JournalTOCs

Digital Health
Number of Followers: 10

This is an Open Access Journal

Open Access journal
ISSN (Print) 2055-2076 - ISSN (Online) 2055-2076
Published by Sage Publications

[1176 journals]

Linking online health information seeking to cancer information overload
among Chinese cancer patients’ family members
- Authors: Yifang Wu; Luxi Zhang, Xinshu Zhao
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundWhile previous studies indicated that seeking online health information could reduce individuals’ cancer information overload, the results are inconsistent and have remained unknown in China. This study focuses on cancer patients’ family members ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-17T07:25:15Z
  DOI: 10.1177/20552076251336308
  Issue No: Vol. 11 (2025)
Outcome measurement instruments in neurodegenerative diseases:
Comprehensive analysis and platform construction
- Authors: Hui Zong; Jiaxue Cha, Liang Tao, Muyun Shi, Yan Zhao, Ru Zhang, Bairong Shen
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectivesThe selection of appropriate outcome measurement instruments (OMIs) in neurodegenerative disease (NDD) researches remains complex and often inconsistent. This study aims to consolidate knowledge on OMIs applied in NDD over the last two decades ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-17T07:24:33Z
  DOI: 10.1177/20552076251335544
  Issue No: Vol. 11 (2025)
Effectiveness of virtual reality interventions in promoting healthy eating
and physical activity among children: A systematic review
- Authors: Yuqi Zhang; Amirrudin Kamsin, Nur Amani Natasha Ahmad Tajuddin, Siti Idayu Hasan
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundChildhood obesity significantly impacts health, making the promotion of healthy behavior (HB) among children crucial to address this issue. Virtual reality (VR) has emerged as a promising tool for encouraging HB in children.ObjectiveThis ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-17T07:23:39Z
  DOI: 10.1177/20552076251331794
  Issue No: Vol. 11 (2025)
RSDCNet: An efficient and lightweight deep learning model for benign and
malignant pathology detection in breast cancer
- Authors: Yuan Liu; Haipeng Li, Zhu Zhu, Chen Chen, Xiaojing Zhang, Gongsheng Jin, Hongtao Li
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundBreast cancer is a leading malignant tumor among women globally, with its pathological classification into benign or malignant directly influencing treatment strategies and prognosis. Traditional diagnostic methods, reliant on manual ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-16T07:53:26Z
  DOI: 10.1177/20552076251336286
  Issue No: Vol. 11 (2025)
Digital clones of the deceased in mental health care: Promises and perils
- Authors: Masaki IwasakiSchool of Law; 26725Seoul National University, Seoul, The Republic of Korea
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  This letter highlights the emerging practice of employing digital clones of deceased individuals in grief care, addressing both their potential therapeutic benefits and the ethical and legal complexities they raise. While such technologies may offer novel ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-16T07:52:51Z
  DOI: 10.1177/20552076251332839
  Issue No: Vol. 11 (2025)
Global trends in internet hospitals and electronic prescriptions: Insights
for China
- Authors: Kang Luo; Chengyu Sun, Furong Yu, Xiaoxia He, Yi Feng, Wensheng Zhu, Xue Yang
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectivesThis study aims to review the research status of Internet hospitals and electronic prescriptions, identify associated research hotspots and frontiers, understand global application progress, and inform future research and development directions ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-16T06:59:26Z
  DOI: 10.1177/20552076251335707
  Issue No: Vol. 11 (2025)
Breathing together: A global hashtag analysis of #LungHealth on platform X
(formerly Twitter)
- Authors: Meisya Rosamystica; Shivanjali Gore, Swapna Sarangi, Maima Matin, Atanas G Atanasov, Zara Arshad, Rahul Kashyap, Faisal A Nawaz
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundThe X platform has gained popularity in healthcare, with posts among physicians increasing by 112% over five years. In the context of pulmonology, #LungHealth is used for engagement, spreading awareness, and disseminating information on ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-16T06:59:06Z
  DOI: 10.1177/20552076251335717
  Issue No: Vol. 11 (2025)
Evaluation of response time in asynchronous telehealth services in
obstetrics and gynecology: A cross-sectional study using a telehealth
service user data
- Authors: Daisuke Shigemi; Rena Toriumi, Ai Ohta, Saki Nakamura, Shunji Suzuki
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveAsynchronous consultations in telehealth provide the convenience of not requiring an appointment. However, some patients may choose to visit a medical facility instead of waiting for a response, and the time delay could negatively affect user ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-16T06:58:49Z
  DOI: 10.1177/20552076251335379
  Issue No: Vol. 11 (2025)
Perceptions of healthcare workers and patients on the implementation of
telemedicine in hard-to-reach areas: A qualitative study from rajasthan,
India
- Authors: Ramesh K Huda; Rahul S Chowhan, Simran Arora, Dileep Seervi, Sakshi Verma, Jayvardhan Singh, Sumit Kalra, Ashok Choudhary, Ramesh K Sangwan, Mukti Khetan, Bontha V Babu
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveTo explore the perceptions of healthcare providers and patients regarding the implementation of telemedicine in hard-to-reach areas of Rajasthan, India, and identify factors influencing its adoption and effectiveness.MethodsA qualitative study ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-16T06:52:52Z
  DOI: 10.1177/20552076251331874
  Issue No: Vol. 11 (2025)
Enhancing wellbeing in cancer care: Engagement in smart-messaging
programmes for symptom management
- Authors: Clement Boutry; Chloe Mays, Anika Kochar, Nima Moghaddam, Priya Patel, Emily Watson, Matthew Dicks, Felicity Gibbons, Sam Malins, James Rathbone
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundDespite improved survival rates, cancer patients often face physical and mental health challenges during and post-treatment. With cancer care services under pressure, these issues may go unnoticed. Holistic Needs Assessments attempt to address ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-16T06:52:38Z
  DOI: 10.1177/20552076251330383
  Issue No: Vol. 11 (2025)
Facial recognition and analysis: A machine learning-based pathway to
corporate mental health management
- Authors: Zicheng Zhang; Tianshu Zhang, Jie Yang
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundIn modern workplaces, emotional well-being significantly impacts productivity, interpersonal relationships, and organizational stability. This study introduced an innovative facial-based emotion recognition system aimed at the real-time ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-15T02:38:18Z
  DOI: 10.1177/20552076251335542
  Issue No: Vol. 11 (2025)
The intention to use short videos for health information among Chinese
adults: Based on the technology acceptance model
- Authors: Junjiang Liu; Rui Huang, Jinluan Ren, Peifan Li, Pengfei Wang
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveThe dissemination of health information plays a crucial role in health promotion. With the evolution of the Internet, short videos have become a significant medium for health information dissemination. This study aims to examine how short video ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-15T02:38:07Z
  DOI: 10.1177/20552076251335519
  Issue No: Vol. 11 (2025)
Development of a deep learning model to predict smoking status in patients
with chronic obstructive pulmonary disease: A secondary analysis of
cross-sectional national survey
- Authors: Sudarshan Pant; Hyung Jeong Yang, Sehyun Cho, EuiJeong Ryu, Ja Yun Choi
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveThis study aims to develop and validate a deep learning model to predict smoking status in patients with chronic obstructive pulmonary disease (COPD) using data from a national survey.MethodsData from the Korea National Health and Nutrition ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-15T02:37:49Z
  DOI: 10.1177/20552076251333660
  Issue No: Vol. 11 (2025)
Inertial sensor-based heel strike and energy expenditure prediction using
a hybrid machine learning approach
- Authors: Kethohalli R Vidyarani; Viswanath Talasila, Raafay Umar, Venkatesan Prem, Ravi Prasad K Jagannath, Gurusiddappa R Prashanth
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveGait analysis plays a critical role in healthcare, biomechanics, and sports science, particularly for estimating energy expenditure (EE). This study introduces a hybrid machine learning approach integrating convolutional neural networks (CNNs), ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-15T02:37:34Z
  DOI: 10.1177/20552076251333375
  Issue No: Vol. 11 (2025)
Assessing patient confidence in telehealth: Comparing across 17 medical
specialties
- Authors: Abdulrahman M Jabour1Department of Public Health; 666532College of Nursing Health Sciences, Jazan University, Jazan, Saudi Arabia
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundTelehealth has become an increasingly vital component of healthcare delivery, particularly after the COVID-19 pandemic. As telemedicine expands its reach, understanding patient confidence in using telehealth services across different medical ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-15T02:37:24Z
  DOI: 10.1177/20552076251330486
  Issue No: Vol. 11 (2025)
A serialization method for digitizing the image-based medical laboratory
report
- Authors: Xiaoyang Ren; Dongwei Dou, Xianying He, Fangfang Cui, Jie Zhao
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundWhen applying for teleconsultations, medical laboratory reports are usually photographed with a mobile phone, and the photographic results are uploaded as teleconsultation application materials. It is very meaningful to extract the content of ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-15T02:21:10Z
  DOI: 10.1177/20552076251334431
  Issue No: Vol. 11 (2025)
Effect of patient satisfaction on the utilization of mHealth services by
patients with chronic disease
- Authors: Jiao Lu; Heng Zhao, Zhilin Ji, Yanan Dong
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundMobile health (mHealth) is considered an effective way to manage chronic disease patients’ health. However, patients often do not use or continue using mHealth services due to concerns about service quality. Patient satisfaction may influence ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-15T02:20:58Z
  DOI: 10.1177/20552076251333983
  Issue No: Vol. 11 (2025)
Deep-learning-based detection of large vessel occlusion: A comparison of
CT and diffusion-weighted imaging
- Authors: JaeYoung Kang; JunYoung Park, YoungJae Kim, BumJoon Kim, SangHee Ha, KwangGi Kim
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundRapid and accurate identification of large vessel occlusion (LVO) is crucial for determining eligibility for endovascular treatment. We aimed to validate whether computed tomography combined with clinical information (CT&CI) or diffusion-...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-15T02:20:35Z
  DOI: 10.1177/20552076251334040
  Issue No: Vol. 11 (2025)
Enhancing stroke-associated pneumonia prediction in ischemic stroke: An
interpretable Bayesian network approach
- Authors: Xingyu Liu; Jiali Mo, Zuting Liu, Yanqiu Ge, Tian Luo, Jie Kuang
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundStroke-associated pneumonia (SAP) is a major cause of mortality following ischemic stroke (IS). However, existing predictive models for SAP often lack transparency and interpretability, limiting their clinical utility. This study aims to develop ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-15T02:20:23Z
  DOI: 10.1177/20552076251333568
  Issue No: Vol. 11 (2025)
The mechanism of word-of-mouth learning on chronic disease patients’
physician choice in online health communities: Latent Dirichlet allocation
analyses and cross-sectional study
- Authors: Linlin Han; Narongsak (Tek) Thongpapanl, Ou Li
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundWord-of-mouth learning (WOML) plays a substantial role in patients’ physician choice behavior. However, there is still a research gap in analyzing the mechanism of WOML on chronic disease patients’ physician choice in online health communities (...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-15T02:20:01Z
  DOI: 10.1177/20552076251332685
  Issue No: Vol. 11 (2025)
Evaluating the effectiveness of a collaborative care digital mental health
intervention on obsessive-compulsive symptoms in adolescents: A
retrospective study
- Authors: Darian Lawrence-Sidebottom; Kelsey McAlister, Monika Roots, Jennifer Huberty
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveObsessive-compulsive (OC) symptoms, characterized by distressing and repetitive thoughts and behaviors, frequently onset during adolescence for individuals with obsessive-compulsive disorder or anxiety disorders. Digital mental health ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-15T02:19:46Z
  DOI: 10.1177/20552076251331885
  Issue No: Vol. 11 (2025)
Digital health interventions for spinal surgery patients: A systematic
scoping review
- Authors: Annemieke Y van der Horst; Saskia M Kelders, Ernst T Bohlmeijer, Karlein M G Schreurs, Jan S Jukema
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  IntroductionThe potential of digital health interventions to optimize healthcare is promising also in the context of spinal surgery. However, a systematic review assessing the quality of digital health interventions for spinal surgery patients and the ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-15T02:19:16Z
  DOI: 10.1177/20552076251328549
  Issue No: Vol. 11 (2025)
Development of a digital treatment analyzer for the management of prostate
cancer patients, with the help of real world data and use of predictive
modelling
- Authors: Lev Korolkov; Heather A Robinson, Konstantinos Mouratis
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  Prostate cancer is the second most diagnosed cancer in the world. Treatment guidelines involve a multitude of therapies, however adherence to them is not fully established, while lack of personalized treatment strategies fails to put the patient as an ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-15T02:18:57Z
  DOI: 10.1177/20552076251326021
  Issue No: Vol. 11 (2025)
Five steps for the deployment of artificial intelligence-driven healthcare
delivery for remote and indigenous populations in Canada
- Authors: Amal Khan; Sandro Galea, Ivar Mendez
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  The integration of artificial intelligence (AI) into healthcare delivery offers transformative potential, especially for remote and underserved populations. In rural and remote regions like northern Saskatchewan, Canada, where Indigenous communities face ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-13T03:36:25Z
  DOI: 10.1177/20552076251334422
  Issue No: Vol. 11 (2025)
Providing digital mental health support and guidance across Colombia: An
observational study
- Authors: Haley M LaMonica; Paula Natalia Bettancourt Niño, Carlos Gómez-Restrepo, Jose Miguel Uribe-Restrepo, Tatiana Colón-Llamas, Andrea Escobar Altare, Ibeth Alexandra Naranjo-Bedoya, Laura Tatiana Morales-Zuluaga, Jaime A Pavlich-Mariscal, Alexandra Pomares-Quimbaya, Angelica María Puentes Mojica, Alvaro Andrés Navarro Mancilla, Esperanza Peña Torres, Frank Iorfino, Carla Gorban, Ian B Hickie, Laura Ospina-Pinillos
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveColombia's mental health system is plagued by significant shortages in services and health professionals. Digital health technologies enable access to information and care, overcoming barriers related to systemic limitations, geographic location, ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-13T03:36:15Z
  DOI: 10.1177/20552076251330766
  Issue No: Vol. 11 (2025)
Exploring artificial intelligence for healthcare from the health
professionals’ perspective: The case of limited resource settings
- Authors: Mulugeta Desalegn Kasaye; Amare Gebrie Getahun, Mulugeta Hayelom Kalayou
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  IntroductionAlthough artificial intelligence (AI) can boost clinical decision-making, personalize patient treatment, and advance the global health sectors, there are unique implementation challenges and considerations in developing countries. The ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-13T03:35:49Z
  DOI: 10.1177/20552076251330552
  Issue No: Vol. 11 (2025)
The current status and future directions of artificial intelligence in the
prediction, diagnosis, and treatment of liver diseases
- Authors: Bo Gao; Wendu DuanDepartment of Hepatobiliary Surgery, 612973Affiliated Hospital of Hebei University, Baoding, China
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  Early detection, accurate diagnosis, and effective treatment of liver diseases are of paramount importance for improving patient survival rates. However, traditional methods are frequently influenced by subjective factors and technical limitations. With ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-13T03:35:39Z
  DOI: 10.1177/20552076251325418
  Issue No: Vol. 11 (2025)
Synergistic evaluation system of “technology and service” in smart
elderly care institutions in China
- Authors: Xiaoyun Liu; Ka-Yin Chau, Xiaoxiao Liu, Yan Wan
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundSmart elderly care faces numerous challenges while aligning with the national strategy of promoting the silver economy. Chief among these challenges is the inconsistent quality of services offered by smart elderly care institutions, which ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-13T03:07:32Z
  DOI: 10.1177/20552076251326681
  Issue No: Vol. 11 (2025)
Translation and psychometric evaluation of the Chinese version of the
Digital Competence Questionnaire for clinical nurses
- Authors: Zhengang Wei; Hongli Liu, Jicheng Zhang, Yan Chen, Lixia Chang, Huiyu Cheng, Xue Bai, Xiaohua Wang, Su Li
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundAdequate digital competence is crucial for clinical nurses to effectively adapt to the evolving digital technologies in their practice. Currently, there is a lack of a brief assessment tool in China that comprehensively measures the digital ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-10T01:49:59Z
  DOI: 10.1177/20552076251332987
  Issue No: Vol. 11 (2025)
Sociodemographic and health predictors of adherence to self-administered
computerized cognitive assessment
- Authors: Marisa Magno; Ana Isabel Martins, Joana Pais, Vítor Tedim Cruz, Anabela G Silva, Nelson Pacheco Rocha
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  IntroductionCognitive assessment is essential to detect early cognitive decline and guide interventions. Self-administered computerized assessment is a promising option for periodic cognitive screening in the general population. One of the most critical ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-10T01:49:43Z
  DOI: 10.1177/20552076251332774
  Issue No: Vol. 11 (2025)
Effects of a virtual simulative diabetes care program on learning ability
and clinical thinking skills in nursing interns
- Authors: Tianhui Xu; Qin Wang, Fang Liu, Li Yang, Rong Wang, Huiting Weng
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  IntroductionDiabetes is a major global health issue, requiring nursing interns to develop essential diabetes management skills. Traditional teaching methods are limited by clinical settings, hindering the development of comprehensive theoretical and ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-10T01:49:10Z
  DOI: 10.1177/20552076251332834
  Issue No: Vol. 11 (2025)
A comparison of the responses between ChatGPT and doctors in the field of
cholelithiasis based on clinical practice guidelines: a cross-sectional
study
- Authors: Tianyang Mao; Xin Zhao, Kangyi Jiang, Qingyun Xie, Manyu Yang, Ruoxuan Wang, Fengwei Gao
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundWith the development of the information age, an increasing number of patients are seeking information about related diseases on the Internet. In the medical field, several studies have confirmed that ChatGPT has great potential for use in ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-04T11:19:21Z
  DOI: 10.1177/20552076251331804
  Issue No: Vol. 11 (2025)
Machine learning-based prediction of in-hospital mortality in patients
with chronic respiratory disease exacerbations
- Authors: Seung Yeob Ryu; Seon Min Lee, Young Jae Kim, Kwang Gi Kim
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveExacerbation of chronic respiratory diseases leads to poor prognosis and a significant socioeconomic burden. To address this issue, an artificial intelligence model must assess patient prognosis early and classify patients into high- and low-risk ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-04T11:15:36Z
  DOI: 10.1177/20552076251326703
  Issue No: Vol. 11 (2025)
Understanding public trust in national electronic health record systems: A
multi-national qualitative research study
- Authors: Kimon Papadopoulos; Elske Ammenwerth, Guillaume Lame, Nina Stahl, Verena Struckmann, Viktor von Wyl, Felix Gille
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveHaving public trust in national electronic health record systems (NEHRs) is crucial for the successful implementation and participation of NEHRs within a nations healthcare system. Yet, a lack of conceptual clarity precludes healthcare ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-03T08:19:42Z
  DOI: 10.1177/20552076251333576
  Issue No: Vol. 11 (2025)
Predicting 30-day readmissions in pneumonia patients using machine
learning and residential greenness
- Authors: Seohyun Choi; Young Jae Kim, Seon Min Lee, Kwang Gi Kim
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  IntroductionIdentifying factors that increase the risk of hospital readmission will help determine high-risk patients and decrease the socioeconomic burden. Pneumonia is associated with high readmission rates. Although residential greenness has been ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-03T06:13:11Z
  DOI: 10.1177/20552076251325990
  Issue No: Vol. 11 (2025)
The relationship between age and physical activity as objectively measured
by accelerometers in older adults with and without dementia
- Authors: Karl Brown; Andrew Shutes-David, Sarah Payne, Adrienne Jankowski, Katie Wilson, Edmund Seto, Debby W Tsuang
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveThis study sought to investigate differences in physical activity and activity fragmentation between older adults with and without dementia and between older adults with dementia with Lewy bodies (DLB) and older adults with Alzheimer's disease (...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-03T06:12:57Z
  DOI: 10.1177/20552076251330808
  Issue No: Vol. 11 (2025)
Exploring the potential of deep learning models integrating transformer
and LSTM in predicting blood glucose levels for T1D patients
- Authors: Xin Xiong; XinLiang Yang, Yunying Cai, Yuxin Xue, JianFeng He, Heng Su
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveDiabetes mellitus is a chronic condition that requires constant blood glucose monitoring to prevent serious health risks. Accurate blood glucose prediction is essential for managing glucose fluctuations and reducing the risk of hypo- and ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-03T06:02:04Z
  DOI: 10.1177/20552076251328980
  Issue No: Vol. 11 (2025)
Beyond the STI clinic: Use of administrative claims data and machine
learning to develop and validate patient-level prediction models for
gonorrhea
- Authors: Lorenzo Argante; Germain Lonnet, Emmanuel Aris, Jane Whelan
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundGonorrhea is a sexually transmitted infection (STI) that, untreated, can result in debilitating complications such as pelvic inflammatory disease, pain, and infertility. A minority of cases are diagnosed in STI clinics in the United States. ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-03T06:01:00Z
  DOI: 10.1177/20552076251331895
  Issue No: Vol. 11 (2025)
Integrated brain tumor segmentation and MGMT promoter methylation status
classification from multimodal MRI data using deep learning
- Authors: Muhammad Sohaib Iqbal; Usama Ijaz Bajwa, Rehan Raza, Muhammad Waqas Anwar
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveGlioblastoma multiforme (GBM) is the most aggressive and prevalent type of brain tumor, with a median survival time of approximately 15 months despite treatment advancements. Determining the O(6)-methylguanine-DNA-methyltransferase (MGMT) ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-03T06:00:08Z
  DOI: 10.1177/20552076251332018
  Issue No: Vol. 11 (2025)
Risk factors and a predictive model for mixed urinary incontinence among
parous women: Insights from a large-scale multicenter epidemiological
investigation
- Authors: Qi Wang; Stefano Manodoro, Huifang Lin, Xiaofang Li, Chaoqin Lin, Xiaoxiang Jiang
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  PurposeThis study aims to identify independent risk factors for mixed urinary incontinence (MUI) in parous women using a multicenter epidemiological study and to establish and validate a predictive nomogram.MethodsA large-scale survey was conducted from ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-03T05:59:49Z
  DOI: 10.1177/20552076251333661
  Issue No: Vol. 11 (2025)
Addressing health service equity through telehealth: A systematic review
of reviews
- Authors: Siyu Wang; Amy von Huben, Prithivi Prakash Sivaprakash, Emily Saurman, Sarah Norris, Andrew Wilson
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveTo synthesize existing reviews on the impact of telehealth programs on health service equity in non-urban areas, focusing on six dimensions of access: accessibility, availability, acceptability, affordability, adequacy, and awareness.MethodsWe ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-02T06:56:41Z
  DOI: 10.1177/20552076251326233
  Issue No: Vol. 11 (2025)
Interactive decision aid on therapy decision making for patients with
chronic kidney disease: A prospective exploratory pilot study
- Authors: Jun-Ming Su; Huey-Liang Kuo, Kai-Ling Yang, Chih-Jung Wu, Ya-Fang Ho
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectivePatients with advanced chronic kidney disease (CKD) face challenging decisions about kidney replacement therapy (KRT) options. This study aimed to evaluate the effectiveness of an Interactive Patient-Decision-Aid App for Kidney Replacement ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-02T06:03:20Z
  DOI: 10.1177/20552076251332832
  Issue No: Vol. 11 (2025)
Machine-learning models for differentiating benign and malignant breast
masses: Integrating automated breast volume scanning intra-tumoral,
peri-tumoral features, and clinical information
- Authors: Meixue Dai; Yueqiong Yan, Zhong Li, Jidong Xiao
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundDifferentiating between benign and malignant breast masses is critical for clinical decision-making. Automated breast volume scanning (ABVS) provides high-resolution three-dimensional imaging, addressing the limitations of conventional ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-02T06:02:37Z
  DOI: 10.1177/20552076251332738
  Issue No: Vol. 11 (2025)
Use machine learning to predict bone metastasis of esophageal cancer: A
population-based study
- Authors: Jun Wan; Jia Zhou
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveThe objective of this study is to develop a machine learning (ML)-based predictive model for bone metastasis (BM) in esophageal cancer (EC) patients.MethodsThis study utilized data from the Surveillance, Epidemiology, and End Results database ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-02T06:01:56Z
  DOI: 10.1177/20552076251325960
  Issue No: Vol. 11 (2025)
Smart insole-based abnormal gait identification: Deep sequential networks
and feature ablation study
- Authors: Beomjoon Park; Minhye Kim, Dawoon Jung, Jinwook Kim, Kyung-Ryoul Mun1Intelligence Technology, Seongbuk-gu, South Korea
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveGait analysis plays a pivotal role in evaluating walking abilities, with recent advancements in digital health stressing the importance of efficient data collection methods. This study aims to classify nine gait types including one normal and ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-01T05:42:18Z
  DOI: 10.1177/20552076251332999
  Issue No: Vol. 11 (2025)
Effects of immersive leisure-based virtual reality cognitive training on
cognitive and physical function in community-based older adults: A
randomized controlled trial
- Authors: I-Ching Chuang; Auwal Abdullahi, I-Chen Chen, Yih-Ru Wu, Ching-Yi Wu
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundOlder adults are at risk of developing cognitive impairments, and cognitive training is commonly used to enhance cognitive function in this population. The effectiveness of cognitive training is further optimized with the integration of leisure-...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-01T05:41:31Z
  DOI: 10.1177/20552076251328491
  Issue No: Vol. 11 (2025)
Impact of connected health on the psychological wellbeing and quality of
life of people with multiple sclerosis and their caregivers: A systematic
review
- Authors: Joan Alaboson; Laura Coffey, Sowmya Shrivastava, Adeola Ade-Alao, Rebecca Maguire
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundConnected health (CH) interventions may improve psychological wellbeing and quality of life (QoL) in caregivers and people with multiple sclerosis (MS); however, this impact has not been rigorously evaluated. This systematic review aims to ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-04-01T05:40:56Z
  DOI: 10.1177/20552076251326230
  Issue No: Vol. 11 (2025)
Use of telehealth by US adults with depression or anxiety disorder:
Results from 2022 Health Information National Trends Survey
- Authors: Pu Bai; Emily Brignone, Bibo Jiang, Casey Pinto, Li Wang
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundTelehealth use has significantly increased recently. However, little is known about its use by individuals with depression or anxiety disorders. This study aims to explore the patterns of telehealth use among those individuals.MethodsData used ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-29T07:12:05Z
  DOI: 10.1177/20552076251321999
  Issue No: Vol. 11 (2025)
Feasibility of a goal sharing digital platform designed to empower
children with disability and their families: A qualitative,
pre-implementation study
- Authors: Meghan Wilson; Bridget O’Connor, Marnie Drake, Adam Scheinberg, Christine Imms, Rose Babic, Danny Hui, George Charalambous, Sarah Knight
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundFamily-centred goal setting is central to optimal care and outcomes for children with a disability. Digital innovations show promise for increased engagement and empowerment of families. While digital products have the potential to improve ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-28T12:53:57Z
  DOI: 10.1177/20552076251319827
  Issue No: Vol. 11 (2025)
Designing digital conversational agents for youth with multiple mental
health conditions: Insights on key features from a youth-engaged
qualitative descriptive study
- Authors: Jingyi Hou; Jamie Gibson, Thalia Phi, Brian Ritchie, Louise Gallagher, Gillian Strudwick, George Foussias, Darren B Courtney, Aristotle Voineskos, Stephanie Ameis, Kristin Cleverley, Lisa D Hawke
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveThis qualitative study aims to examine the key features and design elements of a mental health digital conversational agent (“Digital Conversational Agent” or “DCA”) for youth with multiple mental health conditions.MethodsTwenty-eight youth ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-28T10:00:52Z
  DOI: 10.1177/20552076251330550
  Issue No: Vol. 11 (2025)
Regulating neural data processing in the age of BCIs: Ethical concerns and
legal approaches
- Authors: Hong Yang; Li JiangShanghai International College of Intellectual Property, 12476Tongji University, Shanghai, People's Republic of China
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  Brain–computer interfaces (BCIs) have seen increasingly fast growth under the help from AI, algorithms, and cloud computing. While providing great benefits for both medical and educational purposes, BCIs involve processing of neural data which are ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-28T09:59:55Z
  DOI: 10.1177/20552076251326123
  Issue No: Vol. 11 (2025)
Exploring the acceptability, feasibility and utility of a digital tool for
self-reporting perinatal anxiety and depression in urban obstetric and
paediatric clinics in India
- Authors: Shraddha Lotlikar; Prabha Chandra, Geetha Desai, Sonali Mohanty Quantius, Latha Venkataraman, Madhushree Vijayakumar
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundCommon mental disorders, such as anxiety and depression, affect 13% to 55% of women during the perinatal period in India. However, high-volume obstetric clinics often lack resources for routine mental health assessment. Digital tools could ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-28T09:59:15Z
  DOI: 10.1177/20552076251314101
  Issue No: Vol. 11 (2025)
Smart monitoring technology to support home-based dementia care:
Market-specific business model development and implementation
considerations in the Netherlands
- Authors: Christian Wrede; Annemarie Braakman-Jansen, Lisette van Gemert-Pijnen1Centre for eHealth Wellbeing Research, Department of Psychology, Health & Technology, 3230University of Twente, Enschede, Netherlands
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundRemote monitoring (RM) technology can support home-based dementia care by enabling (in)formal caregivers to monitor the health and safety of people with dementia remotely. However, sustainable implementation is challenged by a lack of viable ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-28T09:06:33Z
  DOI: 10.1177/20552076251331825
  Issue No: Vol. 11 (2025)
Effectiveness of WeChat Official Accounts in health communication: A
comparative study of hospitals and centers for disease control and
prevention on resident participation in Shenzhen
- Authors: Fangfang Gong; Li Zeng, Yi Li, Jingang Shi, Ke Huang, Ying Zhou
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundAs China transitions from a disease-centered to a people-centered healthcare model, hospitals are increasingly involved in health education. This study compares the effectiveness of WeChat Official Accounts (WOAs) operated by hospitals and ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-28T09:06:01Z
  DOI: 10.1177/20552076251331797
  Issue No: Vol. 11 (2025)
Artificial intelligence in psychiatry: A systematic review and
meta-analysis of diagnostic and therapeutic efficacy
- Authors: Moustaq Karim Khan Rony; Dipak Chandra Das, Most. Tahmina Khatun, Silvia Ferdousi, Mosammat Ruma Akter, Mst. Amena Khatun, Most. Hasina Begum, Md Ibrahim Khalil, Mst. Rina Parvin, Daifallah M Alrazeeni, Fazila Akter
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundArtificial Intelligence (AI) has demonstrated significant potential in transforming psychiatric care by enhancing diagnostic accuracy and therapeutic interventions. Psychiatry faces challenges like overlapping symptoms, subjective diagnostic ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-28T09:03:50Z
  DOI: 10.1177/20552076251330528
  Issue No: Vol. 11 (2025)
Healthcare professionals’ perspectives on artificial intelligence
(AI)-based mobile applications (apps) for diabetes education and
behavioural management
- Authors: Phei-Ching Lim; Yung-Wey Chong, Qiu-Ting Chie, Hadzliana Zainal, Kok-Lim Alvin Yau, Soo-Huat Teoh
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundThe global prevalence of diabetes mellitus is escalating rapidly. Similarly in Malaysia, diabetes prevalence among adults rose exponentially. Artificial intelligence (AI) integrated into mobile health applications (apps) presents a promising ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-28T09:03:14Z
  DOI: 10.1177/20552076251329991
  Issue No: Vol. 11 (2025)
Effectiveness of telerehabilitation on the International Classification of
Functioning, Disability, and Health framework outcomes during the COVID-19
pandemic: A systematic review and meta-analysis of randomized controlled
trials
- Authors: Jenjira Thanakamchokchai; Fuengfa Khobkhun, Ruttana Phetsitong, Pakaratee Chaiyawat, Kantheera Areerak, Kanjana Niemrungruang, Jarugool Tretriluxana
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveThis study aimed to synthesize and analyze the evidence on the effectiveness of telerehabilitation categorized according to the International Classification of Functioning, Disability, and Health (ICF) outcomes for physical therapy (PT) during ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-28T09:02:00Z
  DOI: 10.1177/20552076251325993
  Issue No: Vol. 11 (2025)
Height estimation in children and adolescents using body composition big
data: Machine-learning and explainable artificial intelligence approach
- Authors: Dohyun Chun; Taesung Chung, Jongho Kang, Taehoon Ko, Young-Jun Rhie, Jihun Kim
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveTo develop an accurate and interpretable height estimation model for children and adolescents using body composition variables and explainable artificial intelligence approaches.MethodsA light gradient boosting method was employed on a dataset of ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-28T08:22:34Z
  DOI: 10.1177/20552076251331879
  Issue No: Vol. 11 (2025)
Perception, attitude and use of digital health platforms for mental health
promotion among students in a national university in the south-western
part of Nigeria: A cross-sectional study
- Authors: Racheal Oluwabukunmi Ogundipe; Oyenike Elizabeth Omotosho, Yetunde Olufisayo John-Akinola1Department of Health Promotion Education, Faculty of Public Health, College of Medicine, 58987University of Ibadan, Ibadan, Nigeria
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundNigeria faces significant challenges in providing access to and utilisation of professional mental health treatments. Digital health systems play an essential role in healthcare across diverse settings. This study aimed to determine the ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-28T08:22:02Z
  DOI: 10.1177/20552076251331841
  Issue No: Vol. 11 (2025)
Predicting fall risk in older adults: A machine learning comparison of
accelerometric and non-accelerometric factors
- Authors: Ana González-Castro; José Alberto Benítez-Andrades, Rubén González-González, Camino Prada-García, Raquel Leirós-Rodríguez
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectivesAccurate prediction of fall risk in older adults is essential to prevent injuries and improve quality of life. This study evaluates the predictive performance of various machine learning models using accelerometric data, non-accelerometric data, ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-28T08:21:41Z
  DOI: 10.1177/20552076251331752
  Issue No: Vol. 11 (2025)
Virtual reality on perioperative anxiety in pediatric patients: A
narrative review
- Authors: Sabrina Soledad Domene; Daniela Fulginiti, Gabriela D Briceno Silva, Paloma Frei, Gladys Amalia Perez Santiago, Marisa Gasbarra, Isabella Peters, Alexis O’Connell, Ernesto Calderon Martinez
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  Preoperative anxiety is a common response to stress specifically in the pediatric population exhibiting heightened susceptibility to it. High anxiety levels can negatively impact the quality of anesthesia, increase analgesia requirements, and result in ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-28T08:18:56Z
  DOI: 10.1177/20552076251331304
  Issue No: Vol. 11 (2025)
Exploring implementation challenges of decentralized clinical trials: A
qualitative study of policy stakeholder perspectives in Denmark
- Authors: Ida Hestbjerg; Ditte Bonde Stanek, Ulrik Bak Kirk, Christa Thomsen
