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveStudy patterns of physical activity and sedentary behaviour and the influence of demographics and body mass index (BMI) on these behaviours amongst Bahraini adults with type 2 diabetes over 10 weeks using an activity tracker.MethodThis cross-sectional observational study was conducted at a Bahrain government health centre. Thirty-three Bahraini Arab adults, 30–60 years old, with controlled type 2 diabetes, wore a Fitbit Flex 2TM activity tracker for 10 weeks. Data on age, sex, marital and employment status, education and BMI were collected at the start of the study.ResultsA total of N = 32 participants completed the study. The average steps per day were 7859 ± 4131, and there were no differences between baseline, week 5 and 10. A third of participants were sedentary, based on a threshold of 5000 steps/day. Females accumulated fewer average daily steps than males (6728 ± 2936 vs. 10,281 ± 4623, p = 0.018). Daily averages for physical activity intensity were as follows: sedentary (786 ± 109 min), light (250 ± 76 min), moderate (9 ± 10 min) and vigorous (12 ± 18 min). Males had higher daily averages versus females for moderate (13 ± 9 vs. 5 ± 9 min, p = 0.018) and vigorous physical activity (21 ± 23 vs. 5 ± 7 min, p = 0.034). 91% of participants wore the device ≥10 h/day. The adherence rate was 79% based on percentage of days the device was worn continuously over 10 weeks.ConclusionFuture physical activity interventions should target sedentary and female participants with type 2 diabetes. In addition, we need to understand the facilitators and barriers to physical activity and the physical activity preferences of these two subgroups.
Citation: DIGITAL HEALTH
PubDate: 2024-05-17T11:15:14Z
DOI: 10.1177/20552076241251997
Issue No: Vol. 10 (2024)
- Virtual reality after stroke: Identifying important characteristics when
designing experiences to improve engagement in upper limb rehabilitation
Authors: Stephen Isbel, Helen Holloway, Craig Greber, Kelly Nguyen, Jane Frost, Claire Pearce, Nathan M D’Cunha
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveVirtual reality (VR) has been used to improve upper limb function after stroke but there is little to guide product developers in building experiences that engage users in the sustained, repetitive training required. This research sought to understand the characteristics of VR scenarios best suited to engaging someone with a stroke during recovery to achieve therapeutic outcomes.MethodsFive creative immersive VR scenarios were designed by an experienced VR content creator containing unique combinations of VR characteristics. The usefulness of the scenarios was reviewed by expert clinicians experienced in stroke rehabilitation. Following this review, seven stroke survivors participated in each experience and reported on their engagement and motivation. Outcome measures were the User Satisfaction Evaluation Questionnaire and the modified Intrinsic Motivation Inventory. Semi-structured interviews were conducted with five participants following their immersive VR experience and analysed thematically.ResultsExpert clinicians reported potential therapeutic value in the immersive VR scenarios by providing opportunities for repeated and graded practice of upper limb movements. Stroke survivors reported varied levels of enjoyment and engagement for each scenario. They recommended changes to the experiences, primarily relating to the tailoring of the scenarios to match varied upper limb capacities.ConclusionThis study highlights the characteristics of immersive VR scenarios that are important in sustaining motivation and providing high-repetition task-specific movement experiences. Differences in the experience and preferences of stroke participants regarding the characteristics of immersive VR experiences indicate that a variety of experiences are necessary to engage and sustain participation in an immersive VR-related therapy programme.
Citation: DIGITAL HEALTH
PubDate: 2024-05-17T11:14:37Z
DOI: 10.1177/20552076241251634
Issue No: Vol. 10 (2024)
- Simulation-based research for digital health pathologies: A multi-site
mixed-methods study
Authors: Isabel Straw, Joanna Dobbin, Demelza Luna-Reaver, Leonie Tanczer
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundThe advance of digital health technologies has created new forms of potential pathology which are not captured in current clinical guidelines. Through simulation-based research, we have identified the challenges to clinical care that emerge when patients suffer from illnesses stemming from failures in digital health technologies.MethodsClinical simulation sessions were designed based on patient case reports relating to (a) medical device hardware errors, (b) medical device software errors, (c) complications of consumer technology and (d) technology-facilitated abuse. Clinicians were recruited to participate in simulations at three UK hospitals; audiovisual suites were used to facilitate group observation of simulation experience and focused debrief discussions. Invigilators scored clinicians on performance, clinicians provided individual qualitative and quantitative feedback, and extensive notes were taken throughout.FindingsPaired t-tests of pre and post-simulation feedback demonstrated significant improvements in clinician's diagnostic awareness, technical knowledge and confidence in clinical management following simulation exposure (p
Citation: DIGITAL HEALTH
PubDate: 2024-05-17T08:56:22Z
DOI: 10.1177/20552076241247939
Issue No: Vol. 10 (2024)
- Process mining and customer journey mapping in healthcare: Enhancing
patient-centred care in stroke rehabilitation
Authors: Ingy Shafei, Jonathan Karnon, Maria Crotty
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundPatient-centred care and enhancing patient experience is a priority across Australia. Stroke rehabilitation has multiple consumer touchpoints that would benefit from a better understanding of customer journeys, subsequently impacting better patient-centred care, and contributing to process improvements and better patient outcomes. Customer journey mapping through process mining extracts process data from event logs in existing information systems discovering patient journeys, which can be utilized to monitor guideline compliance and uncover nonconformance.MethodologyUtilizing process mining and variant analysis, customer journey maps were developed for 130 stroke rehabilitation patients from referral to discharge. In total, 168 cases from the Australasian Rehabilitation Outcomes Centre dataset were matched with 6291 cases from inpatient stroke data. Variants were explored for age, gender, outcome measures, length of stay and functional independence measure (FIM) change.ResultsThe study illustrated the process, process variants and patient journey map in stroke rehabilitation. Process characteristics of stroke rehabilitation patients were extracted and represented utilizing process mining and results highlighted process variation, attributes, touchpoints and timestamps across stroke rehabilitation patient journeys categorized by patient demographics and outcome variables. Patients demonstrated a mean and median duration of 49.5 days and 44 days, respectively, across the patient journeys. Nine variants were discovered, with 78.46% (n = 102) of patients following the expected sequence of activities in their stroke rehabilitation patient journey. Relationships involving age, gender, length of stay and FIM change along the patient journeys were evident, with four cases experiencing stroke rehabilitation journeys of more than 100 days, warranting further investigation.ConclusionProcess mining can be utilized to visualize and analyse patient journeys and identify gaps in service quality, thus contributing to better patient-centred care and improved patient outcomes and experiences in stroke rehabilitation.
Citation: DIGITAL HEALTH
PubDate: 2024-05-17T05:08:22Z
DOI: 10.1177/20552076241249264
Issue No: Vol. 10 (2024)
- An innovative technological infrastructure for managing SARS-CoV-2 data
across different cohorts in compliance with General Data Protection
Regulation
Authors: Chiara Dellacasa, Maurizio Ortali, Elisa Rossi, Hammam Abu Attieh, Thomas Osmo, Miroslav Puskaric, Eugenia Rinaldi, Fabian Prasser, Caroline Stellmach, Salvatore Cataudella, Bhaskar Agarwal, Juan Mata Naranjo, Gabriella Scipione
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundThe ORCHESTRA project, funded by the European Commission, aims to create a pan-European cohort built on existing and new large-scale population cohorts to help rapidly advance the knowledge related to the prevention of the SARS-CoV-2 infection and the management of COVID-19 and its long-term sequelae. The integration and analysis of the very heterogeneous health data pose the challenge of building an innovative technological infrastructure as the foundation of a dedicated framework for data management that should address the regulatory requirements such as the General Data Protection Regulation (GDPR).MethodsThe three participating Supercomputing European Centres (CINECA - Italy, CINES - France and HLRS - Germany) designed and deployed a dedicated infrastructure to fulfil the functional requirements for data management to ensure sensitive biomedical data confidentiality/privacy, integrity, and security. Besides the technological issues, many methodological aspects have been considered: Berlin Institute of Health (BIH), Charité provided its expertise both for data protection, information security, and data harmonisation/standardisation.ResultsThe resulting infrastructure is based on a multi-layer approach that integrates several security measures to ensure data protection. A centralised Data Collection Platform has been established in the Italian National Hub while, for the use cases in which data sharing is not possible due to privacy restrictions, a distributed approach for Federated Analysis has been considered. A Data Portal is available as a centralised point of access for non-sensitive data and results, according to findability, accessibility, interoperability, and reusability (FAIR) data principles. This technological infrastructure has been used to support significative data exchange between population cohorts and to publish important scientific results related to SARS-CoV-2.ConclusionsConsidering the increasing demand for data usage in accordance with the requirements of the GDPR regulations, the experience gained in the project and the infrastructure released for the ORCHESTRA project can act as a model to manage future public health threats. Other projects could benefit from the results achieved by ORCHESTRA by building upon the available standardisation of variables, design of the architecture, and process used for GDPR compliance.
Citation: DIGITAL HEALTH
PubDate: 2024-05-16T05:33:31Z
DOI: 10.1177/20552076241248922
Issue No: Vol. 10 (2024)
- Application of a WeChat Mini Program to provide pharmaceutical care for
cancer pain patients: A randomized controlled trial
Authors: Qiuling Zhao, Xiuliang Qiu, Wenbin Liu, Zilin Nian, Ting Chen, Juan Chen, Ruixiang Xie, Lin Yang
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThis study aimed to develop an individual WeChat Mini Program to provide pharmaceutical care to better manage cancer pain patients and to evaluate its feasibility and the differences in analgesic efficacy, medication adherence and safety versus conventional pharmacy interventions.MethodsIn this parallel randomized clinical trial, 42 cancer pain patients were equally allocated into the experimental group and the control group. The experimental group received individualized pharmaceutical care based on the “Yao Nin You Wo” WeChat Mini Program, while the control group received conventional care during the 4-week period. Main outcomes contained pain scores, medication adherence, incidences and relief rates of breakthrough pain, and incidences of adverse events. Relief rates of pain were also calculated according to pain scores.ResultsAt the beginning of intervention, none of the pain scores and medication adherence showed relevant differences between the two groups (all P > .05). After intervention, the experimental group had significantly lower pain scores compared to the control group (P = .003). Breakthrough pain of both groups was alleviate; not only the incidence of breakthrough pain considerably was lower at 4 weeks than at baseline, but the relief rate of breakthrough in the experimental group was higher than that in the control group. Compared with the control group, the medication adherence rate of the experimental group was significantly improved (P = .02). Types of adverse events that happened in experimental and groups were similar, but the total incidence of adverse events in the experimental group was lower than that in the control group.ConclusionsWeChat Mini Program is a useful and facilitative tool with the potential to improve cancer pain self-management ability in discharged patients. In addition, pharmacists could play a key role through the Mini Program to connect with patients successfully by providing personalized pharmaceutical services.
Citation: DIGITAL HEALTH
PubDate: 2024-05-16T05:32:30Z
DOI: 10.1177/20552076241255654
Issue No: Vol. 10 (2024)
- Failures and fallacies of eHealth initiatives: Are we finally able to
overcome the underlying theoretical and practical orthodoxies'
Authors: Dalibor Stanimirovic
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
The growing and ubiquitous digitalization trends embodied in eHealth initiatives have led to the widespread adoption of digital solutions in the healthcare sector. These initiatives have been heralded as a potent transformative force aiming to improve healthcare delivery, enhance patient outcomes and increase the efficiency of healthcare systems. However, despite the significant potential and possibilities offered by eHealth initiatives, the article highlights the importance of critically examining their implications and cautions against the misconception that technology alone can solve complex public health concerns and healthcare challenges. It emphasizes the need to critically consider the sociocultural context, education and training, organizational and institutional aspects, regulatory frameworks, user involvement and other important factors when implementing eHealth initiatives. Disregarding these crucial elements can render eHealth initiatives inefficient or even counterproductive. In view of that, the article identifies failures and fallacies that can hinder the success of eHealth initiatives and highlights areas where they often fall short of meeting rising and unjustified expectations. To address these challenges, the article recommends a more realistic and evidence-based approach to planning and implementing eHealth initiatives. It calls for consistent research agendas, appropriate evaluation methodologies and strategic orientations within eHealth initiatives. By adopting this approach, eHealth initiatives can contribute to the achievement of societal goals and the realization of the key health priorities and development imperatives of healthcare systems on a global scale.
Citation: DIGITAL HEALTH
PubDate: 2024-05-16T05:31:59Z
DOI: 10.1177/20552076241254019
Issue No: Vol. 10 (2024)
- Telestroke network to robotic telestroke network: How to upgrade regional
stroke care to include remote robotics'
Authors: Guillaume Charbonnier, Arturo Consoli, Louise Bonnet, Alessandra Biondi, Fabrice Vuillier, Kanty Rabenorosoa, Vitor Mendes Pereira, Thierry Moulin
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveSelected patients with large vessel occlusion (LVO) strokes can benefit from endovascular therapy (EVT). However, the effectiveness of EVT is largely dependent on how quickly the patient receives treatment. Recent technological developments have led to the first neurointerventional treatments using robotic assistance, opening up the possibility of performing remote stroke interventions. Existing telestroke networks provide acute stroke care, including remote administration of intravenous thrombolysis (IVT). Therefore, the introduction of remote EVT in distant stroke centers requires an adaptation of the existing telestroke networks. The aim of this work was to propose a framework for centers that are potential candidates for telerobotics according to the resources currently available in these centers.MethodsIn this paper, we highlight the future challenges for including remote robotics in telestroke networks. A literature review provides potential solutions.ResultsExisting telestroke networks need to determine which centers to prioritize for remote robotic technologies based on objective criteria and cost-effectiveness analysis. Organizational challenges include regional coordination and specific protocols. Technological challenges mainly concern telecommunication networks.ConclusionsSpecific adaptations will be necessary if regional telestroke networks are to include remote robotics. Some of these can already be put in place, which could greatly help the future implementation of the technology.
Citation: DIGITAL HEALTH
PubDate: 2024-05-16T05:31:30Z
DOI: 10.1177/20552076241254986
Issue No: Vol. 10 (2024)
- Circadian rhythm and variability of large and small airway spirometric
variables in healthy individuals
Authors: Yingying Zhang, Yiting Wu, Xue Zhang, Chengjian Lv, Jingwang Lin, Lei Zhao, Yanmei Lin, Min Zhang, Wuping Bao
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveTo assess the diurnal rhythm and variability of lung function in healthy individuals, encompassing both large and small airways.MethodsA prospective study enrolled 35 healthy adults without a history of smoking. Initial spirometry and a bronchodilation test were performed using the Jaeger spirometer, followed by a seven-day continuous home monitoring using the GOSPT2000. We evaluated repeatability using the intraclass correlation coefficient and agreement through linear regression and Bland–Altman analyses. Circadian rhythm and variability in spirometric measurements were analyzed using the coefficient of variation (CV) and daily variation rate.ResultsThe GOSPT2000 demonstrated strong repeatability and high agreement with the Jaeger spirometer. Notable findings included a decrease in nocturnal forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), and FEV3 by 44, 59, and 53 mL, respectively. In contrast, peak expiratory flow at noon showed an increase of 0.143L/min. Small-airway variables, including forced expiratory flow at 50% and 75% of the FVC and maximum midexpiratory flow, showed no significant diurnal variation. The nocturnal CV for large-airway variables was ≤ 4%, while for small-airway variables, it was ≤ 11.89%.ConclusionThis study has established a spectrum of variability for both large and small airways in healthy populations. The variability of small-airway variables is higher than that of large-airway variables. The investigation into the diurnal rhythms and variability characteristics of both large and small airway variables in the healthy population can serve as a foundation for diagnosing asthma or assessing the efficacy of asthma treatments.