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundThe implementation of decentralized clinical trials (DCTs) has received strong political interest in Denmark. Many policy stakeholders may directly or indirectly influence the implementation at a national strategic level. Diverging interests may ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-28T08:18:21Z
  DOI: 10.1177/20552076251330519
  Issue No: Vol. 11 (2025)
The inclusion of implementation outcomes in digital health interventions
for young adults: A scoping review
- Authors: Kennedy Diema Konlan; Zainab Auwalu Ibrahim, Jisu Lee, Hyeonkyeong Lee
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveImplementation outcomes are important in intervention research as a necessary precursor to achieving desired health outcomes. Considering the critical role of implementation outcomes, this study involved a comprehensive review of implementation ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-28T08:02:25Z
  DOI: 10.1177/20552076251330194
  Issue No: Vol. 11 (2025)
Fundus image classification using feature concatenation for early
diagnosis of retinal disease
- Authors: Sara Ejaz; Hafiz U Zia, Fiaz Majeed, Umair Shafique, Stefania Carvajal Altamiranda, Vivian Lipari, Imran Ashraf
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundDeep learning models assist ophthalmologists in early detection of diseases from retinal images and timely treatment.AimOwing to robust and accurate results from deep learning models, we aim to use convolutional neural network (CNN) to provide a ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-28T08:01:45Z
  DOI: 10.1177/20552076251328120
  Issue No: Vol. 11 (2025)
Machine learning-driven prediction of hospital admissions using gradient
boosting and GPT-2
- Authors: Xingyu Zhang; Hairong Wang, Guan Yu, Wenbin Zhang
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundAccurately predicting hospital admissions from the emergency department (ED) is essential for improving patient care and resource allocation. This study aimed to predict hospital admissions by integrating both structured clinical data and ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-28T07:54:23Z
  DOI: 10.1177/20552076251331319
  Issue No: Vol. 11 (2025)
Developing and evaluating a campus-based health management app for nursing
students: A pilot study on usability and user experience
- Authors: Andrew Ke-Ming Lu; Yi-Ling Chung, Yu-Hsia Wang, Sheng-Kai Lin, Jeng-Long Hsieh
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  IntroductionThe application of technology to healthcare is becoming increasingly common. Apps designed for health monitoring among nursing students are currently under development. In this study, we used various health-monitoring devices available in the ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-28T07:53:51Z
  DOI: 10.1177/20552076251330577
  Issue No: Vol. 11 (2025)
Identifying behaviour change techniques, technical features and
implementation options for a virtual reality intervention to motivate
adult smokers to quit: A focus group study with healthcare and virtual
reality experts
- Authors: Tosan Okpako; Corinna Leppin, Alessandro Chincotta, Dimitra Kale, Olga Perski, Jamie Brown
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveIn this study, individuals working in healthcare or virtual reality (VR) were invited to contribute towards developing a VR intervention to encourage adults to quit smoking, by building upon user-generated ideas from a previous co-design study ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-28T07:53:14Z
  DOI: 10.1177/20552076251330510
  Issue No: Vol. 11 (2025)
Digital transformation of nephrology POCUS education—Integrating a
multiagent, artificial intelligence, and human collaboration-enhanced
curriculum with expert feedback
- Authors: Mohammad S Sheikh; Kianoush B Kashani, James R Gregoire, Charat Thongprayoon, Jing Miao, Iasmina M Craici, Wisit Cheungpasitporn, Fawad M Qureshi
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundThe digital transformation in medical education is reshaping how clinical skills, such as point-of-care ultrasound (POCUS), are taught. In nephrology fellowship programs, POCUS is essential for enhancing diagnostic accuracy, guiding procedures, ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-28T07:52:39Z
  DOI: 10.1177/20552076251328807
  Issue No: Vol. 11 (2025)
Acceptance of a sensor-based online psychotherapy for adolescents with
obsessive-compulsive disorder (SSTeP-KiZ)
- Authors: Annika K Alt; Carolin S Klein, Anja Pascher, Annette Conzelmann, Franziska Kosel, Jan Kühnhausen, Karsten Hollmann, Tobias J Renner
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  Background and aimE-mental health interventions with use of cognitive behavioral therapy methods and therapist contact via video have been well established in their effectiveness for various mental illnesses. With the help of sensors worn on the body, ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-28T07:52:05Z
  DOI: 10.1177/20552076251327046
  Issue No: Vol. 11 (2025)
Exploration of digital health literacy among community members and
healthcare teams in the deep south: A quasi-experimental study
- Authors: Gabrielle B Rocque; Nicole L Henderson, Keyonsis Hildreth, Noon Eltoum, Omari Whitlow, Loretta Herring, Stacey Ingram, Daniel I Chu, Connie C Shao, Claudia Hardy, Timiya S Nolan, Chelsea McGowan, Jennifer Young Pierce, Courtney P Williams
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundGiven increasing technology reliance, there is a need for a deeper understanding of individual and community-level comfort with technology as it pertains to basic and more complex healthcare-related skills.MethodsThe objective of this quasi-...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-28T07:51:30Z
  DOI: 10.1177/20552076251325581
  Issue No: Vol. 11 (2025)
Acceptability of an AI-enabled family module in a mobile app for enhanced
diabetes management: Patient and family perspectives
- Authors: Sungwon Yoon; Rena Lau, Yu Heng Kwan, Huiyi Liu, Razeena Sahrin, Jie Kie Phang, Yichi Zhang, Nicholas Graves, Lian Leng Low
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveTo explore the acceptability of family support through an AI-enabled mobile app and identify preferences for its novel family module features among patients with type 2 diabetes (T2DM) and family members.MethodsSemi-structured interviews were ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-25T08:21:15Z
  DOI: 10.1177/20552076251322654
  Issue No: Vol. 11 (2025)
DANCE Rehabilitation EXperience (DANCEREX-DTx): Protocol for a randomized
controlled trial on effectiveness of digital therapeutics in chronic
neurological disabilities
- Authors: Francesca Borgnis; Valeria Blasi, Olivia Realdon, Fabrizia Mantovani, Maria Cotelli, Rosa Manenti, Elena Campana, Viviana Lo Buono, Silvia Marino, Petar Aleksandrov Mavrodiev, Marina Saresella, Pietro Davide Trimarchi, Margherita Alberoni, Maria Pia Amato, Francesca Baglio
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveRehabilitation is an important player in preventing and reducing the high impact of disability on everyday functioning in chronic neurological diseases (CNDs), especially if timely, intensive, and multidimensional. However, to date, ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-25T08:21:11Z
  DOI: 10.1177/20552076251324448
  Issue No: Vol. 11 (2025)
Feasibility and acceptability of a remote computerized cognitive training
- Authors: Marion Ferrandez y Montesinos; Rachid Guerchouche, Justine Lemaire, Esther Brill, Stefan Klöppel, François Bremond, Fabio Solari, Philippe Robert, Guillaume Sacco, Valeria Manera
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  IntroductionComputerized cognitive training (CCT) represents promising solutions for remote training of cognitive abilities in older people with cognitive decline. In the present article, we tested the feasibility and acceptability of a CCT performed at ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-25T08:19:52Z
  DOI: 10.1177/20552076251324015
  Issue No: Vol. 11 (2025)
Acceptability of a smart lighter for tracking cigarette smoking: A focus
group study
- Authors: Lydia Tesfaye; Michael Wakeman, Tim Gregory, Erin Leahy, Gunnar Baskin, Greg Gruse, Brandon Kendrick, Sherine El-Toukhy
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundSmart lighters track cigarette smoking episodes, which can help identify smoking patterns and intervention approaches to promote cessation. We gauged the acceptability of smart lighters among individuals with low socioeconomic status (SES), a ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-25T08:19:50Z
  DOI: 10.1177/20552076251323998
  Issue No: Vol. 11 (2025)
Association between e-health usage and consideration for clinical trial
participation: An exploratory study on the mediating role of
cancer-related self-efficacy and patient-centered communication
- Authors: Siying Gong; Luxi Zhang, Xinshu Zhao
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundThe journey of any new treatment begins in the lab and through a clinical trial. Clinical trials have become an important means to promote public health causes. In digital age, e-health usage (EHU) might be a key factor to promote clinical ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-25T07:25:06Z
  DOI: 10.1177/20552076251328598
  Issue No: Vol. 11 (2025)
Interpretable artificial intelligence (AI) for cervical cancer risk
analysis leveraging stacking ensemble and expert knowledge
- Authors: Priyanka Roy; Mahmudul Hasan, Md Rashedul Islam, Md Palash Uddin
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectivesThis study develops a machine learning (ML)-based cervical cancer prediction system emphasizing explainability. A hybrid feature selection method is proposed to enhance predictive accuracy and stability, alongside evaluation of multiple ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-25T07:23:18Z
  DOI: 10.1177/20552076251327945
  Issue No: Vol. 11 (2025)
Erratum to “A comparison of the sociodemographic, medical, and
psychosocial characteristics of adolescents and young adults diagnosed
with cancer recruited in-person and online: A Canadian cross-sectional
survey”
- Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-25T07:21:19Z
  DOI: 10.1177/20552076251325521
  Issue No: Vol. 11 (2025)
Ethical and pedagogical reflections around serious games in patients
education (PE):
- Authors: Jean-Denis Aubry; Emmanuel Rusch
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundThe serious game in healthcare, part of the e-health segment, aims to develop, in a fun and educational way, the skills needed for better day-to-day management of chronic diseases in patients and their families, using video games as part of ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-25T07:19:49Z
  DOI: 10.1177/20552076241304877
  Issue No: Vol. 11 (2025)
Adopting telehealth service for lymphedema care: Insights from a
Filariasis Management Clinic, Puducherry, India
- Authors: Anoop C Choolayil; Malarvizhi Anandhan, Nileshkumar Elangovan, Sadhishkumar Paranthaman, Anbusivam Sadhasivam, Vijesh Sreedhar Kuttiatt29933Unit of Clinical Molecular Medicine, ICMR - Vector Control Research Centre, Indira Nagar, Puducherry, India
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  Lymphatic filariasis (LF) is a neglected tropical disease affecting communities in tropical and subtropical regions marked by poor socio-economic conditions. Regular hospital-based follow-up and home-based Morbidity Management and Disability Prevention (...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-24T08:00:44Z
  DOI: 10.1177/20552076251326145
  Issue No: Vol. 11 (2025)
A novel and ultralight convolutional neural network model for real-time
detection of infectious lung diseases
- Authors: Eman Alqaissi
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveVectors that cause infectious lung diseases encompass viral, bacterial, fungal, and parasitic agents. Early detection of these infections is critical for timely diagnosis and effective treatment. Several studies have created solutions for early ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-21T03:04:56Z
  DOI: 10.1177/20552076251318155
  Issue No: Vol. 11 (2025)
Digital health interventions for non-older individuals at risk of frailty:
A systematic review and meta-analysis
- Authors: Momoko Tohyama; Ryo Momosaki, Yuka Shirai, Kenta Ushida, Yuki Kato, Miho Shimizu, Issei Kameda, Yuya Sakurai, Asuka Hori, Masatsugu Okamura, Takahiro Tsuge, Hiroki Sato, Yuki Nakashima, Kaori Endo, Shota Hayashi, Norio Yamamoto, Daisuke Matsumoto, Kenichi Fudeyasu, Hidenori Arai
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveFrailty is a significant health problem that results in adverse outcomes, reduced quality of life, and increased medical and nursing care costs. This study aimed to review the effect of digital health interventions on improving physical activity, ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-21T02:47:20Z
  DOI: 10.1177/20552076251328566
  Issue No: Vol. 11 (2025)
Artificial intelligence in obstructive sleep apnea: A bibliometric
analysis
- Authors: Xing An; Jie Zhou, Qiang Xu, Zhihui Zhao, Weihong Li
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveTo conduct a bibliometric analysis using VOSviewer and Citespace to explore the current applications, trends, and future directions of artificial intelligence (AI) in obstructive sleep apnea (OSA).MethodsOn 13 September 2024, a computer search ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-21T02:46:33Z
  DOI: 10.1177/20552076251324446
  Issue No: Vol. 11 (2025)
Effect of AI intervention programs for older adults on the quality of
life: A systematic review and meta-analysis of randomized controlled
trials
- Authors: Kawoun Seo; Taejeong Jang, Jisu Seo
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundThe extension of life expectancy due to medical advancements has resulted in global aging and increased social costs for elder care. Additionally, stringent health measures related to infectious disease pandemics have adversely affected the ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-21T02:45:56Z
  DOI: 10.1177/20552076251324014
  Issue No: Vol. 11 (2025)
Impact of the KARAZ platform's behavioral interventions and incentives on
diabetic glycemic control in Saudi Arabia
- Authors: Sulafa T Alqutub; Faisal Aljehani
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  IntroductionThe glucose management indicator (GMI) and time-in-range (TIR) are important glycemic parameters calculated from continuous glucose monitoring (CGM) values. KARAZ, a hybrid Internet of things—artificial intelligence platform, was developed in ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-21T02:15:43Z
  DOI: 10.1177/20552076251325987
  Issue No: Vol. 11 (2025)
Predictive modeling of pediatric drug-induced liver injury: Dynamic
classifier selection with clustering analysis
- Authors: Zixin Shi; Linjun Huang, Haolin WangCollege of Medical Informatics, 12550Chongqing Medical University, Chongqing, China
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundPediatric populations are more vulnerable to drug-induced liver injury (DILI) due to distinct pharmacokinetic profiles and ongoing physiological maturation processes. However, early identification and assessment of DILI in pediatric patients ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-20T09:18:25Z
  DOI: 10.1177/20552076251330078
  Issue No: Vol. 11 (2025)
The urge for specific standards of telepharmacy services: Lessons learned
from qualitative study in Indonesian community pharmacies
- Authors: Kartika Citra Dewi Permata Sari; Adelia Nathifa Rachma Nur Setiati, Larasati Arrum Kusumawardani, Hasniza Zaman Huri, Mohamed Hassan Elnaem
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveLike many low- and middle-income countries, Indonesia lacks specific standards for telepharmacy practices, which requires adherence to traditional pharmacy guidelines to evaluate the services. This study aims to explore the disparities between ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-20T04:56:20Z
  DOI: 10.1177/20552076251326018
  Issue No: Vol. 11 (2025)
Effectiveness, usability, and patient satisfaction of an mHealth
application with an integrated ePRO system following lumbar degenerative
spinal surgery: A quasi-experimental study
- Authors: Marianne Dyrby Lorenzen; Casper Friis Pedersen, Line Nielsen, Mikkel O. Andersen, Jane Clemensen, Leah Y. Carreon
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  IntroductionThere is a lack of comprehensive clinical research to assess potential benefits of mHealth solutions in post discharge follow-up care after spinal surgery.PurposeThis quasi-experimental study evaluated the effectiveness, usability, and patient ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-20T04:35:01Z
  DOI: 10.1177/20552076251324687
  Issue No: Vol. 11 (2025)
Individual differences in views toward healthcare conversational agents: A
cross-sectional survey study
- Authors: A. Luke MacNeill; Alison Luke, Shelley Doucet
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  Background and ObjectiveTo date, there has been limited research on people's attitudes and design preferences with respect to conversational agents (CAs) that are used for healthcare. Individual differences in attitudes and design preferences have ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-20T04:31:26Z
  DOI: 10.1177/20552076241311066
  Issue No: Vol. 11 (2025)
Corrigendum to “How eHealth use and cancer information seeking influence
older adults’ acceptance of genetic testing: Mediating roles of PIGI and
cancer worry”
- Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-19T01:08:18Z
  DOI: 10.1177/20552076251325278
  Issue No: Vol. 11 (2025)
Quality assessment of temporomandibular disorders-related information on
Chinese social media: A cross-sectional study
- Authors: Yifei Deng; Jianing Zhou, Ming Yang, Yaxin Weng, Xin XiongState Key Laboratory of Oral Diseases & National Center for Stomatology & National Clinical Research Center for Oral Diseases, 168305West China Hospital of Stomatology, Sichuan University, Chengdu, Sichuan, China
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundTemporomandibular disorders (TMDs) affect people's quality of life greatly, and precise understanding of TMDs contributes to a proper treatment choice. Social media is an access to health information, hence it is needed to evaluate the quality ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-18T09:44:20Z
  DOI: 10.1177/20552076251327039
  Issue No: Vol. 11 (2025)
Aged smart-care application program for promoting quality of life among
older adults in the community: Study protocol of a three-arm randomized
controlled trial
- Authors: Yue Xu; Qiyuan Huang, Shuang Teng, Yulong Wang, Huabei Sun, Mei Li, Junxin Li, Muwei Zhu, Xianping Tang
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectivesTo describe a study protocol for a three-arm randomized controlled trial that will evaluate the effectiveness of an Aged Smart-Care (ASC) application program intervention for community-dwelling older adults in China.MethodsThis randomized ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-18T09:08:53Z
  DOI: 10.1177/20552076251326218
  Issue No: Vol. 11 (2025)
Understanding vaccine hesitancy: Insights from social media on polio,
human papilloma virus, and COVID-19 in Zambia
- Authors: Samuel Munalula MunjitaDepartment of Biomedical Sciences; School of Health Sciences, 108234University of Zambia, Lusaka, Zambia
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectivesVaccine hesitancy remains a critical challenge to public health in Zambia and globally, necessitating a deeper understanding of the factors influencing this phenomenon. The study analyzed user-generated Facebook comments from January 2021 to ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-18T09:08:27Z
  DOI: 10.1177/20552076251326131
  Issue No: Vol. 11 (2025)
Recommendations for mobile apps for mental health treatment: Qualitative
interviews with psychiatrists
- Authors: Harleen Gill; Catriona Hippman, Saskia Hanft-Robert, Lena Nugent, Ondřej Nováček, Mostafa M. Kamel, Deirdre Ryan, Regina Demlová, Michael Krausz, Katarina Tabi
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundThe number of mobile apps tailored for people living with mental health conditions has increased tremendously. However, the majority of the existing apps are not evidence-based and are being developed by teams without mental health expertise....
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-17T03:37:50Z
  DOI: 10.1177/20552076251325951
  Issue No: Vol. 11 (2025)
Defining the physiological bounds of left ventricular ejection time with a
- Authors: Lisa M. Hofer; Jon-Émile S. Kenny, Chelsea E. Munding, Isabel Kerrebijn, Sarah Atwi, Aarron Younan, Joseph K. Eibl
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundFlow time (FT) or the left ventricular ejection time (LVET) is the duration of mechanical systole, when the aortic valve is open and ejecting blood. LVET can be measured in the common carotid artery from the time of the systolic upstroke to the ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-17T03:36:50Z
  DOI: 10.1177/20552076251323838
  Issue No: Vol. 11 (2025)
Virtually assisted home rehabilitation after acute stroke (VAST-rehab): A
descriptive pilot study for young and underserved stroke survivors
- Authors: Emily A. Stevens; Neha Muraly, Carolyn P. Da Silva, Lorie Richards, Aylen Sosa, Heather Smith, Allyson Seals Richard, Shehzeen Manji, Mary E. Russell, Sean I. Savitz
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  IntroductionThe objective ofVirtually Assisted home rehabilitation after acute STrokewas to offer a fully remote telerehabilitation intervention to stroke survivors during the COVID-19 pandemic.MethodsParticipants were recruited from acute care, ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-17T03:36:50Z
  DOI: 10.1177/20552076251324443
  Issue No: Vol. 11 (2025)
Criterion validity of a newly developed Apple Watch app (‘MVPA’)
compared to the native Apple Watch ‘activity’ app for measuring
criterion moderate intensity physical activity
- Authors: Ashley Warner; Natalie Vanicek, Amanda Benson, Tony Myers, Grant Abt
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  IntroductionMany people fail to meet physical activity guidelines. One possible solution is wearable technology. Yet it is unclear if popular devices such as the Apple Watch can accurately measure intensity, which is a cornerstone of physical activity ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-17T03:36:50Z
  DOI: 10.1177/20552076251326225
  Issue No: Vol. 11 (2025)
Automated program using convolutional neural networks for objective and
reproducible selection of corneal confocal microscopy images
- Authors: Qincheng Qiao; Wen Xue, Jinzhe Li, Wenwen Zheng, Yongkai Yuan, Chen Li, Fuqiang Liu, Xinguo Hou
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveDiabetic peripheral neuropathy (DPN) is a common complication of diabetes, posing a significant risk for foot ulcers and amputation. Corneal confocal microscopy (CCM) is a rapid, noninvasive method to assess DPN by analysing corneal nerve fibre ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-17T03:36:50Z
  DOI: 10.1177/20552076251326223
  Issue No: Vol. 11 (2025)
Users’ perspectives about challenges of using telemedicine for patient
with diabetes during the COVID-19 pandemic
- Authors: Fatemeh Mirasghari; Haleh Ayatollahi, Farnia Velayati
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  PurposeTelemedicine services have been recognized as a safe and affordable method for providing continuous healthcare services, especially to patients with chronic diseases. Despite all advantages, the use of this technology has faced several challenges ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-17T03:36:50Z
  DOI: 10.1177/20552076251325969
  Issue No: Vol. 11 (2025)
A double-edged sword': Digitalization, health disparities, and the
paradoxical case of e-pharmacy in Ghana
- Authors: Shamshad Khan; Naessiamba Eab-Aggrey
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveWith the ongoing push for greater digitalization of healthcare in low- and middle- income countries (LMICs), the larger questions around who will benefit most from such efforts and what elements of disparities and inequities may further be ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-17T03:36:50Z
  DOI: 10.1177/20552076251326224
  Issue No: Vol. 11 (2025)
Implementation-effectiveness of the power over pain portal for patients
awaiting a tertiary care consultation for chronic pain: A pilot
feasibility study
- Authors: Alesha C. King; Amin Zahrai, Etienne J. Bisson, Yaadwinder Shergill, Danielle Rice, Eugene Wai, Natalie Zur Nedden, Lynn Cooper, Daniel James, Joshua A. Rash, Rachael Bosma, Tim Ramsay, Patricia Poulin
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundThe Power Over Pain (POP) Portal is a digital platform that provides people living with pain (PLWP) flexible access to chronic pain self-management resources.AimsTo (1) determine the feasibility of an adequately-powered multisite trial of the ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-17T03:36:50Z
  DOI: 10.1177/20552076251326229
  Issue No: Vol. 11 (2025)
Mental Health Staff's Views on Social Media Use Among People with
Psychosis: A Cross-Sectional Survey
- Authors: Xiaolong Zhang; Natalie Berry, Sandra Bucci
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectivesThe use of social media is prevalent in society; however, existing evidence is not sufficient to conclude whether the benefits of social media use can outweigh the risks for people with psychosis. In response to a recent call for staff to take a ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-17T03:36:50Z
  DOI: 10.1177/20552076251321059
  Issue No: Vol. 11 (2025)
Leveraging healthcare professionals’ insights to enhance data quality in
medical big data platforms: A qualitative study
- Authors: Huang Jingjing; Huang Sufang, Lang Xiaorong, Liu Yuchen, Zhang Kexin, Liu ShiyaDepartment of Emergency, Tongji Hospital, 12403Tongji Medical College, Huazhong University of Science Technology, Wuhan, Hubei, China
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveThis study aims to explore the awareness, attitudes, and actual usage of medical big data platforms among healthcare professionals to provide practical guidance and theoretical support for improving data quality for the development of medical ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-17T03:36:50Z
  DOI: 10.1177/20552076251326697
  Issue No: Vol. 11 (2025)
VR-NRP: A development study of a virtual reality simulation for training
in the neonatal resuscitation program
- Authors: Mustafa Yalin Aydin; Vernon Curran, Susan White, Lourdes Peña-Castillo, Oscar Meruvia-Pastor
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectivesVirtual reality (VR) offers the potential to provide a lifelike, safe, and interactive environment where healthcare providers can practice and refresh their skills. The Neonatal Resuscitation Program (NRP) is an evidence-based and standardized ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-17T03:36:50Z
  DOI: 10.1177/20552076251323989
  Issue No: Vol. 11 (2025)
Transforming healthcare with chatbots: Uses and applications—A
scoping review
- Authors: Marina Barreda; David Cantarero-Prieto, Daniel Coca, Abraham Delgado, Paloma Lanza-León, Javier Lera, Rocío Montalbán, Flora Pérez
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  PurposeThe COVID-19 pandemic has intensified the demand and use of healthcare resources, prompting the search for efficient solutions under budgetary constraints. In this context, the increasing use of artificial intelligence and telemedicine has emerged ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-17T03:36:50Z
  DOI: 10.1177/20552076251319174
  Issue No: Vol. 11 (2025)
Comment on: Validity and reliability of the Chinese version of digital
health readiness questionnaire among hypertension patients in rural areas
of China
- Authors: Katherine Ning LiInstitute of Sports Science; 66351Xi'an Physical Education University, Shaanxi, China
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  It is the comment on the published article of “Validity and reliability of the Chinese version of digital health readiness questionnaire among hypertension patients in rural areas of China.” The discourse primarily referred the innovative aspects of the d ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-14T12:11:36Z
  DOI: 10.1177/20552076251327519
  Issue No: Vol. 11 (2025)
The global research of artificial intelligence on inflammatory bowel
disease: A bibliometric analysis
- Authors: Suqi Zeng; Chenyu Dong, Chuan Liu, Junhai Zhen, Yu Pu, Jiaming Hu, Weiguo Dong1Department of Gastroenterology, 117921Renmin Hospital of Wuhan University, Wuhan, Hubei, China
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  AimsThis study aimed to evaluate the related research on artificial intelligence (AI) in inflammatory bowel disease (IBD) through bibliometrics analysis and identified the research basis, current hotspots, and future development.MethodsThe related ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-14T11:39:38Z
  DOI: 10.1177/20552076251326217
  Issue No: Vol. 11 (2025)
Advancing personalized medicine in digital health: The role of artificial
intelligence in enhancing clinical interpretation of 24-h ambulatory blood
pressure monitoring
- Authors: Sreyoshi F. Alam; Charat Thongprayoon, Jing Miao, Justin H. Pham, Mohammad S. Sheikh, Oscar A. Garcia Valencia, Gary L. Schwartz, Iasmina M. Craici, Maria L. Gonzalez Suarez, Wisit Cheungpasitporn
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  Background:The use of artificial intelligence (AI) for interpreting ambulatory blood pressure monitoring (ABPM) data is gaining traction in clinical practice. Evaluating the accuracy of AI models, like ChatGPT 4.0, in clinical settings can inform their ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-14T11:39:10Z
  DOI: 10.1177/20552076251326014
  Issue No: Vol. 11 (2025)
Discourses on smoke-free policies on Dutch Twitter: A social network
analysis
- Authors: Roel O Lutkenhaus; Abel Meijberg, Famke JM Mölenberg, Jasper V Been, Martine PA Bouman
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectivesIn highly mediatized societies, online discourses may contribute to whether novel smoke-free policies become a success. This study analyses Dutch public discourses about smoke-free policies on Twitter, which has been known as X since 2023....
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-14T11:38:56Z
  DOI: 10.1177/20552076251325583
  Issue No: Vol. 11 (2025)
Evaluating remote healthcare practices: Experiences and recommendations of
healthcare professionals on smart applications
- Authors: Fadime Baştürk; Arif Osman Tokat, Osman Öztürk, Çiğdem Kader, Levent Işikay
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveWith the digitalization of objects and spaces, healthcare services are being reshaped globally, creating many potential applications. This study aimed to determine the application potential of remote healthcare services (RHS) in a hospital by ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-14T11:38:42Z
  DOI: 10.1177/20552076251315786
  Issue No: Vol. 11 (2025)
Mitigate or exacerbate' Assessing digital engagement's impact on mental
health inequalities across gender and urban–rural divides
- Authors: Yangyang Wang; Chen Li
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectivesMental health inequalities have increasingly become an important factor affecting social well-being. Existing researches have focused on the impact of digital inequalities on mental health, but there is lack of research exploring the impact of ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-13T02:24:55Z
  DOI: 10.1177/20552076251326673
  Issue No: Vol. 11 (2025)
Exploring the potential of telehealth in-flight medical emergencies
- Authors: Yikeun Kim; Sung Chul Bae, Yoo-Seong Song
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  In-flight medical emergencies occur at an average of 127 incidents per one million passengers, without of physicians present at 41.1%. In response, telehealth can play a crucial role in swiftly addressing these emergencies. Adequate internet speed and ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-13T02:24:44Z
  DOI: 10.1177/20552076251326666
  Issue No: Vol. 11 (2025)
Quality evaluation and functional classification of Arabic health apps: A
systematic review
- Authors: Asma AM Abahussin; Ahmed AA Alnakhibi, Bandar MA AlfulaijDepartment of Biomedical Technology, College of Applied Medical Sciences, 37850King Saud University, Riyadh, Saudi Arabia
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveThe study aimed to systematically review mHealth apps available for Arabic speakers regarding quality and functional classification.MethodsA systematic search was conducted on the Apple App Store. Eligible apps were downloaded and tested ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-13T02:24:34Z
  DOI: 10.1177/20552076251326234
  Issue No: Vol. 11 (2025)
Machine learning for clustering and classification of early knee
osteoarthritis using single-leg standing kinematics
- Authors: Ui-Jae Hwang; Kyu Sung Chung, Sung-Min Ha
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveDetection of early osteoarthritis (EOA) of the knee is crucial for effective management and improved outcomes. This study investigated the application of machine learning techniques to single-leg standing (SLS) kinematics to classify and predict ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-13T02:24:26Z
  DOI: 10.1177/20552076251326226
  Issue No: Vol. 11 (2025)
Compassion through technology: Digital empathy concept analysis and
implications in nursing
- Authors: Ebtsam Aly Abou Hashish
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveDigital empathy is an emerging concept in telehealth nursing, emphasizing emotional engagement and compassionate communication in virtual care settings. Despite its importance, digital empathy remains underexplored. This concept analysis aims to ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-13T02:24:14Z
  DOI: 10.1177/20552076251326221
  Issue No: Vol. 11 (2025)
Perceptions about the use of virtual assistants for seeking health
information among caregivers of young childhood cancer survivors
- Authors: Emre Sezgin; Daniel I. Jackson, Kate Kaufman, Micah A. Skeens, Cynthia A. Gerhardt, Emily Moscato
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectivesThis study examined the perceptions of caregivers of young childhood cancer survivors (YCCS) regarding the use of virtual assistant (VA) technology for health information seeking and care management. The study aim was to understand how VAs can ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-13T02:22:50Z
  DOI: 10.1177/20552076251326160
  Issue No: Vol. 11 (2025)
Intrinsic motivations in health and fitness app engagement: A mediation
model of entertainment
- Authors: Thuy Dung Pham Thi; Nam Tien DuongDepartment of Science, Technology, Finance (UEF), Ho Chi Minh City, Vietnam
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveThis study aims to investigate the intrinsic motivations driving continued usage of health and fitness apps, addressing a gap in the literature where user motivation has received limited attention. The study focuses on the role of entertainment ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-13T02:22:39Z
  DOI: 10.1177/20552076251326151
  Issue No: Vol. 11 (2025)
Impact of offline experiences on consumer decisions: Balanced vs.
unbalanced social exchange in switching to online health services
- Authors: Teng Wang; Yongqiang Sun
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveThe diffusion of online health consultations (OHCs) depends on consumers switching from offline to online channels; consumers’ offline experience on health servicer determines their decisions. Our research aims to address this gap in the ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-13T02:22:26Z
  DOI: 10.1177/20552076251326135
  Issue No: Vol. 11 (2025)
Effective behavioral change techniques in m-health app supported
interventions for glycemic control among patients with type 2 diabetes: A
meta-analysis and meta-regression analysis of randomized controlled trials
- Authors: Kaifeng Liu; Yuxuan XiaAcademy of Medical Engineering Translational Medicine, 12605Tianjin University, Tianjin, China
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  ObjectiveThis review examined the effectiveness of mobile-health (m-health) app-supported interventions in improving patient health outcomes. It also sought to describe the behavior change techniques (BCTs) used in these interventions and identify ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-13T02:22:12Z
  DOI: 10.1177/20552076251326126
  Issue No: Vol. 11 (2025)
An assessment of ChatGPT in error detection for thyroid ultrasound
reports: A comparative study with ultrasound physicians
- Authors: Zhirong Xu; Jiayi Ye, Weiwen Luo, Lina Han, Hui Yin, Yanru Li, Qichen Su, Shanshan Su, Guorong Lyu, Shaohui Li
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundThis study evaluates the performance of GPT-4o in detecting errors in ACR TIRADS ultrasound reports and its potential to reduce report generation time.MethodsA retrospective analysis of 200 thyroid ultrasound reports from the Second Affiliated ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-13T02:21:52Z
  DOI: 10.1177/20552076251326019
  Issue No: Vol. 11 (2025)
Translation and psychometric evaluation of the Traditional Chinese eHealth
Literacy Scale among adults with type 2 diabetes in Taiwan: A
cross-sectional study
- Authors: Yen-Ming Huang; Yu-Meng Yang, Tzu Wang, Hsun-Yu Chan
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  IntroductionThe rise in online health information access has shifted health-seeking behaviors, yet a validated tool to assess eHealth literacy for the Taiwanese context remains unavailable. This study aimed to translate the eHealth Literacy Scale (eHEALS) ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-13T02:20:16Z
  DOI: 10.1177/20552076251325967
  Issue No: Vol. 11 (2025)
Distinguishing threshold shoulder range of motion measures collected by a
breast cancer smartphone application: Assessment in healthy adults
- Authors: Justin Pointer; Angelica E Lang, Denise Balogh, Nathaniel D Osgood, Soo Y Kim
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  Background:Shoulder range of motion (ROM) limitations following breast cancer treatments are common. Remotely monitoring ROM changes after treatments through smartphone applications can expand rehabilitation options for breast cancer patients. The aim of ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-13T02:19:57Z
  DOI: 10.1177/20552076251325954
  Issue No: Vol. 11 (2025)
A comprehensive evaluation of interpretable artificial intelligence for
epileptic seizure diagnosis using an electroencephalogram: A systematic
review
- Authors: Daraje Kaba Gurmessa; Worku Jimma
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundEpilepsy is a sensitive social and health issue that causes sudden death in epilepsy. Awake and sleep electroencephalogram (EEG) first test confirms 80% of patients with confirmed epilepsy. Explainable artificial intelligence (XAI) for epileptic ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-13T02:18:24Z
  DOI: 10.1177/20552076251325411
  Issue No: Vol. 11 (2025)
A web-based intervention to support the mental well-being of sexual and
gender minoritised adolescents: Formative evaluation of Oneself
- Authors: Mathijs FG Lucassen; Rajvinder Samra, Katherine E Brown, Katharine A Rimes, Alicia Núñez-García, Louise M Wallace
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundSexual and gender minoritised adolescents are at an increased risk of mental health problems. However, few interventions have been specifically designed to support their mental well-being.ObjectiveThe purpose of this study was to evaluate ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-13T02:18:09Z
  DOI: 10.1177/20552076251321057
  Issue No: Vol. 11 (2025)
Intelligent wearable devices with audio collection capabilities to assess
chronic obstructive pulmonary disease severity
- Authors: Chunbo Zhang; Kunyao Yu, Zhe Jin, Yingcong Bao, Cheng Zhang, Jiping Liao, Guangfa WangDepartment of Respiratory Critical Care Medicine, 26447Peking University First Hospital, Beijing, China
  Abstract: DIGITAL HEALTH, Volume 11, January-December 2025.