Citation: DIGITAL HEALTH
PubDate: 2024-05-16T05:31:11Z
DOI: 10.1177/20552076241254698
Issue No: Vol. 10 (2024)
- A machine learning-based model for “In-time” prediction of
periprosthetic joint infection
Authors: Weishen Chen, Xuantao Hu, Chen Gu, Zhaohui Zhang, Linli Zheng, Baiqi Pan, Xiaoyu Wu, Wei Sun, Puyi Sheng
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundPrevious criteria had limited value in early diagnosis of periprosthetic joint infection (PJI). Here, we constructed a novel machine learning (ML)-derived, “in-time” diagnostic system for PJI and proved its validity.MethodsWe filtered “in-time” diagnostic indicators reported in the literature based on our continuous retrospective cohort of PJI and aseptic prosthetic loosening patients. With the indicators, we developed a two-level ML model with six base learners including Elastic Net, Linear Support Vector Machine, Kernel Support Vector Machine, Extra Trees, Light Gradient Boosting Machine and Multilayer Perceptron), and one meta-learner, Ensemble Learning of Weighted Voting. The prediction performance of this model was compared with those of previous diagnostic criteria (International Consensus Meeting in 2018 (ICM 2018), etc.). Another prospective cohort was used for internal validation. Based on our ML model, a user-friendly web tool was developed for swift PJI diagnosis in clinical practice.ResultsA total of 254 patients (199 for development and 55 for validation cohort) were included in this study with 38.2% of them diagnosed as PJI. We included 21 widely accessible features including imaging indicators (X-ray and CT) in the model. The sensitivity and accuracy of our ML model were significantly higher than ICM 2018 in development cohort (90.6% vs. 76.1%, P = 0.032; 94.5% vs. 86.7%, P = 0.020), which was supported by internal validation cohort (84.2% vs. 78.6%; 94.6% vs. 81.8%).ConclusionsOur novel ML-derived PJI “in-time” diagnostic system demonstrated significantly improved diagnostic potency for surgical decision-making compared with the commonly used criteria. Moreover, our web-based tool greatly assisted surgeons in distinguishing PJI patients comprehensively.Level of evidenceDiagnostic Level III.
Citation: DIGITAL HEALTH
PubDate: 2024-05-16T05:31:09Z
DOI: 10.1177/20552076241253531
Issue No: Vol. 10 (2024)
- Heuristics used for evaluating the usability of mobile health
applications: A systematic literature review
Authors: Zahra Galavi, Somaye Norouzi, Reza Khajouei
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveMobile health applications hold immense potential for enhancing health outcomes. Usability is one of the main factors for the adoption and use of mobile health applications. However, despite the growing importance of mHealth applications, clear standards for their evaluation remain elusive. The present study aimed to determine heuristics for the usability evaluation of health-related applications.MethodsWe systematically searched multiple databases for relevant papers published between January 2008 and April 2021. Articles were reviewed, and data were extracted and categorized from those meeting inclusion criteria by two authors independently. Heuristics were identified based on statements, words, and concepts expressed in the studies. These heuristics were first mapped to Nielsen's heuristics based on their differences or similarities. The remaining heuristics that were very important for mobile applications were categorized into new heuristics.ResultsSeventeen studies met the eligibility criteria. Seventy-nine heuristics were extracted from the papers. After combining the items with the same concepts and removing irrelevant items based on the exclusion criteria, 20 heuristics remained. Common heuristics such as “Visibility of system status” and “Flexibility and efficiency of use” were categorized into 10 previously established heuristics and new heuristics like “Navigation” and “User engagement” were recognized as new ones.ConclusionsIn our study, we have meticulously identified 20 heuristics that hold promise for evaluating and designing mHealth applications. These heuristics can be used by the researchers for the development of robust tools for heuristic evaluation. These tools, when adapted or tailored for health domain applications, have the potential to significantly enhance the quality of mHealth applications. Ultimately, this improvement in quality translates to enhanced patient safety.Protocol Registration(10.17605/OSF.IO/PZJ7H)
Citation: DIGITAL HEALTH
PubDate: 2024-05-16T05:30:58Z
DOI: 10.1177/20552076241253539
Issue No: Vol. 10 (2024)
- Involvement in cyberbullying events and empathy are related to emotional
responses to simulated social pain tasks
Authors: Rosalba Morese, Matteo Angelo Fabris, Claudio Longobardi, Davide Marengo
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
IntroductionThis study aims to explore the relationship between cyberbullying involvement either as a perpetrator or a victim and emotional responses to virtual social exclusion and inclusion. Previous research has predominantly focused on the impacts of in-person bullying. Our study shifts this focus to the cyber realm.MethodsA total of 156 adolescents living in northern Italy were recruited (Mage: 12.26; SD = 0.87; 43% female). After completing measures of empathy and involvement in cyberbullying, adolescents participated in the cyberball tasks. Latent profile analysis was used to identify subgroups.ResultsWe found three groups: Class 3, reporting negative responses to the social exclusion tasks and positive responses to the social inclusion tasks; Class 1, reporting neutral emotional responses to social inclusion and negative emotional responses to social exclusion; and Class 2, showing neutral responses to ‘social exclusion’ tasks and strongly positive responses to ‘social inclusion’ tasks. Linear regression revealed that cyberbullies report a typical emotional response to exclusion and inclusion tasks (Class 3), whereas cybervictims are more likely to report negative responses to both exclusion and inclusion events (Class 1). High levels of empathy are associated with the manifestation of a typical emotional response (Class 3), in contrast to an impaired emotional response characterized by neutral or positive responses to conditions of ‘social exclusion’ and positive responses to conditions of ‘social inclusion’ (Class 2).ConclusionResults underscore the complex interplay between cyberbullying roles and emotional responses to virtual social experiences. Theoretical implications and limitations of the research are discussed.
Citation: DIGITAL HEALTH
PubDate: 2024-05-16T05:30:54Z
DOI: 10.1177/20552076241253085
Issue No: Vol. 10 (2024)
- Real-time personalized feedback in mHealth for adolescents
Authors: Evelien Dietvorst, Manon HJ Hillegers, Jeroen S Legerstee, Lianne P De Vries, Annabel Vreeker, Loes Keijsers
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
Mobile Health (mHealth) interventions have the potential to improve early identification, prevention, and treatment of mental health problems. Grow It! is a multiplayer smartphone app designed for youth aged 12–25, allowing them to monitor their emotions and engage in daily challenges based on Cognitive Behavioral Therapy (CBT) principles. Recently, a personalized mood profile was added to improve the app. We investigated whether real-time personalized feedback on mood enhances app engagement, user experience, and the effects on affective and cognitive well-being.Sample A (N = 1269, age = 18.60 SD = 3.39, 80.6% girls, 95.4% Dutch) played the original app without feedback on their mood, and an independent Sample B (N = 386, age = 16.04 SD = 3.21, 67.6% girls, 82.9% Dutch) received the renewed version with personalized real-time feedback on their mood.Participants who received personal feedback did not have higher app engagement (t(1750,400) = 1.39, P = .206, d = 0.07; t(692,905) = 0.36, P = .971, d = 0.0) nor higher user experience (t(177,596) = 0.21, P = .831, d = 0.02; (t(794) = 1.28, P = .202, d = 0.12; χ2 (659,141) = 2.83, P = .091). Players of the renewed version (Sample B) experienced significant improvements in affective (t(175) = 3.01, P = .003, d = 0.23) and cognitive well-being (t(175) = 3.48, P =
Citation: DIGITAL HEALTH
PubDate: 2024-05-16T05:30:33Z
DOI: 10.1177/20552076241247937
Issue No: Vol. 10 (2024)
- Safety and quality of AI chatbots for drug-related inquiries: A real-world
comparison with licensed pharmacists
Authors: Yasser Albogami, Almaha Alfakhri, Abdulaziz Alaqil, Aljawharah Alkoraishi, Heba Alshammari, Yasmin Elsharawy, Abdullah Alhammad, Abdulaziz Alhossan
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
IntroductionPharmacists play a pivotal role in ensuring patients are administered safe and effective medications; however, they encounter obstacles such as elevated workloads and a scarcity of qualified professionals. Despite the prospective utility of large language models (LLMs), such as Generative Pre-trained Transformers (GPTs), in addressing pharmaceutical inquiries, their applicability in real-world cases remains unexplored.ObjectiveTo evaluate GPT-based chatbots’ accuracy in real-world drug-related inquiries, comparing their performance to licensed pharmacists.MethodsIn this cross-sectional study, authors analyzed real-world drug inquiries from a Drug Information Inquiry Database. Two independent pharmacists evaluated the performance of GPT-based chatbots (GPT-3, GPT-3.5, GPT-4) against human pharmacists using accuracy, detail, and risk of harm criteria. Descriptive statistics described inquiry characteristics. Absolute proportion comparative analyses assessed accuracy, detail, and risk of harm. Stratified analyses were performed for different inquiry types.ResultsSeventy inquiries were included. Most inquiries were received from physicians (41%) and pharmacists (44%). Inquiries type included dosage/administration (34.2%), drug interaction (12.8%) and pregnancy/lactation (15.7%). Majority of inquires included adults (83%) and female patients (54.3%). GPT-4 had 64.3% completely accurate responses, comparable to human pharmacists. GPT-4 and human pharmacists provided sufficiently detailed responses, with GPT-4 offering additional relevant details. Both GPT-4 and human pharmacists delivered 95% safe responses; however, GPT-4 provided proactive risk mitigation information in 70% of the instances, whereas similar information was included in 25.7% of human pharmacists’ responses.ConclusionOur study showcased GPT-4's potential in addressing drug-related inquiries accurately and safely, comparable to human pharmacists. Current GPT-4-based chatbots could support healthcare professionals and foster global health improvements.
Citation: DIGITAL HEALTH
PubDate: 2024-05-15T02:49:49Z
DOI: 10.1177/20552076241253523
Issue No: Vol. 10 (2024)
- Malignancy pattern analysis of breast ultrasound images using clinical
features and a graph convolutional network
Authors: Sidratul Montaha, Sami Azam, Md. Rahad Islam Bhuiyan, Sadia Sultana Chowa, Md. Saddam Hossain Mukta, Mirjam Jonkman
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveEarly diagnosis of breast cancer can lead to effective treatment, possibly increase long-term survival rates, and improve quality of life. The objective of this study is to present an automated analysis and classification system for breast cancer using clinical markers such as tumor shape, orientation, margin, and surrounding tissue. The novelty and uniqueness of the study lie in the approach of considering medical features based on the diagnosis of radiologists.MethodsUsing clinical markers, a graph is generated where each feature is represented by a node, and the connection between them is represented by an edge which is derived through Pearson's correlation method. A graph convolutional network (GCN) model is proposed to classify breast tumors into benign and malignant, using the graph data. Several statistical tests are performed to assess the importance of the proposed features. The performance of the proposed GCN model is improved by experimenting with different layer configurations and hyper-parameter settings.ResultsResults show that the proposed model has a 98.73% test accuracy. The performance of the model is compared with a graph attention network, a one-dimensional convolutional neural network, and five transfer learning models, ten machine learning models, and three ensemble learning models. The performance of the model was further assessed with three supplementary breast cancer ultrasound image datasets, where the accuracies are 91.03%, 94.37%, and 89.62% for Dataset A, Dataset B, and Dataset C (combining Dataset A and Dataset B) respectively. Overfitting issues are assessed through k-fold cross-validation.ConclusionSeveral variants are utilized to present a more rigorous and fair evaluation of our work, especially the importance of extracting clinically relevant features. Moreover, a GCN model using graph data can be a promising solution for an automated feature-based breast image classification system.
Citation: DIGITAL HEALTH
PubDate: 2024-05-15T02:49:29Z
DOI: 10.1177/20552076241251660
Issue No: Vol. 10 (2024)
- Dataset size versus homogeneity: A machine learning study on pooling
intervention data in e-mental health dropout predictions
Authors: Kirsten Zantvoort, Nils Hentati Isacsson, Burkhardt Funk, Viktor Kaldo
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThis study proposes a way of increasing dataset sizes for machine learning tasks in Internet-based Cognitive Behavioral Therapy through pooling interventions. To this end, it (1) examines similarities in user behavior and symptom data among online interventions for patients with depression, social anxiety, and panic disorder and (2) explores whether these similarities suffice to allow for pooling the data together, resulting in more training data when prediction intervention dropout.MethodsA total of 6418 routine care patients from the Internet Psychiatry in Stockholm are analyzed using (1) clustering and (2) dropout prediction models. For the latter, prediction models trained on each individual intervention's data are compared to those trained on all three interventions pooled into one dataset. To investigate if results vary with dataset size, the prediction is repeated using small and medium dataset sizes.ResultsThe clustering analysis identified three distinct groups that are almost equally spread across interventions and are instead characterized by different activity levels. In eight out of nine settings investigated, pooling the data improves prediction results compared to models trained on a single intervention dataset. It is further confirmed that models trained on small datasets are more likely to overestimate prediction results.ConclusionThe study reveals similar patterns of patients with depression, social anxiety, and panic disorder regarding online activity and intervention dropout. As such, this work offers pooling different interventions’ data as a possible approach to counter the problem of small dataset sizes in psychological research.
Citation: DIGITAL HEALTH
PubDate: 2024-05-15T02:49:09Z
DOI: 10.1177/20552076241248920
Issue No: Vol. 10 (2024)
- Revealing patient-reported experiences in healthcare from social media
using thedesign-acquire-process-model-analyse-visualise framework
Authors: Curtis Murray, Lewis Mitchell, Jonathan Tuke, Mark Mackay
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
Understanding patient experience in healthcare is increasingly important and desired by medical professionals in a patient-centred care approach. Healthcare discourse on social media presents an opportunity to gain a unique perspective on patient-reported experiences, complementing traditional survey data. These social media reports often appear as first-hand accounts of patients’ journeys through the healthcare system, whose details extend beyond the confines of structured surveys and at a far larger scale than focus groups. However, in contrast with the vast presence of patient-experience data on social media and the potential benefits the data offers, it attracts comparatively little research attention due to the technical proficiency required for text analysis. In this article, we introduce the design-acquire-process-model-analyse-visualise framework to provide an overview of techniques and an approach to capture patient-reported experiences from social media data. We apply this framework in a case study on prostate cancer data from /r/ProstateCancer, demonstrate the framework’s value in capturing specific aspects of patient concern (such as sexual dysfunction), provide an overview of the discourse, and show narrative and emotional progression through these stories. We anticipate this framework to apply to a wide variety of areas in healthcare, including capturing and differentiating experiences across minority groups, geographic boundaries, and types of illnesses.
Citation: DIGITAL HEALTH
PubDate: 2024-05-15T02:48:09Z
DOI: 10.1177/20552076241251715
Issue No: Vol. 10 (2024)
- Experiences of community health workers on adopting mHealth in rural
Malawi: A qualitative study
Authors: Chiyembekezo Kachimanga, Manuel Mulwafu, Myness Kasanda Ndambo, Jimmy Harare, Joia Murkherjee, Alexandra V Kulinkina, Simon Mbae, Enoch Ndarama, Thomas van den Akker, Ibukun- Oluwa Omolade Abejirinde
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundThe use of mobile health technology (mHealth) by community health workers (CHWs) can strengthen community-based service delivery and improve access to and quality of healthcare.ObjectiveThis qualitative study sought to explore experiences and identify factors influencing the use of an integrated smartphone-based mHealth called YendaNafe by CHWs in rural Malawi.MethodsUsing pre-tested interview guides, between August and October 2022, we conducted eight focus group discussions with CHWs (n = 69), four in-depth interviews with CHW supervisors, and eight key informant interviews in Neno District, Malawi. We audio-recorded and transcribed the interviews verbatim and organized them for analysis in Dedoose V9.0.62. We used an inductive analysis technique to analyze the data. We further applied the six domains of the socio-technical system (STS) framework to map factors influencing the use of YendaNafe.ResultsUser experiences and facilitators and barriers were the two main themes that emerged. mHealth was reported to improve the task efficiency, competence, trust, and perceived professionalism of CHWs. CHWs less frequently referred to cultural factors influencing app uptake. However, for other social systems, they identified relationships and trust with stakeholders, availability of training and programmatic support, and performance monitoring and feedback as influencing the use of YendaNafe. From the STS technical domain, the availability and adequacy of hardware such as phones, mobile connectivity, and usability influenced the use of YendaNafe.ConclusionsDespite the initial discomfort, CHWs found mHealth helpful in supporting their service delivery tasks. Identifying and addressing social and technical factors during mHealth implementation may help improve end users’ attitudes and uptake.
Citation: DIGITAL HEALTH
PubDate: 2024-05-15T02:47:11Z
DOI: 10.1177/20552076241253994
Issue No: Vol. 10 (2024)
- Reliability of continuous vital sign monitoring in post-operative patients
employing consumer-grade fitness trackers: A randomised pilot trial
Authors: Philipp Helmer, Sebastian Hottenrott, Kathrin Wienböker, Rüdiger Pryss, Vasileios Drosos, Anna Katharina Seitz, Daniel Röder, Aleksandar Jovanovic, Jürgen Brugger, Peter Kranke, Patrick Meybohm, Bernd E Winkler, Michael Sammeth
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
IntroductionFitness trackers can provide continuous monitoring of vital signs and thus have the potential to become a complementary, mobile and effective tool for early detection of patient deterioration and post-operative complications.MethodsTo evaluate potential implementations in acute care setting, we included 36 patients after moderate to major surgery in a recent randomised pilot trial to compare the performance of vital sign monitoring by three different fitness trackers (Apple Watch 7, Garmin Fenix 6pro and Withings ScanWatch) with established standard clinical monitors in post-anaesthesia care units and monitoring wards.ResultsDuring a cumulative period of 56 days, a total of 53,197 heart rate (HR) measurements, as well as 12,219 measurements of the peripheral blood oxygen saturation (SpO2) and 28,954 respiratory rate (RR) measurements were collected by fitness trackers. Under real-world conditions, HR monitoring was accurate and reliable across all benchmarked devices (r = [0.95;0.98], p
Citation: DIGITAL HEALTH
PubDate: 2024-05-13T08:21:08Z
DOI: 10.1177/20552076241254026
Issue No: Vol. 10 (2024)
- Black social media influencers engage higher percentages of Black gay and
bisexual men in online outreach for HIV prevention research relative to
paid ads
Authors: Owen B Fahey, Daniel J Carr, Erik MS Ocean, Vasilios Nittas, Chanda Phelan Kane, Peter M Monti, Tyler B Wray
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundInfluencer-based social media marketing campaigns are a popular strategy to engage customers in many non-research industries (e.g., retail), but have been increasingly used in public health campaigns to reach and engage specific populations. However, few studies have directly compared the performance of influencer-based marketing with other ad strategies (e.g., paid ads) in achieving these goals.MethodsFrom March to September 2023, we conducted an influencer-focused marketing campaign in which we identified and partnered with predominantly Black LGBTQ + influencers in the United States South to promote engagement in our ongoing research. We then used web analytics and interest form data to compare performance of influencer posts versus paid ads over the same time period.ResultsWe contacted a total of 358 influencers, 20 of whom ultimately agreed to post (85% Black/African American) and made a total of 28 posts on our behalf. A significantly higher percentage of users who clicked through influencer posts were Black (40% vs. 15%), were not currently using pre-exposure prophylaxis (PrEP) (67% vs. 62%), had no history of PrEP use (78% vs. 72%), and reported higher medical mistrust (12% vs. 8%) compared to those who clicked through paid ads. The percentage of Black men who have sex with men who were at high HIV risk, who were not taking PrEP, had no history of PrEP, or were high in mistrust, were all 2–3 times higher among those who clicked through influencer posts relative to paid ads.ConclusionsInfluencer-focused marketing may be a powerful tool to efficiently reach and engage high-priority and hard to reach populations.
Citation: DIGITAL HEALTH
PubDate: 2024-05-13T08:19:51Z
DOI: 10.1177/20552076241253758
Issue No: Vol. 10 (2024)
- The impact of digital social capital on the health of older adults: A
moderated mediation effect test
Authors: Yupeng Cui, Youshi He, Xinglong Xu, Lulin Zhou, Jonathan Aseye Nutakor
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
PurposeOlder adults are struggling in the digital age due to lower digital literacy and other reasons. The purpose of this study was to explore the relationship between digital social capital, digital divide, learning ability, and health of older adults.MethodsThis study used data from the China General Social Survey (CGSS) to systematically analyze effects of digital social capital on the health of older adults using the moderated mediated effect test.ResultsDigital social capital has a significant positive effect on the health of older adults and significant household and regional heterogeneity. Internet usage has a mediating impact between social capital and the health of older adults. Learning ability positively moderates the effect of internet usage on the health of older adults, but negatively moderates the impact of digital social capital on internet usage. Learning ability moderates the mediating effect of internet usage between social capital and the health of older adults. The stronger the learning ability, the stronger the mediating effect of internet usage between social capital and health of older adults.ConclusionDigital social capital can promote the health of older adults, and internet usage and learning ability can play mediating and moderating roles in the process of digital social capital affecting the health of older adults, revealing that we should cultivate the digital social capital of older adults and improve the digital ability of older adults to improve their health.
Citation: DIGITAL HEALTH
PubDate: 2024-05-10T04:49:59Z
DOI: 10.1177/20552076241253095
Issue No: Vol. 10 (2024)
- Predicting the employment of teledentistry in clinical practice by the
Saudi dental community using a theoretical model
Authors: Angel M Joseph, Rasha A Alsalman, Wjoud A Almasoud, Reem Almutairi, Rawan B Alammari, Yahya AM Deeban, Mohammed Z Mustafa, Amar A Thakare
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
IntroductionThere are very few scholastic studies applying a theory-driven methodology to analyse the employment of teledentistry in clinical practice by the Saudi dental community. The objective of this research was to predict the employment of teledentistry in clinical practice by the Saudi dental community using the UTAUT (Unified Theory of Acceptance and Use of Technology) model.MethodsA countrywide survey was executed from November 2022 to April 2023 among the dental community (pre-graduate students, graduates, post-graduate students, general dentists, and specialist dentists) involved in clinical practice. The survey employed the UTAUT model, which has four fundamental constructs: performance expectancy (PE), effort expectancy (EE), social influence (SI) and facilitating conditions (FC). These constructs are known to impact the user's behavioural intention (BI). The four fundamental constructs were independent, and BI was the dependent variable. A Likert scale with five scores was used to record each variable. Descriptive statistics were used to describe all the constructs. Cronbach's alpha scores were used to measure the inner consistency of the Likert scale. Simple linear regression and multiple linear regression were used to determine the correlation between all the constructs and the overall model's prediction. The Statistical Package for the Social Sciences was applied for analysis. The study had 80% power and an alpha threshold of .05.ResultsThe electronic survey was sent to 3000 participants, out of whom 2143 responded (response rate = 71.43%). PE (R2= 26%, p
Citation: DIGITAL HEALTH
PubDate: 2024-05-09T06:50:27Z
DOI: 10.1177/20552076241253739
Issue No: Vol. 10 (2024)
- Improvements in sleep problems and their associations with mental health
symptoms: A study of children and adolescents participating in a digital
mental health intervention
Authors: Darian Lawrence-Sidebottom, Landry Goodgame Huffman, Aislinn Beam, Amit Parikh, Rachael Guerra, Monika Roots, Jennifer Huberty
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveA growing number of youth are utilizing digital mental health interventions (DMHIs) for treatment of mental health problems such as anxiety, depression, and ADHD. Although these mental health symptoms are closely related to sleep problems, it is unknown whether nonsleep DMHIs indirectly confer improvements in sleep. Using retrospective data, the current study assesses (1) whether youth sleep problems improve over participation in a nonsleep DMHI, and (2) whether mental health symptom severity and improvement are correlated with sleep problem severity over time.MethodsSleep problems and mental health symptoms were assessed every 30 days among children (ages 5–12) and adolescents (ages 13–17) participating in a pediatric digital mental health intervention (DMHI; N = 1219).ResultsChildren and adolescents with elevated sleep problems (39.3%; n = 479) were older (P
Citation: DIGITAL HEALTH
PubDate: 2024-05-09T06:49:50Z
DOI: 10.1177/20552076241249928
Issue No: Vol. 10 (2024)
- Impact of 3D imaging techniques and virtual patients on the accuracy of
planning and surgical placement of dental implants: A systematic review
Authors: Ravinder S Saini, Shashit Shetty Bavabeedu, Syed Altafuddin Quadri, Vishwanath Gurumurthy, Masroor Ahmed Kanji, Mohammed Saheer Kuruniyan, Rayan Ibrahim H Binduhayyim, Anna Avetisyan, Artak Heboyan
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
AimThe integration of advanced technologies, including three-dimensional (3D) imaging modalities and virtual simulations, has significantly influenced contemporary approaches to preoperative planning in implant dentistry. Through a meticulous analysis of relevant studies, this review synthesizes findings related to accuracy outcomes in implant placement facilitated by 3D imaging in virtual patients.MethodsA comprehensive literature search was conducted across relevant databases to identify relevant studies published to date. The inclusion criteria were studies utilizing 3D imaging techniques, virtual patients, and those focusing on the accuracy of dental implant planning and surgical placement. The selected studies were critically appraised for their methodological quality.ResultsAfter a rigorous analysis, 21 relevant articles were included out of 3021 articles. This study demonstrates the versatility and applicability of these technologies in both in vitro and in vivo settings. Integrating Computer-Aided Design/Computer-Aided Manufacturing (CAD/CAM), cone bean computed tomography (CBCT), and advanced 3D reconstruction methodologies showcases a trend toward enhanced precision in implant planning and placement. Notably, the evaluation parameters varied, encompassing distances, discrepancies, and deviations in the implant placement. The ongoing integration of systems such as dynamic navigation systems, augmented reality, and sophisticated software platforms shows a promising trajectory for the continued refinement of virtual reality applications in dental implantology, providing valuable insights for future research and clinical implementation. Moreover, using stereolithographic surgical guides, virtual planning with CBCT data, and 3D-printed templates consistently demonstrates enhanced precision in dental implant placement compared to traditional methods.ConclusionThe synthesis of the available evidence underscores the substantial positive impact of 3D imaging techniques and virtual patients on dental implant planning and surgical placement accuracy. Utilizing these technologies contributes to a more personalized and precise approach that enhances overall treatment outcomes. Future research directions and potential refinements to the application of these technologies in clinical practice should be discussed.
Citation: DIGITAL HEALTH
PubDate: 2024-05-08T06:17:12Z
DOI: 10.1177/20552076241253550
Issue No: Vol. 10 (2024)
- The impact of internet health information usage habits on older
adults’ e-health literacy
Authors: Wei Ye
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveAs the demand and supply sides of popular health services increasingly rely on the Internet, mastering e-health literacy should become an essential skill for older adults. The aim of this article is to analyse the effects of Internet health information usage habits on older adults’ e- health literacy and to investigate the influencing mechanisms.MethodsUsing a combination of random sampling and convenient sampling, data were collected through questionnaire surveys. Data from 776 older adults was analysed using correlation and hierarchical regression to analyse.ResultsThe mean scores for all aspects of older adults’ habits of using health information on the Internet and electronic health literacy were relatively high. There was no statistically significant difference in the predictive power of the three aspects of electronic health literacy among older adults with different genders, health statuses, education levels and ages (p > 0.05). The four factors of older adults’ habits of using Internet health information can increase the explanatory power of application ability, judgment ability and decision-making ability in Model 2 by 53.7%, 46.2% and 57%, respectively, with statistical significance (p
Citation: DIGITAL HEALTH
PubDate: 2024-05-08T06:16:32Z
DOI: 10.1177/20552076241253473
Issue No: Vol. 10 (2024)
- Perceptions of mental health providers of the barriers and facilitators of
using and engaging youth in digital mental-health-enabled measurement
based care
Authors: E.M. Bassi, K.S. Bright, L.G. Norman, K. Pintson, S. Daniel, S. Sidhu, J. Gondziola, J. Bradley, M. Fersovitch, L. Stamp, K. Moskovic, H.M. LaMonica, F. Iorfino, T. Gaskell, S. Tomlinson, D.W. Johnson, G. Dimitropoulos
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectivesIncreased rates of mental health disorders and substance use among youth and young adults have increased globally, furthering the strain on an already burdened mental health system. Digital solutions have been proposed as a potential option for the provision of timely mental health services for youth, with little research exploring mental health professional views about using such innovative tools. In Alberta, Canada, we are evaluating the implementation and integration of a digital mental health (dMH) platform into existing service pathways. Within this paper we seek to explore mental health professionals’ perceptions of the barriers and facilitators that may influence their utilization of digital MH-enabled measurement-based care (MBC) with the youth who access their services.MethodsA qualitative, descriptive methodology was used to inductively generate themes from focus groups conducted with mental health professionals from specialized mental health services and primary care networks in Alberta.ResultsAs mental health professionals considered the barriers and facilitators of using dMH with youth, they referenced individual and family barriers and facilitators to consider. Providers highlighted perceived barriers, including: first, cultural stigma, family apprehension about mental health care, and parental access to dMH and MBC as deterrents to providers adopting digital platforms in routine care; second, perceptions of increased responsibility and liability for youth in crisis; third, perception that some psychiatric and neurodevelopmental disorders in youth are not amenable to dMH; fourth, professionals contemplated youth readiness to engage with dMH-enabled MBC. Participants also highlighted pertinent facilitators to dMH use, noting: first, the suitability of dMH for youth with mild mental health concerns; second, youth motivated to report their changes in mental health symptoms; and lastly, youth proficiency and preference for dMH options.ConclusionsBy identifying professionals’ perceptions of barriers and facilitators for youth users, we may better understand how to address misconceptions about who is eligible and appropriate for dMH through training and education.
Citation: DIGITAL HEALTH
PubDate: 2024-05-08T06:16:03Z
DOI: 10.1177/20552076241253093
Issue No: Vol. 10 (2024)
- Selection of criteria for a telemedicine framework for designing,
implementing, monitoring and evaluating telemedicine interventions:
Validation using a modified Delphi process
Authors: Che Katz, Noemí Robles, David Novillo-Ortiz, Francesc Saigí-Rubió
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectivesThe call to scale up telemedicine services globally as part of the digital health transformation lacks an agreed-upon set of constructs to guide the implementation process. A lack of guidance hinders the development, consolidation, sustainability and optimisation of telemedicine services. The study aims to reach consensus among telemedicine experts on a set of implementation constructs to be developed into an evidence-based support tool.MethodsA modified Delphi study was conducted to evaluate a set of evidence-informed telemedicine implementation constructs comprising cores, domains and items. The study evaluated the constructs consisting of five cores: Assessment of the Current Situation, Development of a Telemedicine Strategy, Development of Organisational Changes, Development of a Telemedicine Service, and Monitoring, Evaluation and Optimisation of Telemedicine Implementation; seven domains: Individual Readiness, Organisational Readiness, Clinical, Economic, Technological and Infrastructure, Regulation, and Monitoring, Evaluation and Optimisation; divided into 53 items. Global telemedicine specialists (n = 247) were invited to participate and evaluate 58 questions. Consensus was set at ≥70%.ResultsForty-five experts completed the survey. Consensus was reached on 78% of the constructs evaluated. Regarding the core constructs, Monitoring, Evaluation and Optimisation of Telemedicine Implementation was determined to be the most important one, and Development of a Telemedicine Strategy the least. As for the domains, the Clinical one had the highest level of consensus, and the Economic one had the lowest.ConclusionsThis research advances the field of telemedicine, providing expert consensus on a set of implementation constructs. The findings also highlight considerable divergence in expert opinion on the constructs of reimbursement and incentive mechanisms, resistance to change, and telemedicine champions. The lack of agreement on these constructs warrants attention and may partly explain the barriers that telemedicine services continue to face in the implementation process.
Citation: DIGITAL HEALTH
PubDate: 2024-05-08T06:15:03Z
DOI: 10.1177/20552076241251951
Issue No: Vol. 10 (2024)
- Comment on “Feasibility and acceptability of ChatGPT generated radiology
report summaries for cancer patients”
Authors: Hinpetch Daungsupawong, Viroj Wiwanitkit
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
Citation: DIGITAL HEALTH
PubDate: 2024-05-08T05:28:23Z
DOI: 10.1177/20552076241253453
Issue No: Vol. 10 (2024)
- Evaluating the effectiveness and feasibility of a digital health
intervention to community-based rehabilitation in older adults: A cluster
randomized controlled trial study protocol
Authors: Caixiu Xue, Lei Xu, Ke Yang, Jia Wang, Xiaohui Xie, Wansha Zhou, Qilan Liu, Renli Deng, Lianhong Wang
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThe escalating global aging population underscores the need to effectively manage geriatric diseases, constituting a significant public health concern. Community-based rehabilitation has emerged as a crucial and accessible paradigm for the rehabilitation of older adults. In China, however, the practical implementation of community-based rehabilitation faces formidable challenges, including a dearth of specialized rehabilitation therapists, substantial disparities between demand and supply, and suboptimal satisfaction rates. We aimed to develop a community-based rehabilitation management platform for older adults centered around digital health technology, with the plan to conduct a cluster randomized controlled trial to gather more evidence to explore the best practices and service models of community-based rehabilitation based on digital health technology.MethodsThis cluster randomized controlled trial will be conducted in Zunyi City, China. We will recruit 286 adults aged ≥60 years and randomly allocate 20 subdistricts in a 1:1 ratio into either the intervention group, which will use the Rehabilitation Journey application, or the control group, which will be given a Rehabilitation Information Booklet for Older Adults. Both groups will undergo a 12-month rehabilitation management program, encompassing six months of guidance and an additional six months of follow-up through online and offline methods. The evaluation indicators will be assessed at enrollment and at 3rd, 6th, and 12th month.DiscussionThis study endeavors to furnish novel insights to develop a tailored community-based rehabilitation management program for older adults, delivering customized, intelligent, and precise rehabilitation services.