  BackgroundIntelligent wearable devices have potential for chronic obstructive pulmonary disease (COPD) monitoring, but the effectiveness of combining cough and blowing sounds for disease assessment is unclear.ObjectiveThe objective was to assess COPD ...
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-13T02:17:55Z
  DOI: 10.1177/20552076251320730
  Issue No: Vol. 11 (2025)
Enhancing physical activity through a relational artificial intelligence
chatbot: A feasibility and usability study
- Authors: Yoo Jung Oh, Kai-Hui Liang, Diane Dagyong Kim, Xuanming Zhang, Zhou Yu, Yoshimi Fukuoka, Jingwen Zhang; Kai-Hui Liang, Diane Dagyong Kim, Xuanming Zhang, Zhou Yu, Yoshimi Fukuoka, Jingwen Zhang
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveThis study presents a pilot randomized controlled trial to assess the usability, feasibility, and initial efficacy of a mobile app-based relational artificial intelligence (AI) chatbot (Exerbot) intervention for increasing physical activity behavior.MethodsThe study was conducted over a 1-week period, during which participants were randomized to either converse with a baseline chatbot without relational capacity (control group) or a relational chatbot using social relational communication strategies. Objectively measured physical activity data were collected using smartphone pedometers.ResultsThe study was feasible in enrolling a sample of 36 participants and with a 94% retention rate after 1 week. Daily engagement rate with the AI chatbot reached over 88% across the groups. Findings revealed that the control group experienced a significant decrease in steps on the final day, whereas the group interacting with the relational chatbot maintained their step counts throughout the study period. Importantly, individuals who engaged with the relational chatbot reported a stronger social bond with the chatbot compared to those in the control group.ConclusionsLeveraging AI chatbot and the relationship-building capabilities of AI holds promise in the development of cost-effective, accessible, and sustainable behavior change interventions. This approach may benefit individuals with limited access to conventional in-person behavior interventions.Clinical trial registrationsClinicalTrials.gov; NCT05794308; https://clinicaltrials.gov/ct2/show/NCT05794308.
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-04T07:10:15Z
  DOI: 10.1177/20552076251324445
  Issue No: Vol. 11 (2025)
Mobile apps for cancer patients: Identifying positive impacts and concerns
- Authors: Wei Leidong, Michelle Monachino, Don Lloyd-Williams, Thi Le Ha Nguyen, Brayal Dsouza, Joaquim Paulo Moreira; Michelle Monachino, Don Lloyd-Williams, Thi Le Ha Nguyen, Brayal Dsouza, Joaquim Paulo Moreira
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundMobile health is being increasingly considered as a strategy to deliver healthcare to people with chronic diseases. This stands particularly true for cancer management where treatment is being progressively administered at home, requiring more involvement, education, and changes in behavior from patients. This article aims to identify the main axes of intervention for behavioral change of mHealth in cancer management and its relative impacts, as well as identify recent evidence on user preferences for optimal engagement in mHealth-based behavioral change strategies.Methodological ApproachA literature search was carried out in the Databases PubMed and Cochrane during the period October–December 2023. The search retrieved 505 initial entries narrowed down to 21 articles included in this commentary.ResultsEvidence is available on Mobile apps for cancer management being used to successfully promote behavioral changes in the areas of treatment adherence, symptoms self-management, communication with healthcare professionals, and holistic well-being in cancer patients. These are activities traditionally relevant in healthcare management interventions and contribute to further developing the relevance of the field of Digital Health in healthcare management.Relevance to Clinical PracticeThe article contributes to a practical understanding of how Mobile interventions are being applied to promote higher self-care, a better emotional status, lesser adverse impacts, and, ultimately, increased survival rates for cancer patients. Several cancer patients’ preferences were identified for the promotion of user engagement related to app design, available features, interoperability, and app creation process, as well as advanced healthcare management intervention. Preferences were found to be different for adolescents and young adult cancer patients when compared to other cohort groups.
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-04T06:42:47Z
  DOI: 10.1177/20552076241305707
  Issue No: Vol. 11 (2025)
Development of an interactive biosensing application for assessing finger
dexterity
- Authors: Michal Greenberg Abrahami, Yehuda Warszawer, Alon Kalron, Emanuel Shirbint, Maria Didikin, Anat Achiron; Yehuda Warszawer, Alon Kalron, Emanuel Shirbint, Maria Didikin, Anat Achiron
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveAccurate finger function assessment is crucial for monitoring the performance of daily hand activities. However, specialized digital applications are lacking for evaluating various finger tasks. This study aims to develop a custom digital biosensing application to assess finger dexterity.MethodsWe developed a digital biosensing application compatible with smartphones and tablets that enables 3-min testing of finger dexterity, measuring velocity and accuracy for each finger and each movement orientation. Data were collected for the dominant hand from a large cohort of healthy volunteers to establish population norms values.ResultsThe construction of the application involved a comprehensive, multi-stage process designed to ensure functionality, user-friendliness, and cross-platform compatibility using the Flutter framework by Google with specific adaptations for Android and iOS. To evaluate the application and construct population norms, 318 healthy subjects, 197 females and 121 males, mean ± age 37.7 ± 13.5 years, were tested. Velocity was faster for the vertical and horizontal tests than all other tests and fastest for finger 2, while the pinch test was the slowest for all fingers. Deviation from any required test orientation was more evident for the circle test and mainly for finger 5, while the vertical and horizontal orientations were the most unerring. Analysis of finger dexterity by age disclosed better performance in the younger age group (
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-04T02:28:43Z
  DOI: 10.1177/20552076241297734
  Issue No: Vol. 11 (2025)
Artificial intelligence (ChatGPT) ready to evaluate ECG in real life'
Not yet!
- Authors: Volkan Çamkıran, Hüseyin Tunç, Batool Achmar, Tuğçe Simay Ürker, İlhan Kutlu, Akin Torun; Hüseyin Tunç, Batool Achmar, Tuğçe Simay Ürker, İlhan Kutlu, Akin TorunBahçeşehir Üniversite Hastanesi Medical Park Göztepe, İstanbul, Turkey
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveThis study aims at evaluating if ChatGPT-based artificial intelligence (AI) models are effective in interpreting electrocardiograms (ECGs) and determine their accuracy as compared to those of cardiologists. The purpose is therefore to explore if ChatGPT can be employed for clinical setting, particularly where there are no available cardiologists.MethodsA total of 107 ECG cases classified according to difficulty (simple, intermediate, complex) were analyzed using three AI models (GPT-ECGReader, GPT-ECGAnalyzer, GPT-ECGInterpreter) and compared with the performance of two cardiologists. The statistical analysis was conducted using chi-square and Fisher exact tests using scikit-learn library in Python 3.8.ResultsCardiologists demonstrated superior accuracy (92.52%) compared to ChatGPT-based models (GPT-ECGReader: 57.94%, GPT-ECGInterpreter: 62.62%, GPT-ECGAnalyzer: 62.62%). Statistically significant differences were observed between cardiologists and AI models (p
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-03T06:32:34Z
  DOI: 10.1177/20552076251325279
  Issue No: Vol. 11 (2025)
The application of artificial intelligence in insomnia, anxiety, and
depression: A bibliometric analysis
- Authors: Enshi Lu, Di Zhang, Mingguang Han, Shihua Wang, Liyun He; Di Zhang, Mingguang Han, Shihua Wang, Liyun He
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundMental health issues like insomnia, anxiety, and depression have increased significantly. Artificial intelligence (AI) has shown promise in diagnosing and providing personalized treatment.ObjectiveThis study aims to systematically review the application of AI in addressing insomnia, anxiety, and depression, identifying key research hotspots, and forecasting future trends through bibliometric analysis.MethodsWe analyzed a total of 875 articles from the Web of Science Core Collection (2000–2024) using bibliometric tools such as VOSviewer and CiteSpace. These tools were used to map research trends, highlight international collaboration, and examine the contributions of leading countries, institutions, and authors in the field.ResultsThe United States and China lead the field in terms of research output and collaborations. Key research areas include “neural networks,” “machine learning,” “deep learning,” and “human-robot interaction,” particularly in relation to personalized treatment approaches. However, challenges around data privacy, ethical concerns, and the interpretability of AI models need to be addressed.ConclusionsThis study highlights the growing role of AI in mental health research and identifies future priorities, such as improving data quality, addressing ethical challenges, and integrating AI more seamlessly into clinical practice. These advancements will be crucial in addressing the global mental health crisis.
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-03T06:32:05Z
  DOI: 10.1177/20552076251324456
  Issue No: Vol. 11 (2025)
Application of large language models in healthcare: A bibliometric
analysis
- Authors: Lanping Zhang, Qing Zhao, Dandan Zhang, Meijuan Song, Yu Zhang, Xiufen Wang; Qing Zhao, Dandan Zhang, Meijuan Song, Yu Zhang, Xiufen Wang
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveThe objective is to provide an overview of the application of large language models (LLMs) in healthcare by employing a bibliometric analysis methodology.MethodWe performed a comprehensive search for peer-reviewed English-language articles using PubMed and Web of Science. The selected articles were subsequently clustered and analyzed textually, with a focus on lexical co-occurrences, country-level and inter-author collaborations, and other relevant factors. This textual analysis produced high-level concept maps that illustrate specific terms and their interconnections.FindingsOur final sample comprised 371 English-language journal articles. The study revealed a sharp rise in the number of publications related to the application of LLMs in healthcare. However, the development is geographically imbalanced, with a higher concentration of articles originating from developed countries like the United States, Italy, and Germany, which also exhibit strong inter-country collaboration. LLMs are applied across various specialties, with researchers investigating their use in medical education, diagnosis, treatment, administrative reporting, and enhancing doctor–patient communication. Nonetheless, significant concerns persist regarding the risks and ethical implications of LLMs, including the potential for gender and racial bias, as well as the lack of transparency in the training datasets, which can lead to inaccurate or misleading responses.ConclusionWhile the application of LLMs in healthcare is promising, the widespread adoption of LLMs in practice requires further improvements in their standardization and accuracy. It is critical to establish clear accountability guidelines, develop a robust regulatory framework, and ensure that training datasets are based on evidence-based sources to minimize risk and ensure ethical and reliable use.
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-03T06:31:24Z
  DOI: 10.1177/20552076251324444
  Issue No: Vol. 11 (2025)
Improving machine learning algorithm for risk of early pressure injury
prediction in admission patients using probability feature aggregation
- Authors: Shu-Chen Chang, Shu-Mei Lai, Mei-Wen Wu, Shou-Chuan Sun, Mei-Chu Chen, Chiao-Min Chen; Shu-Mei Lai, Mei-Wen Wu, Shou-Chuan Sun, Mei-Chu Chen, Chiao-Min Chen
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectivePressure injuries (PIs) pose a significant concern in hospital care, necessitating early and accurate prediction to mitigate adverse outcomes.MethodsThe proposed approach receives multiple patients records, selects key features of discrete numerical based on their relevance to PIs, and trains a random forest (RF) machine learning (ML) algorithm to build a predictive model. Pairs of significant categorical features with high contributions to the prediction results are grouped, and the PI risk probability for each group is calculated. High-risk group probabilities are then added as new features to the original feature subset, generating a new feature subset to replace the original one, which is then used to retrain the RF model.ResultsThe proposed method achieved an accuracy of 83.44%, sensitivity of 84.59%, specificity of 83.42%, and an area under the curve of 0.84.ConclusionThe ML-based approach, coupled with feature aggregation, enhances predictive performance, aiding clinical teams in understanding crucial features and the model's decision-making process.
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-03T06:30:54Z
  DOI: 10.1177/20552076251323300
  Issue No: Vol. 11 (2025)
Using machine learning to identify frequent attendance at accident and
emergency services in Lanarkshire
- Authors: Fergus Reid, S. Josephine Pravinkumar, Roma Maguire, Ashleigh Main, Haruno McCartney, Lewis Winters, Feng Dong; S. Josephine Pravinkumar, Roma Maguire, Ashleigh Main, Haruno McCartney, Lewis Winters, Feng Dong
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundFrequent attenders to accident and emergency (A&E) services pose complex challenges for healthcare providers, often driven by critical clinical needs. Machine learning (ML) offers potential for predictive approaches to managing frequent attendance, yet its application in this area is limited. Existing studies often focus on specific populations or models, raising concerns about generalisability. Identifying risk factors for frequent attendance and high resource use is crucial for effective prevention strategies.ObjectivesThis research aims to evaluate the strengths and weaknesses of ML approaches in predicting frequent A&E attendance in NHS Lanarkshire, Scotland, identify associated risk factors and compare findings with existing research to uncover commonalities and differences.MethodHealth and social care data were collected from 17,437 A&E patients in NHS Lanarkshire (2021–2022), including clinical, social and demographic information. Five classification models were tested: multinomial logistic regression (LR), random forests (RF), support vector machine (SVM) classifier, k-nearest neighbours (k-NN) and multi-layer perceptron (MLP) classifier. Models were evaluated using a confusion matrix and metrics such as precision, recall, F1 and area under the curve. Shapley values were used to identify risk factors.ResultsMLP achieved the highest F1 score (0.75), followed by k-NN, RF and SVM (0.72 each), and LR (0.70). Key health conditions and risk factors consistently predicted frequent attendance across models, with some variation highlighting dataset-specific characteristics.ConclusionsThis study underscores the utility of combining ML models to enhance prediction accuracy and identify risk factors. Findings align with existing research but reveal unique insights specific to the dataset and methodology.
  Citation: DIGITAL HEALTH
  PubDate: 2025-03-03T06:29:56Z
  DOI: 10.1177/20552076251315293
  Issue No: Vol. 11 (2025)
Evaluating YouTube as a source of information on amyotrophic lateral
sclerosis
- Authors: Liamuel Giancarlo V Untalan, John Paulo D Malanog, Roland Dominic G Jamora; John Paulo D Malanog, Roland Dominic G Jamora
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundAmyotrophic lateral sclerosis (ALS) is a rare neurodegenerative disease that leads to progressive motor weakness and eventual death. Recent years have seen an increase in online information on ALS, with the popular video platform YouTube becoming a prominent source. We aimed to evaluate the quality, reliability, actionability, and understandability of ALS videos on YouTube.MethodsA search was performed using the keyword “Amyotrophic Lateral Sclerosis” on YouTube. A total of 240 videos were viewed and assessed by two independent raters. Video characteristics such as type of uploader, views, likes, comments, and Video Power Index were also collected.ResultsVideos had moderate to low quality and reliability (Global Quality Scale [GQS] and modified DISCERN [mDISCERN] median 2.5), and poor understandability and actionability (PEMAT total median 8.5). Among the general video characteristics, only length of video showed a significant positive correlation across the tools (with mDISCERN [p
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-28T08:29:45Z
  DOI: 10.1177/20552076251324439
  Issue No: Vol. 11 (2025)
The application of artificial intelligence in stroke research: A
bibliometric analysis
- Authors: Yun Peng, Zhen Zhao, Yutong Rao, Ke Sun, Jiayi Zou, Guanqing Liu; Zhen Zhao, Yutong Rao, Ke Sun, Jiayi Zou, Guanqing Liu
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundCurrently, artificial intelligence (AI) has been widely used for the prediction, diagnosis, evaluation and rehabilitation of stroke. However, the quantitative and qualitative description of this field is still lacking.ObjectiveThis study aimed to summarize and elucidate the research status and changes in hotspots on the application of AI in stroke over the past 20 years through bibliometric analysis.Materials and MethodsPublications on the application of AI in stroke in the past two decades were retrieved from the Web of Science Core Collection. Microsoft Excel was used to analyze the annual publication volume. The cooperation network map among countries/regions was generated on an online platform (https://bibliometric.com/). CiteSpace was used to visualize the co-occurrence of institutions and analyze the timeline view of references and burst keywords. The network visualization map of keywords co-occurrence was generated by VOSviewer.ResultsA total of 4437 publications were included. The annual number of published documents shows an upwards trend. The USA published the most documents and has the top 3 most productive institutions. Journal of Neuroengineering and Rehabilitation and Stroke are the journals with the most publications and citations, respectively. The keywords co-occurrence network classified the keywords into four themes, that is "rehabilitation," "machine learning," "recovery" and "upper limb function." The top 3 keywords with the strongest burst strength were "arm," "upper limb" and "therapy." The most recent keywords that burst after 2020 and last until 2023 included "scores," "machine learning," "natural language processing" and "atrial fibrillation."ConclusionThe USA shows a leading position in this field. At present and in the next few years, research in this field may focus on the prediction/rapid diagnosis of potential stroke patients by using machine learning, deep learning and natural language processing.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-28T08:28:24Z
  DOI: 10.1177/20552076251323833
  Issue No: Vol. 11 (2025)
Biobeat monitor utilization in various healthcare settings: A systematic
review
- Authors: Yifan Zhang, Jill Querney, Yamini Subramani, Kendra Naismith, Priyanka Singh, Lee-Anne Fochesato, Nida Fatima, Natasha Wood, Richard Malthaner, Mahesh Nagappa; Jill Querney, Yamini Subramani, Kendra Naismith, Priyanka Singh, Lee-Anne Fochesato, Nida Fatima, Natasha Wood, Richard Malthaner, Mahesh Nagappa
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundEnsuring accurate and continuous monitoring of patients’ physiological parameters is paramount for evaluating their health status and guiding clinical decision-making. Technological advancements have the potential to significantly improve patient care and outcomes by offering a seamless continuum of healthcare experiences. Biobeat Technologies Ltd has pioneered a non−invasive wearable approach to acquiring advanced hemodynamic parameters, employing devices such as the BB−613WP wrist monitor and the BB−613P chest patch. Biobeat devices have been applicable across many clinical settings, as substantiated by a growing body of research. This systematic review endeavours to comprehensively consolidate the evidence regarding using Biobeat monitors across various clinical scenarios.MethodsFrom 2016 to 2024, a thorough literature search was conducted across multiple databases. The inclusion criteria for selected studies comprised adult patients aged 18 years or older in any healthcare setting, employing Biobeat monitoring devices (wrist monitors and/or chest patches), reporting at least one outcome or finding, and presenting fully published original research studies, including randomized controlled trials and prospective or retrospective cohort studies. The quality and risk of bias assessment for the studies was performed using the Newcastle−Ottawa scale and COSMIN scoring system.ResultsAmong 27 studies identified, 15 met the inclusion criteria, involving 4248 patients. These included 14 prospective observational studies and one retrospective cohort study; no randomized control trials were identified. Notably, eight studies were conducted in ambulatory settings, with 1 study focusing on patients undergoing labor and delivery. Additionally, three studies were carried out in general inpatient wards, 1 in a medical ICU and another in a cardiac surgery ICU (CSICU). Furthermore, 1 study presented results from 3 separate investigations— 2 in ambulatory settings and 1 in the CSICU. Across all studies, Biobeat devices were consistently utilized, with each study reporting positive outcomes associated with their use.ConclusionThis systematic review demonstrates that Biobeat's non-invasive wearable devices have been effectively utilized across various clinical settings, consistently contributing to positive patient outcomes. The versatility and reliability of these devices highlight their potential to enhance patient care and support clinical decision-making, warranting further research to explore their broader applications.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-27T06:56:31Z
  DOI: 10.1177/20552076251324012
  Issue No: Vol. 11 (2025)
Usability of a mobile app for suicide risk awareness in South Korea
- Authors: Moon-Heum Cho, Seokwon Hwang, Ye Jin Kim, Dong Hun Lee, Hong Jin Jeon, Kyung-Eun Park; Seokwon Hwang, Ye Jin Kim, Dong Hun Lee, Hong Jin Jeon, Kyung-Eun Park
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundSuicide rates have significantly increased in South Korea, yet many individuals lack adequate support. Barriers such as reluctance to seek mental health help and fear of social stigma contribute to this gap. A mobile app focused on suicide risk awareness could provide accessible support, though none are currently available for public use in South Korea. This study conducted a usability test on a newly developed suicide risk awareness app using a mixed methods approach.MethodsThirty-eight students from a large university in South Korea participated in the study, with 19 in a high-risk suicide group and 19 in a nonrisk suicide group. After using the app for 2 weeks, all participants completed an online usability survey, and 19 students took part in individual interviews.ResultsIndependent samples t-tests showed that participants, regardless of risk group, rated the app positively for ease of use, accessibility, design, perceived learning, and satisfaction. Regression analysis identified perceived learning as the strongest predictor of satisfaction, followed by ease of use. The qualitative analysis highlighted areas for improvement, including providing direct and guided feedback on suicide risk.ConclusionThe study demonstrated the potential of a mobile app to enhance suicide risk awareness among young adults in South Korea. Moreover, user engagement with the app can be improved by ensuring confidentiality and fostering perceived learning.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-27T06:55:43Z
  DOI: 10.1177/20552076251322666
  Issue No: Vol. 11 (2025)
Cost-effectiveness of a digital supportive healthcare pathway for type 2
diabetes compared to usual care in Belgium
- Authors: Lisa Van Wilder, Delphine De Smedt, Lieselotte Sandra, Dorien Vandormael, Joyce Kaes, Ruben Willems, Jeroen Trybou; Delphine De Smedt, Lieselotte Sandra, Dorien Vandormael, Joyce Kaes, Ruben Willems, Jeroen Trybou
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveTo evaluate the cost-effectiveness of a digital supportive healthcare pathway in patients with type 2 diabetes mellitus (T2DM) compared to usual care.MethodsAn age-dependent Markov model was applied from a healthcare payer perspective projecting results of a clinical trial study over a time horizon of 22 years assuming a continuous implementation of the intervention every year, 24/7. The setting was Flanders (Belgium). One-way and probabilistic sensitivity analyses were performed.ResultsThe hybrid care pathway led to a quality-adjusted life year (QALY) gain of 5.97, while the costs increased with €663,036. This resulted in an incremental cost-effectiveness ratio of €110,989/QALY. With a cost-effectiveness threshold of 45,000€/QALY, the hybrid care pathway was found not cost-effective compared to the usual care trajectory. Sensitivity analyses showed that over 50% of iterations exceeded the threshold, with a cost-effectiveness probability of 13.12% at €45,000/QALY.ConclusionsThis cost-effectiveness analysis indicates that a hybrid care pathway is unlikely to be a cost-effective approach compared to the standard care trajectory in patients with T2DM. Nevertheless, the exploration of technology-driven healthcare pathways are vital for advancing patient well-being, emphasizing the need for further research to optimize resource utilization and enhance outcomes effectively.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-27T06:55:18Z
  DOI: 10.1177/20552076251319169
  Issue No: Vol. 11 (2025)
Design of a mobile application for universal screening for women of
child-bearing age engaged in comprehensive addiction and recovery
environments (WE-CARE) for substance use and women from the general
population
- Authors: Autumn Shifflett, Lacey Karpisek, Kajal Patel, Yi Cui, Maayan Lawental, Golfo Tzilos Wernette, H Katie Chang, Krystyna R. Isaacs, Tony X Ma; Lacey Karpisek, Kajal Patel, Yi Cui, Maayan Lawental, Golfo Tzilos Wernette, H Katie Chang, Krystyna R. Isaacs, Tony X Ma
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveFormative research was conducted to identify barriers to universal screening for alcohol/substance use, depression, and anxiety in women of childbearing age (WOCA,18–44 years of age) drawn from the general population and from women in a residential treatment program for alcohol and/or drug use.MethodsE-surveys (n = 467), focus groups with women aged 18–44 (n = 30), and in-depth interviews (IDIs) with healthcare providers (HCPs, n = 8) were conducted to create a user-centered design for a mHealth application.ResultsE-surveys revealed that 80% of the women were asked about alcohol use at a visit with their HCP, while 70% were asked about drug use. Only 35% of the respondents indicated an HCP discussed their answers with them. Two focus groups with WOCA revealed minimal to no prior knowledge of risk factors related to alcohol and substance use. Barriers to treatment identified included a lack of readiness to change, cost, minimal social support systems, and a perceived sense of public stigma. In-depth interviews with HCPs revealed not all HCPs use screenings for substance use due to lack of time to conduct a screening, unfamiliarity with standardized screening tools, insufficient training to provide proper follow-up care, no mandates requiring such screenings, and a concern that asking follow-up questions may negatively impact their relationship with the client.ConclusionResults from the formative research studies were used to inform the design and development of the WE-CARE app prototype. The prototype, which includes educational content on alcohol and substance use disorders (SUDs), a moderated discussion forum, FAQs, and a Chatbot to encourage participants to make an appointment with an SUD treatment center if indicated, is undergoing pilot testing.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-27T06:54:34Z
  DOI: 10.1177/20552076251318054
  Issue No: Vol. 11 (2025)
Radiomics analysis based on plain X-rays to detect spinal fractures with
posterior wall injury
- Authors: Wangmi Liu, Xiaxuan Zhang, Ruofu Tang, Chengcheng Yu, Guofang Sun; Xiaxuan Zhang, Ruofu Tang, Chengcheng Yu, Guofang Sun
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  PurposeSpinal fractures, particularly those involving posterior wall injury, pose a heightened risk of instability and significantly influence treatment strategies. This study aimed to improve early diagnosis and treatment planning for spinal fractures through radiomics analysis based on plain X-ray imaging.MethodsThis retrospective study analyzed plain X-rays of patients with spinal fractures at the thoracolumbar junction. Radiomic features were extracted from both anteroposterior and lateral plain spine radiographs to evaluate the utility of radiomics in detecting posterior wall injury. Diagnostic accuracy, sensitivity, and specificity of the radiomics models were assessed and compared with the performance of a spine surgeon.ResultsA total of 100 patients were included in the study, and four radiomic features were identified to construct radiomic signatures. In the training set, the RandomForest, ExtraTrees, and eXtreme Gradient Boosting (XGBoost) models achieved an area under the curve (AUC) of 1. In the validation set, the highest AUC value was 0.889, achieved by the RandomForest and XGBoost models. The diagnostic accuracy, sensitivity, and specificity of the radiomics models outperformed those of the spine surgeon.ConclusionsRadiomics analysis based on plain X-ray imaging demonstrates significant potential for detecting posterior wall injury following spinal fractures. This approach offers a promising tool for early diagnosis and informed clinical decision-making in the management of spinal fractures.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-26T07:11:22Z
  DOI: 10.1177/20552076251324436
  Issue No: Vol. 11 (2025)
Structuring and centralizing breast cancer real-world biomarker data from
pathology reports through C-LAB® artificial intelligence platform
- Authors: Florent Le Borgne, Camille Garnier, Camille Morisseau, Yanis Navarrete, Yanina Echeverria, Juan Mir, Jaume Calafell, Tanguy Perennec, Olivier Kerdraon, Jean-Sébastien Frenel, Judith Raimbourg, Mario Campone, Maria Fe Paz, François Bocquet; Camille Garnier, Camille Morisseau, Yanis Navarrete, Yanina Echeverria, Juan Mir, Jaume Calafell, Tanguy Perennec, Olivier Kerdraon, Jean-Sébastien Frenel, Judith Raimbourg, Mario Campone, Maria Fe Paz, François Bocquet
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  PurposeTo evaluate the effectiveness of C-LAB®, an artificial intelligence (AI) platform, in extracting, structuring, and centralizing biomarker data from breast cancer pathology reports within the challenging, heterogeneous dataset of the Institut de Cancérologie de l’Ouest (ICO).MethodsC-LAB® was deployed at the ICO to analyze HER2 and hormonal receptor data from breast cancer pathology reports. During the development phase, 292 anatomic pathology reports were used to design and refine the rule-based extraction algorithm through an iterative process of monitoring and adjustments. After finalizing the algorithm, it was applied to a total of 2323 anatomic pathology reports. To evaluate the platform's accuracy, performance metrics could only be calculated for a subset of these reports that were also available in the structured National Epidemiological Strategy and Medical Economics (ESME) database. Out of the 2323 pathology reports belonging to 487 patients analyzed by C-LAB®, 666 corresponded to 97 patients present in the ESME database. These reports were used as the gold standard for performance assessment, as ESME provides structured data against which the outputs of the C-LAB® algorithm could be compared.ResultsC-LAB® achieved over 80% agreement with human extractions (precision, recall, and F1-score) in structuring biomarker data from complex, unstructured pathology reports, despite dataset variability and optical character recognition errors. While the ESME database served as a benchmark, its reliance on single manual data entry without secondary review introduces potential inaccuracies, suggesting the observed performance reflects close alignment between human and algorithmic extractions rather than absolute accuracy. C-LAB® demonstrates significant potential to reduce manual workload, centralize data, and enable scalable, real-time reporting.ConclusionAI technologies like C-LAB® show significant potential in creating accessible and actionable digital factories from complex pathology data, aiding in the precision management of diseases such as breast cancer diagnostics and treatment.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-26T07:11:00Z
  DOI: 10.1177/20552076251323110
  Issue No: Vol. 11 (2025)
Data governance and open sharing in the fields of life sciences and
medicine: A bibliometric analysis
- Authors: Yanrui Qiu, Zhimin Hu; Zhimin HuSchool of Health Policy Management, 12501Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing, China
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveThis study aims to conduct a bibliometric analysis of literature related to data governance and open sharing in the fields of life sciences and medicine, so as to clarify the characteristics of publications and explore research hotspots and trends.MethodsA total of 2529 valid documents published in the Web of Science Core Collection database from 2000 to 2024 were included in this study. VOSviewer was used for co-occurrence analysis, while CiteSpace was employed for clustering, burst detection, and thematic evolution analysis.ResultsBetween 2000 and 2024, the number of studies on data governance and open sharing in the fields of life sciences and medicine has increased annually, indicating the growing importance of research in this area. The USA led as the country with the most research output in this field. The University of Oxford was the institution with the highest publication volume, Amy L. McGuire was the most active author, and the Journal of Medical Internet Research and the Journal of the American Medical Informatics Association were the most frequent publication outlets. The most cited reference was ‘Comment: The FAIR Guiding Principles for Scientific Data Management and Stewardship’.ConclusionsTopics such as the FAIR principles, ethical issues, public attitudes toward data sharing, data quality, databases, and big data analysis techniques are hotspots in this field. Potential research frontiers include the FAIR principles, data quality, public trust and attitudes toward data sharing, the application of artificial intelligence technology in data governance and sharing, governance and sharing of epidemiological and public health data, governance and sharing of data on chronic diseases such as diabetes, and the construction of data governance models.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-26T07:10:19Z
  DOI: 10.1177/20552076251320302
  Issue No: Vol. 11 (2025)
Medication counseling for OTC drugs using customized ChatGPT-4: Comparison
with ChatGPT-3.5 and ChatGPT-4o
- Authors: Keisuke Kiyomiya, Tohru Aomori, Hisakazu Ohtani; Tohru Aomori, Hisakazu Ohtani
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundIn Japan, consumers can purchase most over-the-counter (OTC) drugs without pharmacist guidance. Recently, generative artificial intelligence (AI) has become increasingly popular. Therefore, medical professionals need to consider the use of generative AI by consumers for medication counseling. We have previously reported responses in Japanese from ChatGPT-3.5 to 264 questions regarding whether each of 22 OTC drugs can be taken under 12 typical patient conditions. The proportion of responses that satisfied the criteria of 1) accuracy, 2) relevance, and 3) reliability with respect to package insert instructions was 20.8%. In November 2023, GPTs were launched, enabling us to construct a customized ChatGPT, using natural language. In the present study, we compared performance in providing medication guidance among a newly customized GPT, the latest non-customized version ChatGPT-4o, and the previous version, ChatGPT-3.5. The aim was to determine whether the customization and version update of ChatGPT improved performance and to evaluate its potential usefulness.MethodsWe configured customized ChatGPT-4 by executing five instructions in Japanese and uploaded the text of package inserts for 22 OTC drugs as knowledge. We asked the same 264 questions as in our previous study.ResultsWith the customized ChatGPT-4, the percentages of responses that satisfied the criteria of accuracy, relevance, and reliability were 93.2%, 100%, and 60.2%, respectively. Additionally, 56.1% of responses satisfied all three criteria, 2.7-fold higher compared with ChatGPT-3.5 and 1.3-fold higher compared with ChatGPT-4o.ConclusionThe performance of our customized GPT far exceeded that of ChatGPT-3.5. In particular, the proportion of appropriate responses to the questions using brand names was significantly improved. ChatGPT can be customized by providing drug package insert information and using appropriate prompt engineering, potentially offering helpful tools in clinical pharmacy.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-26T06:17:49Z
  DOI: 10.1177/20552076251323810
  Issue No: Vol. 11 (2025)
Preferences of community-dwelling older adults with multimorbidity for
digital empowerment interventions: Protocol for a discrete choice
experiment study
- Authors: Yawen Ren, Li Yang, Wenguang Wang, Runtian Lv, Xiuqing Fang, Yueling Wang, Jiayi Zhong; Li Yang, Wenguang Wang, Runtian Lv, Xiuqing Fang, Yueling Wang, Jiayi Zhong
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  IntroductionThe development of digital health technologies has had a positive impact on the management of comorbidity in the elderly. At the same time, it also sets certain requirements for the digital health literacy of older adults. To effectively implement interventions that improve digital health literacy among older adults with multimorbidity, it is essential to involve patients in decision-making processes and to develop interventions that align with their preferences.Methods and analysisThis study will use Discrete choice experiment to investigate the needs and preferences of older adults with multimorbidity for digital health literacy intervention. Attributes were identified through a literature review, in-depth interviews, and expert consultation. The attributes are intervention provider, content, type, frequency, material, learning mode, supplementary services, and cost. According to the identified attributes and levels, the choice set questionnaires were generated using Stata software. Furthermore, the study will assess the impact of demographic factors, social support, self-efficacy, levels of digital health literacy, and technology anxiety on the selection of preferences. A mixed logic model will analyze respondents’preferences for different intervention attributes and levels. A latent class model will identify heterogeneous group preferences.ResultsWe anticipate a minimum of 300 older adults with multimorbidity in community enrolling and completing data collection. Study results will be published in peer-reviewed scientiﬁc journals.ConclusionsThe collection and analysis of patient preference information will greatly improve the accuracy and effectiveness of digital health literacy interventions in the development, adoption, and economic evaluation.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-26T06:17:16Z
  DOI: 10.1177/20552076251319662
  Issue No: Vol. 11 (2025)
Leveraging of digital triage to enhance access in obstetric emergencies in
the maternity units: A scoping review of digital triage interventions in
healthcare
- Authors: Mxolisi Welcome Ngwenya, Livhuwani Muthelo, Mellitah Molatelo Rasweswe, Tebogo Maria Mothiba; Livhuwani Muthelo, Mellitah Molatelo Rasweswe, Tebogo Maria Mothiba
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundPregnancy and childbirth are supposed to give a new beautiful meaning to life and it is a time of enormous delight and anticipation for both the women and their families. In these times, not only a baby is born, but a mother is born. However, inaccessibility and delays in obstetric care remain a common concern, particularly in low- and middle-income countries. Digital health technologies are being implemented to improve healthcare access worldwide, but there is a lack of documented data on available digital triage interventions. This article sought to examine and critique existing digital triage interventions in the healthcare system with reference to obstetrics.MethodsAdopting a scoping review approach, using the five iterative steps proposed by Arksey and O'Malley, approximately 17 studies retrieved from databases like PubMed, Elsevier, EBSCohost, and google scholar were reviewed. Only the literature from 2014–2024 was included.ResultsThe review revealed that there are various types of digital triage interventions. However, they are flooded with weaknesses and threats among of which are diagnosis inaccuracy, insufficient information, and shortage of resources.ConclusionsThe study recommends that strengths, weaknesses, opportunities, and threats should not be overlooked, particularly when aiming to leverage digital health to improve access to emergency care in maternity units through digital triage. However, they should serve as a reference for the development of optimal digital triage systems for maternity and emergency units. Furthermore, the findings should also be a benchmark for digital triaging improvement strategies in the healthcare context.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-25T07:07:09Z
  DOI: 10.1177/20552076241302003
  Issue No: Vol. 11 (2025)
Tales of hope and hesitation: Smoking cessation experts’ views on the
opportunities and risks of digital behaviour change interventions
- Authors: Arian Kashefi, Stephen Lee Murphy, Lieven De Marez, Peter Conradie, Mariek M. P. Vanden Abeele; Stephen Lee Murphy, Lieven De Marez, Peter Conradie, Mariek M. P. Vanden Abeeleimec-mict-UGent, Department of Communication Sciences, 129188Ghent University, Ghent, Belgium
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveSmoking remains a global health challenge, with 1.14 billion active smokers worldwide. Many of these smokers seek cessation support. The rise of mHealth offers novel intervention methods, providing monitoring and tailored feedback. This study aimed to map the opportunities and challenges of integrating digital behaviour change interventions (mHealth) into smoking cessation practices by understanding professionals’ perceptions of these tools.MethodsA qualitative study was conducted involving semi-structured interviews with 11 experienced smoking cessation professionals in Flanders, Belgium. Data collection occurred between January and April 2023. Inductive thematic analysis was performed to identify key patterns and themes in the experts’ views regarding mHealth interventions.ResultsThe analysis revealed four primary themes: (1) The Inexorable March of Technology – experts acknowledged the inevitability of technology in smoking cessation but varied in enthusiasm; (2) The Shimmering Mirage of Possibility – technology was viewed as supplementary, offering efficiency and support but limited in depth; (3) The Footnotes to Enthusiasm – experts expressed concerns over privacy, inclusivity, and the potential for technology to displace human care; and (4) The Human Anchor – the irreplaceable role of human connection and therapeutic alliance, which digital tools might not be able to replicate.ConclusionExperts believe mHealth interventions can augment smoking cessation support but should not replace human-driven care. A blended approach, integrating digital tools with traditional therapeutic relationships, offers the most promise. Addressing concerns about privacy, inclusivity, and most importantly the limits of digital therapeutic alliances is essential for successful mHealth implementation in smoking cessation.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-24T06:53:10Z