Citation: DIGITAL HEALTH
PubDate: 2024-05-08T05:28:12Z
DOI: 10.1177/20552076241252648
Issue No: Vol. 10 (2024)
- Sequential graph convolutional network and DeepRNN based hybrid framework
for epileptic seizure detection from EEG signal
Authors: Ferdaus Anam Jibon, A. R. Jamil Chowdhury, Mahadi Hasan Miraz, Hwang Ha Jin, Mayeen Uddin Khandaker, Sajia Sultana, Sifat Nur, Fazlul Hasan Siddiqui, AHM Kamal, Mohammad Salman, Ahmed A. F. Youssef
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
Automated epileptic seizure detection from ectroencephalogram (EEG) signals has attracted significant attention in the recent health informatics field. The serious brain condition known as epilepsy, which is characterized by recurrent seizures, is typically described as a sudden change in behavior caused by a momentary shift in the excessive electrical discharges in a group of brain cells, and EEG signal is primarily used in most cases to identify seizure to revitalize the close loop brain. The development of various deep learning (DL) algorithms for epileptic seizure diagnosis has been driven by the EEG's non-invasiveness and capacity to provide repetitive patterns of seizure-related electrophysiological information. Existing DL models, especially in clinical contexts where irregular and unordered structures of physiological recordings make it difficult to think of them as a matrix; this has been a key disadvantage to producing a consistent and appropriate diagnosis outcome due to EEG's low amplitude and nonstationary nature. Graph neural networks have drawn significant improvement by exploiting implicit information that is present in a brain anatomical system, whereas inter-acting nodes are connected by edges whose weights can be determined by either temporal associations or anatomical connections. Considering all these aspects, a novel hybrid framework is proposed for epileptic seizure detection by combined with a sequential graph convolutional network (SGCN) and deep recurrent neural network (DeepRNN). Here, DepRNN is developed by fusing a gated recurrent unit (GRU) with a traditional RNN; its key benefit is that it solves the vanishing gradient problem and achieve this hybrid framework greater sophistication. The line length feature, auto-covariance, auto-correlation, and periodogram are applied as a feature from the raw EEG signal and then grouped the resulting matrix into time-frequency domain as inputs for the SGCN to use for seizure classification. This model extracts both spatial and temporal information, resulting in improved accuracy, precision, and recall for seizure detection. Extensive experiments conducted on the CHB-MIT and TUH datasets showed that the SGCN-DeepRNN model outperforms other deep learning models for seizure detection, achieving an accuracy of 99.007%, with high sensitivity and specificity.
Citation: DIGITAL HEALTH
PubDate: 2024-05-08T05:27:35Z
DOI: 10.1177/20552076241249874
Issue No: Vol. 10 (2024)
- Discovery, development, and deployment of a user-centered point-of-care
digital information system to treat and track hypertension and diabetes
patients under India Hypertension Control Initiative 2019–2022, India
Authors: Parasuraman Ganeshkumar, Aarti Bhatnagar, Daniel Burka, Kiran Durgad, Ashish Krishna, Bidisha Das, Mahima Chandak, Meenakshi Sharma, Roopa Shivasankar, Anupam Khungar Pathni, Abhishek Kunwar, Prabhdeep Kaur
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundHypertension affects 28.5% of Indians aged 18–69. Real-time registration and follow-up of persons with hypertension are possible with point-of-care digital information systems. We intend to describe herein the experiences of discovering, developing, and deploying a point-of-care digital information system for public health facilities under the India Hypertension Control Initiative.MethodsWe have adopted an agile and user-centered approach in each phase in selected states of India since 2017. A multidisciplinary team adopted a hybrid approach with quantitative and qualitative methods, such as contextual inquiries, usability testing, and semi-structured interviews with healthcare workers, to document and monitor utility and usability. ResultsDuring the discovery phase, we adopted a storyboard technique to understand the requirement of a digital information system. The participatory approach in discovery phase co-designed the information system with the nurses and doctors at Punjab state of India. Simple, which is the developed information system, has a front-end Android mobile application for healthcare workers and a backend dashboard for program managers. As of October 2022, over 24,31,962 patients of hypertension and 8,99,829 diabetes were registered in the information system of 10,017 health facilities. The median duration of registering a new patient was 50 seconds, and for recording a follow-up visit was 14 seconds in the app. High satisfaction was reported in 100 app users’ quarterly interviews.ConclusionSimple was implemented by administering a user-centered approach and agile techniques. It demonstrated high utility and usability among users, highlighting the benefits of a user-centered approach for effective digital health solutions.
Citation: DIGITAL HEALTH
PubDate: 2024-05-07T07:15:50Z
DOI: 10.1177/20552076241250153
Issue No: Vol. 10 (2024)
- Novel use of structural equation modeling to examine the development of a
framework of patient-centered two-way referral systems for building
digital subjective well-being healthcare: A cross-sectional survey in
Central China
Authors: Xintong Wen, Qingyuan Song, Shuang OuYang, Zhiwei Yao, Ying Luo
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundDigital health technologies are progressively assuming significant roles in aspects encompassing in-hospital management, patient-centered design, and tiered referral systems. Nevertheless, current studies do not involve exploration into the potential value and mechanisms of digital health in a patient-centered context. This study aimed to explore the development of a framework of comprehensive, evidence-based digital health technologies for the construction of welfare-oriented healthcare.MethodsFrom March to June 2023, a cross-sectional online study was performed, involving 335 respondents with prior referral experiences hailing from the Central China region. Data on welfare-oriented healthcare factors (clinical pathway management, medical structure configuration, healthcare service accessibility, two-way referrals) underwent factor analysis in advance, and correlation between these factors and their association with two-way referrals was evaluated by testing for direct and indirect (mediating) effects.ResultsFirstly, there existed a significant positive correlation between integrative medical indicators and welfare-centered healthcare (β = 0.02–0.16, p
Citation: DIGITAL HEALTH
PubDate: 2024-05-07T06:17:32Z
DOI: 10.1177/20552076241253079
Issue No: Vol. 10 (2024)
- Multiplatform web app (My Way Up) plus motivational interviewing for
improving treatment retention in patients with onset of alcohol-related
liver disease and alcohol use disorder – an example of participatory
research
Authors: Elsa Caballeria, Mercè Balcells-Oliveró, Ramón Bataller, Pol Bruguera, Noel Cabrera, Alexandra Estruch, Neus Freixa, Óscar Garcia-Pañella, Mireia Graell, Jordi Gratacós-Gines, Pablo Guzman, Anna Hernández-Rubio, Anna Lligoña, Martina Pérez-Guasch, María Teresa Pons-Cabrera, Elisa Pose, Paola Zuluaga, Hugo López-Pelayo
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundWith the aim of improving treatment retention in patients with the onset of alcohol-related liver disease (ArLD), we designed a blended intervention (brief motivational intervention + ‘serious game’ (SG)). We present the participatory design methodology and outcomes and the usability assessment of the intervention.Methods(1) The design of the SG was based on the outcomes of two 3-h co-creation sessions with 37 participants (healthcare and technology professionals, patients, and patients’ relatives). The brief face-to-face motivational intervention was based on the 5 As Model and adapted to the ArLD population. (2) Usability pilot study: 20 participants (10 ArLD patients + 10 healthcare professionals) received the intervention. System Usability Scale (SUS) and Post-Study System Usability Questionnaire (PSSUQ) were applied to assess the SG usability and patients’ satisfaction with it. Weekly semi-structured interviews on the phone were conducted to identify the preferred elements in the SG and those aspects that should be improved.Results(1) Design: an SG in the form of a gamified web app, consisting of a daily activity for six weeks and adapted brief motivational interviewing. (2) Usability pilot study: usability results were excellent for both patients and healthcare professionals (SUS median score = 85). The general usability, the quality of the information provided by the SG and the quality of the interface were very positively rated in the PSSUQ (overall median score = 2, IQR = 1–2). The best-rated aspects were the provision of feedback, the use of metaphors and the application of audiovisual material. Changes in the design, response mechanics and content were applied after the study.ConclusionsThe usability and acceptability of an intervention for increasing retention to treatment in patients with recent onset of ArLD and AUD were excellent for patients and healthcare professionals. A randomized-controlled trial is required to test the efficacy of this approach.
Citation: DIGITAL HEALTH
PubDate: 2024-05-07T06:14:09Z
DOI: 10.1177/20552076241242787
Issue No: Vol. 10 (2024)
- Embracing digital health: German otolaryngology patients’ usage and
prospects of digital information and communication technologies for
cross-sectoral care
Authors: Martin Holderried, Ansgar Hoeper, Leonie Stauss, Friederike Holderried, Anne Herrmann-Werner, Hans A Kestler, Christian Ernst, Friederike Baerhold, Sven Becker
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThe usage of digital information and communication technologies in European healthcare is growing. Unlike numerous technological possibilities, the present use of these technologies and perspectives towards them in relation to otolaryngology care have so far been of less interest. This study evaluates the utilisation of and attitudes towards digital information and communication technologies in cross-sectoral otolaryngology care among German patients.MethodsA structured interview-based study was conducted at the outpatient facility of a tertiary hospital in Germany. It focused on chief complaints, current use of digital technologies, estimated benefits of increased digital technology use in otolaryngology care, and sociodemographic data. The detailed statistical analysis employed Chi-squared tests and multivariate logistic regression.ResultsA total of 208 otolaryngology patients completed the interview. Digital communication technologies exhibited a high penetration rate (91.8%) and were regularly used in daily life (78.7%) and for health reasons (73.3%). Younger age (p ≤ 0.003) and higher education levels (p ≤ 0.008) were significantly correlated with the increased digital communication technology use. The overall potential of eHealth technologies was rated significantly higher by younger patients (p ≤ 0.001). The patients’ chief complaints showed no significant influence on the current and potential use of these technologies for cross-sectoral otolaryngology care.ConclusionRegardless of their chief complaints, German otolaryngology patients regularly use digital information and communication technologies for health reasons and express interest in their further use for cross-sectoral care. To enhance digital patient communication in otolaryngology, attention should be given to treatment quality, usability, data security and availability and financial remuneration for service providers.
Citation: DIGITAL HEALTH
PubDate: 2024-05-07T05:24:54Z
DOI: 10.1177/20552076241249280
Issue No: Vol. 10 (2024)
- Longitudinal clinical decision support for assessing decisions over time:
State-of-the-art and future directions
Authors: Tyler J Loftus, Jeremy A Balch, Jenna L Marquard, Jessica M Ray, Brian S Alper, Neeraj Ojha, Azra Bihorac, Genevieve Melton-Meaux, Gopal Khanna, Christopher J Tignanelli
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectivePatients and clinicians rarely experience healthcare decisions as snapshots in time, but clinical decision support (CDS) systems often represent decisions as snapshots. This scoping review systematically maps challenges and facilitators to longitudinal CDS that are applied at two or more timepoints for the same decision made by the same patient or clinician.MethodsWe searched Embase, PubMed, and Medline databases for articles describing development, validation, or implementation of patient- or clinician-facing longitudinal CDS. Validated quality assessment tools were used for article selection. Challenges and facilitators to longitudinal CDS are reported according to PRISMA-ScR guidelines.ResultsEight articles met inclusion criteria; each article described a unique CDS. None used entirely automated data entry, none used living guidelines for updating the evidence base or knowledge engine as new evidence emerged during the longitudinal study, and one included formal readiness for change assessments. Seven of eight CDS were implemented and evaluated prospectively. Challenges were primarily related to suboptimal study design (with unique challenges for each study) or user interface. Facilitators included use of randomized trial designs for prospective enrollment, increased CDS uptake during longitudinal exposure, and machine-learning applications that are tailored to the CDS use case.ConclusionsDespite the intuitive advantages of representing healthcare decisions longitudinally, peer-reviewed literature on longitudinal CDS is sparse. Existing reports suggest opportunities to incorporate longitudinal CDS frameworks, automated data entry, living guidelines, and user readiness assessments. Generating best practice guidelines for longitudinal CDS would require a greater depth and breadth of published work and expert opinion.
Citation: DIGITAL HEALTH
PubDate: 2024-05-02T07:26:16Z
DOI: 10.1177/20552076241249925
Issue No: Vol. 10 (2024)
- Improved interpretable machine learning emergency department triage tool
addressing class imbalance
Authors: Clarisse SJ Look, Salinelat Teixayavong, Therese Djärv, Andrew FW Ho, Kenneth BK Tan, Marcus EH Ong
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThe Score for Emergency Risk Prediction (SERP) is a novel mortality risk prediction score which leverages machine learning in supporting triage decisions. In its derivation study, SERP-2d, SERP-7d and SERP-30d demonstrated good predictive performance for 2-day, 7-day and 30-day mortality. However, the dataset used had significant class imbalance. This study aimed to determine if addressing class imbalance can improve SERP's performance, ultimately improving triage accuracy.MethodsThe Singapore General Hospital (SGH) emergency department (ED) dataset was used, which contains 1,833,908 ED records between 2008 and 2020. Records between 2008 and 2017 were randomly split into a training set (80%) and validation set (20%). The 2019 and 2020 records were used as test sets. To address class imbalance, we used random oversampling and random undersampling in the AutoScore-Imbalance framework to develop SERP+-2d, SERP+-7d, and SERP+-30d scores. The performance of SERP+, SERP, and the commonly used triage risk scores was compared.ResultsThe developed SERP+ scores had five to six variables. The AUC of SERP+ scores (0.874 to 0.905) was higher than that of the corresponding SERP scores (0.859 to 0.894) on both test sets. This superior performance was statistically significant for SERP+-7d (2019: Z = −5.843, p
Citation: DIGITAL HEALTH
PubDate: 2024-05-02T07:25:49Z
DOI: 10.1177/20552076241240910
Issue No: Vol. 10 (2024)
- Using FLO text-messages to enhance health behaviours and self-management
of long-term conditions in South-Asian patients
Authors: Tahreem Chaudhry, Paula Ormandy, Cristina Vasilica
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectivesCultural and communication differences faced by South-Asian (SA) ethnic minority groups have led to challenges in the delivery of health care and complex management of long-term conditions (LTCs). We aim to explore the use of text-messaging in SA communities, through the Florence (FLO) health messaging system utilised within U.K. health sectors, to enhance positive health behaviours and self-management.MethodsA mixed-methods approach was used for this study involving two phases. Phase 1 includes the administration of the patient activation measure to assess SA patient level of skills, knowledge, and confidence to self-manage their LTC; whilst in Phase 2 semi-structured interviews were conducted, exploring the experiences of users and non-users of FLO text messaging to promote self-management behaviours.FindingsForty participants (Florence users (n = 20) and non-users (n = 20)) completed the patient activation survey once after using FLO, and took part in interviews. Differences were noted to exist between the two groups (p
Citation: DIGITAL HEALTH
PubDate: 2024-05-02T04:02:07Z
DOI: 10.1177/20552076241242558
Issue No: Vol. 10 (2024)
- YouTube as a source of information for cryptococcal infection: A
cross-sectional study
Authors: Kaijun Liao, Zhiqiang Feng, Dongli Lu, Jianping Xia, Zhaochun Wu, Zhenghua Jiang, Kun Chen, Hongqiang Qiu
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveImmunocompromised individuals, particularly HIV patients, worldwide are at risk from cryptococcal infection. There are a number of videos of cryptococcal infection and more and more individuals may search these videos, but the quality of videos on YouTube is unclear. This study set out to assess the content and quality of YouTube videos regarding cryptococcal infection.MethodsThe keywords “Cryptococcus,” “Cryptococcosis” and “Cryptococcal infection” were searched on YouTube. The videos were evaluated and graded by two impartial raters. A 14-point content score was used to categorize videos as bad, good or exceptional. The reliability and quality were evaluated utilizing the DISCERN instrument and a 5-point global quality score. Videos were then divided into groups based on uploading sources and content types.ResultsA total of 46 videos were located, and the ratings provided by the two raters were identical. Our scoring algorithm determined that 54.3% (n = 25), 32.6% (n = 15) and 13.0% (n = 6) of the videos were poor, decent and exceptional, respectively. Regarding quality, no difference was identified between the various video categories. The global quality scale, number of views, days posted, content score and DISCERN showed a significant positive relationship.ConclusionsProfessional individuals or healthcare organizations should be encouraged to submit high-quality videos for the expanding internet population, as only a small proportion of available videos had exceptional quality.
Citation: DIGITAL HEALTH
PubDate: 2024-05-01T04:41:48Z
DOI: 10.1177/20552076241249668
Issue No: Vol. 10 (2024)
- Optimizing lung cancer classification through hyperparameter tuning
Authors: Syed Muhammad Nabeel, Sibghat Ullah Bazai, Nada Alasbali, Yifan Liu, Muhammad Imran Ghafoor, Rozi Khan, Chin Soon Ku, Jing Yang, Sana Shahab, Lip Yee Por
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
Artificial intelligence is steadily permeating various sectors, including healthcare. This research specifically addresses lung cancer, the world's deadliest disease with the highest mortality rate. Two primary factors contribute to its onset: genetic predisposition and environmental factors, such as smoking and exposure to pollutants. Recognizing the need for more effective diagnosis techniques, our study embarked on devising a machine learning strategy tailored to boost precision in lung cancer detection. Our aim was to devise a diagnostic method that is both less invasive and cost-effective. To this end, we proposed four methods, benchmarking them against prevalent techniques using a universally recognized dataset from Kaggle. Among our methods, one emerged as particularly promising, outperforming the competition in accuracy, precision and sensitivity. This method utilized hyperparameter tuning, focusing on the Gamma and C parameters, which were set at a value of 10. These parameters influence kernel width and regularization strength, respectively. As a result, we achieved an accuracy of 99.16%, a precision of 98% and a sensitivity rate of 100%. In conclusion, our enhanced prediction mechanism has proven to surpass traditional and contemporary strategies in lung cancer detection.
Citation: DIGITAL HEALTH
PubDate: 2024-05-01T04:41:17Z
DOI: 10.1177/20552076241249661
Issue No: Vol. 10 (2024)
- Handling of outcome missing data dependent on measured or unmeasured
background factors in micro-randomized trial: Simulation and application
study
Authors: Masahiro Kondo, Koji Oba
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundMicro-randomized trials (MRTs) enhance the effects of mHealth by determining the optimal components, timings, and frequency of interventions. Appropriate handling of missing values is crucial in clinical research; however, it remains insufficiently explored in the context of MRTs. Our study aimed to investigate appropriate methods for missing data in simple MRTs with uniform intervention randomization and no time-dependent covariates. We focused on outcome missing data depending on the participants’ background factors.MethodsWe evaluated the performance of the available data analysis (AD) and the multiple imputation in generalized estimating equations (GEE) and random effects model (RE) through simulations. The scenarios were examined based on the presence of unmeasured background factors and the presence of interaction effects. We conducted the regression and propensity score methods as multiple imputation. These missing data handling methods were also applied to actual MRT data.ResultsWithout the interaction effect, AD was biased for GEE, but there was almost no bias for RE. With the interaction effect, estimates were biased for both. For multiple imputation, regression methods estimated without bias when the imputation models were correct, but bias occurred when the models were incorrect. However, this bias was reduced by including the random effects in the imputation model. In the propensity score method, bias occurred even when the missing probability model was correct.ConclusionsWithout the interaction effect, AD of RE was preferable. When employing GEE or anticipating interactions, we recommend the multiple imputation, especially with regression methods, including individual-level random effects.
Citation: DIGITAL HEALTH
PubDate: 2024-05-01T04:39:49Z
DOI: 10.1177/20552076241249631
Issue No: Vol. 10 (2024)
- A mobile app to predict and manage behavioral and psychological symptoms
of dementia: Development, usability, and users’ acceptability
Authors: Eunhee Cho, Minhee Yang, Jiyoon Jang, Jungwon Cho, Bada Kang, Yoonhyung Jang, Min Jung Kim
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
Objective: Non-pharmacological interventions are considered the first-line treatment for behavioral and psychological symptoms of dementia (BPSD); however, traditional approaches have shown only small effect sizes. Mobile technology offers an opportunity to improve BPSD assessment and management in people living with dementia (PLWD). We aimed (1) to develop a mobile application (app) featuring a real-time BPSD diary, machine-learning-based BPSD prediction, and individualized non-pharmacological care programs, including therapeutic use of music and reminiscent content, and (2) to test its usability, acceptability, and preliminary efficacy among PLWD and caregivers. Methods: An Android-based app was developed through the following three phases: (1) needs assessment, (2) software development and initial testing with experts, and (3) beta-testing with end users who were dyads of PLWD and caregivers. The preliminary efficacy, usability, and acceptability of the app were assessed using validated BPSD questionnaires and face-to-face interviews with the dyads. Logs of the dyads’ program participation (i.e., types, time, and duration), BPSD diaries, and engagement levels of PLWD were also collected through the app. Results: Five dyads created BPSD diaries (range: 22–48) over 3 weeks. Overall, the BPSD symptoms decreased after the beta-testing period. Each dyad participated in the care programs for 106–204 min, during which music alone was most frequently used. Engagement levels ranged from 3.38 to 4.94 (out of 5). Conclusions: The app was deemed usable, acceptable, and feasible for PLWD and caregivers. The upgraded app will be further tested and can be easily implemented at home or in the community.
Citation: DIGITAL HEALTH
PubDate: 2024-05-01T04:38:32Z
DOI: 10.1177/20552076241249277
Issue No: Vol. 10 (2024)
- Burst versus continuous delivery design in digital mental health
interventions: Evidence from a randomized clinical trial
Authors: Marta Anna Marciniak, Lilly Shanahan, Kenneth S L Yuen, Ilya Milos Veer, Henrik Walter, Oliver Tuescher, Dorota Kobylińska, Raffael Kalisch, Erno Hermans, Harald Binder, Birgit Kleim
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveDigital mental health interventions delivered via smartphone-based apps effectively treat various conditions; however, optimizing their efficacy while minimizing participant burden remains a key challenge. In this study, we investigated the potential benefits of a burst delivery design (i.e. interventions delivered only in pre-defined time intervals) in comparison to the continuous delivery of interventions.MethodsWe randomly assigned 93 participants to the continuous delivery (CD) or burst delivery (BD) group. The CD group engaged in ReApp, a mobile app that increases positive cognitive reappraisal with a consistent delivery schedule that provides five prompts per day throughout the 3-week-long study, while the BD group received five daily prompts only in the first and third weeks of the study.ResultsNo significant differences were found between the groups in terms of adherence, mental health outcomes (specifically depressive and anxiety symptoms), level of perceived stress, and perceived helpfulness of intervention. The BD group showed a significantly decreased perceived difficulty of intervention over time.ConclusionsThe results suggest that the burst delivery may be as suitable for digital mental health interventions as the continuous delivery. The perceived difficulty of the intervention declined more steeply for the BD group, indicating that it improved the feasibility of the positive cognitive reappraisal intervention without hurting its efficacy. This outcome may inform the design of less burdensome interventions with improved outcomes in future research.
Citation: DIGITAL HEALTH
PubDate: 2024-05-01T04:37:46Z
DOI: 10.1177/20552076241249267
Issue No: Vol. 10 (2024)
- Willingness to pay for health apps, its sociodemographic correlates, and
reasons for being unwilling to pay
Authors: Hao Liu, Zhenzhen Xie, Calvin Or
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundKnowledge about whether, how much, and why individuals are willing to pay for health apps is limited.ObjectivesThis study aimed to examine (1) the proportion of individuals willing to pay for health apps, (2) their willingness to pay (WTP; i.e. the maximum price the individual is willing to pay) for health apps, (3) the sociodemographic correlates determining whether individuals are willing to pay for these apps, (4) the sociodemographic correlates of their WTP, and (5) reasons for being unwilling to pay.MethodsSix hundred adults were invited to participate in a questionnaire survey examining their sociodemographic characteristics, WTP for health apps, and reasons for being unwilling to pay. Sociodemographic characteristics and WTP for health apps were analyzed using descriptive statistics. Logistic regression was applied to examine the sociodemographic variables correlated with whether individuals were willing to pay for health apps. Among those who were willing to pay, log-linear regression was conducted to examine the sociodemographic correlates of their WTP. The reasons for unwillingness to pay were descriptively analyzed.ResultsA total of 577 individuals completed the questionnaire. Of them, 58.9% were willing to pay for health apps. Their median WTP was HK$50 (HK$1 ≈ US$0.13). Participants with a bachelor's degree or higher and those who had previously installed health apps were more inclined to pay for health apps. WTP was positively associated with the maximum price previously paid for a health app. The most frequently cited reasons for being unwilling to pay were the belief that the government should provide free health apps, distrust in health apps, and a lack of awareness of health apps and their benefits.ConclusionsThis study provides insights that can inform strategies to enhance the marketability, affordability, and accessibility of health apps.
Citation: DIGITAL HEALTH
PubDate: 2024-05-01T04:36:46Z
DOI: 10.1177/20552076241248925
Issue No: Vol. 10 (2024)
- Acceptability, usability, and credibility of a mindfulness-based digital
therapeutic for pediatric concussion: A mixed-method study
Authors: Veronik Sicard, Kiarah O’Kane, Olivier Brown, Lauren Butterfield, Rachel Kardish, Esther Choi, Katherine Healey, Noah Silverberg, Andra M Smith, Gary Goldfield, Bechara J Saab, Clare Gray, Kristian Goulet, Peter Anderson, Craig Mackie, Sonja Roth, Martin Osmond, Roger Zemek, Molly Cairncross, Andrée-Anne Ledoux
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundThe ability to cope with concussion symptoms and manage stress is an important determinant of risk for prolonged symptoms.ObjectiveThis open-label mixed-methods pilot study assessed the acceptability and credibility of a mindfulness-based intervention delivered through a digital therapeutic (DTx; therapeutic smartphone app) for pediatric concussion.MethodsParticipants aged 12 to 18 years were recruited from an emergency department within 48 hours of a concussion (acute cohort) or from a tertiary care clinic at least 1-month post-concussion (persisting symptoms cohort). Participants completed a novel 4-week mindfulness-based intervention, for 10 to 15 minutes/day, at a minimum of 4 days/week. At 2 weeks, participants completed a credibility and expectancy questionnaire. At 4 weeks, participants completed questionnaires assessing satisfaction, usability and working alliance, as well as a semi-structured phone interview.ResultsTen participants completed the study outcomes (7 acute; 3 persisting symptoms). The intervention was perceived as credible (median/max possible = 6.50/9.00 [6.83,8.75]) and DTx was usable (median/max possible = 70.00/100.00 [55.00,82.50]). Participants rated their satisfaction with the DTx (median/max possible = 27.00/32.00 [24.50,29.50]) and the working alliance with the digital mindfulness guides (median/max possible = 3.92/5.00 [3.38–4.33]) as high. Four themes were identified from the qualitative data: (a) positive attributes; (b) negative attributes; (c) ideas for modifications; and (d) technical issues.ConclusionResults show modifications to the DTx, instructions and mindfulness intervention, and potential ways to increase adherence by leveraging positive attributes. A randomized control trial will assess the effectiveness of the DTx MBI to decrease the risk of persisting symptoms and reduce the symptom burden following pediatric concussion.
Citation: DIGITAL HEALTH
PubDate: 2024-05-01T04:33:46Z
DOI: 10.1177/20552076241248296
Issue No: Vol. 10 (2024)
- Patient perspectives on informed consent for medical AI: A web-based
experiment
Authors: Hai Jin Park
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveDespite the increasing use of AI applications as a clinical decision support tool in healthcare, patients are often unaware of their use in the physician's decision-making process. This study aims to determine whether doctors should disclose the use of AI tools in diagnosis and what kind of information should be provided.MethodsA survey experiment with 1000 respondents in South Korea was conducted to estimate the patients’ perceived importance of information regarding the use of an AI tool in diagnosis in deciding whether to receive the treatment.ResultsThe study found that the use of an AI tool increases the perceived importance of information related to its use, compared with when a physician consults with a human radiologist. Information regarding the AI tool when AI is used was perceived by participants either as more important than or similar to the regularly disclosed information regarding short-term effects when AI is not used. Further analysis revealed that gender, age, and income have a statistically significant effect on the perceived importance of every piece of AI information.ConclusionsThis study supports the disclosure of AI use in diagnosis during the informed consent process. However, the disclosure should be tailored to the individual patient's needs, as patient preferences for information regarding AI use vary across gender, age and income levels. It is recommended that ethical guidelines be developed for informed consent when using AI in diagnoses that go beyond mere legal requirements.
Citation: DIGITAL HEALTH
PubDate: 2024-05-01T04:32:46Z
DOI: 10.1177/20552076241247938
Issue No: Vol. 10 (2024)
- The relationship between therapeutic alliance, frequency of consultation
and uptake of telemedicine among patients seeking treatment for early
psychosis: A moderated mediation model
Authors: PV AshaRani, Yeow Wee Brian Tan, Ellaisha Samari, Peizhi Wang, Laxman Cetty, Pratika Satghare, Swapna K Verma, Charmaine Tang, Mythily Subramaniam
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundTelehealth services ensure the delivery of healthcare services to a wider range of consumers through online platforms. Nonetheless, the acceptance and uptake of telehealth remain elusive. This study aims to understand the (a) uptake and (b) acceptability of telemedicine, (c) if therapeutic alliance mediates the relationship between the frequency of consultations with clinicians and the uptake of telemedicine in patients with early psychosis, and (d) role of education in moderating the relationship between therapeutic alliance and the uptake of telemedicine for their mental healthcare.MethodsA convenience sample of outpatients (n = 109) seeking treatment for early psychosis and their care providers (n = 106) were recruited from a tertiary psychiatric care centre. Sociodemographic and clinical characteristics, therapeutic alliance (Working Alliance Inventory), and telemedicine use were captured through self-administered surveys. The moderated mediation analysis was performed using PROCESS macro 3.4.1 with therapeutic alliance and level of education as the mediating and moderating factors, respectively.ResultsThe acceptance of telemedicine was high (possibly will use: 47.7%; definitely will use: 26.6%) whilst the uptake was low (11%). Therapeutic alliance mediated the relationship between the frequency of consultation and the uptake of telemedicine (β: 0.326; CI: 0.042, 0.637). This effect was moderated by the level of education (β: −0.058; p
Citation: DIGITAL HEALTH
PubDate: 2024-05-01T04:31:46Z
DOI: 10.1177/20552076241247194
Issue No: Vol. 10 (2024)
- Rapid review on the incentives of digital health apps for physicians and
psychotherapists: A German perspective
Authors: Cordula C. J. Kreuzenbeck, Brit S. Schneider, Sophie X. Brenner, Florian Koerber
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThe Digital Healthcare Act introduced the prescription of digital health applications under specific circumstances in 2019 in Germany. They are funded through the social security system. In market access of prescribed digital health applications, Germany is pioneering the market. There remains a gap in understanding the views of medical professionals on the incentives of using these applications. As prescription of digital health applications starts emerging in other European countries, we sought to generate an overview of incentives and barriers that affect the caregivers in their decision of promoting and prescribing them in Germany.MethodsA Rapid Review of the Web of Science database for the years 2017–2023 was performed using sensitive search strings. Two reviewers conducted a three-phase selection via title, abstract and full-text independently from each other. The quality of studies was assessed systematically by both reviewers. Both quantitative and qualitative studies with primary data were included, and study quality was assessed using a standardised form.ResultsOut of 1643 initial results, 12 studies with information from 9302 physicians and psychotherapists were identified. Eight quantitative and four qualitative studies were included in the analysis. Key findings suggest that while between 40% and 60% of caregivers see relevant incentives mainly based on patients’ benefits, 20–40% see relevant barriers. For the physicians’ daily work, there were slightly more barriers than incentives identified in the quantitative synthesis. The qualitative studies revealed no relevant incentives that were not attributed to patients.ConclusionWhile digital health solutions offer promising avenues for enhancing patient care, their acceptance among healthcare professionals hinges on addressing prevailing concerns. Continuous research and dialogues with the medical community are pivotal to harness the full potential of digital health applications in healthcare.
Citation: DIGITAL HEALTH
PubDate: 2024-05-01T04:30:49Z
DOI: 10.1177/20552076241242781
Issue No: Vol. 10 (2024)
- Explainable machine learning for predicting conversion to neurological
disease: Results from 52,939 medical records
Authors: Christina Felix, Joshua D Johnston, Kelsey Owen, Emil Shirima, Sidney R Hinds, Kenneth D Mandl, Alex Milinovich, Jay L Alberts
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThis study assesses the application of interpretable machine learning modeling using electronic medical record data for the prediction of conversion to neurological disease.MethodsA retrospective dataset of Cleveland Clinic patients diagnosed with Alzheimer's disease, amyotrophic lateral sclerosis, multiple sclerosis, or Parkinson's disease, and matched controls based on age, sex, race, and ethnicity was compiled. Individualized risk prediction models were created using eXtreme Gradient Boosting for each neurological disease at four timepoints in patient history. The prediction models were assessed for transparency and fairness.ResultsAt timepoints 0-months, 12-months, 24-months, and 60-months prior to diagnosis, Alzheimer’s disease models achieved the area under the receiver operating characteristic curve on a holdout test dataset of 0.794, 0.742, 0.709, and 0.645; amyotrophic lateral sclerosis of 0.883, 0.710, 0.658, and 0.620; multiple sclerosis of 0.922, 0.877, 0.849, and 0.781; and Parkinson’s disease of 0.809, 0.738, 0.700, and 0.651, respectively.ConclusionsThe results demonstrate that electronic medical records contain latent information that can be used for risk stratification for neurological disorders. In particular, patient-reported outcomes, sleep assessments, falls data, additional disease diagnoses, and longitudinal changes in patient health, such as weight change, are important predictors.