  DOI: 10.1177/20552076251322060
  Issue No: Vol. 11 (2025)
Harnessing digital health data for suicide prevention and care: A rapid
review
- Authors: Laura Bennett-Poynter, Sridevi Kundurthi, Reena Besa, Dan W. Joyce, Andrey Kormilitzin, Nelson Shen, James Sunwoo, Patrycja Szkudlarek, Lydia Sequiera, Laura Sikstrom; Sridevi Kundurthi, Reena Besa, Dan W. Joyce, Andrey Kormilitzin, Nelson Shen, James Sunwoo, Patrycja Szkudlarek, Lydia Sequiera, Laura Sikstrom
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  Background and aimSuicide is a global public health issue disproportionately impacting equity-deserving groups. Recent advances in Artificial Intelligence and increased access to a variety of digital data sources have enabled the development of novel and personalized suicide prevention strategies. However, standards on how to harness these data in a comprehensive and equitable way remain unclear. The primary aim of this study is to identify considerations for the collection and use of digital health data for suicide prevention and care. The results will inform the development of a data governance framework for a multinational suicide prevention mHealth platform.MethodWe used a modified Cochrane Rapid Reviews Method. Inclusion criteria focused on primary studies published in English from 2007 to the present that referenced the use of digital health data in the context of suicide prevention and care. Screening and data extraction was performed independently by multiple reviewers, with disagreements resolved through discussion. Qualitative and quantitative synthesis methods were employed to identify emergent themes.ResultsOur search identified 2453 potential articles, with 70 meeting inclusion criteria. We found little consensus on best practices for the collection and use of digital health data for suicide prevention and care. Issues of data quality, fairness and equity persist, compounded by inadequate consideration of key governance issues including privacy and trust, especially in multinational initiatives.ConclusionsRecommendations for future research and practice include prioritizing engagement with knowledge users, establishing robust data governance frameworks aligned with clinical guidelines, and leveraging advanced analytics, such as natural language processing, to improve the quality of health equity data.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-24T06:52:41Z
  DOI: 10.1177/20552076241308615
  Issue No: Vol. 11 (2025)
Preliminary study: Data analytics for predicting medication adherence in
Malaysian arthritis patients
- Authors: Firdaus Aziz, Shubathira Sooriamoorthy, Bryan Liew, Sharifah M. Syed Ahmad, Wei Wen Chong, Sorayya Malek, Adliah Mhd Ali; Shubathira Sooriamoorthy, Bryan Liew, Sharifah M. Syed Ahmad, Wei Wen Chong, Sorayya Malek, Adliah Mhd Ali
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveIn multi-ethnic Malaysian populations, understanding and improving medication adherence in arthritis patients is crucial for enhancing treatment outcomes. Non-adherence, whether intentional or due to complex factors, can lead to severe long-term consequences such as increased disability and disease progression. This study analysed and predicted Malaysian arthritis medication adherence using 13 machine learning models.MethodsA majority of 151 responders (82.1%) were female and 58.3% had comorbid illnesses. Notably, 90.07% of respondents were non-adherence to their prescription, with significant differences by occupation and aids in medication. This study's machine learning models perform better with recursive feature elimination for feature selection. Key variables included occupation, presence of other diseases, religion, income, medication aid, marital status, and number of medications taken per day. These variables were used to build predictive models for medication adherence.ResultsResults from machine learning algorithms showed varied performance. Support vector machine, gradient boosting, and random forest models performed best with AUC values of 0.907, 0.775, and 0.632 utilizing all variables. When using selected variables, random forest (AUC = 0.883), gradient boosting (AUC = 0.872), and Bagging (AUC = 0.860) performed best. Model interpretation using SHapley Additive exPlanations analysis identified occupation as the most important variable affecting medication adherence. The study also found that unemployment, concomitant disease, income, medication aid type, marital status, and daily medication count are connected with non-adherence.ConclusionThe findings underscore the multifaceted nature of medication adherence in arthritis, highlighting the need for personalized approaches to improve adherence rates.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-24T06:43:05Z
  DOI: 10.1177/20552076241309505
  Issue No: Vol. 11 (2025)
A 5G network based conceptual framework for real-time malaria parasite
detection from thick and thin blood smear slides using modified YOLOv5
model
- Authors: Swati Lipsa, Ranjan Kumar Dash, Korhan Cengiz, Nikola Ivković, Adnan Akhunzada; Ranjan Kumar Dash, Korhan Cengiz, Nikola Ivković, Adnan Akhunzada
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveThis paper aims to address the need for real-time malaria disease detection that integrates a faster prediction model with a robust underlying network. The study first proposes a 5G network-based healthcare system and then develops an automated malaria detection model capable of providing an accurate diagnosis, particularly in areas with limited diagnostic resources.MethodsThe proposed system leverages a deep learning-based YOLOv5x algorithm to detect malaria parasites in thick and thin blood smear samples. The YOLOv5x network architecture was modified by introducing two squeeze-and-excitation network (SENet) layers just before the Upsample layers. The system is designed to operate over 5G networks efficiently, enabling remote and smart healthcare solutions.ResultsThe modified YOLOv5x model demonstrated improved accuracy and precision in detecting malaria parasites on microscopic slides. The inclusion of SENet layers optimized the network’s performance, making it suitable for real-time disease detection over a 5G network.ConclusionOur model exemplifies how a generic one-stage object detection algorithm, such as YOLOv5x, can be repurposed to detect objects as small as malaria parasites from microscopic visuals in a cost-effective manner over the 5G network. By integrating the computational efficiency of deep learning with the connectivity of 5G networks, this system can significantly enhance remote diagnostic capabilities and contribute to smart healthcare solutions.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-21T10:17:30Z
  DOI: 10.1177/20552076251321540
  Issue No: Vol. 11 (2025)
A user-centred website to support advance care planning for people with
dementia and their family caregivers: Development and usability study
- Authors: Fanny Monnet, Charlèss Dupont, Lara Pivodic, Tinne Smets, Aline De Vleminck, Chantal Van Audenhove, Lieve Van den Block; Charlèss Dupont, Lara Pivodic, Tinne Smets, Aline De Vleminck, Chantal Van Audenhove, Lieve Van den Block
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveAdvance care planning (ACP) is a dynamic communication process about future care preferences. We aimed to develop and test an ACP support website for people with dementia and their family caregivers.MethodsWe adopted a user-centred design process to develop and test the ACP support website. A content specification phase included needs assessment, evidence synthesis and translation into preliminary content. A creative design phase included storyboarding, iterative prototyping, and usability testing. People with dementia and family caregivers engaged in usability testing across four iterations, using System Usability Scale questionnaires, and think-aloud and semi-structured interviews. An advisory group with people with dementia, family caregivers, and diverse regional stakeholders was involved. Descriptive statistics and qualitative framework analysis were applied.ResultsWebsite goals were: providing ACP information and facilitating ACP conversations. A ‘what matters most’ approach (i.e. enabling users to reflect on ‘what matters most’ in the present and the future) and non-linear navigation were favoured. We tested the website with 17 people with dementia and 26 family caregivers. Feedback addressed design, navigation and content. Usability scores of 76.4 for family caregivers and 81.3 for people with dementia were achieved. Family caregivers and people with dementia highlighted the value of information and interactive ACP tools, the need for language simplification and harmonised layout. People with dementia experienced challenges in using interactive tools.ConclusionThe user-centred development process, involving diverse stakeholders, led to the development of an ACP support website deemed usable and useful. Future evaluation should focus on acceptability, feasibility and effectiveness of the ACP support website.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-21T10:16:41Z
  DOI: 10.1177/20552076241304018
  Issue No: Vol. 11 (2025)
Sleep as a window of cardiometabolic health: The potential of digital
sleep and circadian biomarkers
- Authors: Willem J van den Brink, Johanneke E Oosterman, Dagmar J Smid, Herman J de Vries, Douwe E Atsma, Sebastiaan Overeem, Suzan Wopereis; Johanneke E Oosterman, Dagmar J Smid, Herman J de Vries, Douwe E Atsma, Sebastiaan Overeem, Suzan Wopereis
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  Digital biomarkers are quantifiable and objective indicators of a person's physiological function, behavioral state or treatment response, that can be captured using connected sensor technologies such as wearable devices and mobile apps. We envision that continuous and 24-h monitoring of the underlying physiological and behavioral processes through digital biomarkers can enhance early diagnostics, disease management, and self-care of cardiometabolic diseases. Cardiometabolic diseases, which include a combination of cardiovascular and metabolic disorders, represent an emerging global health threat. The prevention potential of cardiometabolic diseases is around 80%, indicating a promising role for interventions in the lifestyle and/or the environmental context. Disruption of sleep and circadian rhythms are increasingly recognized as risk factors for cardiometabolic disease. Digital biomarkers can be used to measure around the clock, that is, day and night, to quantify not only sleep patterns but also diurnal fluctuations of certain biomarkers and processes. In this way, digital biomarkers can support the delivery of optimal timed medical care. Night-time cardiometabolic patterns, such as blood pressure dipping, are predictive of cardiometabolic health outcomes. In addition, the sleep period provides an opportunity for digital cardiometabolic health monitoring with relatively low influence of artifacts, such as physical activity and eating. Digital biomarkers that utilize sleep as a window of health can be used during daily life to enable early diagnosis of cardiometabolic diseases, facilitate remote patient monitoring, and support self-management in people with cardiometabolic diseases. This review describes the influence of sleep and circadian rhythms on cardiometabolic disease and highlights the state-of-the-art sleep and circadian digital biomarkers which could be of benefit in the prevention of cardiometabolic disease.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-20T06:52:16Z
  DOI: 10.1177/20552076241288724
  Issue No: Vol. 11 (2025)
Leveraging BiLSTM-CRF and adversarial training for sentiment analysis in
nature-based digital interventions: Enhancing mental well-being through
MOOC platforms
- Authors: Juanjuan Zang; Shandong Women's University, Jinan, Shandong, China
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveThis study aims to leverage annotated textual data from a Massive Open Online Course (MOOC) platform to conduct sentiment analysis of learners’ interactions with nature-based digital interventions, which seeks to enhance sentiment classification and provide insights into learners’ affective experiences, ultimately facilitating timely psychological interventions and improving curriculum design.MethodsThis study leverages the extensive corpus of annotated textual data available on a MOOC platform, encompassing learners’ assessments, inquiries, and recommendations. By performing meticulous sentiment analysis, we aim to understand the subjective sentiments of learners engaging with nature-based digital interventions. To achieve this, we integrate a Bidirectional Long Short-Term Memory (BiLSTM) network with a Conditional Random Field (CRF). The BiLSTM captures word associations in both forward and backward directions, feeding these results into the CRF network to establish the conditional distribution between the feature function and labels. This ensures high-quality feature extraction, precise label assignment, and the derivation of evaluation metrics. Furthermore, adversarial training is introduced to enhance aspect sentiment classification. This involves incorporating perturbations in the embedding space, generating adversarial samples at the embedding layer and semantic feature fusion layer, and combining these with the original samples for model training.ResultsExperimental outcomes demonstrate that the proposed model achieves precision, recall, and F1 scores of 83.71, 85.66, and 84.67 on the SemEval-2014 dataset, and 80.63, 83.06, and 81.76 on the Coursera dataset.ConclusionNotably, the sentiment prediction efficacy surpasses that of comparative models, underscoring the proficiency of the proposed scheme. By harnessing the proposed model, educators and administrators can effectively sift through learners’ affective information, facilitating timely psychological interventions and curriculum guidance. This study contributes to the growing body of research on digital mental health interventions within natural settings, providing valuable insights into how technology can support and enhance mental well-being in these contexts.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-19T07:05:41Z
  DOI: 10.1177/20552076251317345
  Issue No: Vol. 11 (2025)
Efficacy of a physical rehabilitation program using virtual reality in
patients with chronic tendinopathy: A randomized controlled trial protocol
(VirTendon-Rehab)
- Authors: David Lucena-Anton, Juan G Dominguez-Romero, Juan C Chacon-Barba, María José Santi-Cano, Carlos Luque-Moreno, Jose A Moral-Munoz; Juan G Dominguez-Romero, Juan C Chacon-Barba, María José Santi-Cano, Carlos Luque-Moreno, Jose A Moral-Munoz
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectivesTo analyze the efficacy of a virtual reality (VR)-based rehabilitation program in people with chronic tendinopathy (CT) on pain, muscle activation pattern, range of motion, muscle strength, kinesiophobia, physical function, quality of life, and user satisfaction compared to a control group. In addition, the relationship between these variables and the clinical profile of this population will be analyzed.DesignA 12-week, single-blind, low-risk, randomized controlled trial.MethodsSixty patients diagnosed with CT will be enrolled and randomly assigned to two groups. The control group will receive a physical exercise program without VR support (45 min), whereas the experimental group will receive an additional 15-min intervention through a physical exercise program delivered by VR. Both groups will receive three sessions per week, and the outcomes will be collected at baseline, after 12 weeks, and at the 24-week follow-up. Stratified groups will be established according to tendinopathy location (shoulder rotator cuff, elbow, patella, and Achilles tendon). Statistical analyses using SPSS v.24 will include descriptive analysis, stratified analysis by tendinopathy location, normality checks, intragroup and intergroup differences, effect sizes, and variable relationships.DiscussionThe results of this project may have a significant impact on the knowledge of using VR in tendinopathy management, understanding how the outcomes are related, and characterizing the clinical profiles of the population diagnosed with CT. If these results are confirmed, VR would be clinically useful for the treatment of these conditions.Trial registration numberNCT06056440.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-19T06:56:29Z
  DOI: 10.1177/20552076241297043
  Issue No: Vol. 11 (2025)
Functional characteristics of sleep monitoring devices in China: A
real-world cross-sectional study
- Authors: Le Yang, Bingtao Weng, Xingyan Xu, Zhi Huang, Run Ding, Miaomiao Si, Yingxin Fu, Yurui Zhu, Yu Jiang, Beibei Rao, Xinyi Zhang, Qingwei Zhou, Shenglan Lin, Yansong Guo, XiaoXu Xie; Bingtao Weng, Xingyan Xu, Zhi Huang, Run Ding, Miaomiao Si, Yingxin Fu, Yurui Zhu, Yu Jiang, Beibei Rao, Xinyi Zhang, Qingwei Zhou, Shenglan Lin, Yansong Guo, XiaoXu Xie
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundSleep monitoring devices present potential improvements to address the challenges of sleep disorders. However, systematic evaluations are lacking. This study investigates the functional characteristics of existing sleep monitoring devices in the Chinese market and delves into population preferences.ObjectiveWe aim to summarize the characteristics of mobile health devices with sleep monitoring function in China, analyzing product features and market prices, and collect population preferences for mobile health devices, providing a concrete basis for the ongoing development of mobile health technologies.MethodsData on 203 sleep devices were gathered from four major mobile shopping platforms (Tmall, JD.com, Pinduoduo, and Suning) using relevant keywords. A two-level variance model was employed to analyzed the link between device features and sales. Additionally, a structured questionnaire assessed public usage and attitudes towards these devices, with 167 responses collected via social networks.ResultsOur study found that smart bracelets, which make up 82.6% of sleep monitoring devices, effectively track heart rate, physical activity blood oxygen saturation, sleep duration, and assess sleep quality. Most devices cost under 500 RMB, influencing sales (β = −1.111 to −3.490, p
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-19T06:55:54Z
  DOI: 10.1177/20552076251320752
  Issue No: Vol. 11 (2025)
Perceptions regarding cardiovascular health and preparedness for digital
health education among adolescents in an urban community of Nepal: A
qualitative study
- Authors: Dayana Shakya, Karin Flodin, Dip Raj Thapa, Madhusudan Subedi, Nawi Ng, Abhinav Vaidya, Natalia Oli, Alexandra Krettek; Karin Flodin, Dip Raj Thapa, Madhusudan Subedi, Nawi Ng, Abhinav Vaidya, Natalia Oli, Alexandra Krettek
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundCardiovascular disease (CVD) is the leading cause of death in Nepal. As CVD risks can develop early in life, a life course approach for non-communicable disease (NCD) prevention is needed. Due to its potentially acceptable delivery mode, digital health education could be a promising way forward to increase adolescents’ CVD knowledge.PurposeThe purpose of this study was to explore adolescents’ CVD perceptions and their perceptions and preparedness for digital cardiovascular health education through mobile games.MethodsTwelve focus group discussions were conducted with adolescents, Grades 8–10, from two public and four private Nepalese schools. A qualitative study with a deductive thematic analysis was performed, guided by the health belief model (HBM) and the technology acceptance model (TAM).ResultsThe analysis resulted in 6 themes and 13 sub-themes concerning perceptions of CVD and 5 themes and 10 sub-themes on perceptions and preparedness for digital cardiovascular health education through mobile games. The adolescents viewed CVD as a serious disease with consequences. A healthy diet and physical activity were important for prevention. Benefits were the positive impacts of a heart-healthy lifestyle. Barriers were the temptation of consuming unhealthy food, lack of healthy food environments, time and motivation. The adolescents also stressed their own ability to prevent CVD. Digital cardiovascular health education through mobile games was desirable. Constraints were accessibility and technical issues, parental allowance, available time and whether the game was engrossing enough.ConclusionThe adolescents perceived CVD as serious, with benefits and barriers connected to its prevention. Digital cardiovascular health education through mobile games was viewed positively but not without constraints for successful implementation.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-19T06:28:05Z
  DOI: 10.1177/20552076251321068
  Issue No: Vol. 11 (2025)
ACU-Net: Attention-based convolutional U-Net model for segmenting brain
tumors in fMRI images
- Authors: Md Alamin Talukder, Md Abu Layek, Md Aslam Hossain, Md Aminul Islam, Mohammad Nur-e-Alam, Mohsin Kazi; Md Abu Layek, Md Aslam Hossain, Md Aminul Islam, Mohammad Nur-e-Alam, Mohsin Kazi
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveAccurate segmentation of brain tumors in medical imaging is essential for diagnosis and treatment planning. Current techniques often struggle with capturing complex tumor features and are computationally demanding, limiting their clinical application. This study introduces the attention-based convolutional U-Net (ACU-Net) model, designed to improve segmentation accuracy and efficiency in fMRI images by incorporating attention mechanisms that selectively highlight critical features while preserving spatial context.MethodsThe ACU-Net model combines convolutional neural networks (CNNs) with attention mechanisms to enhance feature extraction and spatial coherence. We evaluated ACU-Net on the BraTS 2018 and BraTS 2020 fMRI datasets using rigorous data splitting for training, validation, and testing. Performance metrics, particularly Dice scores, were used to assess segmentation accuracy across different tumor regions, including whole tumor (WT), tumor core (TC), and enhancing tumor (ET) classes.ResultsACU-Net demonstrated high segmentation accuracy, achieving Dice scores of 99.23%, 99.27%, and 96.99% for WT, TC, and ET, respectively, on the BraTS 2018 dataset, and 98.72%, 98.40%, and 97.66% for WT, TC, and ET on the BraTS 2020 dataset. These results indicate that ACU-Net effectively captures tumor boundaries and subregions with precision, surpassing traditional segmentation approaches.ConclusionThe ACU-Net model shows significant potential to enhance clinical diagnosis and treatment planning by providing precise and efficient brain tumor segmentation in fMRI images. The integration of attention mechanisms within a CNN architecture proves beneficial for identifying complex tumor structures, suggesting that ACU-Net can be a valuable tool in medical imaging applications.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-18T07:10:40Z
  DOI: 10.1177/20552076251320288
  Issue No: Vol. 11 (2025)
Barriers to and enhancement of the utilization of digital mental health
interventions in low-resource settings: Perceptions of young people in
Uganda
- Authors: Erisa Sabakaki Mwaka, Datsun Bazzeketa, Joy Mirembe, Reagan D. Emoru, Adelline Twimukye, Apollo Kivumbi; Datsun Bazzeketa, Joy Mirembe, Reagan D. Emoru, Adelline Twimukye, Apollo Kivumbi
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  IntroductionDigital mental health (DMH) enhances access to healthcare, particularly in low- and middle-income countries where investment in mental healthcare is low. However, utilization among young people (YP) is low. This study aimed to explore YP's perceptions of the barriers to the using of DMH interventions in low-resource settings.MethodsA qualitative descriptive approach was used. Six face-to-face focus group discussions were conducted with 50 YP from nine universities in Uganda. The median age was 24 years (range 21–25 years) and respondents were drawn from diverse academic programmes with the majority being medical students (54%). A thematic approach was used to interpret the results.ResultsThree themes were identified from the data including perceptions of using DMH services, the perceived barriers to utilization, and suggestions for enhancement of DMH for YP in low-resource settings. Most respondents had a positive attitude towards DMH. The perceived barriers to utilization of DMH included the fear of stigma, affordability, inequitable access, privacy and confidentiality concerns, and app-related challenges. Access and use of DMH can be enhanced through public engagement, creating awareness, enhanced training, and access to affordable DMH interventions.ConclusionDMH was deemed important in extending healthcare to YP, particularly in health systems where traditional mental health services are not readily available. However, several factors hinder equitable access to DMH in low-resource settings. There is a need for long-term investment in digital health technologies.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-17T06:31:30Z
  DOI: 10.1177/20552076251321698
  Issue No: Vol. 11 (2025)
MRpoxNet: An enhanced deep learning approach for early detection of
monkeypox using modified ResNet50
- Authors: Vandana, Chetna Sharma, Mohd Asif Shah; Chetna Sharma, Mohd Asif Shah
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveTo develop an enhanced deep learning model, MRpoxNet, based on a modified ResNet50 architecture for the early detection of monkeypox from digital skin lesion images, ensuring high diagnostic accuracy and clinical reliability.MethodsThe study utilized the Kaggle MSID dataset, initially comprising 1156 images, augmented to 6116 images across three classes: monkeypox, non-monkeypox, and normal skin. MRpoxNet was developed by extending ResNet50 from 177 to 182 layers, incorporating additional convolutional, ReLU, dropout, and batch normalization layers. Performance was evaluated using metrics such as accuracy, precision, recall, F1 score, sensitivity, and specificity. Comparative analyses were conducted against established models like ResNet50, AlexNet, VGG16, and GoogleNet.ResultsMRpoxNet achieved a diagnostic accuracy of 98.1%, outperforming baseline models in all key metrics. The enhanced architecture demonstrated superior robustness in distinguishing monkeypox lesions from other skin conditions, highlighting its potential for reliable clinical application.ConclusionMRpoxNet provides a robust and efficient solution for early monkeypox detection. Its superior performance suggests readiness for integration into diagnostic workflows, with future enhancements aimed at dataset expansion and multimodal adaptability to diverse clinical scenarios.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-17T06:31:03Z
  DOI: 10.1177/20552076251320726
  Issue No: Vol. 11 (2025)
Knowledge mapping of online healthcare: An interdisciplinary visual
analysis using VOSviewer and CiteSpace
- Authors: Xue Ding, Dingming Lu, Ruoxi Wei, Fangfang Zhu; Dingming Lu, Ruoxi Wei, Fangfang Zhu
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundOnline healthcare has been regarded as a permanent component and complementation in routine worldwide healthcare. Although there have been large-scale related studies in this field, studies are scattered across disciplines. Numerous publications are needed to systematically and comprehensively identify the status quo, development, and future hotspots in this field.MethodsPublications on online healthcare were screened from the WoS database. By using VOSviewer and CiteSpace, this study analyzed 4636 articles in this field with 60,306 associated references. First, countries/regions distributions, institutions distributions, influential journals, and productive authors were obtained. Then, co-citation analysis, co-occurrence analysis, timeline analysis, and burst detection were further conducted to sketch the panorama of online healthcare.ResultsThere were 147 countries/regions participated in and contributed to this field in total. Accounting for over half of the total number of publications, the USA, England, Australia, China, and Canada played significant roles in this area. Among the 24,362 authors, Guo XT was the most influential author. The International Journal of Environmental Research and Public Health was the journal with the most publications and citations. Studies in this field have shifted from basic research to applied practice research. COVID-19, mental health, healthcare, and healthcare workers were the most common keywords, indicating that studies on the impact of online healthcare on healthcare workers, online healthcare service for COVID-19, and mental health will be promising areas in the future.ConclusionsResearch on online healthcare is booming, while worldwide cooperation is still regionalized. Cross-regional cooperation among institutions and scholars is needed to enhance in the future. Online healthcare services for specific health fields and specific groups are the current and developing topics in this field.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-17T05:08:12Z
  DOI: 10.1177/20552076251320761
  Issue No: Vol. 11 (2025)
Thanks to Reviewers
- Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-17T04:35:34Z
  DOI: 10.1177/20552076251316494
  Issue No: Vol. 11 (2025)
A model for predicting factors affecting health information avoidance on
WeChat
- Authors: Minghong Chen, Xiumei Huang, Yinger Wu, Shijie Song, Xianjun Qi; Xiumei Huang, Yinger Wu, Shijie Song, Xianjun Qi
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveWeChat serves as a crucial source of health information, distinguished by its highly personalized nature. Avoidance of such personalized health information has a direct impact on individuals’ health decision-making. This study aims to identify the factors influencing personalized health information avoidance on WeChat and to construct a hierarchical framework illustrating the relationships among these factors.MethodsA hybrid method was utilized. Semi-structured interviews and grounded theory were used to identify the influencing factors. The interpretive structural modeling (ISM) method was adopted to develop a hierarchical model of the identified factors, followed by matrice d'impacts croises-multiplication appliqué a un classemen (MICMAC) to analyze the dependence and driving power of each factor.ResultsThe 20 predictors of personalized health information avoidance were broadly categorized into three groups: personal, informational, and social factors. These factors collectively form a three-tier explanatory framework, consisting of the top, middle and bottom layers. At the root layer, health characteristics and cognition exerted a strong driving force, while negative emotions and affective factors at the top layer showed a high degree of dependence. In contrast, the decision-making cognition, informational factors, and social factors in the middle layer exhibited relatively weaker driving force and dependence power.ConclusionThis study bridged the research gap of information avoidance by providing new insights targeting the factors influencing personalized health information avoidance behavior on WeChat. It also contributed to enhancing personal health information management and the health information services provided on WeChat.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-14T06:25:23Z
  DOI: 10.1177/20552076251314277
  Issue No: Vol. 11 (2025)
Process evaluation of technologically assisted senior care using mixed
methods: Results of the virtual assisted living (VAL, German: VBW Virtuell
Betreutes Wohnen) project
- Authors: Eva Jansen, Juliana Supplieth, Sonia Lech, Jan Zöllick, Johanna Schuster; Juliana Supplieth, Sonia Lech, Jan Zöllick, Johanna Schuster14903Charité—Universitätsmedizin Berlin, corporate member of Freie Universität Berlin, Humboldt-Universität zu Berlin, Institute of Medical Sociology Rehabilitation Science, Berlin, Germany
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveTechnologically assisted support systems and social support in everyday life can help senior citizens live longer independently in their homes. The purpose of this process evaluation is to investigate an innovative care model integrating monitoring technology with social support services, aimed at enabling senior citizens to live independently and extend their longevity in their homes.MethodsData collection of this mixed-method study was conducted through three distinct sources: expert interviews with employees of the participating social service, focus groups with seniors participating in the intervention, and involved consortium partners in the project. Following Kuckartz's methodology, we employed a structural qualitative content analysis using MAXQDA software. Additionally, a portion of the standardized survey administered post-intervention to participants was analyzed using descriptive statistics.ResultsThe focus groups identified key challenges related to technical implementation such as false alarms and the failure of sensors as well as communication between invested parties. However, significant potential was noted in the practical execution of the intervention and social care. Interview participants emphasized the need for improved technical implementation. Results from the questionnaires indicate a generally positive perception of the intervention, particularly regarding its social dimensions.ConclusionsSurveying individuals who implement and utilize assistive technology can yield valuable insights into its effectiveness. Additionally, it is crucial to comprehensively and in detail capture the experiences of those involved in testing new care models. Future research on assistive technologies for older adults should integrate both technical and social support components, while also addressing secure data protection measures and the paradox of reassurance.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-13T09:22:24Z
  DOI: 10.1177/20552076241308445
  Issue No: Vol. 11 (2025)
Diagnostic accuracy and radiographic interpretation of pre-eruptive
intra-coronal resorption among dental practitioners using eye-tracking
technology
- Authors: Jamila Almashaikhi, Heba M. Elkhodary, Ghalia Y. Bhadila, Osama M. Felemban, Amani A. Al Tuwirqi; Heba M. Elkhodary, Ghalia Y. Bhadila, Osama M. Felemban, Amani A. Al Tuwirqi
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundPre-eruptive intra-coronal resorption (PEIR) is a condition in which unerupted teeth exhibit coronal radiolucency consistent with resorptive loss of coronal tooth structure. These lesions are discovered incidentally on routine radiographs.AimTo measure the radiographic interpretation and diagnostic accuracy of PEIR among dental practitioners at King Abdulaziz University Dental Hospital using eye-tracking technology.MethodsIn this cross-sectional study, 125 interns, general dentists, and postgraduate residents examined five panoramic radiographs, including a case of impaction, and the rest were radiographs with PEIR of different severities. In this study, PEIR recognition was assessed using a validated questionnaire uploaded to an eye-tracking device (Sensomotoric Instruments SMI).ResultsThe findings revealed an association between the severity of the PEIR lesion and the detection of the affected teeth. As the severity increased, the participants were more able to identify the affected teeth, and the percentage of overlooking decreased. The dentist's level of education and years of clinical experience influenced the diagnostic accuracy and radiographic interpretation of the PEIR lesions.ConclusionsThe diagnostic accuracy and radiographic interpretation of PEIR lesions were affected by participants’ level of education and years of clinical experience. Based on this study, PEIR lesions may remain undetected until they reach advanced stages.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-13T09:22:04Z
  DOI: 10.1177/20552076251315620
  Issue No: Vol. 11 (2025)
Development of an evidence-informed implementation strategy for a digital
supportive care platform for brain tumour patients, their carers and
healthcare professionals
- Authors: Verena Schadewaldt, Teresa O’Brien, Mahima Kalla, Meinir Krishnasamy, Kara Burns, Sarah CE Bray, Cecily Gilbert, Richard De Abreu Lourenco, Joseph Thomas, Daniel Capurro, Wendy Chapman, Ann Borda, Rana S Dhillon, James R Whittle, Katharine J Drummond; Teresa O’Brien, Mahima Kalla, Meinir Krishnasamy, Kara Burns, Sarah CE Bray, Cecily Gilbert, Richard De Abreu Lourenco, Joseph Thomas, Daniel Capurro, Wendy Chapman, Ann Borda, Rana S Dhillon, James R Whittle, Katharine J Drummond
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundImplementation challenges of digital health solutions (DHSs) comprise complexities of behavioural change, resource limitation, inertia in existing systems, and failure to include consumer preferences. Understanding the factors which contribute to successful implementation of DHS is essential. We report the development of an implementation strategy for Brain Tumours Online (BT Online), a digital supportive care platform for patients with brain tumours, their carers and healthcare professionals.AimTo develop an evidence-informed implementation strategy for BT Online, considering the specific barriers and facilitators to implementing DHS for adults with a brain tumour and their carers and healthcare professionals.MethodsA rapid review methodology was used to summarise factors relevant to implementation of DHS for people affected by cancer. Themes from the review were supported by implementation guidelines for DHS and the combined evidence informed the implementation strategy. Each theme was matched with specific steps for implementing BT Online.ResultsThe rapid review identified 10 themes, namely, awareness of the new digital platform; institutional integration and support; data security, the quality, usability and accessibility of the platform; belief in the benefit of the platform; the need for holistic and tailored features; the timing of introducing the platform; engagement of healthcare professionals; and the re-definition of roles and workload. The themes were matched with 51 concrete implementation steps.DiscussionThe purpose of the strategy was to minimise risk of implementation failure, consider the specific context of care and generate a reference framework to evaluate BT Online prior to upscaling to use outside the research context. Our method contributes a novel approach of developing an evidence-informed rigorous implementation strategy if existing implementation frameworks do not apply.The implementation plan of a digital health platform to support patients with a brain tumour, their carers and healthcare profesionalsWhy was the study done' We explored the best way to introduce a digital supportive care platform to patients with a brain tumour, their carers and healthcare professionals. To reach and engage all users, barriers and enablers specific to their needs must be considered when implementing such a platform into existing care pathways.Why is the study important' In Australia, there is no digital platform that provides people affected by a brain tumour with information, self-help tools and connection with peers in one place. Our team of patients, carers, healthcare professionals, digital health researchers and digital software developers has built a digital platform that meets all these needs. It is called BT Online (https://braintumoursonline.org/). This study presents the implementation plan of BT Online.What did the researchers do' To identify potential barriers and enablers to implement the platform we searched the research literature and published guidelines on the implementation of digital health solutions. From these resources we developed a structured implementation plan.What did we find' The implementation plan consisted of 10 themes that guided the implementation steps when introducing BT Online to its users. For example, the timing of introducing the platform to its users is important and user data needs to be safe. Furthermore, healthcare professionals are important to engage patients and carers in platform participation. In total, we followed 51 implementation steps. Having a well-constructed implementation plan increases the chance that BT Online will be successful for patients with a brain tumour, their carers and healthcare professionals.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-13T09:21:04Z
  DOI: 10.1177/20552076251316713
  Issue No: Vol. 11 (2025)
Test–retest reliability of the computer-based cognitive assessment tool
for community-dwelling older adults in Japan: The Otassha study
- Authors: Jou-Yin Chen, Hisashi Kawai, Junta Takahashi, Manami Ejiri, Keigo Imamura, Shuichi P Obuchi; Hisashi Kawai, Junta Takahashi, Manami Ejiri, Keigo Imamura, Shuichi P Obuchi
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  AimEarly detection of dementia is crucial for effective intervention. The computer-based cognitive assessment tool (CompBased-CAT) was designed to assess cognitive function using a tablet computer. While its predictive validity for mild cognitive impairment (MCI) is established, its test–retest reliability remains unclear. This study aimed to evaluate the test–retest reliability of CompBased-CAT among older Japanese adults in a community setting.MethodsThis is a methodological study that examined the test–retest reliability of CompBased-CAT. Community-dwelling older adults aged 65 years or older who participated in both the pre-test and post-test were included. Pre-test assessments were conducted during the 2023 Otassha study from 28 September to 8 October 2023, with the post-test administered 62 days (±14 days) later. Subtest scores were normalized to z-scores, and the total CompBased-CAT score was computed by summing these z-scores. Test–retest reliability was assessed using paired t-tests and intraclass correlation coefficient (ICC) two-way random-effects models, with analyses stratified by age, sex, and MCI status.ResultsA total of 80 participants (mean age: 76.8 years; 27.5% male) were analyzed. Paired t-tests showed no significant difference in total scores between the pre-test (mean = −0.75; standard deviation [SD] = 3.73) and the post-test (mean = −0.42; SD = 4.45). The total score exhibited an ICC of 0.64 (95% confidence interval [CI] = 0.49–0.75) for all participants, increasing to 0.74 (95% CI = 0.48–0.88) among those with MCI.ConclusionCompBased-CAT demonstrated good test–retest reliability, with an ICC of 0.64 among all participants, which increased to 0.74 among participants with MCI over a 2-month period, indicating its potential for monitoring cognitive function through repeated assessments.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-13T09:20:24Z
  DOI: 10.1177/20552076251317627
  Issue No: Vol. 11 (2025)
Development and implementation of a home-based prehabilitation app for
older patients undergoing elective colorectal cancer surgery. A
Prospective Cohort Study
- Authors: Thomas GC Timmers, Lennaert CB Groen, Hermien Schreurs, Emma RJ Bruns; Lennaert CB Groen, Hermien Schreurs, Emma RJ Bruns
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundPrehabilitation optimizes colorectal cancer patients’ health during the preoperative waiting period, by increasing functional capacity, reducing postoperative complications, and speeding recovery. However, challenges in implementation include patients’ willingness to attend multiple hospital visits, and hospitals needing trained personnel, facilities, and financial resources. An app-based prehabilitation program could address these issues by allowing patients to participate from home with remote support from healthcare professionals.ObjectiveTo develop and evaluate the feasibility of a digital application to offer multimodal home-based prehabilitation for older patients undergoing surgery for colorectal cancerMaterials and MethodsThis single-center prospective cohort study at Northwest Clinics (Alkmaar and Den Helder, The Netherlands) included patients scheduled for elective surgery for the curative treatment of colorectal cancer. The Patient Journey App was used to deliver prehabilitation. The primary outcome was the number of patients who downloaded and activated the app. Secondary outcomes included usage information, videos viewed, questionnaires answered, and signals triggered.ResultsNinety-seven patients were included in the study (age 72.1 [SD 0.8], 62.9% male). All patients used the app daily for a median of 29 days (IQR 23–28). Exercise videos were viewed most. Ninety-five patients activated daily exercise and protein shake reminders. Patients provided 1367 answers, triggering 79 signals related to smoking cessation, nutrition, and exercise. Response rates to in-app questionnaires were high, up to 90.5%.ConclusionsHome-based prehabilitation via an app for older colorectal cancer surgery patients is feasible. Given the effectiveness of prehabilitation programs, the scarcity of healthcare professionals, and patients’ reluctance for frequent in-person visits, home-based prehabilitation programs via an app could become a valuable added modality for offering these programs.Trial Registration2020.0600 (VU University Medical Center).