Citation: DIGITAL HEALTH
PubDate: 2024-04-29T03:21:38Z
DOI: 10.1177/20552076241249286
Issue No: Vol. 10 (2024)
- Immersive virtual reality in the promotion of health and well-being for
people in residential aged care without cognitive impairment: A scoping
review
Authors: Helen Holloway, Brenda Conroy, Stephen Isbel, Nathan M D’Cunha
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveSustaining the health and well-being of older people living in residential aged care (RAC) requires new means of providing safe and stimulating recreational and therapeutic programs such as using virtual reality (VR). The aim of the scoping review was to investigate the utility of immersive VR interventions using head-mounted display technology to promote the health and well-being of people without cognitive impairment living in RAC.MethodThe following databases were searched from inception until January 2024: PubMed, PsycINFO, Scopus, Cochrane and CINAHL. The eligibility criteria were quantitative, qualitative or mixed methods studies published in English, conducted in RAC, using VR with head-mounted display with people without cognitive impairment.ResultsOf the 274 articles identified, 9 articles with a total of 310 residents and 50 staff met the inclusion criteria. Seven factors to either impede or enable the use of VR with head-mounted displays in RAC were: residents’ agency; the nature of the VR experience; the content of the experience; the ease of use and comfort of the technology; the role of RAC staff; and the role of residents’ family members.ConclusionImmersive VR has potential as a tool to promote the health and well-being of people without cognitive impairment living in RAC. Small sample sizes, variations in study design, and selection bias mean that generalisability of the results is limited. Further research is recommended to inform the design and implementation of immersive VR programs tailored specifically for this population.
Citation: DIGITAL HEALTH
PubDate: 2024-04-27T06:18:44Z
DOI: 10.1177/20552076241249568
Issue No: Vol. 10 (2024)
- Trend of mortality and length of stay in the emergency department
following implementation of a centralized sepsis alert system
Authors: Liza Grosman-Rimon, Leon Rivlin, Rosa Spataro, Zhiqiang Zhu, Jane Casey, Susan Tory, Jhanvi Solanki, Pete Wegier
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
IntroductionSepsis alerts based on laboratory and vital sign criteria were found insufficient to improve patient outcomes. While most early sepsis alerts were implemented into smaller scale operating systems, a centralized new approach may provide more benefits, overcoming alert fatigue, improving deployment of staff and resources, and optimizing the overall management of sepsis. The objective of the study was to assess mortality and length of stay (LOS) trends in emergency department (ED) patients, following the implementation of a centralized and automated sepsis alert system.MethodsThe automated sepsis alert system was implemented in 2021 as part of a hospital-wide command and control center. Administrative data from the years 2018 to 2021 were collected. Data included ED visits, in-hospital mortality, triage levels, LOS, and the Canadian Triage and Acuity Scale (CTAS).ResultsMortality rate for patients classified as CTAS I triage level was the lowest in 2021, after the implementation of the automated sepsis alert system, compared to 2020, 2019, and 2018 (p
Citation: DIGITAL HEALTH
PubDate: 2024-04-27T05:55:34Z
DOI: 10.1177/20552076241250255
Issue No: Vol. 10 (2024)
- Three cycles of mobile app design to improve HIV self-management: A
development and evaluation study
Authors: Gwang Suk Kim, Layoung Kim, Seoyoung Baek, Mi-So Shim, SangA Lee, Ji Min Kim, Jong Yae Yoon, Jin Kim, JunYong Choi, Jae-Phil Choi
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveEmploying three cycles of Design Science Research (DSR) to develop a mobile app ‘ESSC (Excellent Self Supervised HIV Care)’ to improve self-management among people living with HIV (PLWH).MethodsThis study is based on the DSR framework comprising three iterative cycles. In the Relevance cycle, PLWH participated in a survey of mobile health (mHealth) experiences and needs. In the Rigor cycle, the information-motivation-behavioural skills (IMB) model was applied to foundations of the app, and HIV specialists verified the contents. Experts evaluated the heuristic system and the app quality with the Mobile Application Rating Scale (MARS). In the Design cycle, ESSC was built on the findings of the other two cycles, and end-users tested the usability using uMARS.ResultsThe contents of the app were developed based on user requirements. The IMB model led ESSC to supplement motivational components for self-management, which built five functions: information contents; health life records including mental and sexual health; interactive counselling with healthcare providers; setting health goals after watching videos; and my page for self-reflection. To reduce social stigma and promote acceptance of the information-driven app, we created animated characters with neutral and bright features. The HIV specialists evaluated content validity as highly appropriate. The MARS score by the overall raters was between 3-acceptable and 4-good: functionality, 4.38; information, 4.12; aesthetics, 3.96; engagement, 3.37; and subjective quality, 3.25.ConclusionsThe DSR approach is effective for implementing usable and useful mHealth. The ESSC app would be feasible and contribute PLWH to retention in care.
Citation: DIGITAL HEALTH
PubDate: 2024-04-27T05:54:55Z
DOI: 10.1177/20552076241249294
Issue No: Vol. 10 (2024)
- Understanding the role and impact of electronic health records in labor
and delivery nursing practice: A scoping review protocol
Authors: Crystal A Bignell, Olga Petrovskaya
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundElectronic health records have a significant impact on nursing practice, particularly in specializations such as labor and delivery, or acute care maternity nursing practice. Although primary studies on the use of electronic health records in labor and delivery have been done, no reviews on this topic exist. Moreover, the topic of labor and delivery nurses’ organizing work in the electronic health record-enabled context has not been addressed.ObjectiveTo (a) synthesize research on electronic health record use in labor and delivery nursing and (b) map how labor and delivery nursing organizing work is transformed by the electronic health record (as described in the reviewed studies).MethodsThe scoping review will be guided by a modified methodology based on selected recommendations from the Joanna Briggs Institute and the Preferred Reporting Items for Systematic reviews and Meta-Analyses Extension for Scoping Reviews. A comprehensive search will be conducted in the following databases: CINAHL Complete, MEDLINE, Academic Search Complete, Web of Science, Scopus and Dissertations and Theses Abstracts and Indexes. Included sources will be primary research, dissertations, or theses that address the use of electronic health records in labor and delivery nursing practice in countries with high levels of electronic health record adoption. Data extracted from included sources will be analyzed thematically. Further analysis will theorize labor and delivery nurses’ organizing work in the context of electronic health record use by utilizing concepts from Davina Allen's Translational Mobilization Theory. Findings will be presented in tabular and descriptive formats.ConclusionThe findings of this review will help understand transformations of nursing practice in the electronic health record-enabled labor and delivery context and identify areas of future research. We will propose an extension of the Translational Mobilization Theory and theorize nurses’ organizing work involving the use of the electronic health record.
Citation: DIGITAL HEALTH
PubDate: 2024-04-25T07:38:57Z
DOI: 10.1177/20552076241249271
Issue No: Vol. 10 (2024)
- Exploring physiotherapists’ perceptions of telerehabilitation for
musculoskeletal disorders: Insights from focus groups
Authors: Lee Lee Sia, Shobha Sharma, Saravana Kumar, Devinder Kaur Ajit Singh
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThis study explored the perceived benefits, barriers, and recommendations of telerehabilitation for musculoskeletal disorders among physiotherapists in Malaysia.MethodsThis study employed an exploratory qualitative methodology to gather the perceptions of government-employed physiotherapists in Malaysia regarding the benefits, barriers, and recommendations for telerehabilitation in treating musculoskeletal disorders. The researchers conducted semistructured focus group discussions (FGDs) via Google Meet, which were recorded, transcribed, and analyzed using thematic analysis.ResultsFive FGDs were conducted with 24 participants, 37.5% of whom had prior experience with telerehabilitation. The data analysis returned three main themes: (1) perceived benefits, (2) barriers, and (3) recommendations. Four subthemes were derived from perceived benefits: (1a) saving time and money, (1b) convenience, (1c) clients responsible for their treatment, and (1d) alternatives for infectious diseases. Perceived barriers revealed three subthemes: (2a) technology, (2b) organization, and (2c) personal barriers. Finally, participants provided recommendations for improving telerehabilitation services, including training programs to facilitate greater acceptance of this modality.ConclusionThe findings of this study offer crucial insights into the evolving landscape of telerehabilitation in Malaysia. These findings revealed a greater prevalence of barriers to enablers among Malaysian physiotherapists, potentially influenced by varying experience levels. Despite the prevailing lack of experience among participants, this research underscores the significance of identifying barriers and enablers in implementing telerehabilitation with participants offering recommendations for integrating telerehabilitation into their practices. This study provides clear insights and a roadmap for stakeholders aiming to shape the future of telerehabilitation among physiotherapists in Malaysia.
Citation: DIGITAL HEALTH
PubDate: 2024-04-25T07:38:27Z
DOI: 10.1177/20552076241248916
Issue No: Vol. 10 (2024)
- Co-producing digital mental health interventions: A systematic review
Authors: Rebecca Brotherdale, Katherine Berry, Alison Branitsky, Sandra Bucci
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveSmartphone apps (apps) are widely recognised as promising tools for improving access to mental healthcare. However, a key challenge is the development of digital interventions that are acceptable to end users. Co-production with providers and stakeholders is increasingly positioned as the gold standard for improving uptake, engagement, and healthcare outcomes. Nevertheless, clear guidance around the process of co-production is lacking. The objectives of this review were to: (i) present an overview of the methods and approaches to co-production when designing, producing, and evaluating digital mental health interventions; and (ii) explore the barriers and facilitators affecting co-production in this context.MethodsA pre-registered (CRD42023414007) systematic review was completed in accordance with The Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines. Five databases were searched. A co-produced bespoke quality appraisal tool was developed with an expert by experience to assess the quality of the co-production methods and approaches. A narrative synthesis was conducted.ResultsTwenty-six studies across 24 digital mental health interventions met inclusion criteria. App interventions were rarely co-produced with end users throughout all stages of design, development, and evaluation. Co-producing digital mental health interventions added value by creating culturally sensitive and acceptable interventions. Reported challenges included resource issues exacerbated by the digital nature of the intervention, variability across stakeholder suggestions, and power imbalances between stakeholders and researchers.ConclusionsVariation in approaches to co-producing digital mental health interventions is evident, with inconsistencies between stakeholder groups involved, stage of involvement, stakeholders’ roles and methods employed.
Citation: DIGITAL HEALTH
PubDate: 2024-04-25T07:38:04Z
DOI: 10.1177/20552076241239172
Issue No: Vol. 10 (2024)
- Exploring patient perspectives on electronic patient-reported outcome
measures in home-based cancer palliative care: A qualitative study
Authors: Letteria Consolo, Ilaria Basile, Stella Colombo, Daniele Rusconi, Loredana Pasquot, Tiziana Campa, Augusto Caraceni, Maura Lusignani
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundElectronic patient-reported outcomes (ePROMs) enhance symptom management and patients’ engagement in palliative cancer care. However, integrating them into this setting brings challenges, including patients’ familiarity with technological devices and declining health status. Prioritizing the patient's acceptability and feasibility is crucial for their adoption. However, more knowledge is needed about patients’ perspectives on the adoption of ePROMs in the community, especially for home-based palliative care.AimExplore patient viewpoints on utilizing ePROMs for symptom reporting in home-based oncology palliative care.DesignA qualitative interpretative approach was used to evaluate patients’ points of view on using ePROMs in this specific care setting. Semistructured interviews were carried out. Data were analyzed using a reflexive thematic analysis.Setting/participantsA total of 25 patients receiving oncological home palliative care from the advanced palliative care unit of the Fondazione IRCCS Istituto Nazionale dei Tumori in Milan, Italy, were invited to participate. Twenty interviews were conducted, as five patients declined due to deteriorating health.ResultsFour themes were identified: (1) strategic value of ePROMs and subjective appreciation; (2) enhancing patient centeredness through ePROMs; (3) exploring and addressing concerns about the use of ePROMs and (4) intersecting factors influencing the efficacy of ePROMsConclusionDespite initial reticence, home palliative care patients consider ePROMs as potentially valuable allies monitoring symptoms, enhancing their quality of life, and amplifying their voices on less explored aspects of care. Continuous dialog between healthcare professionals and patients is crucial for addressing patient skepticism about ePROMs and their impact on the human aspect of care.
Citation: DIGITAL HEALTH
PubDate: 2024-04-25T06:52:07Z
DOI: 10.1177/20552076241249962
Issue No: Vol. 10 (2024)
- Feasibility and effectiveness study of applying a hallucinogen harm
reduction and integration model to a mindfulness thinking intervention
using virtual reality: A randomized controlled trial
Authors: Yanying Chen, Tianyang Wang, Yuxi Tan, Duo Li
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThe purpose of this study was to investigate whether a virtual reality (VR) program designed and developed based on the hallucinogenic harm reduction and integration (PHRI) clinical model could be more effective in guiding positive thinking training, improving positive thinking awareness and ability, and, to some extent, facilitating personal efficacy and emotional state compared to a traditional VR program that places users in a virtual natural ecological environment to guide positive thinking training. We also sought to understand the factors that may influence the effectiveness of VR interventions and user experience.MethodSeventy-six randomly recruited participants were divided into a control group and an experimental group of 38 participants, each according to a random number table, and were trained in VR meditation for eight weeks. The experimental group used a PHRI-based mindfulness program, while the control group used a traditional mindfulness meditation program. We used The Mindful Attention Awareness Scale and the PAD emotional three-dimensional scale to assess the level of state mindfulness and changes in the emotional state before and at the end of the experiment. The Immersive Tendencies Questionnaire measured the user's sense of presence and immersion in the virtual environment. The Five Facet Mindfulness Questionnaires and the Depression Anxiety and Stress Scale (DASS-21) were used at the baseline assessment stage before and at the 4-week follow-up after the experiment to assess the change in trait mindfulness levels due to the mindfulness training. The Five Facet Mindfulness Questionnaires and the DASS-21 were used to assess changes in mindfulness and mental health trait levels.ResultsAt the end of the experiment, the MMSQ score was significantly lower in the control group than in the experimental group, while the ITQ score was significantly higher than in the experimental group, and both scores were statistically significant (p
Citation: DIGITAL HEALTH
PubDate: 2024-04-25T06:51:29Z
DOI: 10.1177/20552076241249869
Issue No: Vol. 10 (2024)
- Can social media be used to increase fruit and vegetable consumption'
A pilot intervention study
Authors: Lily Hawkins, Claire Farrow, Meshach Clayton, Jason M Thomas
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundExposure to social norms about fruit and vegetable intake has been shown to increase individuals’ consumption of these foods. Further, exposure to socially endorsed ‘healthy’ food posts can increase consumption of low energy-dense (LED), relative to high energy-dense (HED) foods. The current pilot study aimed to investigate whether exposure to healthy eating (vs. control) social media accounts can shift normative perceptions about what others eat, eating intentions and self-reported food consumption.MethodsIn a 2 (condition) × 2 (type of food consumed) mixed factorial design, 52 male and female students were asked to follow either healthy eating (intervention) or interior design (control) Instagram accounts over a two-week period. Baseline and post-intervention measures assessed normative perceptions of Instagram users’ consumption of fruit and vegetables (LED foods), and energy dense snacks and sugar sweetened beverages (HED foods). Participants’ intentions to consume, and self-reported consumption of these foods, were also measured.ResultsThere were no significant changes in perceptions about what others eat, or participants’ own eating intentions (ps > 0.05). However, the intervention increased participants’ self-reported consumption of LED foods by 1.37 servings (per day) and decreased consumption of HED foods by 0.81 items (per day), compared to the control condition (ps
Citation: DIGITAL HEALTH
PubDate: 2024-04-24T09:00:19Z
DOI: 10.1177/20552076241241262
Issue No: Vol. 10 (2024)
- A pilot randomized control trial on the feasibility, acceptability, and
initial effects of a digital-assisted parenting intervention for promoting
mental health in Malaysian adolescents
Authors: Nor Sheereen Zulkefly, Anis Raihan Dzeidee Schaff, Nur Arfah Zaini, Firdaus Mukhtar, Rahima Dahlan
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveOnline parenting interventions for enhancing child development, specifically mental health is relatively new in Malaysia. This pilot study tests the feasibility, acceptability, and initial effects of a universal digital-assisted parenting intervention (DaPI) in promoting mental health in adolescents by improving parental behaviors and self-efficacy.MethodsA two-arm pilot randomized controlled trial was conducted. Twenty-four mothers of adolescent aged 10 to 14 years from a non-clinical sample were recruited online and randomly allocated into two groups (intervention [DaPI] and waitlist-control [WLC]). Eight weekly sessions were delivered online via technological devices. Feasibility outcomes were based on the participants’ engagement in DaPI and study retention. Primary (parental behaviors and self-efficacy) and secondary (adolescent mental health) outcomes were assessed using an online survey at baseline (T0), post-intervention (T1), and 1-month follow-up (T2). Data were analyzed using descriptive and inferential statistics and an intention-to-treat approach.ResultsThe DaPI was well received by the mothers. Retention was high (81.8%) in both groups and intervention adherence was excellent (91.6%). Within-group analyses showed a significant decrease in physical control at T2 and an increase in parental self-efficacy at T1 and T2 among the DaPI mothers. No significant differences were observed in adolescents’ mental health at any time point. As for the WLC group, there were no significant differences in all the outcome variables across the three assessment moments. Between groups analyses revealed DaPI mothers had significant differences in proactive parenting at T1, and in positive reinforcement and lax control at T2. There were no significant differences in adolescents’ mental health between the groups at any time point.DiscussionThe DaPI is feasible and acceptable in the Malaysian context. Findings show promise regarding the initial effects of the DaPI. However, a larger RCT is needed to determine its effectiveness in promoting mental health of adolescents.Trial registrationhttps://www.irct.ir/; identifier: IRCT20211129053207N1.
Citation: DIGITAL HEALTH
PubDate: 2024-04-24T03:43:32Z
DOI: 10.1177/20552076241249572
Issue No: Vol. 10 (2024)
- How institutional logics shape the adoption of virtual reality in mental
health care: A qualitative study
Authors: Morten D Terkildsen, Stina Bollerup, Camilla Palmhøj, Lotte G Jensen, Stina Lou
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveTo analyse institutional logics’ role in adopting virtual reality in mental health care.MethodsData were collected via qualitative, semi-structured interviews with four frontline staff and seven administrative and service staff, two focus group interviews with three frontline staff and four administrative and service staff, and via participant observation in meetings between stakeholders working on virtual reality. Data were collected from May 2021 to February 2022, analysed using thematic analysis, and theoretically driven by the framework of Institutional logics.ResultsWe identified two different forms of institutional logics being drawn upon by frontline staff and administrative and service staff, respectively, when working with the adoption of virtual reality in mental health care. Frontline staff drew mainly on a Professional logic; administrative and service staff drew on a Diffusion logic. Each logic defined a unique focal point, causal pathway, and perceptions of a meaningful adoption process for virtual reality.ConclusionsBy taking institutional logics as our theoretical and analytical point of departure, this study demonstrates how the meaning of virtual reality and its adoption in mental health care is grounded in multiple and sometimes conflicting institutional logics. Acknowledging the existence and influence of often multiple institutional logics in the adoption process is crucial to guide the future adoption of virtual reality in mental health care. Organising collaborative venues for stakeholders where their multiple institutional logics are made the subject of joint reflection is essential to counter frictions.
Citation: DIGITAL HEALTH
PubDate: 2024-04-24T03:27:21Z
DOI: 10.1177/20552076241248914
Issue No: Vol. 10 (2024)
- COVID-19 surveillance based on consumer wearable devices
Authors: Chunbo Zhang, Aijun Sun, Jiping Liao, Chunbo Zhang, Kunyao Yu, Xiaoyu Ma, Guangfa Wang
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundConsumer wearable devices such as wristbands and smartwatches have potential application value in communicable disease surveillance.ObjectiveWe investigated the ability of wearable devices to monitor COVID-19 patients of varying severity.MethodsCOVID-19 patients with mobile phones supporting wearable device applications were selected from Dalian Sixth People Hospital. Physiological parameters from the wearable devices and electronic questionnaires were collected from the device wearing until 14 days post-discharge. Clinical information during hospitalization was also recorded. Based on imaging data, the patients were categorized into the milder group without pneumonia and the more severe group with pneumonia. We plotted the curves of the physiological parameters of the two groups to compare the differences and changes.ResultsNinety-eight patients were included in the analysis. The mean age was 39.6 ± 10.5 years, including 45 males (45.9%). There were 24 asymptomatic patients, 10 mild patients, 60 moderate patients, and 4 severe patients. Compared with the milder group, the more severe group had higher heart rate-related parameters, while the heart rate variability (HRV) was the opposite. In the more severe group, the heart rate-related parameters showed a downward trend from 0 to 7 days after the fever resolution. Among them, the resting heart rate and sleep heart rate decreased on the 25th day after the onset and were close to the milder group 1 week after discharge.ConclusionsConsumer wearable devices have the potential to monitor respiratory infections. Heart rate-related parameters obtained from these devices can be sensitive indicators of COVID-19 severity and correlate with disease evolution.Trial registrationClinicalTrials.gov NCT04459637.
Citation: DIGITAL HEALTH
PubDate: 2024-04-24T03:26:51Z
DOI: 10.1177/20552076241247374
Issue No: Vol. 10 (2024)
- Critical success factors for creating sustainable digital health
applications: A systematic review of the German case
Authors: Lukas Schramm, Claus-Christian Carbon
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThe Covid-19 pandemic has accelerated the adoption of digital technologies to address social needs, leading to increased investments in digital healthcare applications. Germany implemented a special law called the “Digitales Versorgungsgesetz” (DVG—Digital Supply Act) in 2019, which enables the reimbursement of digital health applications, including digital therapeutics (DTx), through a fast-track process. The Federal Institute for Drugs and Medical Devices (BfArM), the German federal authority responsible for overseeing digital health applications, has implemented legislative adjustments since the law's introduction, which have increased requirements for these applications and potentially led to the removal of some from the directory as well as a slowdown in the addition of new ones. To counteract this trend, this work aimed to identify key success factors for digital health applications (DiGAs).MethodsThis research identifies critical success factors through a structured literature review for developing sustainable digital health applications within the European healthcare systems, specifically DiGAs. The study aims to support the ongoing digital transformation in healthcare.ResultsThe identified success factors that significantly impact the sustainability of DiGAs include patient-centered design, application effectiveness, user-friendliness, and adherence to data protection and information security regulations using standardized approaches. These factors are crucial in preventing the failure of DiGA manufacturers in European countries.ConclusionBy considering and implementing these critical success factors, DiGA manufacturers can enhance their chances of long-term success and contribute to the digital transformation of the healthcare system in Europe.
Citation: DIGITAL HEALTH
PubDate: 2024-04-24T01:10:13Z
DOI: 10.1177/20552076241249604
Issue No: Vol. 10 (2024)
- Factors influencing patient engagement in mental health chatbots: A
thematic analysis of findings from a systematic review of reviews
Authors: Mohsen Khosravi, Ghazaleh Azar
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
IntroductionMental health disorders affect millions of people worldwide. Chatbots are a new technology that can help users with mental health issues by providing innovative features. This article aimed to conduct a systematic review of reviews on chatbots in mental health services and synthesized the evidence on the factors influencing patient engagement with chatbots.MethodsThis study reviewed the literature from 2000 to 2024 using qualitative analysis. The authors conducted a systematic search of several databases, such as PubMed, Scopus, ProQuest, and Cochrane database of systematic reviews, to identify relevant studies on the topic. The quality of the selected studies was assessed using the Critical Appraisal Skills Programme appraisal checklist and the data obtained from the systematic review were subjected to a thematic analysis utilizing the Boyatzis's code development approach.ResultsThe database search resulted in 1494 papers, of which 10 were included in the study after the screening process. The quality assessment of the included studies scored the papers within a moderate level. The thematic analysis revealed four main themes: chatbot design, chatbot outcomes, user perceptions, and user characteristics.ConclusionThe research proposed some ways to use color and music in chatbot design. It also provided a systematic and multidimensional analysis of the factors, offered some insights for chatbot developers and researchers, and highlighted the potential of chatbots to improve patient-centered and person-centered care in mental health services.
Citation: DIGITAL HEALTH
PubDate: 2024-04-22T08:30:21Z
DOI: 10.1177/20552076241247983
Issue No: Vol. 10 (2024)
- Virtual reality interventions designed to support parents during and
throughout the first year after birth: A scoping review
Authors: Victoria Fallon, Sian M Davies, Sergio Silverio, Lisa Creagh
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveVirtual reality (VR) has become increasingly popular in clinical and health settings where it has been used for a wide range of purposes. A recent scoping review explored VR applications to assist pregnant women and found that VR was a useful method to be used for a range of different purposes in both pregnancy and labour. However, no such review exists for the period after birth.MethodWe aimed to search for studies that used VR to support parents during birth and in the first year postpartum (Population) in different settings (Context), and finally provided data on the characteristics, reported effectiveness and experience of VR interventions (Concept). Two hundred and fifty-one studies were identified, of which ten were eligible. Two authors independently extracted data including study design, participants and results.ResultsFindings indicate that VR has been used effectively in this context to alleviate depression anxiety, and multiple domains of pain and to improve childbirth satisfaction. The majority of the studies explored the use of VR technology on outcomes such as pain and anxiety during labour and birth. The studies included used a broad range of VR hardware and software. All of the studies reported positive experiences of using VR.ConclusionsAcross these studies, VR was found to be effective in terms of both physiological and psychological outcomes. There are many unexplored maternal and infant focused applications of VR which warrant further investigation as emerging evidence indicates this is becoming an increasingly accessible method to improve maternal and infant health outcomes from pregnancy through to parenthood.
Citation: DIGITAL HEALTH
PubDate: 2024-04-22T07:41:44Z
DOI: 10.1177/20552076241245373
Issue No: Vol. 10 (2024)
- Evaluating ChatGPT's efficacy in assessing the safety of non-prescription
medications and supplements in patients with kidney disease
Authors: Mohammad S. Sheikh, Erin F. Barreto, Jing Miao, Charat Thongprayoon, James R Gregoire, Benjamin Dreesman, Stephen B. Erickson, Iasmina M. Craici, Wisit Cheungpasitporn
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundThis study investigated the efficacy of ChatGPT-3.5 and ChatGPT-4 in assessing drug safety for patients with kidney diseases, comparing their performance to Micromedex, a well-established drug information source. Despite the perception of non-prescription medications and supplements as safe, risks exist, especially for those with kidney issues. The study's goal was to evaluate ChatGPT's versions for their potential in clinical decision-making regarding kidney disease patients.MethodThe research involved analyzing 124 common non-prescription medications and supplements using ChatGPT-3.5 and ChatGPT-4 with queries about their safety for people with kidney disease. The AI responses were categorized as “generally safe,” “potentially harmful,” or “unknown toxicity.” Simultaneously, these medications and supplements were assessed in Micromedex using similar categories, allowing for a comparison of the concordance between the two resources.ResultsMicromedex identified 85 (68.5%) medications as generally safe, 35 (28.2%) as potentially harmful, and 4 (3.2%) of unknown toxicity. ChatGPT-3.5 identified 89 (71.8%) as generally safe, 11 (8.9%) as potentially harmful, and 24 (19.3%) of unknown toxicity. GPT-4 identified 82 (66.1%) as generally safe, 29 (23.4%) as potentially harmful, and 13 (10.5%) of unknown toxicity. The overall agreement between Micromedex and ChatGPT-3.5 was 64.5% and ChatGPT-4 demonstrated a higher agreement at 81.4%. Notably, ChatGPT-3.5's suboptimal performance was primarily influenced by a lower concordance rate among supplements, standing at 60.3%. This discrepancy could be attributed to the limited data on supplements within ChatGPT-3.5, with supplements constituting 80% of medications identified as unknown.ConclusionChatGPT's capabilities in evaluating the safety of non-prescription drugs and supplements for kidney disease patients are modest compared to established drug information resources. Neither ChatGPT-3.5 nor ChatGPT-4 can be currently recommended as reliable drug information sources for this demographic. The results highlight the need for further improvements in the model's accuracy and reliability in the medical domain.
Citation: DIGITAL HEALTH
PubDate: 2024-04-17T10:23:34Z
DOI: 10.1177/20552076241248082
Issue No: Vol. 10 (2024)
- The acceptability, usability, engagement and optimisation of a mHealth
service promoting healthy lifestyle behaviours: A mixed method feasibility
study
Authors: Callum Regan, Phillip Von Rosen, Susanne Andermo, Maria Hagströmer, Unn-Britt Johansson, Jenny Rossen
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveMobile health (mHealth) services suffer from high attrition rates yet represent a viable strategy for adults to improve their health. There is a need to develop evidence-based mHealth services and to constantly evaluate their feasibility. This study explored the acceptability, usability, engagement and optimisation of a co-developed mHealth service, aiming to promote healthy lifestyle behaviours.MethodsThe service LongLife Active® (LLA) is a mobile app with coaching. Adults were recruited from the general population. Quantitative results and qualitative findings guided the reasoning for the acceptability, usability, engagement and optimisation of LLA. Data from: questionnaires, log data, eight semi-structured interviews with users, feedback comments from users and two focus groups with product developers and coaches were collected. Inductive content analysis was used to analyse the qualitative data. A mixed method approach was used to interpret the findings.ResultsThe final sample was 55 users (82% female), who signed up to use the service for 12 weeks. Engagement data was available for 43 (78%). The action plan was the most popular function engaged with by users. The mean scores for acceptability and usability were 3.3/5.0 and 50/100, respectively, rated by 15 users. Users expressed that the service’s health focus was unique, and the service gave them a ‘kickstart’ in their behaviour change. Many ways to optimise the service were identified, including to increase personalisation, promote motivation and improve usability.ConclusionBy incorporating suggestions for optimisation, this service has the potential to support peoples’ healthy lifestyle behaviours.
Citation: DIGITAL HEALTH
PubDate: 2024-04-17T10:22:49Z
DOI: 10.1177/20552076241247935
Issue No: Vol. 10 (2024)
- A pilot randomised controlled trial exploring the feasibility and efficacy
of a human-AI sleep coaching model for improving sleep among university
students
Authors: Jintana Liu, Sakura Ito, Tra My Ngo, Ashwini Lawate, Qi Chwen Ong, Tatiana Erlikh Fox, Si Yuan Chang, Duy Phung, Elizabeth Nair, Malar Palaiyan, Shafiq Joty, John Abisheganaden, Chuen Peng Lee, May Oo Lwin, Yin Leng Theng, Moon-Ho Ringo Ho, Michael Chia, Iva Bojic, Josip Car
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveSleep quality is a crucial concern, particularly among youth. The integration of health coaching with question-answering (QA) systems presents the potential to foster behavioural changes and enhance health outcomes. This study proposes a novel human-AI sleep coaching model, combining health coaching by peers and a QA system, and assesses its feasibility and efficacy in improving university students’ sleep quality.MethodsIn a four-week unblinded pilot randomised controlled trial, 59 university students (mean age: 21.9; 64% males) were randomly assigned to the intervention (health coaching and QA system; n = 30) or the control conditions (QA system; n = 29). Outcomes included efficacy of the intervention on sleep quality (Pittsburgh Sleep Quality Index; PSQI), objective and self-reported sleep measures (obtained from Fitbit and sleep diaries) and feasibility of the study procedures and the intervention.ResultsAnalysis revealed no significant differences in sleep quality (PSQI) between intervention and control groups (adjusted mean difference = −0.51, 95% CI: [−1.55–0.77], p = 0.40). The intervention group demonstrated significant improvements in Fitbit measures of total sleep time (adjusted mean difference = 32.5, 95% CI: [5.9–59.1], p = 0.02) and time in bed (adjusted mean difference = 32.3, 95% CI: [2.7–61.9], p = 0.03) compared to the control group, although other sleep measures were insignificant. Adherence was high, with the majority of the intervention group attending all health coaching sessions. Most participants completed baseline and post-intervention self-report measures, all diary entries, and consistently wore Fitbits during sleep.ConclusionsThe proposed model showed improvements in specific sleep measures for university students and the feasibility of the study procedures and intervention. Future research may extend the intervention period to see substantive sleep quality improvements.