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-13T09:20:11Z
  DOI: 10.1177/20552076251317760
  Issue No: Vol. 11 (2025)
Possible challenges to the widespread use of colposcopic artificial
intelligence auxiliary diagnostic system in clinical practice
- Authors: Hongnan Ye; Research, Beijing Alumni Association of China Medical University, Beijing, China
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-13T08:17:15Z
  DOI: 10.1177/20552076251320312
  Issue No: Vol. 11 (2025)
COVID-19 recognition from chest X-ray images by combining deep learning
with transfer learning
- Authors: Chang-Jiang Zhang, Lu-Ting Ruan, Ling-Feng Ji, Li-Li Feng, Fu-Qin Tang; Lu-Ting Ruan, Ling-Feng Ji, Li-Li Feng, Fu-Qin Tang
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveBased on the current research status, this paper proposes a deep learning model named Covid-DenseNet for COVID-19 detection from CXR (computed tomography) images, aiming to build a model with smaller computational complexity, stronger generalization ability, and excellent performance on benchmark datasets and other datasets with different sample distribution features and sample sizes.MethodsThe proposed model first extracts and obtains features of multiple scales from the input image through transfer learning, followed by assigning internal weights to the extracted features through the attention mechanism to enhance important features and suppress irrelevant features; finally, the model fuses these features of different scales through the multi-scale fusion architecture we designed to obtain richer semantic information and improve modeling efficiency.ResultsWe evaluated our model and compared it with advanced models on three publicly available chest radiology datasets of different types, one of which is the baseline dataset, on which we constructed the model Covid-DenseNet, and the recognition accuracy on this test set was 96.89%, respectively. With recognition accuracy of 98.02% and 96.21% on the other two publicly available datasets, our model performs better than other advanced models. In addition, the performance of the model was further evaluated on external test sets, trained on data sets with balanced sample distribution (experiment 1) and unbalanced sample distribution (experiment 2), identified on the same external test set, and compared with DenseNet121. The recognition accuracy of the model in experiment 1 and experiment 2 is 80% and 77.5% respectively, which is 3.33% and 4.17% higher than that of DenseNet121 on external test set. On this basis, we also changed the number of samples in experiment 1 and experiment 2, and compared the impact of the change in the number of training set samples on the recognition accuracy of the model on the external test set. The results showed that when the number of samples increased and the sample features became more abundant, the trained Covid-DenseNet performed better on the external test set and the model became more robust.ConclusionCompared with other advanced models, our model has achieved better results on multiple datasets, and the recognition effect on external test sets is also quite good, with good generalization performance and robustness, and with the enrichment of sample features, the robustness of the model is further improved, and it has better clinical practice ability.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-13T08:16:57Z
  DOI: 10.1177/20552076251319667
  Issue No: Vol. 11 (2025)
Protocol for a usability and pilot implementation study of a digital
medical device to assess pain in non-verbal people with dementia in
Portuguese residential care facilities
- Authors: Mara Pereira Guerreiro, Isa Brito Félix, Morgane Tomé, Kreshnik Hoti, Catarina Ramos, Beatriz Dias, Teresa Andrade, José Brito, Jeff Hughes; Isa Brito Félix, Morgane Tomé, Kreshnik Hoti, Catarina Ramos, Beatriz Dias, Teresa Andrade, José Brito, Jeff Hughes
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectivePeople living with moderate to severe dementia (PLWD) are often unable to self-report pain. This matter is of particular concern given that up to 80% experience chronic pain. Mistreated or untreated pain in PLWD is associated with symptoms such as agitation and aggression, and unnecessary use of antipsychotic agents. Further, it can also engender mental burden in formal caregivers. The PainChek® App, a regulatory cleared class I medical device, enables the assessment and monitoring of pain in people who cannot verbalise it, such as those with moderate to severe dementia. To date there are no data on the real-world use of the PainChek® App in Portugal. To address this gap, we report the protocol of a pilot study, which combines usability evaluation and implementation research.MethodsUsability evaluation of the PainChek® (Portuguese) App will be guided by the ISO framework, focused on effectiveness, efficiency and user satisfaction. Implementation research will combine qualitative interviews to inform the implementation process, a longitudinal study of formal caregivers’ psychological variables, implementation outcomes, plus qualitative interviews to explore the ‘hows’ and ‘whys’ of implementation. The NASSS framework will be used as an implementation framework, together with the COM-B model and the Theoretical Domains Framework.ResultsThe usability and implementation studies have received ethics approval from the Egas Moniz Ethics Committee, under numbers 1367 and 64/24, respectively.ConclusionThis study is expected to inform the scale-up of the PainChek® (Portuguese) App in real-world settings and establish a foundation for a larger effectiveness and implementation study.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-13T08:14:26Z
  DOI: 10.1177/20552076241311326
  Issue No: Vol. 11 (2025)
Focus groups on digital cognitive assessment in the context of Alzheimer's
disease
- Authors: Sophie M van der Landen, Rosanne L van den Berg, Matthijs J Keijzer, Mariska N van Liere, Casper de Boer, Leonie N C Visser, Wiesje M van der Flier, Hanneke F M Rhodius-Meester, Sietske A M Sikkes; Rosanne L van den Berg, Matthijs J Keijzer, Mariska N van Liere, Casper de Boer, Leonie N C Visser, Wiesje M van der Flier, Hanneke F M Rhodius-Meester, Sietske A M Sikkes
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  IntroductionDigital cognitive assessments (DCAs) may facilitate early recognition of cognitive decline in the context of Alzheimer's disease (AD). While DCAs are increasingly emerging, they are often not used in practice. We assessed facilitators and barriers of using DCAs according to older individuals and patients.MethodsIn five focus groups, we presented three different DCAs to older individuals with unimpaired cognition (n = 14), subjective cognitive decline (n = 11) and mild cognitive impairment with biomarker-confirmed AD (n = 4) and their caregivers (n = 2). Participants discussed facilitators and barriers that could influence the use of DCAs. Focus groups were recorded, and transcripts were analysed using thematic analysis.ResultsThree main themes were identified: (1) test motivation (‘Do I want to know my brain health'’), facilitated by early disease recognition, while impeded by fear of dementia; (2) digital test suitability (‘Do I want to use a digital test'’), enabling at-home testing, while lacking personal contact; and (3) digital test characteristics (‘What makes a digital test a good test for me'’), where user-friendliness was emphasized as a key facilitator to overcome digital incompetence.DiscussionParticipants recognized the added value of DCAs, and multiple factors were identified as facilitators and barriers for their use. Similar factors could be a barrier for one, yet facilitated others, underlining the need for a personalized approach. Strategies to minimize barriers and exploit facilitators would ultimately foster implementation.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-12T09:47:58Z
  DOI: 10.1177/20552076251318903
  Issue No: Vol. 11 (2025)
Feasibility, acceptability, usability and quality of life levels in
post-stroke patients undergoing telerehabilitation: Results from a
multicentric pilot study
- Authors: Luisa Cacciante, Sara Federico, Lorenza Maistrello, Pawel Kiper, Roberto De Icco, Tommaso Milanesi, Cristina Tassorelli, Roberto Gatti, Johanna Jonsdottir, Marco Franceschini, Michela Goffredo, Rocco Salvatore Calabrò, Andrea Turolla; Sara Federico, Lorenza Maistrello, Pawel Kiper, Roberto De Icco, Tommaso Milanesi, Cristina Tassorelli, Roberto Gatti, Johanna Jonsdottir, Marco Franceschini, Michela Goffredo, Rocco Salvatore Calabrò, Andrea Turolla,
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveThis study aims to evaluate the feasibility of an integrated multi-domain telerehabilitation (TR) system in stroke patients and to observe whether there are changes in the quality of life (QoL) levels of patients undergoing TR treatments.MethodsPatients were enrolled for a longitudinal multicentric pilot study conducted in six Italian research hospitals (IRCCS). The primary outcome was the feasibility of an integrated TR system, assessed by calculating treatment adherence and by collecting data from the Technology Acceptance Model and the System Usability Scale (SUS). Information on time and travel distance savings was also collected. As secondary outcomes, we evaluated changes in QoL levels with the EuroQol 5-dimensions (EQ-5D) and the Short Form-36 (SF-36) and in caregiver burden through the Zarit Burden Inventory.ResultsWe enrolled 84 patients. Our system turned out to be feasible (treatment adherence = 85%), usable (SUS = 73.36/100, classifying it as a ‘good’ system) and well accepted by patients. Quality of life levels improved significantly from pre- to post-treatment (EQ-5D: p = 0.0014; SF-36 general health: p = 0.047). Caregivers perceived little or no significant care burden.ConclusionsTelerehabilitation has been confirmed to be a feasible, usable and acceptable solution to guarantee continuity of care and improve accessibility to rehabilitation treatments to post-stroke patients. Furthermore, the strength of TR is in the possibility to improve patients’ QoL, which in turn could impact on functioning.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-12T09:36:16Z
  DOI: 10.1177/20552076241308617
  Issue No: Vol. 11 (2025)
A qualitative analysis of Arabic language websites about extraction of
third molars
- Authors: Muath Saad Alassaf, Mohannad Mohammed Abu Aof, Osama Ayidh Alharbi, Abdulrahman Turkustani, Moataz Ibrahim Karbouji, Nebras Althagafi, Ahmed Mohammed Almghamsi, Ghayda Yousof Zolaly, Shadia Elsayed; Mohannad Mohammed Abu Aof, Osama Ayidh Alharbi, Abdulrahman Turkustani, Moataz Ibrahim Karbouji, Nebras Althagafi, Ahmed Mohammed Almghamsi, Ghayda Yousof Zolaly, Shadia Elsayed
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  AimThe study aimed to evaluate the quality and readability of Arabic-language web-based online information regarding surgical third molar extraction.MethodsIn this observational web-based analytical study, the top 150 Arabic search results for surgical wisdom tooth extraction were collected from Google, Yahoo, and Bing. The quality of the websites was evaluated using the DISCERN tool and the Journal of American Medical Association (JAMA) guidelines for online content analysis. Readability was measured using the Flesch-Reading Ease (FRE) scale, the Flesch-Kincaid Grade Level (FKGL) scale, and the Simplified- Measure of Gobbledygook (SMOG).ResultsA total of 450 websites related to the extraction of wisdom teeth were initially identified. 146 websites were included in the final analysis after exclusion according to specific exclusion criteria. Significant difference was observed in the domain of treatment alternatives and the quality of information provided, according to the DISCERN criteria. The median scores for reliability-related questions ranged from 1.5 to 4.5. The overall quality rating had a median score of 2.5 (IQR = 0.5). There were significant differences in the number of achieved JAMA items per webpage between the groups (P-value = 0.000). However, there was no significant difference in the DISCERN quality evaluations between the affiliations (P-value = 0.450).Conclusionthe study results indicating a broad spectrum in the explicitness and relevance of information with moderate quality across the evaluated websites and the investigation revealed significant variations in the content quality and readability provided by websites belonging to various affiliations, with non-profit websites generally achieving higher scores in JAMA criteria and readability measures.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-11T07:39:23Z
  DOI: 10.1177/20552076251321053
  Issue No: Vol. 11 (2025)
Satisfaction with a daily supportive text messaging program aimed at
promoting psychological well-being of educators: Outcomes from
implementation of the Wellness4Teachers
- Authors: Belinda Agyapong, Pamela Brett-MacLean, Raquel da Luz Dias, Yifeng Wei, Vincent Israel Opoku Agyapong; Pamela Brett-MacLean, Raquel da Luz Dias, Yifeng Wei, Vincent Israel Opoku Agyapong
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundThe Wellness4Teachers program, a daily supportive text messaging program, was launched in Canada to mitigate psychological problems among teachers.ObjectiveThis study assessed subscribers’ experiences and satisfaction with the Wellness4Teachers supportive text messaging program.MethodsThis cross-sectional study was conducted during the 2022/2023 academic year and employed a mixed-methods approach. Data was collected from educators who self-subscribed to the free Wellness4Teachers program in Canada. Participants’ satisfaction levels were assessed using a Likert scale to evaluate the responses to different aspects of the program. Frequency counts and percentages were used to summarize quantitative responses. Qualitative data from responses to open-ended questions in the satisfaction survey were analyzed using content analysis.ResultsOverall, 88.3% of the participants were female, and 11.7% were male. 68.2% of participants agreed that Wellness4Teachers text messages helped them manage stress, 63.1% agreed it helped them cope with anxiety, and 74.4% felt connected to a support system. Additionally, 73.6% reported improved overall mental well-being due to the program, and 72.2% felt hopeful about managing concerns with their mental health. Further, 95.4% of respondents reported that the text messages were always or often positive, and 93.3% deemed them to be always or often affirming. Elementary school teachers and administrators had significantly higher mean satisfaction scores compared to senior high school teachers and educators who selected "other" as their major role in school. Qualitative feedback highlighted preferences for message delivery times and suggestions for enhancing personalization features to improve the Wellness4Teachers program.ConclusionThis study demonstrates that educators were highly satisfied with and receptive to the Wellness4Teachers program. With the addition of enhanced personalization features suggested by the educators, the program may help to effectively supplement support for teachers’ psychological well-being while offsetting challenges associated with time constraints and geographical location.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-11T07:39:08Z
  DOI: 10.1177/20552076251317558
  Issue No: Vol. 11 (2025)
Care partner experience with telepresence robots in long-term care during
COVID-19 pandemic
- Authors: Grace Hu, Joey Wong, Lily Haopu Ren, Sarah Kleiss, Annette Berndt, Lily Wong, Ali Hussein, Nazia Ahmed, Jim Mann, Lillian Hung; Joey Wong, Lily Haopu Ren, Sarah Kleiss, Annette Berndt, Lily Wong, Ali Hussein, Nazia Ahmed, Jim Mann, Lillian Hung
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveAs people living with dementia move into long-term care (LTC), their care partners face a difficult role change from primary caregiver to visitor, losing a significant degree of control and direct care involvement. The COVID-19 pandemic exacerbated these challenges with health risks, changing care home protocols, and government policies. To help address these challenges, this study aimed to investigate the experiences of care partners who used telepresence robots to maintain contact with and care for their loved ones during the pandemic.MethodsThis study was guided by the Collaborative Action Research (CAR) approach. Along with interdisciplinary researchers and trainees, our team included patient and family partners as co-researchers throughout the project. We conducted semi-structured interviews with 20 care partners who used the robots in five urban Canadian LTC homes between May 2021 and August 2023.ResultsThematic analysis identified four key themes characterizing their experiences using the robot: (a) decreases care partner burden, (b) facilitates care partner–staff relationship, (c) creates relational autonomy, and (d) expands the scope of what is possible.ConclusionThe results of the study suggest that telepresence robots can play a useful role in enhancing the caregiving experience for informal care partners in multifaceted ways. Care partners reported positive benefits of having the robot assist their virtual visits. However, further research is needed to determine the sustainability of robot implementation among diverse geographic regions and care home compositions.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-06T07:52:07Z
  DOI: 10.1177/20552076251319820
  Issue No: Vol. 11 (2025)
An evidence-based tailored eHealth patient education tool for patients
with knee osteoarthritis: A randomized controlled trial
- Authors: Chong Li, Chuanmei Zhu, Kangping Song, Xiaona Xiang, Xiaoyi Wang, Jing Hu, Zhao Li, Yi Deng, Daoxin Jiang, Lixin Guo, Ting Ren, Luwen Zhu, Haibo Ai, Shaojun Zhang, Zhongliang Liu, Yonghong Yang, Siyi Zhu, Chengqi He; Chuanmei Zhu, Kangping Song, Xiaona Xiang, Xiaoyi Wang, Jing Hu, Zhao Li, Yi Deng, Daoxin Jiang, Lixin Guo, Ting Ren, Luwen Zhu, Haibo Ai, Shaojun Zhang, Zhongliang Liu, Yonghong Yang, Siyi Zhu, Chengqi He
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundAn evidence-based eHealth education tool was developed for patients with knee osteoarthritis (KOA). This study aimed to evaluate the long-term effectiveness on patient knowledge level and analyzed the patient's satisfaction with the proposed tool.MethodsA two-arm randomized controlled trial was performed, with 218 KOA patients allocated 1:1 to two groups by balanced block randomization. Both groups received usual care and additionally, the eHealth group used the proposed eHealth tool during the process. The primary outcome measure was the validated osteoarthritis patient knowledge questionnaire (PKQ-OA). The secondary outcome was patients’ satisfaction with the eHealth tool. Measurements were taken at baseline, post-intervention (T1), 1-month (T2), 3-month (T3), and 6-month (T4) follow-up. Statistical analyses, including ANOVA and chi-square tests, were employed to compare outcomes between the groups.ResultsThe results of the PKQ-OA indicated that patients in the eHealth group (38.7% ± 25%, 95% confidence interval (CI), 33.9%–43.4%) performed significantly better than the usual care group (28.8% ± 21.1%, 95% CI, 24.9%–32.9%) in terms of correct responses to the knowledge assessment in T1(P = 0.001). In addition, a higher percentage of patients in the eHealth group (39.4%) achieved a score of at least 50% on the knowledge assessment compared to the usual care group (14.6%). Patients who received eHealth education perceived it as a valuable tool for education. The analysis of follow-up data showed that the knowledge level of the eHealth group was higher than that of the control group at 6 months (F = 0.727, P = 0.471), but there was no significant difference.ConclusionPatients educated using an evidence-based eHealth education tool showed significant improvements in knowledge and were more likely to achieve an adequately informed status. The evidence-based eHealth tool could offer a low-cost, effective educational device.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-06T07:51:26Z
  DOI: 10.1177/20552076251317230
  Issue No: Vol. 11 (2025)
Erratum to “Artificial intelligence-enabled non-invasive ubiquitous
anemia screening: The HEMO-AI pilot study on pediatric population”
- Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-06T06:44:13Z
  DOI: 10.1177/20552076251316131
  Issue No: Vol. 11 (2025)
Digital health technologies and innovation patterns in diabetes ecosystems
- Authors: Odile-Florence Giger, Estelle Pfitzer, Wasu Mekniran, Hannes Gebhardt, Elgar Fleisch, Mia Jovanova, Tobias Kowatsch; Estelle Pfitzer, Wasu Mekniran, Hannes Gebhardt, Elgar Fleisch, Mia Jovanova, Tobias Kowatsch
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundThe global rise in type-2 diabetes (T2D) has prompted the development of new digital technologies for diabetes management. However, despite the proliferation of digital health companies for T2D care, scaling their solutions remains a critical challenge. This study investigates the digital transformation of T2D ecosystems and seeks to identify key innovation patterns. We examine: (1) What are emerging organizations in digital diabetes ecosystems' (2) What are the value streams in digital T2D ecosystems' (3) Which innovation patterns are present in digital T2D ecosystems'MethodsWe conducted a literature review and market analysis to characterize organizations and value streams in T2D ecosystems, pre- and post-digital transformation. We used the e3-value methodology to visualize T2D ecosystems (RQ1 and RQ2) and conducted expert interviews to identify emerging innovation patterns in digital diabetes ecosystems (RQ3).ResultsOur analyses revealed the emergence of eight organization segments in digital diabetes ecosystems: real-world evidence analytics, healthcare management platforms, clinical decision support, diagnostic and monitoring, digital therapeutics, wellness, online community, and online pharmacy (RQ1). Visualizing the value streams among these organizations highlights the crucial importance of individual health data (RQ2). Furthermore, our analysis revealed four major innovation patterns within the digital diabetes ecosystem: open ecosystem strategies, outcome-based payment models, platformization, and user-centric software (RQ3).ConclusionsOur findings illustrate the transition from traditional value chains in T2D care to platform-based and outcome-oriented models. These innovation patterns can inform strategic decisions for companies and healthcare providers, potentially helping anticipate new digital trends in diabetes care and across other chronic disease ecosystems.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-05T08:24:20Z
  DOI: 10.1177/20552076241311740
  Issue No: Vol. 11 (2025)
Cost-effectiveness of the Floodlight® MS app in Austria. Unlocking the
mystery of costs and outcomes of a digital health application for patients
with multiple sclerosis
- Authors: Evelyn Walter, Matthäus Traunfellner, Franz Meyer, Christian Enzinger, Michael Guger, Christian Bsteh, Patrick Altmann, Harald Hegen, Christoph Goger, Veronika Mikl; Matthäus Traunfellner, Franz Meyer, Christian Enzinger, Michael Guger, Christian Bsteh, Patrick Altmann, Harald Hegen, Christoph Goger, Veronika Mikl
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveMultiple sclerosis (MS) is a chronic inflammatory demyelinating disease affecting 2.9 million people worldwide, often leading to permanent disability. MS patients frequently use eHealth tools due to their relatively young age. The Floodlight® MS app is a scientifically designed smartphone application that helps patients monitor hand motor skills, walking ability and cognition between medical appointments. This study assesses the cost-effectiveness of using the Floodlight® MS app alongside standard-of-care (SoC) versus SoC alone in patients with relapsing-remitting MS (RRMS) from the perspective of the healthcare system.MethodsA 10-year decision-analytic model was developed to assess the cost-effectiveness of incorporating the Floodlight® MS app alongside SoC. The analysis included treatment-naive individuals and those already on drug therapy, modelling the app's role in early detection of disease progression and relapses to improve quality-of-life.ResultsFor treatment-naive patients, using the Floodlight® MS app resulted in a 2,660 € increase in total costs but yielded potential medical-cost savings of 786 € through health improvements. These patients experienced fewer relapses and slower disability progression, translating to a quality-of-life improvement of 4.5 months in perfect health and an incremental-cost-effectiveness-ratio (ICER) of 7,071 €. Pre-treated patients showed similar trends, with medical-cost savings of 718 €, an ICER of 7,864 €, and a quality-of-life improvement of 4.2 months. Higher effectiveness (+5%) led to an additional 8.3 months in perfect health and a reduction in overall costs.ConclusionThe analysis demonstrates that the Floodlight® MS app is a cost-effective digital health application, encouraging broader discussions on maximizing the potential of software-as-medical-devices within the care pathway.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-04T12:43:15Z
  DOI: 10.1177/20552076251314550
  Issue No: Vol. 11 (2025)
MoodMover: Development and usability testing of an mHealth physical
activity intervention for depression
- Authors: Yiling Tang, Madelaine Gierc, Henry La, Juehee Kim, Sam Liu, Raymond W Lam, Eli Puterman, Guy Faulkner; Madelaine Gierc, Henry La, Juehee Kim, Sam Liu, Raymond W Lam, Eli Puterman, Guy Faulkner
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundPhysical activity (PA) is recognized as a modifiable lifestyle factor for managing depression. An application(app)-based intervention to promote PA among individuals with depression may be a viable alternative or adjunct to conventional treatments offering increased accessibility.ObjectiveThis paper describes the early stages of the development process of MoodMover, a 9-week app-based intervention designed to promote PA for people with depression, including its usability testing.MethodsDevelopment of MoodMover followed the initial stages of the Integrate, Design, Assess, and Share (IDEAS) framework. The development process included (1) identifying intervention needs and planning; (2) intervention development; and (3) usability testing and refinement. Usability testing employed a mixed-methods formative approach via virtual semi-structured interviews involving goal-oriented tasks and administration of the mHealth App Usability Questionnaire (MAUQ).ResultsDrawing on formative research, a multidisciplinary research team developed the intervention, guided by the Multi-Process Action Control framework. Nine participants engaged in the usability testing with the MoodMover prototypes receiving an average MAUQ score of 5.79 (SD = 1.04), indicating good to high usability. Necessary modifications were made based on end-users' feedback.ConclusionsThe development of MoodMover, the first theoretically informed app-based PA intervention for individuals with depression, may provide another treatment option, which has wide reach. The comprehensive usability testing indicated interest in the app and strong perceptions of usability enabling a user-centered approach to refine the app to better align with end-users' preferences and needs. Testing the feasibility and preliminary efficacy of the refined MoodMover is now recommended.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-04T06:27:44Z
  DOI: 10.1177/20552076251317756
  Issue No: Vol. 11 (2025)
Effects of a 12-week digital training equipment program on cognitive
function and mental health in older women: a randomized controlled trial
- Authors: Byung-Sun Lee, Bong Du Choi, Ho Sung Park, Chae Won Seo, Kyung-Ae Kim; Bong Du Choi, Ho Sung Park, Chae Won Seo, Kyung-Ae KimHealth Care, Human IT Solution, Seoul, Republic of Korea
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveCombined interventions of physical activity and cognitive training have been shown to effectively enhance physical and mental factors in older adults. Digital-based tools offer various social advantages and may be more effective in improving the physical and mental well-being of older adults. As digital health content can simultaneously provide physical activity and cognitive training, this study aims to investigate the effects of a digital-based physical and cognitive training program on the physical and mental health of older women and to explore the potential of digital tools for older adults.MethodsThe participants, older women, engaged in the program three times a week for 12 weeks, with each session lasting 30 minutes (10 minutes for each of the three components). The content included digital health tools such as VR, motion tracking, and touchscreens designed for cognitive and physical fitness. Measurements were taken one week before and after the intervention for both groups, assessing body composition, cognitive function, depression, and quality of life.ResultsData from 36 participants were analyzed. Interaction effects were observed in body fat mass (P = .011) and body fat percentage (P = .01), with improvements noted in the digital intervention group. Cognitive function (P = .017) and depression (P = .017) also showed significant improvements in the digital intervention group. Quality of life subdomains, including Physical Function (P = .009), Limitation of Physical Function (P = .004), and Pain (P = .002), demonstrated significant interaction effects, though no interaction effects were found in other subdomains.ConclusionThis study found that digital-based combined interventions did not significantly impact body comb position but did improve cognitive function and depression in older women. These findings suggest that digital tools can be effectively utilized for the comprehensive management of cognitive function and mental health well-being. Such insights contribute to promoting healthy aging and provide an efficient method for managing the mental and physical health of older adults.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-04T06:27:14Z
  DOI: 10.1177/20552076251314353
  Issue No: Vol. 11 (2025)
Overcoming barriers and enabling artificial intelligence adoption in
allied health clinical practice: A qualitative study
- Authors: Jane Hoffman, Rachel Wenke, Rebecca L Angus, Lucy Shinners, Brent Richards, Laetitia Hattingh; Rachel Wenke, Rebecca L Angus, Lucy Shinners, Brent Richards, Laetitia Hattingh
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundArtificial intelligence (AI) has the potential to revolutionise healthcare. If the implementation is successful it has the potential to improve healthcare outcomes for patients and organisations. Little is known about the perceptions of allied health professionals (AHPs) towards AI in healthcare.ObjectiveThis study investigated barriers and enablers to AI implementation in the delivery of healthcare from the AHPs perspective.MethodsQualitative methodology informed by behaviour change theory using focus groups with AHPs at a health service in Queensland, Australia.ResultsTwenty-four barriers and 24 enablers were identified by 25 participants across four focus groups. Barriers included: lack of AI knowledge, explainability challenges, risk to professional practice, negative impact on professional practice, and role replacement. Enablers include AI training and education, regulation, reputation, understanding the healthcare benefits of AI and engaging clinical champions.ConclusionsAHPs have concerns about the impact and trustworthiness of AI and the readiness of organisations to support its use. Organisations must take a proactive approach and adopt targeted and multifaceted strategies to address barriers. This may include workforce upskilling, clear communication of the benefits of AI use of local champions and ongoing research.
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-04T06:26:09Z
  DOI: 10.1177/20552076241311144
  Issue No: Vol. 11 (2025)
The satisfaction of clients and caregivers with telehealth speech-language
pathology services
- Authors: Reem S. W. Alyahya
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectivesTo investigate the satisfaction of service users with different aspects related to telehealth speech-language pathology (SLP) services, and examine the influence of client's demographics, medical diagnosis, and type of clinical services on the level of satisfaction with telehealth services.MethodsA questionnaire was developed and validated to measure the satisfaction of clients and caregivers with telehealth services. Phone survey was used to collect data from clients and caregivers of clients who received telehealth SLP services.Results302 clients and caregivers completed the survey. Most of the respondents were strongly satisfied with different aspects related to telehealth, including their whole telehealth experience (82.12%), the perceived benefits from telehealth (69.21%), and the perceived quality of healthcare received through telehealth (87.75%). The levels of satisfaction with these aspects were not related to age or gender, but they were significantly influenced by the SLP diagnosis (p
  Citation: DIGITAL HEALTH
  PubDate: 2025-02-03T04:44:28Z
  DOI: 10.1177/20552076241313163
  Issue No: Vol. 11 (2025)
Efficacy of in-person and telepractice-based extended Lee Silverman Voice
Treatment LOUD® on dysarthria and dysphagia in adults with cerebral palsy
- Authors: Kyung Min Kim, Sang Ah Park, Seong Hye Hwang, Alyssia Park, Hyang Hee Kim, Jihye Hwang, Sung-Rae Cho; Sang Ah Park, Seong Hye Hwang, Alyssia Park, Hyang Hee Kim, Jihye Hwang, Sung-Rae Cho
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveWith advancements in digital health technologies, telepractice has become crucial for providing accessible medical interventions. Cerebral palsy (CP) frequently results in comorbidities including dysarthria and dysphagia, leading to restrictions in activities. This study evaluates the efficacy of both in-person and telepractice-based intensive voice therapy in improving speech, swallowing functions, and related quality of life measures in adults with CP.MethodsThe Lee Silverman Voice Treatment (LSVT) LOUD®, a speech-behavior therapy, was administered to 16 CP subjects (9 men and 7 women; mean age = 43.4 ± 10.43 years) via in-person extended version of LSVT LOUD® (LSVT-X), and telepractice program of LSVT-X (LSVT-X e-LOUD®). Outcomes, including maximum phonation time (MPT), voice intensity, diadochokinetic rate (DDK), Voice Handicap Index (VHI), swallowing quality of life (SWAL-QOL), and Videofluoroscopic Dysphagia Scale (VDS), were assessed pre- and post-treatment.ResultsSignificant improvements were noted in speech functions: MPT (p = 0.006), voice intensity (p = 0.004), DDK /puh/ (p = 0.043), and DDK/puh-tuh-kuh/ (p = 0.031). Swallowing function improved significantly in the pharyngeal phase on the VDS. Quality of life measures showed improvement in physical (p = 0.003), emotional (p = 0.000), total score of VHI (p = 0.001), fear (p = 0.031), sleep (p = 0.013), fatigue (p = 0.003), and total score of SWAL-QOL (p = 0.019). These improvements were consistent in both LSVT-X and LSVT-X e-LOUD® groups.ConclusionsBoth in-person and telepractice-based LSVT-X enhance speech, swallowing functions, and quality of life in adults with CP. LSVT-X e-LOUD® can be utilized as a digital therapeutic intervention for dysphagia and dysarthria, offering accessible and flexible treatment options aligned with digital health advancements.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-31T02:01:56Z
  DOI: 10.1177/20552076251315296
  Issue No: Vol. 11 (2025)
Performance assessment of ChatGPT 4, ChatGPT 3.5, Gemini Advanced Pro 1.5
and Bard 2.0 to problem solving in pathology in French language
- Authors: Georges Tarris, Laurent Martin; Laurent Martin
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  Digital teaching diversifies the ways of knowledge assessment, as natural language processing offers the possibility of answering questions posed by students and teachers.ObjectiveThis study evaluated ChatGPT's, Bard's and Gemini's performances on second year of medical studies’ (DFGSM2) Pathology exams from the Health Sciences Center of Dijon (France) in 2018–2022.MethodsFrom 2018 to 2022, exam scores, discriminating powers and discordance rates were retrieved. Seventy questions (25 first-order single response questions and 45 second-order multiple response questions) were submitted on May 2023 to ChatGPT 3.5 and Bard 2.0, and on September 2024 to Gemini 1.5 and ChatGPT-4. Chatbot's and student's average scores were compared, as well as discriminating powers of questions answered by chatbots. The percentage of student–chatbot identical answers was retrieved, and linear regression analysis correlated the scores of chatbots with student's discordance rates. Chatbot's reliability was assessed by submitting the questions in four successive rounds and comparing score variability using a Fleiss’ Kappa and a Cohen's Kappa.ResultsNewer chatbots outperformed both students and older chatbots as for the overall scores and multiple-response questions. All chatbots outperformed students on less discriminating questions. Oppositely, all chatbots were outperformed by students to questions with a high discriminating power. Chatbot's scores were correlated to student discordance rates. ChatGPT 4 and Gemini 1.5 provided variable answers, due to effects linked to prompt engineering.ConclusionOur study in line with the literature confirms chatbot's moderate performance for questions requiring complex reasoning, with ChatGPT outperforming Google chatbots. The use of NLP software based on distributional semantics remains a challenge for the generation of questions in French. Drawbacks to the use of NLP software in generating questions include the generation of hallucinations and erroneous medical knowledge which have to be taken into count when using NLP software in medical education.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-31T02:01:13Z
  DOI: 10.1177/20552076241310630
  Issue No: Vol. 11 (2025)
Implementing a teleophthalmology referral platform in routine practice:
Understanding a digital health intervention implementation using
normalisation process theory
- Authors: Sarah Abdi, Dilisha Patel, Josie Carmichael, Konstantinos Balaskas, Ann Blandford; Dilisha Patel, Josie Carmichael, Konstantinos Balaskas, Ann Blandford
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveDigital health interventions have the potential to improve clinical processes and patient outcomes; however, many face challenges during the adoption and implementation stages, hindering their overall impact. Our study uses normalisation process theory (NPT) as a theoretical approach to explore the facilitators and barriers to the implementation of a teleophthalmology referral platform in the United Kingdom, as an illustrative case of the implementation of a digital health intervention in routine practice.MethodsSemistructured interviews were conducted with 24 health professionals (18 optometrists and 6 ophthalmologists) involved in the implementation of a teleophthalmology referral platform. NPT guided data collection and analysis.ResultsMost participants were ready to engage with the teleophthalmology referral platform, recognising its potential value and benefits. However, during implementation, participants’ perceptions varied; a major factor was whether their expectations from the technology were met, particularly regarding the feedback from the secondary eye care component of the referral platform. Several additional factors were identified that would influence the adoption of the platform. These included individual aspects (e.g. participants’ IT skills), technology-related factors (e.g. the time required to complete referrals) and organisational factors (e.g. investment in community optometry services).ConclusionsTo successfully implement the teleophthalmology platform into routine practice, particularly on a large scale, multiple factors at different levels must be considered. This study highlights the complexity associated with implementing digital health interventions in routine practice and the contribution of NPT in untangling some of these complexities.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-31T02:00:44Z
  DOI: 10.1177/20552076241303812
  Issue No: Vol. 11 (2025)
How eHealth use and cancer information-seeking influence older adults’
acceptance of genetic testing: Mediating roles of PIGI and cancer worry
- Authors: Yinxia Zhu, Qian Erica Xiao, Man Chon Ao, Xinshu Zhao; Qian Erica Xiao, Man Chon Ao, Xinshu Zhao
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveThis study aims to investigate how eHealth use and information-seeking behavior affect older adults’ acceptance of genetic testing, focusing on their participation in genetic tests and their willingness to adopt lifestyle changes based on test results. The research highlights the mediating roles of the perceived importance of genetic information (PIGI) and cancer worry.MethodsThis cross-sectional study used secondary data from the Health Information National Trends Survey (HINTS 5, Cycle 4), conducted in 2020. The analysis included 1852 adults aged 60 and above. Two mediation models were tested using SPSS 25. Model 1 examined the relationship between eHealth use, perceived importance of genetic information (PIGI), and genetic test behavior. Model 2 analyzed how cancer information-seeking influences willingness to change lifestyle behavior (WCLB) based on genetic test results, with cancer worry as a mediator.ResultsBoth models showed statistically significant mediation effects (p
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-31T01:38:52Z
  DOI: 10.1177/20552076251317658
  Issue No: Vol. 11 (2025)
Validity of a smartphone application for self-monitoring psychiatric
symptoms in patients with schizophrenia
- Authors: Sung-Wan Kim, Jae-Kyeong Kim, Min Jhon, Ju-Wan Kim, Seunghyong Ryu, Ju-Yeon Lee, Jae-Min Kim; Jae-Kyeong Kim, Min Jhon, Ju-Wan Kim, Seunghyong Ryu, Ju-Yeon Lee, Jae-Min Kim
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveDespite increasing research on digital technologies for psychiatric disorders, studies specifically examining self-monitoring of symptoms with smartphone applications by patients with schizophrenia remain limited. This study aims to evaluate the validity and reliability of self-monitoring psychiatric symptoms using a smartphone application among patients with schizophrenia at Mindlink, a community-based early intervention center.MethodsFifty-three young patients with schizophrenia spectrum disorders participated. They rated their psychiatric symptoms across five domains—delusions, hallucinations, anxiety, depression, and perceived stress—using an 11-point Likert scale at baseline, 1 week, 8 weeks, and 16 weeks. Test–retest reliability was assessed using intraclass correlation coefficients (ICCs) between baseline and 1-week ratings. Concurrent validity was determined by correlating app-based ratings with established self-report and clinician-administered scales, including the Eppendorf Schizophrenia Inventory, Hamilton Program for Schizophrenia Voices Questionnaire, Beck Depression Inventory, Generalized Anxiety Disorder-7, and Perceived Stress Scale. The accuracy of the app's depression rating was assessed using receiver operating characteristic (ROC) analysis.ResultsICCs for test–retest reliability were high across all symptom domains, ranging from 0.741 to 0.876 (p
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-31T01:38:25Z
  DOI: 10.1177/20552076251317556
  Issue No: Vol. 11 (2025)
Can intelligent virtual assistants improve cognitive function in older
adults' A two-wave mediation study
- Authors: Tianxin Cai, Shilong Ma, Renyao Zhong; Shilong Ma, Renyao Zhong
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectivesThis study aims to investigate whether social networks and physical exercise mediate the relationship between new and constant use of intelligent virtual assistants (IVAs) and cognitive function in older adults in China and explore the potential differences across living arrangements and education levels.MethodsThis study utilized nationally representative longitudinal data from 2018 to 2020, including a sample of 8343 older adults aged 60–80. A path-analytic model was employed to examine the relationship between IVAs use and cognitive function, as well as the mediating roles of social networks and physical exercise.ResultsConstant and new IVAs use significantly affected cognitive function, mediated by social networks and physical exercise. For older adults living alone, both types of IVAs use had positive direct and indirect effects. Those living with children benefited mainly from new IVAs use, with indirect effects through physical exercise. Among less educated older adults, only new IVAs use showed significant effects through social networks. For those with higher education, both constant and new IVAs use positively impacted cognitive function directly and indirectly.ConclusionsThe use of IVAs can significantly improve cognitive function in older adults, with social networks and physical exercise being important mediating pathways. Policy suggestions were provided based on these results.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-30T08:18:51Z
  DOI: 10.1177/20552076251317355
  Issue No: Vol. 11 (2025)
Interest, uptake, and feasibility trial of a real-life digital health
intervention to improve lifestyle in Brazil
- Authors: Raquel B De Boni, Jurema C Mota, Michael Duncan, Leonardo Linconl, Giovanna Lucieri Alonso Costa, Sofia Pimentel, Kamila P Sales, Fabiana Gaspar, Fernanda Adaes, Felipe B Schuch, Vicent Balanzá-Martinez, Flavio Kapczinski, Renato S Lima; Jurema C Mota, Michael Duncan, Leonardo Linconl, Giovanna Lucieri Alonso Costa, Sofia Pimentel, Kamila P Sales, Fabiana Gaspar, Fernanda Adaes, Felipe B Schuch, Vicent Balanzá-Martinez, Flavio Kapczinski, Renato S Lima
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectivePromoting healthy lifestyle behaviors is essential for preventing and managing chronic and mental health conditions. This study aims to present a digital health platform accessible via PC or smartphone, VIVA!, designed to foster lifestyle change among the Brazilian population. It evaluates interest, uptake, acceptability, usability, adherence, and retention over 12 weeks.MethodsA fully online feasibility trial was conducted from April to December 2023. Participants were adults who lived in Rio de Janeiro and reported using the Brazilian Public Health System. Interest in VIVA! and uptake were assessed via recruitment metrics. Acceptability and usability were measured using the Mobile App Rating Scale (MARS). Adherence was calculated as the percentage of completed challenges over 12 weeks, and retention was tracked at 4, 8, and 12 weeks.ResultsOf 3812 individuals reached, 27.2% expressed interest in the app, with an uptake rate of 65.4%. A total of 401 participants were enrolled, predominantly women (73.3%) with higher education (61.6%). MARS acceptability scores were 2.9 for quality and 3.5 for specificity. Usability scores averaged around 3, with aesthetics rated the highest. Retention at 12 weeks was 4.5%, and the average adherence rate was 11.2%.ConclusionThe VIVA! attracted women and highly-educated individuals, but its effectiveness was constrained by high attrition. These findings highlight key challenges in DHI implementation, including the need for robust outreach, iterative improvements, and strategies to sustain engagement. Addressing digital access, literacy inequities, and strengthening regulations are critical for the future success and equity of DHIs in public health systems.Trial registrationThe trial was registered at the Brazilian Clinical Trials Registry (Registro Brasileiro de Ensaios Clínicos -REBEC)—number RBR-2ssyb6q.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-30T08:18:32Z
  DOI: 10.1177/20552076251316719
  Issue No: Vol. 11 (2025)
Assessing the costs and savings of telemedicine: Insights from the
consolidated framework for implementation research
- Authors: Julia Ivanova, Beju Shah, Carrie Foote, Mollie R. Cummins; Beju Shah, Carrie Foote, Mollie R. Cummins
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  Cost reduction is an often-cited reason to use telemedicine. In assessing telemedicine's cost and value, providers often turn to published cost analyses in the scientific literature for guidance. In this commentary on existing telemedicine cost analysis literature, we discuss the generalizability of these analyses and identify the Consolidated Framework for Implementation Research framework to help assess the applicability of a given cost analysis using inner- and outer-setting constructs. Outer-setting factors—location, practice type, and specialty—can substantially affect telemedicine cost and value, as can the implementation setting. While the body of evidence shows telemedicine may often reduce costs, there is still a need for robust cost analyses to guide implementation decisions as telemedicine becomes a mainstay of healthcare provision. Along with a call for more cost research, we ultimately argue that providers should consider a more holistic, value-based approach to determining when and how telemedicine implementation could benefit healthcare delivery.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-29T09:42:04Z
  DOI: 10.1177/20552076251314552
  Issue No: Vol. 11 (2025)
Digital tool as speech and language therapy for patients with post-stroke
aphasia
- Authors: Gerardo Ruiz Ares, Marta Martin Alonso, Ricardo Rigual, Carlos Hervás Testal, Gabriel Torres Iglesias, Laura Casado Fernandez, Elena de Celis Ruiz, Jorge Rodríguez Pardo, Jenny Carvajal Muñoz, Laura González Martín, María Alonso de Leciñana, Blanca Fuentes; Marta Martin Alonso, Ricardo Rigual, Carlos Hervás Testal, Gabriel Torres Iglesias, Laura Casado Fernandez, Elena de Celis Ruiz, Jorge Rodríguez Pardo, Jenny Carvajal Muñoz, Laura González Martín, María Alonso de Leciñana, Blanca Fuentes
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  IntroductionNew technologies could play a role in post-stroke aphasia (PSA). Our aims were to develop a digital tool; to evaluate its acceptance and usability by patients and caregivers; and to demonstrate its effectiveness in improving language skills in patients with PSA, applying it from the acute phase.MethodsThe study consisted of two phases: development of a digital tool; and an interventional before-and-after study. During the first week of admission, the digital tool, VerbalizAPP®, was installed for use with the help of family/caregivers. PSA was evaluated by a summarised version of the Boston Diagnostic Aphasia Examination (sBDAE) with 0–64 points. After 3 months of using VerbalizAPP®, the sBDAE and scales to assess user satisfaction were applied.ResultsForty patients (29 men, mean age 68.3 years) were included. Aphasia description: Broca's 12 (15.0%), Wernicke's 13 (32.5%), mixed/global 15 (37.5%) cases. Patients began using VerbalizAPP® 4.8 days (range 2–7) after stroke onset. A significant improvement in sBDAE scores was found after 3 months of VerbalizAPP® use: 35.1 (SD 17.6) versus 51.1 (SD 14.4) points; p
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-29T09:41:39Z
  DOI: 10.1177/20552076251314551
  Issue No: Vol. 11 (2025)
Developing a codesigned text message-based digital oral health education
resource (TOOTH)
- Authors: Shalinie King, Lauren Adriel Church, Edel O’Hagan, Dion Candelaria, Aboli Pawar, Ashley Cooper, Rebecca Chen, Alice Gibson; Lauren Adriel Church, Edel O’Hagan, Dion Candelaria, Aboli Pawar, Ashley Cooper, Rebecca Chen, Alice Gibson
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveDigital health technologies offer a more equitable way of providing access to health education. This study engaged consumers and clinicians from two Australian regions with a high burden of oral disease to develop a digital oral health resource called “TOOTH” tailored for adults.MethodsA total of three focus groups (one clinician and two consumers) were conducted to identify themes that were used to draft text message content. The study team reviewed, refined, and mapped these messages to behaviour change techniques and developed videos to support key concepts.ResultsEight multidisciplinary clinicians (dentist, oral health therapist, dietician, cardiac nurse, and dental nurse), 75% female with a mean age of 48 ± 10 years, and seven consumers from a metropolitan and a regional area in New South Wales, 43% female with a mean age 62 ± 11.6 years participated in the focus groups. Key themes identified by clinicians included (1) limited support from medical and allied health teams for integrating oral health care into the healthcare system, (2) government-supported measures enable prevention, and (3) poor oral health of Australians. Consumers highlighted (1) barriers to accessing oral health care, (2) negative attitudes towards oral health, and (3) limited oral health-related knowledge. The final resource developed includes 81 text messages sequenced to ensure a gradual increase in knowledge and skill and five supporting videos.ConclusionsThe TOOTH resource has the potential to improve awareness and knowledge regarding oral disease prevention, enabling more equitable access to oral health education and better self-management of oral health.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-29T08:27:43Z
  DOI: 10.1177/20552076241311730
  Issue No: Vol. 11 (2025)
Assessing the acceptability of a sleep-targeted digital intervention among
geriatric inpatients: A preliminary study
- Authors: Alex Chanteclair, Marion Lartigau, Nathalie Salles, Julien Coelho, Florian Pécune, Pierre Philip, Clément Champeville; Marion Lartigau, Nathalie Salles, Julien Coelho, Florian Pécune, Pierre Philip, Clément Champeville
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundSleep complaints are a major concern for the aging population. Insomnia affects quality of life, and is associated with unfavorable geriatric outcomes. Digital technologies offer promising perspectives to assess and support various health conditions, including for the older adults. Among them, the KANOPEE app, a smartphone-based virtual autonomous conversational agent, has been validated in general adult population for insomnia assessment and behavioral interventions. This research primarily aimed to assess the acceptability of the KANOPEE application for evaluating insomnia in older adults. The secondary objective aimed to identify the determinants influencing the acceptability of the app.MethodsThis cross-sectional study included geriatric department inpatients aged 70 or older, undergoing a standardized interview with the app, addressing sleep complaints. Acceptability was assessed using the Acceptability E-Scale (AES) and ECA Trust Questionnaire (ETQ). Sociodemographic and geriatric data were collected for multivariate linear regression analysis to assess determinants of acceptability scores.ResultsFifty inpatients were included (mean age: 85.8 years, men: 48%), 40% declaring a novice level of familiarity with technology. Insomnia Severity Index mean score was of 8.2, with significant clinical insomnia for 12%. The KANOPEE app showed good acceptability on AES and ETQ scales (respectively 22/30 and 18/24). Familiarity with technology increased credibility in the app (β = 1.5, CI [0.1, 2.9]). Acceptability increased with multimorbidity score (β = 1.5, CI [0.1, 2.2]), and decreased with hearing impairment (β = 1.5, CI [−10.6, −2.8]). Higher depression score decreased trust in the app (β = −0.7, CI [−0.9, −0.4).ConclusionThis preliminary investigation confirms that older adults can use autonomous virtual agent based applications to quantify their sleep complaints. Understanding and tailoring the unique needs of older users are paramount for successful digital interventions. Further research is needed to confirm these preliminary findings and assess the broader impact of digital apps in a larger sample.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-29T05:24:28Z
  DOI: 10.1177/20552076241293935
  Issue No: Vol. 11 (2025)
Impact of #PCOSweightloss: A global X hashtag analysis study of weight
loss narratives in the PCOS community
- Authors: Munaza Afaq, Kumar Vashisht, Areen Al-Dhoon, Divya Abraham, Atanas G. Atanasov, Maima Matin, Zara Arshad, Rahul Kashyap, Faisal A. Nawaz; Kumar Vashisht, Areen Al-Dhoon, Divya Abraham, Atanas G. Atanasov, Maima Matin, Zara Arshad, Rahul Kashyap, Faisal A. Nawaz
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  IntroductionSocial media plays a significant role in fostering communities around health and wellness. The hashtag #PCOSweightloss has become a pivotal forum on the platform X, where individuals exchange experiences, share information, and motivate each other concerning weight loss strategies connected with Polycystic Ovary Syndrome (PCOS).ObjectiveThis study aims to analyze the trends of #PCOSweightloss on X to gain insights into the social media metrics, including overall hashtag reach, key themes, and potential influence on management practices of PCOS.MethodsFedica research analytics tool (https://fedica.com) was utilized to automatically evaluate quantitatively the posts on the platform X containing #PCOSweightloss from July 1st, 2017 to July 1st, 2023.ResultsDuring the defined period, 1570 users shared 12,200 posts on X, gathering 2,967,001 views. The highest number of posts originated from the United States (38.6%), followed by India (30.6%) and the United Kingdom (10.6%). Top co-occurring hashtags associated with #PCOSweightloss included #PCOS, #healthyfood, #pcosmood and #diabetes. Physicians were key contributors to the discourse, contributing to 30% of the ten most engaging posts and 20% of the top ten influencers. Sentiment analysis revealed a predominant neutral sentiment (98%), while 1% each revealed positive and negative sentiments.ConclusionOur study affirms the substantial presence of #PCOSweightloss in the PCOS discourse on X. The involvement of physicians is particularly noteworthy, as it emphasizes the importance of expert-guided discussions in the online health discourse. On the background of geographical variances and the challenge of engaging a wider audience due to the limited follower counts of many participants, there is a clear opportunity for further community engagement and outreach. The prevailing neutral tone showcases a community engaged primarily in an informational exchange, setting the stage for more profound discussions of novel PCOS weight loss strategies.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-29T05:22:47Z
  DOI: 10.1177/20552076251314100
  Issue No: Vol. 11 (2025)
Health information analysis of cryptorchidism-related short videos:
Analyzing quality and reliability
- Authors: Yuqiang Sun, Xingjian Liu, Xintao Zhang, Qiongqian Xu, Aiwu Li; Xingjian Liu, Xintao Zhang, Qiongqian Xu, Aiwu LiDepartment of Pediatric Surgery, 91623Qilu Hospital of Shandong University, Jinan, China
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveCryptorchidism is a common congenital anomaly in children. Short video content has increased awareness and urged timely intervention, but there is a lack of understanding about the quality and reliability of these videos. This survey assesses the quality and credibility of cryptorchidism-related videos on Chinese short video platforms to ensure accurate information for caregivers and healthcare professionals.MethodsWe analyzed 228 videos from TikTok, Bilibili, and Kwai on 16 May 2024. Using the Journal of the American Medical Association (JAMA) criteria, Global Quality Score (GQS), and modified DISCERN score, we evaluated the videos based on six factors: definition, risk factors, symptoms, tests, treatment, and outcome.ResultsVideo quality varied significantly across platforms (P = .03). Medical professionals’ videos had higher JAMA scores than those by nonmedical professionals (P
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-28T09:18:09Z
  DOI: 10.1177/20552076251317578
  Issue No: Vol. 11 (2025)
Integrating AI·IoT-OAHPs with existing elderly care systems
- Authors: Yanyi Wu
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  This letter addresses the integration of artificial intelligence and the Internet of Things-based older adult healthcare programs with existing community and institutional elderly care systems. It highlights the current disconnect leading to service duplication and resource inefficiencies, proposes multifaceted integration approaches, and underscores the importance of supportive policies. International examples are referenced to demonstrate successful models, emphasizing the need for coordinated care to enhance service delivery and optimize resource use.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-28T09:17:28Z
  DOI: 10.1177/20552076251317378
  Issue No: Vol. 11 (2025)
Recommendations for improving accessibility of digital health
interventions for cardiometabolic disease for ethnically diverse
populations
- Authors: Mel Ramasawmy, Nushrat Khan, David Sunkersing, Madiha Sajid, Shivali H Modha, Manoj Mistry, Wasim Hanif, Fiona Stevenson, Kiran Patel, Kamlesh Khunti, Paramjit Gill, Lydia Poole, Amitava Banerjee; Nushrat Khan, David Sunkersing, Madiha Sajid, Shivali H Modha, Manoj Mistry, Wasim Hanif, Fiona Stevenson, Kiran Patel, Kamlesh Khunti, Paramjit Gill, Lydia Poole, Amitava Banerjee
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  Digital health interventions (DHIs), such as apps, websites and wearables, are being presented as solutions or enablers to manage the burden of cardiometabolic disease in healthcare. However, the potential benefits of DHIs may not be reaching the most in-need populations, who may face intersecting barriers to accessing health services and digital solutions. The Digital Interventions for South Asians in Cardiometabolic Disease (DISC) study used a mixed-method approach to focus on people of a South Asian background, a high-risk group for cardiometabolic disease. A one-day workshop was held in May 2023 with key stakeholders, including people with lived experience, health professionals, technology innovators and policymakers (n = 34), to develop recommendations arising from the DISC study findings. Discussions covered four areas: actions to support individuals to access and benefit from DHIs; translating learning about inclusive design into practice; the role of regulation and evaluation to improve inclusivity of DHIs used within the health service; and improving quality of data collection and use to reduce inequalities related to digital health and cardiometabolic disease. Our recommendations align with recent national strategies and provide specific examples of actions that can be taken to address digital inequalities for ethnic minority populations.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-28T09:16:51Z
  DOI: 10.1177/20552076241272600
  Issue No: Vol. 11 (2025)
eNSMBL-PASD: Spearheading early autism spectrum disorder detection through
advanced genomic computational frameworks utilizing ensemble learning
models
- Authors: Ayesha Karim, Nashwan Alromema, Sharaf J Malebary, Faisal Binzagr, Amir Ahmed, Yaser Daanial Khan; Nashwan Alromema, Sharaf J Malebary, Faisal Binzagr, Amir Ahmed, Yaser Daanial Khan
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveAutism spectrum disorder (ASD) is a complex neurodevelopmental condition influenced by various genetic and environmental factors. Currently, there is no definitive clinical test, such as a blood analysis or brain scan, for early diagnosis. The objective of this study is to develop a computational model that predicts ASD driver genes in the early stages using genomic data, aiming to enhance early diagnosis and intervention.MethodsThis study utilized a benchmark genomic dataset, which was processed using feature extraction techniques to identify relevant genetic patterns. Several ensemble classification methods, including Extreme Gradient Boosting, Random Forest, Light Gradient Boosting Machine, ExtraTrees, and a stacked ensemble of classifiers, were applied to assess the predictive power of the genomic features. TheEnsemble Model Predictor for Autism Spectrum Disorder (eNSMBL-PASD) model was rigorously validated using multiple performance metrics such as accuracy, sensitivity, specificity, and Mathew's correlation coefficient.ResultsThe proposed model demonstrated superior performance across various validation techniques. The self-consistency test achieved 100% accuracy, while the independent set and cross-validation tests yielded 91% and 87% accuracy, respectively. These results highlight the model's robustness and reliability in predicting ASD-related genes.ConclusionThe eNSMBL-PASD model provides a promising tool for the early detection of ASD by identifying genetic markers associated with the disorder. In the future, this model has the potential to assist healthcare professionals, particularly doctors and psychologists, in diagnosing and formulating treatment plans for ASD at its earliest stages.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-27T08:32:11Z
  DOI: 10.1177/20552076241313407
  Issue No: Vol. 11 (2025)
Psychometric properties of a physiotherapy care satisfaction scale using
telerehabilitation in caregivers of pediatric patients during the COVID-19
pandemic
- Authors: Jessica Liz Gonzalez Ccosi, Deysi Pedraza Ricra, Miguel Basauri-Delgado, Jacksaint Saintila; Deysi Pedraza Ricra, Miguel Basauri-Delgado, Jacksaint Saintila
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundEvidence on the psychometric properties of satisfaction scales in telerehabilitation is limited, especially in specific populations such as caregivers of children.ObjectiveTo determine the psychometric properties of a physiotherapy care satisfaction scale using telerehabilitation in caregivers of pediatric patients during the COVID-19 pandemic.MethodsA total of 155 caregivers were evaluated between June and December 2020. Approximately 91% (141) were women. Evidence of content validity for the scale was obtained through evaluation by three expert judges, who confirmed the adaptation of the University of Washington Telemedicine Patient Satisfaction Survey, in which the word “telemedicine” was replaced with “telerehabilitation” and “physician” with “physical therapist.”ResultsFor the confirmatory factor analysis, two models were tested. The first one-factor model with nine items did not fit satisfactorily based on the goodness-of-fit indices (χ2/df = 13.96, comparative fit index [CFI] = 0.963, non-normed fit index [NNFI] = 0.951, root mean square error of approximation [RMSEA] = 0.290 [0.265, 0.316], and standardized root mean square residual [SRMR] = 0.178). In contrast, the second one-factor model, which involved respecification of Items 6 and 7, was considered acceptable (χ2/df = 1.60, CFI = 0.998, NNFI = 0.998, RMSEA = 0.062 [0.021, 0.096], and SRMR = 0.057). Reliability was acceptable, with a value of 0.888. Additionally, network analysis confirmed the direct relationship between the items, with Item 7 showing the greatest strength centrality.ConclusionThe instrument demonstrated sufficient evidence of validity and reliability in the Peruvian context, supporting its use with pediatric patients.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-27T07:00:05Z
  DOI: 10.1177/20552076251315299
  Issue No: Vol. 11 (2025)
Can the number of confirmed COVID-19 cases be predicted more accurately by
including lifestyle data' An exploratory study for data-driven prediction
of COVID-19 cases in metropolitan cities using deep learning models
- Authors: Sungwook Jung
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveThe COVID-19 outbreak has significantly impacted human lifestyles and life patterns. Therefore, data related to human social life may tell us the increase or decrease in the number of confirmed COVID-19 cases. However, although the number of confirmed cases is affected by social life, it is difficult to find studies that attempt to predict the number of confirmed cases using various lifestyle data. This paper attempted an exploratory data analysis to see if the number of confirmed cases could be predicted more accurately by including various lifestyle data.MethodsWe included taking public transportation, watching a movie at the cinema, and accommodation at a motel in the lifestyle data. Finally, a ‘lifestyle addition’ set was constructed that added lifestyle data to the number of past confirmed cases and search term frequency data. The deep learning algorithms used in the analysis are deep neural networks (DNNs) and recurrent neural networks (RNNs). Performance differences across data sets and between deep learning models were tested to be statistically significant.ResultsAmong metropolitan cities in South Korea, Seoul (9.6 million) with the largest population and Busan (3.4 million) with the second largest population had the lowest error rate in ‘lifestyle addition’ set. When predicting with the ‘lifestyle addition’ set, in Seoul, the error rate was reduced to 20.1%, and in Busan, the graph of the actual number of confirmed cases and the predicted graph were almost identical.ConclusionsThrough this study, we were able to identify three notable results that could contribute to predicting the number of patients infected with epidemic in the future.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-27T06:59:24Z
  DOI: 10.1177/20552076251314528
  Issue No: Vol. 11 (2025)
Novel approach for noninvasive pelvic floor muscle strength measurement
using extracorporeal surface perineal pressure measurement and machine
learning modeling
- Authors: Ui-jae Hwang, Sun-hee Ahn, Hyeon-ju Lee, Yurin Jeon, Myung Jae Jeon; Sun-hee Ahn, Hyeon-ju Lee, Yurin Jeon, Myung Jae Jeon
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveAccurate measurement of pelvic floor muscle (PFM) strength is crucial for the management of pelvic floor disorders. However, the current methods are invasive, uncomfortable, and lack standardization. This study aimed to introduce a novel noninvasive approach for precise PFM strength quantification by leveraging extracorporeal surface perineal pressure (ESPP) measurements and machine learning algorithms.MethodsTwenty-one healthy women participated in this study. ESPP measurements were obtained using a 10 × 10 pressure array sensor during maximal voluntary PFM contractions in a seated position. Simultaneously, transabdominal ultrasound was used to measure bladder base displacement (mm) as a reference for PFM contraction strength. Seven ESPP variables were calculated based on ESPP data and intra- and inter-rater reliabilities were assessed. Machine learning algorithms predicted bladder base displacement from ESPP variables.ResultsThe ESPP measurements demonstrated good to excellent intra-rater (ICC = 0.881) and inter-rater (ICC = 0.967) reliability. Significant correlations were observed between bladder base displacement and middle (r = .619, P