Citation: DIGITAL HEALTH
PubDate: 2024-04-17T10:22:09Z
DOI: 10.1177/20552076241241244
Issue No: Vol. 10 (2024)
- YouTubeTM as a source of information on autosomal dominant polycystic
kidney disease: A quality analysis
Authors: Tamer Selen, Ozgur Merhametsiz
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
IntroductionAutosomal dominant polycystic kidney disease (ADPKD) is the most common inherited kidney disease in adults. As a social media platform, YouTube has tremendous potential to both support and hinder public health efforts. The aim of this study was to assess the reliability and quality of the most viewed English-language YouTube videos on ADPKD.MethodsA YouTube search was conducted on 3 August 2023, using the keyword ADPKD disease and the top 200 videos were analyzed for relevance. Videos in the “Short” category that were duplicates, were not in English, were not audio or visual, and contained advertisements were excluded. Two reviewers divided the 159 included videos into groups based on their source and content.ResultsIn 106 (66.7%) of the 159 videos, general information about the disease was given, 58 (36.5%) discussed medical treatment, 11 (6.9%) discussed surgical treatment, 30 (18.9%) included patient images and radiological images, and eight (5%) discussed the genetic and pathological features of the disease. Additionally, 16 (10.1%) videos fell into the “other” category. According to the Journal of the American Medical Association, Quality Criteria for Consumer Health Information and Global Quality Scale scoring systems, videos uploaded by health associations and foundations received the highest scores (3 (1–4), 54 (28–70), 4 (1–5), respectively).ConclusionAcademic institutions and other official health organizations such as Health Associations/Foundations need to use YouTube more effectively to disseminate accurate, reliable and useful health-related information to society.
Citation: DIGITAL HEALTH
PubDate: 2024-04-17T07:51:31Z
DOI: 10.1177/20552076241248109
Issue No: Vol. 10 (2024)
- Application of machine learning in the management of lymphoma: Current
practice and future prospects
Authors: Junyun Yuan, Ya Zhang, Xin Wang
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
In the past decade, digitization of medical records and multiomics data analysis in lymphoma has led to the accessibility of high-dimensional records. The digitization of medical records, the visualization of extensive volume data extracted from medical images, and the integration of multiomics methods into clinical decision-making have produced many datasets. As a promising auxiliary tool, machine learning (ML) intends to extract homologous features in large-scale data sets and encode them into various patterns to complete complicated tasks. At present, artificial intelligence and digital mining have shown promising prospects in the field of lymphoma pathological image analysis. The paradigm shift from qualitative analysis to quantitative analysis makes the pathological diagnosis more intelligent and the results more accurate and objective. ML can promote accurate lymphoma diagnosis and provide patients with prognostic information and more individualized treatment options. Based on the above, this comprehensive review of the general workflow of ML highlights recent advances in ML techniques in the diagnosis, treatment, and prognosis of lymphoma, and clarifies the boundedness and future orientation of the ML technique in the clinical practice of lymphoma.
Citation: DIGITAL HEALTH
PubDate: 2024-04-16T08:17:11Z
DOI: 10.1177/20552076241247963
Issue No: Vol. 10 (2024)
- Tweaking algorithms. Technopolitical issues associated with artificial
intelligence based tuberculosis detection in global health
Authors: Pierre-Marie David, Julien Onno, Jessica Pourraz, Faiz Ahmad Khan
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
Computer-aided detection algorithms based on artificial intelligence are increasingly being tested and used as a means for detecting tuberculosis in countries where the epidemic is still present. Computer-aided detection tools are often presented as a global solution that can be deployed in all the geographical areas concerned by tuberculosis, but at the same time, they need to be adjusted and calibrated according to local populations’ characteristics. The aim of this article is to analyze the tensions between the standardization of computer-aided detection algorithms and their local adaptation and the political issues associated with these tensions. We undertook a qualitative analysis of practices associated with tuberculosis detection algorithms in different contexts, contrasting the perspectives of various stakeholders. Algorithms embed the promise of standardization through automation and the bypassing of variable human expertise such as that of radiologists, they are nonetheless objects of local practices that we have characterized as “tweaking.” This work of tweaking reveals how the technology is situated but also the many concerns of the users and workers (insertion in care, control over infrastructure, and political ownership). This should be better considered to truly make computer-aided detection innovative tools for tuberculosis management in global health.
Citation: DIGITAL HEALTH
PubDate: 2024-04-16T08:16:43Z
DOI: 10.1177/20552076241239778
Issue No: Vol. 10 (2024)
- Institutional health communication and social media: Exploring Italian
hospitals’ use of social media pages
Authors: Carlotta Fiammenghi, Loredana Covolo, Anna Vanoncini, Umberto Gelatti, Elisabetta Ceretti
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
IntroductionHospitals play a potentially crucial role in public health, and social media can be powerful tools to reach their target audiences but are hospitals exploiting them to their full potential'MethodsWe retrieved the institutional webpages and the social media profiles (Facebook, Instagram, X (Twitter), YouTube, LinkedIn, WhatsApp and Telegram) of all Italian public hospitals located in regional capitals (N = 194). From 1 March to 30 April 2022, we analysed these profiles, noting the number of followers and of posts published, the date of the last post, and the availability of a social media policy. We selected the most active 53 social media profiles (belonging to 33 hospital facilities) for a closer content analysis. Engagement was measured through numbers of reactions, comments and shares to posts published from 1 to 30 April 2022.ResultsAbout 36.6% of hospitals had a social media profile, and 18.3% had a social media policy. Most (87%) used Facebook as their main platform. They posted most frequently about hospital events and activities (48.3% of the socially active set). Overall, engagement was modest, as on average 0.62% of potential users reacted to a post. The same post often appeared without modifications across different platforms (82.3% of cases for Instagram, 37.8% for X (Twitter) compared to Facebook).ConclusionsItalian public hospitals did not seem to have a clear social media policy nor strategy, and social media remained underused. Italian hospitals, therefore, appeared to be missing valuable opportunities to reach out to their patients and communities.
Citation: DIGITAL HEALTH
PubDate: 2024-04-16T04:22:42Z
DOI: 10.1177/20552076241245921
Issue No: Vol. 10 (2024)
- A locally optimised machine learning approach to early prognostication of
long-term neurological outcomes after out-of-hospital cardiac arrest
Authors: Vincent Pey, Emmanuel Doumard, Matthieu Komorowski, Antoine Rouget, Clément Delmas, Fanny Vardon-Bounes, Michaël Poette, Valentin Ratineau, Cédric Dray, Isabelle Ader, Vincent Minville
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundOut-of-hospital cardiac arrest (OHCA) represents a major burden for society and health care, with an average incidence in adults of 67 to 170 cases per 100,000 person-years in Europe and in-hospital survival rates of less than 10%. Patients and practitioners would benefit from a prognostication tool for long-term good neurological outcomes.ObjectiveWe aim to develop a machine learning (ML) pipeline on a local database to classify patients according to their neurological outcomes and identify prognostic features.MethodsWe collected clinical and biological data consecutively from 595 patients who presented OHCA and were routed to a single regional cardiac arrest centre in the south of France. We applied recursive feature elimination and ML analyses to identify the main features associated with a good neurological outcome, defined as a Cerebral Performance Category score less than or equal to 2 at six months post-OHCA.ResultsWe identified 12 variables 24 h after admission, capable of predicting a six-month good neurological outcome. The best model (extreme gradient boosting) achieved an AUC of 0.96 and an accuracy of 0.92 in the test cohort.ConclusionWe demonstrated that it is possible to build accurate, locally optimised prediction and prognostication scores using datasets of limited size and breadth. We proposed and shared a generic machine-learning pipeline which allows external teams to replicate the approach locally.
Citation: DIGITAL HEALTH
PubDate: 2024-04-15T04:25:39Z
DOI: 10.1177/20552076241234746
Issue No: Vol. 10 (2024)
- The most used questionnaires for evaluating the usability of robots and
smart wearables: A scoping review
Authors: Khadijeh Moulaei, Reza Moulaei, Kambiz Bahaadinbeigy
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundAs the field of robotics and smart wearables continues to advance rapidly, the evaluation of their usability becomes paramount. Researchers may encounter difficulty in finding a suitable questionnaire for evaluating the usability of robotics and smart wearables. Therefore, the aim of this study is to identify the most commonly utilized questionnaires for assessing the usability of robots and smart wearables.MethodsA comprehensive search of databases, including PubMed, Web of Science, and Scopus, was conducted for this scoping review. Two authors performed the selection of articles and data extraction using a 10-field data extraction form. In cases of disagreements, a third author was consulted to reach a consensus. The inclusions were English-language original research articles that utilized validated questionnaires to assess the usability of healthcare robots and smart wearables. The exclusions comprised review articles, non-English publications, studies not focused on usability, those assessing clinical outcomes, articles lacking questionnaire details, and those using non-validated or researcher-made questionnaires. Descriptive statistics methods (frequency and percentage), were employed to analyze the data.ResultsA total of 314 articles were obtained, and after eliminating irrelevant and duplicate articles, a final selection of 50 articles was included in this review. A total of 17 questionnaires were identified to evaluate the usability of robots and smart wearables, with 10 questionnaires specifically for wearables and 7 questionnaires for robots. The System Usability Scale (50%) and Post-Study System Usability Questionnaire (19.44%) were the predominant questionnaires utilized to assess the usability of smart wearables. Moreover, the most commonly used questionnaires for evaluating the usability of robots were the System Usability Scale (56.66%), User Experience Questionnaire (16.66%), and Quebec User Evaluation of Satisfaction with Assistive Technology (10%).ConclusionCommonly employed questionnaires serve as valuable tools in assessing the usability of robots and smart wearables, aiding in the refinement and optimization of these technologies for enhanced user experiences. By incorporating user feedback and insights, designers can strive towards creating more intuitive and effective robotic and wearable solutions.
Citation: DIGITAL HEALTH
PubDate: 2024-04-10T05:44:04Z
DOI: 10.1177/20552076241237384
Issue No: Vol. 10 (2024)
- “Simply complicated”: Uncovering the processes of lifestyle behavior
change among college and university students with access to a digital
multiple lifestyle intervention
Authors: Katarina Åsberg, Ann Catrine Eldh, Marie Löf, Marcus Bendtsen
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundOne approach to promoting healthy lifestyle behaviors is to target students with digital interventions. One of these is the digital intervention Buddy. This study aimed to understand why college and university students’ chose to participate in a digital multiple lifestyle behavior intervention trial (Buddy), and their subsequent experiences of the behavior-change process.MethodsCollege and university students taking part in a trial of the Buddy intervention were individually interviewed after completing the 4-month intervention. Participants were guided to narrate their experiences and actions that followed signing up. Altogether, 50 interviews were conducted via telephone. The verbatim transcribed texts were analyzed qualitatively.ResultsThe analysis generated seven personas, which illustrated the students’ different levels of engagement with the intervention and the behavior-change process. These were: the Occupied, the Kickstarter, the Aimless, the Reflective, the Goal-oriented, the Compliant, and the Personally developed. Buddy worked best for students who had clear ideas about what they wanted to change and why, and who were aware of their needs, and those who could translate information and reflection into action and had the mental and physical energy needed to make changes.ConclusionsThe progress of behavior change depends on the interaction between the digital mode of delivery, the intervention materials of Buddy, the individual's expectations, needs, and skills, and their current life situation. This suggests that designing lifestyle interventions could benefit from more often considering the various personas’ different intentions, knowledge, and contexts. By doing so, interventions are likely to emerge that can better match different needs in the target population.
Citation: DIGITAL HEALTH
PubDate: 2024-04-09T02:49:50Z
DOI: 10.1177/20552076241245905
Issue No: Vol. 10 (2024)
- Exploring the use, usefulness and ease of use of digital occupational
health services: A descriptive correlational study of customer experiences
Authors: Sari Nissinen, Sanna Pesonen, Pauliina Toivio, Erja Sormunen
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
ObjectiveThis study examined the customer experiences of use, perceived usefulness and ease of use of digital occupational health (OH) services.MethodsA cross-sectional study based on an electronic survey was conducted between December 2022 and January 2023. A total of 9871 OH customers responded to the survey. The sample was restricted to respondents who used digital OH services (n = 7275). An analysis of variance was run to test the relationships between respondents’ characteristics and the rate of usefulness, and ease of use variables.ResultsThe most commonly used digital services were appointment booking, access to health information recorded by professionals and prescription renewal, and the digital services provided by physicians and nurses. Respondents expressed quite high satisfaction with the digital services, but not as much with their usefulness and ease of use. Females, individuals under 50 years of age, those with higher education, working in white-collar or managerial positions and possessing proficient information and communication technology (ICT) skills gave the most positive evaluations regarding usefulness and ease of use.ConclusionsThere was a certain level of mixed experiences among respondents regarding the usefulness and ease of use of digital OH services. We can also conclude that individuals who possess the necessary ICT skills can more easily take full advantage of the available digital services. When customers are proficient in using digital services, they can confidently interact with professionals. Regardless of the user's age, gender, education or profession, it is crucial for service providers always to strive to improve the usability of digital services.
Citation: DIGITAL HEALTH
PubDate: 2024-04-09T02:49:08Z
DOI: 10.1177/20552076241242668
Issue No: Vol. 10 (2024)
- Comparative effectiveness of interventions on promoting physical activity
in older adults: A systematic review and network meta-analysis
Authors: Shuang Wu, Guangkai Li, Beibei Shi, Hongli Ge, Si Chen, Xianliang Zhang, Qiang He
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
BackgroundDespite the well-established health benefits of physical activity, a large population of older adults still maintain sedentary life style or physical inactivity. This network meta-analysis (NMA) aimed to compare the effectiveness of wearable activity tracker-based intervention (WAT), electronic and mobile health intervention (E&MH), structured exercise program intervention (SEP), financial incentive intervention (FI) on promoting physical activity and reducing sedentary time in older adults.MethodsThe systematic review based on PRISMA guidelines, a systematic literature search of PubMed, Web of Science, Google Scholar, EMbase, Cochrane Library, Scopus were searched from inception to December 10th 2022. The randomized controlled trials (RCT) were included. Two reviewers independently conducted study selection, data extraction, risk of bias and certainty of evidence assessment. The effect measures were standard mean differences (SMD) and 95% confidence interval (CI) in daily steps, moderate-to-vigorous physical activity (MVPA) and sedentary time.ResultsA total of 69 studies with 14,120 participants were included in the NMA. Among these included studies, the results of daily steps, MVPA and sedentary time was reported by 55, 25 and 15 studies, respectively. The NMA consistency model analysis suggested that the following interventions had the highest probability (surface under the cumulative ranking, SUCRA) of being the best when compared with control: FI + WAT for daily steps (SUCRA = 96.6%; SMD = 1.32, 95% CI:0.77, 1.86), WAT + E&MH + SEP for MVPA (SUCRA = 91.2%; SMD = 0.94, 95% CI: 0.36, 1.52) and WAT + E&MH + SEP for sedentary time (SUCRA = 80.3%; SMD = −0.50, 95% CI: −0.87, −0.14). The quality of the evidences of daily steps, MVPA and sedentary time was evaluated by very low, very low and low, respectively.ConclusionsIn this NMA, there's low quality evidence that financial incentive combined with wearable activity tracker is the most effective intervention for increasing daily steps of older adults, wearable activity tracker combined with electronic and mobile health and structured exercise program is the most effective intervention to help older adults to increase MVPA and reduce sedentary time.
Citation: DIGITAL HEALTH
PubDate: 2024-04-09T02:48:28Z
DOI: 10.1177/20552076241239182
Issue No: Vol. 10 (2024)
- Feasibility and usability of remote monitoring in Alzheimer's disease
Authors: Marijn Muurling, Casper de Boer, Chris Hinds, Alankar Atreya, Aiden Doherty, Vasilis Alepopoulos, Jelena Curcic, Anna-Katharine Brem, Pauline Conde, Sajini Kuruppu, Xavier Morató, Valentina Saletti, Samantha Galluzzi, Estefania Vilarino Luis, Sandra Cardoso, Tina Stukelj, Milica Gregorič Kramberger, Dora Roik, Ivan Koychev, Ann-Cecilie Hopøy, Emilia Schwertner, Mara Gkioka, Dag Aarsland, Pieter Jelle Visser
Abstract: DIGITAL HEALTH, Volume 10, Issue , January-December 2024.
IntroductionRemote monitoring technologies (RMTs) can measure cognitive and functional decline objectively at-home, and offer opportunities to measure passively and continuously, possibly improving sensitivity and reducing participant burden in clinical trials. However, there is skepticism that age and cognitive or functional impairment may render participants unable or unwilling to comply with complex RMT protocols. We therefore assessed the feasibility and usability of a complex RMT protocol in all syndromic stages of Alzheimer's disease and in healthy control participants.MethodsFor 8 weeks, participants (N = 229) used two activity trackers, two interactive apps with either daily or weekly cognitive tasks, and optionally a wearable camera. A subset of participants participated in a 4-week sub-study (N = 45) using fixed at-home sensors, a wearable EEG sleep headband and a driving performance device. Feasibility was assessed by evaluating compliance and drop-out rates. Usability was a