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-24T08:18:58Z
  DOI: 10.1177/20552076251316730
  Issue No: Vol. 11 (2025)
The intersection of digital health and artificial intelligence: Clearing
the cloud of uncertainty
- Authors: Pooyeh Graili, Bijan Farhoudi; Bijan Farhoudi
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  Digital health (DH) and artificial intelligence (AI) in healthcare are rapidly evolving but were addressed synonymously by many healthcare authorities and practitioners. A deep understanding and clarification of these concepts are fundamental and a prerequisite for developing robust frameworks and practical guidelines to ensure the safety, efficacy, and effectiveness of DH solutions and AI-embedded technologies. Categorizing DH into technologies (DHTs) and services (DHSs) enables regulatory, HTA, and reimbursement bodies to develop category-specific frameworks and guidelines for evaluating these solutions effectively. DH is the key in generating real-world data, which is increasingly important in decision-making processes. The potential benefits of DHTs in improving health outcomes and reducing health system costs can position them alongside traditional health technologies in certain medical conditions. AI, one of the potential tools for DH, can be embedded in technologies, such as medical devices or applications, to enhance functionality and performance. AI excels at handling numerical and perceptual data. In the context of numerical data, machine learning algorithms enable prediction, classification, and clustering. In managing perceptual data, AI recognizes image/video, voice, and text. In recent years, generative AI, a form of AI that generates new content by employing a combination of a wide range of learning approaches, has become prominent in research and influences the health sector. A thorough understanding of DH and AI, along with accurate terminology use, would facilitate the timely generation of regulatory and HTA-grade evidence that helps improve health outcomes and decision-making certainty.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-24T08:18:15Z
  DOI: 10.1177/20552076251315621
  Issue No: Vol. 11 (2025)
Enhancing prostate cancer segmentation in bpMRI: Integrating zonal
awareness into attention-guided U-Net
- Authors: Chao Wei, Zheng Liu, Yibo Zhang, Lianhui Fan; Zheng Liu, Yibo Zhang, Lianhui Fan
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  PurposeProstate cancer (PCa) is the second most common cancer in males worldwide, requiring improvements in diagnostic imaging to identify and treat it at an early stage. Bi-parametric magnetic resonance imaging (bpMRI) is recognized as an essential diagnostic technique for PCa, providing shorter acquisition times and cost-effectiveness. Nevertheless, accurate diagnosis using bpMRI images is difficult due to the inconspicuous and diverse characteristics of malignant tumors and the intricate structure of the prostate gland. An automated system is required to assist the medical professionals in accurate and early diagnosis with less effort.MethodThis study recognizes the impact of zonal features on the advancement of the disease. The aim is to improve the diagnostic performance through a novel automated approach of a two-step mechanism using bpMRI images. First, pretraining a convolutional neural network (CNN)-based attention-guided U-Net model for segmenting the region of interest which is carried out in the prostate zone. Secondly, pretraining the same type of Attention U-Net is performed for lesion segmentation.ResultsThe performance of the pretrained models and training an attention-guided U-Net from the scratch for segmenting tumors on the prostate region is analyzed. The proposed attention-guided U-Net model achieved an area under the curve (AUC) of 0.85 and a dice similarity coefficient value of 0.82, outperforming some other pretrained deep learning models.ConclusionOur approach greatly enhances the identification and categorization of clinically significant PCa by including zonal data. Our approach exhibits exceptional performance in the accurate segmentation of bpMRI images compared to current techniques, as evidenced by thorough validation of a diverse dataset. This research not only enhances the field of medical imaging for oncology but also underscores the potential of deep learning models to progress PCa diagnosis and personalized patient care.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-24T08:17:47Z
  DOI: 10.1177/20552076251314546
  Issue No: Vol. 11 (2025)
The use of an automatic remote weight management system to track treatment
response, identified drugs supply shortage and its consequences: A pilot
study
- Authors: Idan Roth, Ohad Cohen; Ohad Cohen
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveThe objective of this pilot study is to evaluate the feasibility of using an automatic weight management system to follow patients’ response to weight reduction medications and to identify early deviations from weight trajectories.MethodsThe pilot study involved 11 participants using Semaglutide for weight management, monitored over a 12-month period. A cloud-based, Wi-Fi-enabled remote weight management system collected and analyzed daily weight data from smart scales. The system's performance was evaluated during a period marked by a Semaglutide supply shortage.ResultsParticipants achieved a cumulative weight loss of 85 kg until a supply shortage-induced trough in October 2022. This was followed by a 6–8 week plateau and a subsequent 13 kg cumulative weight gain. The study demonstrated the feasibility of digitally monitoring weight without attrition over 12 months and highlighted the impact of anti-obesity drug (AOD) supply constraints on weight trajectories.ConclusionsThe remote weight management system proved important for improving clinic efficacy and identifying trends impacting obesity outcomes through electronic data monitoring. The system's potential in increasing medication compliance and enhancing overall clinical outcomes warrants further research, particularly in light of the challenges posed by AOD supply fluctuations.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-24T08:17:09Z
  DOI: 10.1177/20552076251314090
  Issue No: Vol. 11 (2025)
Perceptions and experiences of commercial virtual reality games in early
postoperative rehabilitation among cardiac surgical patients: A
qualitative study
- Authors: Zhongkang Wu, Xihan Li, Lin Li, Yan Zhang, Xiao Shen; Xihan Li, Lin Li, Yan Zhang, Xiao Shen
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundAlthough numerous studies have focused on the outcomes of virtual reality games for early rehabilitation in postoperative cardiac surgical patients, research on these patients’ perceptions and experiences with virtual reality games has been limited.ObjectivesThe aim of this qualitative study was to provide insights into the perceptions and experiences of postoperative cardiac surgery patients in using commercial virtual reality games during early rehabilitation.MethodsA cohort of postoperative cardiac surgery patients (n = 12) who used the same VR games during the early rehabilitation period at the cardiac surgery intensive care unit (ICU) of a tertiary hospital in Nanjing, China, was enrolled in this study, conducted between January 2023 and December 2023. Data were collected through individual in-depth interviews and analyzed using Colaizzi's phenomenological method.ResultsTwo themes emerged from the interviews: (1) the benefits of virtual reality games for rehabilitation, including (i) enhancing enthusiasm for rehabilitation, (ii) helping patients to focus attention, (iii) increasing individual exercise, (iv) providing enjoyment, and (v) regulating negative emotions; (2) shortcomings in the use of virtual reality games, including (i) producing stress, (ii) insufficient operating space, (iii) discomfort while wearing, (iv) difficulty in mastering the application, and (v) individualized needs.ConclusionCardiac patients believed that the use of commercial virtual reality games during early postoperative rehabilitation was beneficial to rehabilitation, but they highlighted some shortcomings that require improvement. The results of this study provide a certain theoretical basis for the further promotion and application of commercial virtual reality games in clinical practice in the future.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-23T11:21:56Z
  DOI: 10.1177/20552076251315793
  Issue No: Vol. 11 (2025)
Development and implementation of a digiphysical screening model with
nationwide reach to diagnose familial hypercholesterolemia
- Authors: Karin Littmann, Gustav Kindborg, Matthias Lidin, Linda Mellbin, Daniel Eriksson Hogling, Jonas Brinck; Gustav Kindborg, Matthias Lidin, Linda Mellbin, Daniel Eriksson Hogling, Jonas Brinck
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundFamilial hypercholesterolemia (FH) is a hereditary dyslipidemia that confers a severely elevated risk for development of early atherosclerotic cardiovascular disease if left untreated. FH is underdiagnosed in most countries including Sweden.AimTo develop and evaluate the implementation of a digiphysical screening model to diagnose FH in the clinical routine.MethodsA digiphysical screening model for FH, containing digital and physical related activities was developed and fully implemented in routine clinical care in the Stockholm region, Sweden 2022. The centerpiece of the model is a tailormade interactive web-based platform designed to facilitate communication and secure medical information exchange between its participants and the healthcare professionals. The screening model includes, (i) cascade screening of relatives to patients with a confirmed FH diagnosis and (ii) systematic selective screening of patients with established atherosclerotic coronary artery disease.ResultsUntil October 2023, 338 index patients were included in the cascade screening. They invited 954 relatives nationwide, 616 (64.6%) accepted participation, 346 (36.3%) were completely screened, and 141 (14.8%) have received a FH diagnosis (40.8% of all completely screened). Selective screening was performed in 2867 patients with coronary artery disease, 355 (12.4%) were identified with increased risk for FH and underwent a genetic test. Of these, 153 (3.8%) had a genetic test result and 52 (1.8%) were diagnosed with FH.ConclusionsA digiphysical screening model with a nationwide reach to diagnose FH was successfully implemented in routine clinical care. The model has potential to facilitate FH screening and provide health economic benefits long term.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-23T11:21:39Z
  DOI: 10.1177/20552076241311156
  Issue No: Vol. 11 (2025)
Comparing the performance of ChatGPT and ERNIE Bot in answering questions
regarding liver cancer interventional radiology in Chinese and English
contexts: A comparative study
- Authors: Xue-ting Yuan, Chen-ye Shao, Zhen-zhen Zhang, Duo Qian; Chen-ye Shao, Zhen-zhen Zhang, Duo Qian
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  IntroductionThis study aims to critically assess the appropriateness and limitations of two prominent large language models (LLMs), enhanced representation through knowledge integration (ERNIE Bot) and chat generative pre-trained transformer (ChatGPT), in answering questions about liver cancer interventional radiology. Through a comparative analysis, the performance of these models will be evaluated based on their responses to questions about transarterial chemoembolization and hepatic arterial infusion chemotherapy in both English and Chinese contexts.MethodsA total of 38 questions were developed to cover a range of topics related to transarterial chemoembolization (TACE) and hepatic arterial infusion chemotherapy (HAIC), including foundational knowledge, patient education, and treatment and care. The responses generated by ERNIE Bot and ChatGPT were rigorously evaluated by 10 professionals in liver cancer interventional radiology. The final score was determined by one seasoned clinical expert. Each response was rated on a five-point Likert scale, facilitating a quantitative analysis of the accuracy and comprehensiveness of the information provided by each language model.ResultsERNIE Bot is superior to ChatGPT in the Chinese context (ERNIE Bot: 5, 89.47%; 4, 10.53%; 3, 0%; 2, 0%; 1, 0% vs ChatGPT: 5, 57.89%; 4, 5.27%; 3, 34.21%; 2, 2.63%; 1, 0%; P = 0.001). However, ChatGPT outperformed ERNIE Bot in the English context (ERNIE Bot: 5, 73.68%; 4, 2.63%; 3, 13.16; 2, 10.53%；1, 0% vs ChatGPT: 5, 92.11%; 4, 2.63%; 3, 5.26%; 2, 0%; 1, 0%; P = 0.026).ConclusionsThis study preliminarily demonstrated that ERNIE Bot and ChatGPT effectively address questions related to liver cancer interventional radiology. However, their performance varied by language: ChatGPT excelled in English contexts, while ERNIE Bot performed better in Chinese. We found that choosing the appropriate LLMs is beneficial for patients in obtaining more accurate treatment information. Both models require manual review to ensure accuracy and reliability in practical use.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-23T10:28:08Z
  DOI: 10.1177/20552076251315511
  Issue No: Vol. 11 (2025)
Continuous atrial fibrillation monitoring using a wearable smartwatch:
Using long-term Holter as reference
- Authors: Yannan Pan, Erdong Chen, Shihui Jie, Dongbo Huo, Zhongru Ding, Jing Zhou, Jie Jiang, Jianping Li, Yong Huo; Erdong Chen, Shihui Jie, Dongbo Huo, Zhongru Ding, Jing Zhou, Jie Jiang, Jianping Li, Yong Huo
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundWearables satisfactorily detect atrial fibrillation (AF) longer than 1 hour. Our study aims to evaluate smartwatch performances for long-term AF monitoring, including AF with short durations.MethodsThis prospective study enrolled AF patients from 2020 to 2023. Diagnostic efficacy of the Amazfit smartwatch, with AF-identifying algorithms from photoplethysmography (PPG) and single-lead electrocardiogram (ECG), was compared with a 7-day Holter. Primary analysis included smartwatch diagnostics to identify AF longer than 5 minutes. Secondary analyses evaluated smartwatch performances under different settings and compared AF burdens between the smartwatch and Holter.ResultsThe study analyzed 72 patients (48 males, mean age 65.4 ± 8.5) with 914 AF episodes lasting 834.7 hours, including 142 longer-than-5-minute AF episodes. Smartwatch recording time was 8927.6 hours. By individual, sensitivities and specificities of AF longer than 5 minutes were 100.0% and 83.7% for PPG and 89.7% and 67.4% for the ECG algorithm. Positive and negative predictive values were 94.9% and 99.9% for PPG and 77.6% and 99.8% for ECG. Optimal AF durations to be identified by PPG and ECG algorithms were 1.358 and 16.708 minutes. Smartwatch performances varied across AF durations and between day-time and night-time. Strong correlations (PPG: ρ = 0.877; ECG: ρ = 0.769) and excellent agreements (PPG: ICC = 0.976; ECG: ICC = 0.927) were found between AF burdens calculated from smartwatch and Holter.ConclusionsCompared with long-term Holter, the wearable smartwatch had satisfying qualitative and quantitative diagnostic performances for continuous AF monitoring. Susceptibility to false positives led to modest specificity. Smartwatch performances were affected by AF durations and time periods.RegistrationChiCTR2000040035.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-23T10:27:38Z
  DOI: 10.1177/20552076251314105
  Issue No: Vol. 11 (2025)
Evaluation of reporting trends in the MAUDE Database: 1991 to 2022
- Authors: Meital Mishali, Nadav Sheffer, Oren Mishali, Maya Negev; Nadav Sheffer, Oren Mishali, Maya Negev
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  Adverse event reporting for medical devices is critical for risk mitigation. The Food and Drug Administration's (FDA) Manufacturer and User Facility Device Experience (MAUDE) database serves as a key tool for post-market surveillance, receiving reports from various sources. Ensuring information integrity, especially across diverse reporting sources, is paramount. Periodic regulatory updates aim to enhance reporting.ObjectiveAnalyze reporting patterns, assess the potential contribution of 2015 and 2018 regulatory updates on reporting rates for varies reporters. Additionally, evaluating reporting schedule compliance by comparing average reporting times to regulatory requirements for these reporters.MethodsData from 1991–2022 was retrieved from the MAUDE database. Annual report counts were filtered by reporter and event type. Average reporting time was calculated as the difference between received and awareness dates.ResultsThe 2018 Voluntary Malfunction Summary Reporting (VMSR) program correlated with an increase in manufacturers’ event reporting, peaking at 3,135,501 events in 2022. Distributors’ reports surged post-2018, while voluntary reports steadily increased since 1997, spiking notably in 2015 with the Electronic Medical Device Reporting (eMDR) submission update. Reporting times for deaths averaged 80 days for manufacturers, far exceeding regulatory requirements, compared to 40 days for distributors and 46 days for user facilities. Malfunction events had longer average reporting times for manufacturers (89 days) compared to distributors (44 days).ConclusionsChanges in reporting trends around 2015 and 2018 may be linked to regulatory updates. Tailoring regulatory changes for each reporter type, like user-friendly electronic forms, new reporting programs, and exemptions cancelations, can improve reporting. Balancing the volume of reports among different reporters is crucial for enhancing database integrity, transparency, and overall health outcomes.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-23T10:27:18Z
  DOI: 10.1177/20552076251314094
  Issue No: Vol. 11 (2025)
Systematic optimization and evaluation of a Dutch sexual health
intervention: Role model stories for chlamydia prevention, testing, and
treatment
- Authors: Gido Metz, Rosa RLC Thielmann, Hanneke Roosjen, Sarah E. Stutterheim, Rik Crutzen; Rosa RLC Thielmann, Hanneke Roosjen, Sarah E. Stutterheim, Rik Crutzen
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundThe rapidly evolving nature of eHealth necessitates regular optimization and subsequent evaluation. Within the Dutch sexual health intervention Sense.info, we utilized a mixed-methods cyclic evaluation process to assess and optimize the potential impact of the chlamydia page. This paper reports on the page's optimization through the development of role model stories for chlamydia prevention and the subsequent evaluation of these stories.MethodThe experiences of 10 young individuals served as the basis of role model stories using the behavior change principle modeling based on social cognitive theory. These stories aimed to motivate young individuals to undergo sexually transmitted infection testing, use condoms, and notify sexual partners. Once the stories were posted online, we tracked use data between July and September 2022 and investigated end-user perspectives through a think-aloud study combined with semistructured interviews (N = 20, Mage = 19.7, SDage = 2.65). Template analyses were used for the analysis of the think-aloud study.ResultsUse data revealed that all stories were accessed by website visitors, yet other page elements on the chlamydia page interacted with more. The exploration of end-user perspectives indicated a positive impact of the personal stories on normalization, self-efficacy, and skills related to chlamydia preventive behaviors. Mixed results were found regarding some conditions for the effectiveness of the behavior change principle modeling.Discussion and conclusionThis study provided valuable insights into the cyclic evaluation process for evaluating and optimizing web-based public health interventions, as well as the potential impact of role model stories on sexual health prevention. Also, aspects of the stories that could be optimized in future optimization rounds were identified. Overall, this research contributes to enhancing the impact of eHealth interventions through iterative evaluation and optimization processes.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-23T10:26:38Z
  DOI: 10.1177/20552076241308447
  Issue No: Vol. 11 (2025)
Overview of basic design recommendations for user-centered explanation
interfaces for AI-based clinical decision support systems: A scoping
review
- Authors: Ian-C. Jung, Katharina Schuler, Maria Zerlik, Sophia Grummt, Martin Sedlmayr, Brita Sedlmayr; Katharina Schuler, Maria Zerlik, Sophia Grummt, Martin Sedlmayr, Brita Sedlmayr1Institute for Medical Informatics University Hospital Carl Gustav Carus, TUD Dresden University of Technology, Dresden, Germany
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveThe application of artificial intelligence (AI)-based clinical decision support systems (CDSS) in the healthcare domain is still limited. End-users’ difficulty understanding how the outputs of opaque black AI models are generated contributes to this. It is still unknown which explanations are best presented to end users and how to design the interfaces they are presented in (explanation user interface, XUI). This article aims to provide an overview of recommendations for the user-centered design of XUIs from the scientific literature.MethodsA scoping review was conducted to identify recommendations for the design of XUIs. Articles published between 2017 and 2022 in English or German, presenting original research or literature reviews, focusing on XUIs for end users or domain experts, which are intended for presentation in graphical user interfaces and from which recommendations could be extracted were included in the review. Articles were retrieved from Scopus, Web of Science, IEEE Explore, PubMed, ACM Digital Library, and PsychInfo. A mind map was created to organize and summarize the identified recommendations.ResultsFrom the 47 included articles, 240 recommendations for the user-centered design were extracted. The organization in a mind map resulted in 64 summarized recommendations.ConclusionThis review provides a synopsis of basic recommendations for the user-centered design of XUIs, focusing on the healthcare domain. During the analysis of the articles, it became clear that no specific and directly implementable design recommendations for AI-based CDSS can be given, but only basic recommendations for raising awareness about the user-centered design of XUIs.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-23T10:26:09Z
  DOI: 10.1177/20552076241308298
  Issue No: Vol. 11 (2025)
A systematic review of the effect of personal health records on patient
activation
- Authors: Irina Osovskaya, Ann Blandford, Henry WW Potts; Ann Blandford, Henry WW Potts
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundPersonal health records (PHRs) or patient portals have been on the healthcare policy agenda for many countries as a promising mechanism to support patient-centred healthcare by making medical records accessible to patients and those assisting patients in health self-management. Studies on clinical outcome have been inconsistent. To help us to understand why, we propose to look at measures that precede clinical outcome, specifically patient engagement and activation. Patient activation describes the knowledge, skills and confidence a person has in managing their own health and healthcare.ObjectiveTo systematically review the current evidence on the impact of PHRs on patient activation.MethodsA literature search was conducted for randomised controlled trials and quasi-experimental studies published up to January 2024 across EMBASE, PsycINFO, CINAHL and PubMed. Publications were included in the study if they examined any association between PHR use and activation.ResultsThe search initially produced 3062 papers for review, of which 88 full-text articles were screened for eligibility. Two reviewers assessed 22 of these articles, and 8 papers were identified as meeting the selection criteria. Among these, seven studies found no statistically significant differences in activation. However, one study reported a significantly greater effect than the others. Data from seven randomised controlled trials and quasi-experimental studies examining the effects of PHRs on patient activation and similar measures were extracted for meta-analysis. Overall, the use of PHRs was associated with a 0.41 standardised mean difference increase in activation (95% confidence interval 0.31–0.51). There was a high degree of heterogeneity (I² = 98%), with one study showing a much larger effect size compared to the rest.ConclusionMost studies showed little impact on activation, but one study found a large effect. This study notably offered PHRs combined with health coaching and training in the use of the system to their intervention group, which may indicate an important requirement for how to get the best out of a PHR system.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-22T09:17:51Z
  DOI: 10.1177/20552076251315295
  Issue No: Vol. 11 (2025)
The engagement of healthcare providers in implementing the selfBACK
randomised controlled trial – A mixed-methods process evaluation
- Authors: Mette Jensen Stochkendahl, Barbara I Nicholl, Karen Wood, Frances S Mair, Paul Jarle Mork, Karen Søgaard, Charlotte DN Rasmussen; Barbara I Nicholl, Karen Wood, Frances S Mair, Paul Jarle Mork, Karen Søgaard, Charlotte DN Rasmussen
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundPeople with low back pain (LBP) are often recommended to self-manage their condition, but it can be challenging without support. Digital health interventions (DHIs) have shown promise in supporting self-management of LBP, but little is known about healthcare providers’ (HCPs) engagement in implementing these.AimsWe aimed to examine HCPs’ engagement in patient recruitment for the selfBACK app clinical trial and explore their perceptions of the app.MethodsIn a mixed-methods design, we conducted a process evaluation alongside the selfBACK trial, triangulating quantitative data from trial recruitment logs and a vignette-based survey, and qualitative data from trial procedure documents, interviews with HCPs, and survey free-text responses. From 2019 to 2020, we recruited 57 HCPs from Norway and 39 health clinics in Denmark and collected quantitative and qualitative data in parallel. Results were integrated using displays.ResultsOverall, 825 patients were recruited by the HCPs. The vignette-based survey showed high agreement among HCPs (n = 62) with the self-management plans generated by the app (84.1–88.9%) but also highlighted concerns about tailoring and content. Interviews with HCPs (n = 19) revealed challenges with recruitment due to busy schedules, competing tasks, and varying levels of interest and engagement in the study.ConclusionsThe study identified factors that impact HCPs’ engagement in recruiting patients for the selfBACK trial and highlighted overall positive views of the selfBACK app, although some concerns about the content and tailoring of the app were raised. Understanding HCP motivations and workload is crucial for the successful implementation of DHIs in clinical practice.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-22T09:16:50Z
  DOI: 10.1177/20552076241313159
  Issue No: Vol. 11 (2025)
Real-time mechanism-based interventions for daily alcohol challenges:
Protocol for ecological momentary assessment and intervention
- Authors: Shuyan Liu, Matthias Haucke, Rika Groß, Kay Schneider, Jaekyung Shin, Fabian Arntz, Patrick Bach, Tobias Banaschewski, Christian Beste, Lorenz Deserno, Ulrich Ebner-Priemer, Tanja Endrass, Marvin Ganz, Ali Ghadami, Marco Giurgiu, Andreas Heinz, Falk Kiefer, Reinhold Kliegl, Bernd Lenz, Marta Anna Marciniak, Andreas Meyer-Lindenberg, Andreas B. Neubauer, Michael Rapp, Michael N. Smolka, Jens Strehle, Rainer Spanagel, Gianna Spitta, Heike Tost, Henrik Walter, Hilmar Zech, Dominic Reichert, Markus Reichert; Matthias Haucke, Rika Groß, Kay Schneider, Jaekyung Shin, Fabian Arntz, Patrick Bach, Tobias Banaschewski, Christian Beste, Lorenz Deserno, Ulrich Ebner-Priemer, Tanja Endrass, Marvin Ganz, Ali Ghadami, Marco Giurgiu, Andreas Heinz, Falk Kiefer, Reinhold Kliegl, Bernd Lenz, Marta Anna Marciniak, Andreas Meyer-Lindenberg, Andreas B. Neubauer, Michael Rapp, Michael N. Smolka, Jens Strehle, Rainer Spanagel, Gianna Spitta, Heike Tost, Henrik Walter, Hilmar Zech, Dominic Reichert, Markus Reichert
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundAdvancing evidence-based, tailored interventions for substance use disorders (SUDs) requires understanding temporal directionality while upholding ecological validity. Previous studies identified loneliness and craving as pivotal factors associated with alcohol consumption, yet the precise directionality of these relationships remains ambiguous.ObjectiveThis study aims to establish a smartphone-based real-life intervention platform that integrates momentary assessment and intervention into everyday life. The platform will explore the temporal directionality of contextual influences on alcohol use among individuals experiencing loneliness and craving.MethodsWe will target 180 individuals aged 18 to 70 in Germany who report loneliness, alcohol cravings, and meet risk or binge drinking criteria (over 14 standard drinks per week or five drinks in a single day for males, and over seven drinks per week or four drinks in a single day for females). Using a Within-Person-Encouragement-Design and Just-In-Time-Adaptive-Interventions, we will manipulate the contexts of loneliness and alcohol craving with cognitive reappraisal and physical activity interventions against a control condition (working memory task).ResultsRecruitment started in June 2024, with data collection and processing expected by June 2027.ConclusionOur real-life intervention platform endeavors to serve as a robust tool for discerning the directionality of the effects from time series data in everyday life influences on alcohol use for the future study. Ultimately, it will pave the way for low-threshold prevention, clinical treatment, and therapy to target diverse contexts of everyday life in SUD.Trial registrationGerman Clinical Trials Register DRKS00033133.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-22T09:16:04Z
  DOI: 10.1177/20552076241311731
  Issue No: Vol. 11 (2025)
Comparison of health technology assessments for digital therapeutics in
Germany, the United Kingdom and France
- Authors: Emanuele Arcà, Dorothea Heldt, Martina Smith; Dorothea Heldt, Martina Smith
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveDigital therapeutics (DTx) are promising technologies. However, current assessment and access frameworks, when they exist, are heterogeneous and fragmented. We analysed and compared health technology assessment (HTA) criteria for DTx across European countries that had assessed the same DTx products. This allowed us to conduct a direct comparison of these countries’ DTx assessment frameworks.MethodsA scoping review identified HTA DTx documents from European HTA bodies with specific DTx frameworks in place. The HTAs of the same DTx products assessed across multiple countries were then extracted and analysed.ResultsDeprexis and Velibra were the only DTx products assessed by multiple countries: Deprexis, indicated for depression, was assessed by Germany, the United Kingdom (UK) and France; and Velibra, indicated for anxiety, was assessed by Germany and the UK. There are notable differences among those frameworks, but what they share is an emphasis on the context-specific positioning of products within the disease landscape, choice of comparators and usage and usability data. Safety data are not a major focus in DTx assessments in these countries, but a request is often made for the addition and evaluation of risk-flagging systems.ConclusionThe majority of European countries do not have a specific framework in place for the assessment of DTx, and major differences exist among recently implemented frameworks. The current policy landscape at the European Union level offers an opportunity to establish a harmonized framework for the assessment of DTx and possibly faster access and integration of such promising technologies.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-22T09:15:13Z
  DOI: 10.1177/20552076241308704
  Issue No: Vol. 11 (2025)
Effectiveness of digital behaviour change interventions for promoting
physical activity in adolescents with overweight and obesity: A systematic
review and meta-analysis
- Authors: Puteri Shanaz Jahn Kassim, Noor Azimah Muhammad, Nur Faraheen Abdul Rahman, Sherina Mohd Sidik, Cecilia A. Essau, Shamsul Azhar Shah; Noor Azimah Muhammad, Nur Faraheen Abdul Rahman, Sherina Mohd Sidik, Cecilia A. Essau, Shamsul Azhar Shah
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundObesity in adolescence is associated with many adverse health and well-being outcomes. Physical activity plays an important role in the prevention of obesity; however, many adolescents with overweight and obesity are physically inactive. Digital behaviour change interventions (DBCI) are increasingly used to increase physical activity; however, there is insufficient evidence on their use in promoting physical activity among adolescents with overweight and obesity, including its active components. This review aims to evaluate the effectiveness of DBCIs in promoting physical activity among adolescents with overweight and obesity and to identify the behaviour change techniques (BCTs) used in these interventions.MethodsSeven electronic databases were searched for randomized controlled trials and quasi-experimental studies evaluating DBCIs for physical activity promotion in adolescents with overweight and obesity aged 10–19 years. The risk of bias in the included studies was assessed using Cochrane's Risk of Bias tool 2 and ROBINS-I tool. Meta-analyses were performed using random effects models. The BCTs used within the DBCIs were coded using the Behaviour Change Technique taxonomy (v1).ResultsEighteen studies involving 1769 participants were included. DBCIs showed a large, significant positive effect on total physical activity (SMD = 1.61, 95% CI: [0.56, 2.66], p = 0.003), but non-significant effects on moderate-to-vigorous physical activity (MVPA) (SMD = 0.16, 95% CI: [−0.39, 0.71], p = 0.56) and step count (SMD = −0.10, 95% CI: [−0.52, 0.32], p = 0.65). High heterogeneity was observed in total physical activity and MVPA analyses. The most frequently used BCTs were information about health consequences, goal setting, self-monitoring, feedback on behaviour and social support.ConclusionDBCIs can effectively promote overall physical activity among adolescents with overweight and obesity, although their impact on MVPA and step count remains unclear. Future research should focus on optimizing BCT combinations and improving long-term engagement while addressing intervention effect variability.Systematic review registrationPROSPERO CRD42021270008
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-21T10:44:22Z
  DOI: 10.1177/20552076251314904
  Issue No: Vol. 11 (2025)
Social media activism and women's health: Endometriosis awareness and
support
- Authors: Hyunjin Seo, K. Macy Burkett, Moses Okocha, Huong Ha, Rim Chaif, Nazra Izhar, Michaella Barros Coelho, Blessing Jona, Azhar Iqbal; K. Macy Burkett, Moses Okocha, Huong Ha, Rim Chaif, Nazra Izhar, Michaella Barros Coelho, Blessing Jona, Azhar Iqbal
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveWhile endometriosis is estimated to affect 1 in 10 women globally, awareness of the disease as well as research and funding to fight the disease remains lacking as compared with other chronic diseases. This study examines how social media users utilized Instagram to raise awareness of and mobilize activism around endometriosis by analyzing prominent topics, word associations, and feminism themes in endometriosis-tagged posts on Instagram.MethodsWe used a mixed-method approach of combining computational analyses (topic modeling and word association) and human coding (qualitative thematic analysis) of Instagram posts on endometriosis.ResultsOur results show that while Instagram users discussed a variety of issues related to the disease on the social media platform, these issues tend to focus on four topic areas: (i) living with endometriosis, (ii) pregnancy and endometriosis, (iii) awareness and empowerment, and (iv) women's health and wellbeing. In addition, social media users widely incorporated three feminism themes: (i) bringing attention to invisible disability, (ii) solidarity and support among women, and (iii) advocacy and activism around women's health.ConclusionPeople with endometriosis use Instagram to create an online public sphere for raising awareness of the disease, offering networking and support to other endometriosis patients, and creating a movement for enhancing women's health and wellness. In doing so, they are also addressing sexism and misconceptions about endometriosis that have traditionally contributed to normalizing women's experience of pain associated with endometriosis and consequently delaying endometriosis diagnoses.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-21T09:07:46Z
  DOI: 10.1177/20552076251314905
  Issue No: Vol. 11 (2025)
Challenges and opportunities of artificial intelligence in African health
space
- Authors: Muslim A. Alaran, Salim Kamaludeen Lawal, Mustapha Husseini Jiya, Salihu Alhassan Egya, Mohamed Mustaf Ahmed, Abdullateef Abdulsalam, Usman Abubakar Haruna, Muhammad Kabir Musa, Don Eliseo Lucero-Prisno; Salim Kamaludeen Lawal, Mustapha Husseini Jiya, Salihu Alhassan Egya, Mohamed Mustaf Ahmed, Abdullateef Abdulsalam, Usman Abubakar Haruna, Muhammad Kabir Musa, Don Eliseo Lucero-Prisno
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  The application of artificial intelligence (AI) to healthcare in Africa has the potential to transform productivity, diagnosis, disease surveillance, and resource allocation by improving accuracy and efficiency. However, to fully realize its benefits, it is necessary to consider issues concerning data privacy, equity, infrastructure integration, and ethical policy development. The use of these tools may improve the detection of diseases, the distribution of resources, and the continuity of care. The use of AI allows for the development of policies that are tailored to address health disparities based on evidence. While AI may increase accessibility and affordability through telehealth, remote monitoring, and cost reductions, significant barriers remain. Ethical guidelines are needed to ensure AI decisions align with medical standards and patient autonomy. Strict privacy and security controls are crucial to protecting sensitive health data. This article evaluates the current and potential roles of AI in the African health sector. It identifies opportunities to address challenges through tailored interventions and an AI framework to simulate policy impacts.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-21T08:39:32Z
  DOI: 10.1177/20552076241305915
  Issue No: Vol. 11 (2025)
Delving into the world of webCHAT – an e-mental health support service
for distressed youths in Singapore
- Authors: Yi Ping Lee, Ying Ying Lee, Hamidah Binte Otheman, Charmaine Tang, Mythily Subramaniam, Swapna K Verma; Ying Ying Lee, Hamidah Binte Otheman, Charmaine Tang, Mythily Subramaniam, Swapna K Verma
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundYoung people face high rates of mental health issues, yet many do not seek professional help. In 2017, CHAT launched webCHAT – a free, anonymous, one-on-one synchronous web-based text service managed by case managers (CMs) to support young people aged 16 to 30 who may be hesitant about engaging in face-to-face mental health services.ObjectiveThis study aimed to explore the perspectives and experiences of users who accessed webCHAT for mental health support in Singapore.MethodsA qualitative thematic analysis was conducted using transcripts of webCHAT sessions to identify main themes.ResultsMany users accessed webCHAT to seek support with emotional and behavioural concerns, valuing its immediacy and anonymity over traditional appointment-based services. A desire to ‘get better’ and self-realisation emerged as important motivators for seeking help, with webCHAT offering a supportive space for reflection. Key barriers to seeking additional support included fear of stigma, concerns about leaving a ‘medical record’, potential hospitalisation, and treatment costs.ConclusionswebCHAT appears to be a viable early intervention and preventive approach, providing young people with a pathway towards in-person support services. Professional guidance from CMs is essential in encouraging users to pursue further support, emphasising the importance of human expertise in digital mental health platforms. By fostering early help-seeking and self-realisation, webCHAT has the potential to reduce the long-term impact of mental health challenges. Future research could explore webCHAT's long-term effects and identify improvements to facilitate users’ transitions to in-person support.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-20T05:09:43Z
  DOI: 10.1177/20552076251314912
  Issue No: Vol. 11 (2025)
A multi-patch-based deep learning model with VGG19 for breast cancer
classifications in the pathology images
- Authors: Anitha Ponraj, Palanigurupackiam Nagaraj, Duraisamy Balakrishnan, Parvathaneni Naga Srinivasu, Jana Shafi, Wonjoon Kim, Muhammad Fazal Ijaz; Palanigurupackiam Nagaraj, Duraisamy Balakrishnan, Parvathaneni Naga Srinivasu, Jana Shafi, Wonjoon Kim, Muhammad Fazal Ijaz
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  PurposeBreast cancer encompasses various subtypes with distinct prognoses, necessitating accurate stratification methods. Current techniques rely on quantifying gene expression in limited subsets. Given the complexity of breast tissues, effective detection and classification of breast cancer is crucial in medical imaging. This study introduces a novel method, MPa-DCAE, which uses a multi-patch-based deep convolutional auto-encoder (DCAE) framework combined with VGG19 to detect and classify breast cancer in histopathology images.MethodsThe proposed MPa-DCAE model leverages the hierarchical feature extraction capabilities of VGG19 within a DCAE framework, designed to capture intricate patterns in histopathology images. By using a multi-patch approach, regions of interest are extracted from pathology images to facilitate localized feature learning, enhancing the model's discriminatory power. The auto-encoder component enables unsupervised feature learning, increasing resilience and adaptability to variations in image features. Experiments were conducted at various magnifications on the CBIS-DDSM and MIAS datasets to validate model performance.ResultsExperimental results demonstrated that the MPa-DCAE model outperformed existing methods. For the CBIS-DDSM dataset, the model achieved a precision of 97.96%, a recall of 94.85%, and an accuracy of 98.36%. For the MIAS dataset, it achieved a precision of 97.99%, a recall of 97.2%, and an accuracy of 98.95%. These results highlight the model's robustness and potential for clinical application in computer-assisted diagnosis.ConclusionThe MPa-DCAE model, integrating VGG19 and DCAE, proves to be an effective, automated approach for diagnosing breast cancer. Its high accuracy and generalizability make it a promising tool for clinical practice, potentially improving patient care in histopathology-based breast cancer diagnosis.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-17T04:10:36Z
  DOI: 10.1177/20552076241313161
  Issue No: Vol. 11 (2025)
Using a mobile app comprising neurofeedback-based meditation and binaural
beat music to treat PTSD symptoms: A qualitative analysis
- Authors: Yun-Jung Choi, Na-Rae Lee; Na-Rae LeeRed Cross College of Nursing, 26729Chung-Ang University, Seoul, South Korea
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundThere is a significant need for an effective and convenient symptom management and healing program for people experiencing post-traumatic stress disorder (PTSD) symptoms; however, research on this topic is lacking.ObjectiveThis study explored the experiences of individuals with PTSD who used a mobile traumatic stress management app with neurofeedback-based meditation and binaural beat music to promote their psychological recovery.MethodsThis study used a qualitative research method to explore the experiences of neurofeedback-based meditation and binaural beat music using a mobile traumatic stress management app to promote the psychological recovery of people experiencing chronic traumatic stress. The research question was “What is the experience of neurofeedback-based meditation and binaural beat music using the mobile traumatic stress management app'”ResultsThe thematic content analysis of in-depth interviews held with nine participants derived 26 codes, eight sub-categories, three categories, and one theme (“Holding hope for healing from agony of mind and body”).ConclusionNeurofeedback-based meditation and binaural beat music using a mobile app helped people with PTSD symptoms to truly understand the symptoms caused by traumatic stress. Continued use of this program provides the participants with physical and psychological stability; it instills them with faith and hope.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-17T04:09:36Z
  DOI: 10.1177/20552076241311053
  Issue No: Vol. 11 (2025)
Comparative study of imputation strategies to improve the sarcopenia
prediction task
- Authors: Shakhzod Karimov, Dilmurod Turimov, Wooseong Kim, Jiyoun Kim; Dilmurod Turimov, Wooseong Kim, Jiyoun Kim
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveSarcopenia, a condition characterized by the progressive loss of skeletal muscle mass and strength, poses significant challenges in research due to missing data. Incomplete datasets undermine the accuracy and reliability of studies, necessitating effective imputation techniques. This study conducts a comparative analysis of three advanced methods—multiple imputation by chained equations (MICE), support vector regression, and K-nearest neighbors (KNN)—to address data completeness issues in sarcopenia research.MethodsFollowing imputation, we utilized machine learning models, including logistic regression, gradient boosting, support vector machine, and random forest, to classify sarcopenia. The methodology encompassed rigorous data preprocessing, normalization, and the synthetic minority oversampling technique to address class imbalance and ensure unbiased model performance.ResultsThe results revealed substantial variations in model accuracy based on the imputation method employed. The gradient boosting model consistently exhibited superior performance across all imputation strategies, demonstrating its robustness with imputed datasets. Additionally, KNN and MICE emerged as effective imputation techniques, preserving the original data distribution and enabling more accurate classification outcomes.ConclusionThis study underscores the pivotal role of imputation methods in maintaining data integrity and enhancing predictive accuracy in sarcopenia research. The gradient boosting model's reliability across all strategies highlights its potential as a robust classifier, while the suitability of KNN and MICE for preserving data distribution supports their application in similar research contexts. These findings contribute to more reliable and valid insights in sarcopenia studies, ultimately supporting improved clinical outcomes.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-17T04:09:16Z
  DOI: 10.1177/20552076241301960
  Issue No: Vol. 11 (2025)
Enhance health evidence quality in classification tasks: A triangulation
approach utilizing case-based reasoning and process features
- Authors: Ruihua Guo, Ross Smith, Qifan Chen, Angus Ritchie, Simon Poon; Ross Smith, Qifan Chen, Angus Ritchie, Simon Poon
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveMachine learning (ML) has enabled healthcare discoveries by facilitating efficient modeling, such as for cancer screening. Unlike clinical trials, real-world data used in ML are often gathered for multiple purposes, leading to bias and missing information for a specific classification task. This challenge is especially pronounced in healthcare because of stringent ethical considerations and resource constraints.This study proposed an integrated approach to enhance the quality of health evidence from a classification task for predicting Medicare's Diagnosis-Related Groups of ischemic heart disease (IHD) patients.MethodsEligible participants were identified from the Medical Information Mart for Intensive Care IV (MIMIC IV), a publicly available hospital database. Six ML models were selected for model triangulation. Sequential triangulation was employed via Local Process Mining (LPM) and Qualitative Comparative Analysis (QCA).ResultsA total of 1545 IHD hospitalizations from 916 patients were identified from the MIMIC IV. Eight health process features were identified through LPM aligned with clinical knowledge. The correlation coefficients for process features, ranging from 0.24 to 0.42, are higher than those for non-process features ranged from 0.02 to 0.36. A total of 56 unique combinations were identified from the QCA, with 28 configurations having raw coverage lower than 1.0%. The overall model performance (i.e. weighted F1 and area under the curve scores) increased after adopting this integrated approach. The proportion of cases misclassified by any of the six models decreased by 47% after incorporating process features (from 5.29% to 2.91%) and further decreased to 0.0% after applying the QCA solutions.ConclusionThe integrated approach demonstrates its ability to enhance quality of a classification task through its clinical relevance, improved model performance, and reduced case-level error rates. However, more scalable QCA methods are needed for larger datasets. Developing health process feature engineering for broader applications can be a future direction.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-17T03:30:16Z
  DOI: 10.1177/20552076251314097
  Issue No: Vol. 11 (2025)
Inverse Gini indexed averaging: A multi-leveled ensemble approach for skin
lesion classification using attention-integrated customized ResNet
variants
- Authors: Anwar Hossain Efat, SM Mahedy Hasan, Md Palash Uddin, Faysal Hossain Emon; SM Mahedy Hasan, Md Palash Uddin, Faysal Hossain Emon
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveTo improve the accuracy and explainability of skin lesion detection and classification, particularly for several types of skin cancers, through a novel approach based on the convolutional neural networks with attention-integrated customized ResNet variants (CRVs) and an optimized ensemble learning (EL) strategy.MethodsOur approach utilizes all ResNet variants combined with three attention mechanisms: channel attention, soft attention, and squeeze-excitation attention. These attention-integrated ResNet variants are aggregated through a unique multi-level EL strategy. We propose an innovative weight optimization method, inverse Gini indexed averaging (IGIA), which is further extended to multi-leveled IGIA (ML-IGIA) to determine the optimal weights for each model within multiple ensemble levels. For interpretability, we employ gradient class activation map to highlight the regions responsible for classification dominance, enhancing the model’s transparency.ResultsOur method was evaluated on the Human Against Machines 10000 dataset, achieving a superior accuracy of 94.52% with the ML-IGIA approach, outperforming existing methods.ConclusionsThe proposed CRV-based ensemble model with ML-IGIA demonstrates robust performance in skin lesion classification, offering both high accuracy and enhanced interpretability. This approach addresses the current research gap in effective weight optimization in EL and supports timely, automated skin disease detection.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-17T03:29:16Z
  DOI: 10.1177/20552076241312936
  Issue No: Vol. 11 (2025)
User-centred design of ChestCare: mHealth app for pulmonary rehabilitation
for patients with COPD; a mixed-methods sequential approach
- Authors: Suad J. Ghaben, Arimi Fitri Mat Ludin, Nazlena Mohamad Ali, Devinder Kaur Ajit Singh; Arimi Fitri Mat Ludin, Nazlena Mohamad Ali, Devinder Kaur Ajit Singh
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundThe increasing prevalence and burden of chronic obstructive pulmonary disorder (COPD), the challenges in implementing pulmonary rehabilitation (PR) programs and the limited availability of alternatives and supportive programs to serve patients with COPD necessitate the development of pulmonary telerehabilitation (PTR) systems to provide patients with COPD with PR programs.ObjectiveThis study aimed to design and develop the ChestCare mobile Health app using user-centred design (UCD) approach. Thus, it provided PTR for patients with COPD, enhancing their self-management of symptoms and improving their compliance with PR programs.MethodsIn this mixed-methods sequential research, we deployed the UCD iterative design through the prototype app design and development sequence. The first phase was built based on the results of a previous needs assessment study and an analysis of related apps. This produced the initial mock-up, the foundation for the focus group discussions with physiotherapists and patients. Six physiotherapists with cardiorespiratory specialisation evaluated each app module and item of the latest mock-up using the content validity index (CVI) document. The I-CVI (S-CVI/Ave) and (S-CVI/UA) were computed. Qualitative and quantitative data were integrated, and decisions were made by comparing their results.ResultsThe UCD iterative design through sequential MMR has generated four mock-up app versions. The latest version identified 13 modules through 150 items validated by six experts using a CVI document. The I-CVI calculation of 145 items was 1, while 0.83 for the remaining items, was within accepted values. The S-CVI scored 99.4, indicating an overall validity of the ChestCare app as a PTR system for patients with COPD.ConclusionsThe development and validation of the ChestCare app resulted from conducting UCD iterative design and sequential MMR, which identified 13 functionalities, including symptom assessment, tracking lung volume, functional capacity test, action plan, intervention program, COPD education, COPD community, monitoring and reminders.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-17T03:28:56Z
  DOI: 10.1177/20552076241307476
  Issue No: Vol. 11 (2025)
Prediction of reduced left ventricular ejection fraction using atrial
fibrillation or flutter electrocardiograms: A machine-learning study
- Authors: Soonil Kwon, SooMin Chung, So-Ryoung Lee, Kwangsoo Kim, Junmo Kim, Dahyeon Baek, Hyun-Lim Yang, Eue-Keun Choi, Seil Oh; SooMin Chung, So-Ryoung Lee, Kwangsoo Kim, Junmo Kim, Dahyeon Baek, Hyun-Lim Yang, Eue-Keun Choi, Seil Oh
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveAlthough the evaluation of left ventricular ejection fraction (LVEF) in patients with atrial fibrillation (AF) or atrial flutter (AFL) is crucial for appropriate medical management, the prediction of reduced LVEF (
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-17T03:28:15Z
  DOI: 10.1177/20552076241311460
  Issue No: Vol. 11 (2025)
Do you have depression' A summative content analysis of mental
health-related content on TikTok
- Authors: Roxanne Turuba, Marco Zenone, Raman Srivastava, Jonathan Stea, Yuri Quintana, Nikki Ow, Kirsten Marchand, Amanda Kwan, Anna-Joy Ong, Xiaoxu Ding, Cassia Warren, Alessandro R Marcon, Jo Henderson, Steve Mathias, Skye Barbic; Marco Zenone, Raman Srivastava, Jonathan Stea, Yuri Quintana, Nikki Ow, Kirsten Marchand, Amanda Kwan, Anna-Joy Ong, Xiaoxu Ding, Cassia Warren, Alessandro R Marcon, Jo Henderson, Steve Mathias, Skye Barbic
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundTikTok is a global social media platform with over 1 billion active users. Presently, there are few data on how TikTok users navigate the platform for mental health purposes and the content they view.ObjectiveThis study aims to understand the patterns of mental health-related content on TikTok and assesses the accuracy and quality of the advice and information provided.MethodsWe performed a summative content analysis on the top 1000 TikTok videos with the hashtag #mentalhealth between October 12 and 16, 2021. Six content themes were developed to code the data: (1) a personal story, perspective, or confessional, (2) advice and information, (3) emoting, (4) references to death, (5) references to science or research, and (6) a product or service for sale. Advice and information were further assessed by clinical experts.ResultsA total of 970 mental health-related videos were pulled for our analysis (n = 30 removed due to non-English content). The most prevalent content themes included a personal story, perspective, or confessional (n = 574), advice and information (n = 319), emoting (n = 198), references to death (n = 128). Advice and information were considered misleading in 33.0% of videos (n = 106), with misleading content performing better. Few videos included references to scientific evidence or research (n = 37).ConclusionHealthcare practitioners and researchers may consider increasing their presence on the platform to promote the dissemination of evidence-based information to a wider and more youth-targeted population. Interventions to reduce the amount of misinformation on the platform and increase people's ability to discern between anecdotal and evidence-based information are also warranted.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-17T03:27:55Z
  DOI: 10.1177/20552076241297062
  Issue No: Vol. 11 (2025)
Assessing static balance control improvement following cataract surgery
using a smartphone
- Authors: Lorrana de Souza Azevedo, Newton Quintino Feitosa, Enzo Gabriel Rocha Santos, Márcio Augusto Moraes Alvarez, Lucciano Augusto Ximenes Norat, Gabriel Izan Santos Botelho, Anderson Belgamo, Gustavo Henrique Lima Pinto, Ketlin Jaqueline Santana Castro, Bianca Callegari, Anselmo de Athayde Costa e Silva, Raílson Cruz Salomão, André dos Santos Cabral, Alexandre Antônio Marques Rosa, Givago Silva Souza; Newton Quintino Feitosa, Enzo Gabriel Rocha Santos, Márcio Augusto Moraes Alvarez, Lucciano Augusto Ximenes Norat, Gabriel Izan Santos Botelho, Anderson Belgamo, Gustavo Henrique Lima Pinto, Ketlin Jaqueline Santana Castro, Bianca Callegari, Anselmo de Athayde Costa e Silva, Raílson Cruz Salomão, André dos Santos Cabral, Alexandre Antônio Marques Rosa, Givago Silva Souza
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  IntroductionCataract remains a prevalent cause of visual impairment among the elderly, significantly increasing the risk of falls due to compromised visual function. Cataract removal surgery has been established as a means to enhance visual acuity and balance control. The advent of novel technologies, such as accelerometers integrated into smartphones, provides an opportunity to assess static balance control. We compared static balance measurements obtained from accelerometer built-in smartphones before and after cataract removal surgery.MethodsOur sample comprised 29 individuals diagnosed with bilateral cataracts scheduled for cataract removal surgery. We evaluated participants’ visual acuity and recorded their body oscillations using the smartphone's embedded accelerometer during static balance maintenance, both with eyes open and closed, before and after surgery. We compared quantitative parameters of static balance evaluation at both time points.ResultsA significant improvement in visual acuity following cataract removal surgery was observed. Moreover, under open-eye conditions, all quantitative parameters of static balance evaluation exhibited significant improvement, with only one parameter showing improvement under closed-eye conditions.ConclusionInertial sensors integrated into smartphones serve as practical tools for monitoring changes in static balance control resulting from cataracts, as well as for evaluating balance recovery after lens extraction surgery.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-17T03:27:36Z
  DOI: 10.1177/20552076241277468
  Issue No: Vol. 11 (2025)
Chatbot-delivered mental health support: Attitudes and utilization in a
sample of U.S. college students
- Authors: Gavin N. Rackoff, Zhenyu Z. Zhang, Michelle G. Newman; Zhenyu Z. Zhang, Michelle G. NewmanDepartment of Psychology, 8082The Pennsylvania State University, University Park, PA, USA
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveChatbots’ rapid advancements raise the possibility that they can be used to deliver mental health support. However, public utilization of and opinions toward chatbots for mental health support are poorly understood.MethodsSurvey study of 428 U.S. university students who participated in early 2024, just over one year after the release of ChatGPT. Descriptive analyses examined utilization of and attitudes toward both traditional mental health services (i.e. psychotherapy, counseling, or medication) and chatbot-delivered mental health support.ResultsNearly half (49%) of participants reported having used a chatbot for any purpose, yet only 5% reported seeking mental health support from a chatbot (8% when only considering participants with probable depression or generalized anxiety disorder). Attitudes toward traditional mental health services were broadly positive, and attitudes toward chatbot-delivered support were neutral and significantly less positive (d = 1.18, p
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-17T03:16:55Z
  DOI: 10.1177/20552076241313401
  Issue No: Vol. 11 (2025)
Interactive 3D visualisation technique used in pulmonary rehabilitation in
chronic obstructive pulmonary disease: A randomised controlled study
evaluating quality of life, compliance and use of health care
- Authors: Pernilla Sönnerfors, Anna-Karin Nordlin, Maria Nykvist, Ulrika Thunström, Ulrika Einarsson; Anna-Karin Nordlin, Maria Nykvist, Ulrika Thunström, Ulrika Einarsson
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  IntroductionA pulmonary rehabilitation (PR) programme, including exercise training, education, and behaviour change, is highly recommended in treatment guidelines for chronic obstructive pulmonary disease (COPD). A new PR educational material for PR using an interactive three-dimensional (3D) visualisation technique was developed. There is little known regarding using 3D in this setting. The aim was to evaluate, within a PR programme setting, differences between outcomes of education through interactive 3D compared with education by means of 2D visualisation in patients with COPD regarding health-related quality of life (HRQL), physical capacity, exercise self-efficacy, compliance to exercise training, compliance to medication, and the use of health care and to describe learning styles.MethodsPatients were cluster randomised to PR at the University Hospital clinic, including exercise training, for 10 weeks with education by 3D (n = 27) or education by traditional 2D technique (n = 17). At follow-ups, HRQL, physical capacity, self-efficacy, handgrip strength, compliance to exercise training, compliance to medication and health care utilisation were assessed.ResultsNo significant differences were observed in HRQL in relation to COPD between the groups after 10 weeks. Differences were found in handgrip strength at baseline and at 10 weeks (p
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-17T03:16:37Z
  DOI: 10.1177/20552076241308940
  Issue No: Vol. 11 (2025)
The impacts on population health by China's regional health data centers
and the potential mechanism of influence
- Authors: Jiaoli Cai, Yue Li, Peter C Coyte; Yue Li, Peter C Coyte
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundChina recently established a series of pilot regional health data centers with a mandate to acquire, consolidate, analyze, and translate data into evidence for health policy decision-making. This experiment with “big data” has the potential to influence population health and is the focus of this study.MethodsThis study used national longitudinal survey data from the China Family Panel Studies over the period 2014–2020 to empirically assess the impact of China's establishment of pilot regional health data centers on population health and health inequality. A difference-in-differences model was employed to investigate the policy effect on population health, with additional exploration of possible mechanisms of influence. The corrected concentration index was used to measure health inequality, while Wagstaff decomposition method was applied to examine the marginal influence of the policy effect on health inequality.ResultsOverall health status of local residents has improved after the establishment of the pilot regional health data centers. Using mechanism analysis, the findings demonstrated that improvements to population health were driven by promoting healthy lifestyles and innovations in medical practices. Furthermore, due to differences in individual e-health literacy, the pilot centers produced “pro-rich” health inequality where high-income groups benefited more from the establishment of the pilot centers in terms of health than low-income groups.ConclusionsThis study has highlighted the potential to improve population health, in general, with the advent of big data centers, but for these benefits be unevenly distributed among the resident population.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-17T03:07:57Z
  DOI: 10.1177/20552076251314102
  Issue No: Vol. 11 (2025)
Perceptions of community healthcare workers on the use of teledentistry
for seniors in Singapore: A qualitative study
- Authors: Milawaty Nurjono, Ezra Ho, Jing Yi Lee, Roland Petcu, Christina PC Sim; Ezra Ho, Jing Yi Lee, Roland Petcu, Christina PC Sim
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveThis study aimed to identify barriers and facilitators surrounding the implementation of TDOCS from Community Health Workers (CHW)'s perspective before TDOCS implementation.MethodsA descriptive qualitative study was conducted through semistructured interviews with a purposive sampling of CHWs from partner nursing homes and home care teams. A French framework outlining barriers to asynchronous oral teleconsultation adoption was used to develop the topic guide for this study. Then, the Consolidated Framework for Implementation research (CFIR 1.0) was used to guide coding and analysis.ResultsA total of 43 CHWs from participating institutions were interviewed prior to receiving teledentistry training. Perceived barriers included low awareness about the importance of dental care, limiting conditions to T-DOCS participation among beneficiaries, limited understanding of T-DOCS, perceived low self-efficacy among CHWs, manpower shortages, perceived low priority of dental care, competition with other nursing duties and restricted access due to COVID-19. Facilitators included existing relationships between CHWs and beneficiaries, receptivity towards participation, CHWs’ motivation to upskill and improve dental care for seniors, prior experience with other telemedicine technology, perceived need for change, supportive management, colleagues and existing impediments to access dental care.ConclusionThis study identified barriers and facilitators to implementing T-DOCS from the CHWs’ perspectives. We recommend for targeted CHW training, programme champions, workflow integration and incentivisation schemes. Addressing challenges like manpower strain and resource limitations through efficient scheduling and capacity building is vital for sustainability. Policy-level support, including legal frameworks, funding and regulatory structures, is essential for integrating teledentistry into mainstream healthcare.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-16T12:20:04Z
  DOI: 10.1177/20552076241312562
  Issue No: Vol. 11 (2025)
Text messaging to improve connection between adolescents and their health
care providers
- Authors: Claire R Galvin, Astrid M De Souza, James E Potts, Penny L Sneddon, Shubhayan Sanatani, Kathryn R Armstrong; Astrid M De Souza, James E Potts, Penny L Sneddon, Shubhayan Sanatani, Kathryn R Armstrong
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveAdolescence marks a time of transition where teenagers are learning to advocate for themselves. In those with underlying chronic conditions such as adolescent dysautonomia, improving communication between clinic visits may improve connection with their health care provider which may aide management. Our aims were as follows: (1) to implement a text message platform to increase communication between adolescent patients and health care provider (HCP); (2) to evaluate its effect on quality of life (QoL), symptom burden, and patient engagement; and (3) to determine patient satisfaction with the platform.MethodsParticipants (age 12–18) with access to a personal mobile phone were recruited from a pediatric dysautonomia clinic. A weekly automated text message asking “How are you'” was sent to participants (WelTel Inc.). Responses were triaged to HCP and responded to within 48 hours.ResultsTwenty-six participants with median (interquartile range) age of 16.8 (15.7–17.4) years completed the study. Duration of the text messaging intervention was 33 (26.8–37.3) weeks. A total of 896 automated weekly messages were sent, which resulted in 206 (23%) care conversations. Participants found texting useful (96%) and produced feelings of connection to their HCP (92%). There was no change in overall QoL or symptom burden (p > 0.05).ConclusionA text message platform was successfully implemented in adolescents seen in our Dysautonomia Clinic. Patients were engaged, satisfied with the platform, and felt connected to their HCP despite no changes in QoL or symptom burden.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-16T01:57:24Z
  DOI: 10.1177/20552076241309228
  Issue No: Vol. 11 (2025)
Digital healthcare services in community pharmacies in Switzerland:
Pharmacist and patient acceptability, and pharmacist readiness–the
Pneumoscope™ pilot study
- Authors: Claudine Backes, Coralie Godot, Cédric Yan Gujer, Noémie Obegi, Alexandre Perez, Alain Gervaix, Marie P. Schneider; Coralie Godot, Cédric Yan Gujer, Noémie Obegi, Alexandre Perez, Alain Gervaix, Marie P. Schneider
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundThe integration of artificial intelligence (AI)-based pharmaceutical services in community pharmacy (CP) settings has the potential to enhance point-of-care services and improve informed patient access to healthcare. The Pneumoscope™, an innovative AI-powered digital stethoscope that analyses lung sounds to detect specific respiratory pathologies, could be a valuable tool for pharmacists in conducting respiratory screening. To understand how this device can be implemented in the healthcare system, this exploratory research aims to assess the acceptability of pharmacists and patients, and the pharmacists’ readiness to use the Pneumoscope™ in CPs for respiratory disease management.MethodsA 2-stage exploratory approach was conducted using mixed methods: 1) a qualitative analysis of pharmacists’ acceptability and readiness was developed using semi-structured interviews and focus groups ; 2) followed by a quantitative cross-sectional survey of patients’ acceptability of the device in CPs.ResultsPharmacists were generally positive about the integration of e-health services into their daily clinical practice, recognizing their potential to improve advanced pharmaceutical triage and collaboration with physicians. Most patients were satisfied with the care provided by CPs, and their acceptability to use the Pneumoscope™ was significantly associated with their level of confidence in AI (p = 0.0092) and with the location of their CP (p = 0.0276).ConclusionsDigital devices such as the Pneumoscope™ have the potential to reinforce the pharmacists’ clinical roles within an interprofessional team and improve patient care, but further scientific evaluation and implementation are necessary to support its integration and ensure its reimbursement by health insurers.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-16T01:56:26Z
  DOI: 10.1177/20552076241313164
  Issue No: Vol. 11 (2025)
How does mHealth benefit older Chinese adults’ quality of life'
Examining the roles of eHealth literacy, health motivation, and patient
activation
- Authors: Sha Sarah Qiu, Jizhou Francis Ye, Fei You, Muhan Liu, Xinshu Zhao; Jizhou Francis Ye, Fei You, Muhan Liu, Xinshu Zhao
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundChina is experiencing a rapid increase in its aging population, leading to the emergence of significant challenges to improve the quality of life (QoL) of older adults. The study aims to explore the potential benefits of using mobile health technology in improving the QoL for older Chinese adults.MethodThis study utilized a subsample of adults aged 60 and above from a cross-sectional, population-based national survey conducted among Chinese adults (N = 852). A moderated mediation analysis was conducted to investigate the impact of mHealth use on older Chinese adults’ QoL, focusing on the mediating roles of eHealth literacy and patient activation and the moderating effect of motivation for health promotion and prevention.ResultThe results indicate that mHealth use directly enhances the QoL of older Chinese adults (β = .061, p
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-13T08:48:29Z
  DOI: 10.1177/20552076241313160
  Issue No: Vol. 11 (2025)
Corrigendum to “Toward reliable diabetes prediction: Innovations in data
engineering and machine learning applications”
- Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-13T07:31:46Z
  DOI: 10.1177/20552076251313644
  Issue No: Vol. 11 (2025)
The potential of digital health interventions to address health system
challenges in Southeast Asia: A scoping review
- Authors: Vanita Singh, Rosemol Johnson K, Anil G. Jacob, Oommen John; Rosemol Johnson K, Anil G. Jacob, Oommen John
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveThe World Health Organization (WHO) Southeast Asian region, comprising a quarter of the global population, faces significant healthcare challenges influenced by diverse economic and social conditions. The objective of this study is to map the Digital Health Intervention (DHI) functionalities across the nine axes of the healthcare system challenges (HSCs) model; we use WHO's DHIs classification framework and the Health System Challenges model. Our study findings help identify the gaps in the potential of the existing DHIs in addressing health system challenges in low- and middle-income countries.MethodsUsing SCOPUS, EMBASE and PUBMED databases, a scoping review was conducted to identify the existing DHIs in the Southeast Asia region and map the DHIs with the HSCs related to availability, information, utilization, quality, accountability, efficiency, equity, cost and acceptability.ResultsOut of 278 studies, 337 DHI functionalities were identified. A majority of the identified DHIs address challenges related to information (35.6%), availability (34.7%) and utilization, while less than 10% of the DHIs address challenges related to quality (8.9%), accountability (5%) and efficiency (3.9%) in the health system.ConclusionMost of the existing DHIs in SEA region address challenges related to availability of services and lack of information, while they fall short in addressing challenges related to quality of services, efficiency, and accountability. Acknowledging the inter-linkages across the health system challenges, this gap identification may well guide future investments and planning in DHIs.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-10T08:59:14Z
  DOI: 10.1177/20552076241311062
  Issue No: Vol. 11 (2025)
Assessing intention to use mobile phone–based self-management support
among adults with type 2 diabetes in Saudi Arabia: A cross-sectional study
- Authors: Hind M AlOsaimi, Mohammed Kanan, Mohammed AlOtaibi, Saba Alhejaili, Bayader Alshammari, Aleya Khalaf, Amal Hajea, Ryoof Saleh, Futoon Jamal, Amnah AbuShahin, Bandar Alanazi, Raghad Alshanbari, Ashwag Alsubaie, Ghadi Alasmari, Rana S Alshahrani; Mohammed Kanan, Mohammed AlOtaibi, Saba Alhejaili, Bayader Alshammari, Aleya Khalaf, Amal Hajea, Ryoof Saleh, Futoon Jamal, Amnah AbuShahin, Bandar Alanazi, Raghad Alshanbari, Ashwag Alsubaie, Ghadi Alasmari, Rana S Alshahrani
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  IntroductionThe use of mobile phone technology for chronic illness self-management is growing, and it may help people with type 2 diabetes mellitus (T2DM). Innovative methods are needed to improve patient involvement and disease management in the Kingdom of Saudi Arabia due to the high incidence of T2DM.ObjectiveThe purpose of this study was to explore how the T2DM patients in KSA utilizes their mobile phones for self-management.MethodsA cross-sectional study was conducted between April and June 2025 among T2DM patients who were attending endocrinologists for their diabetes management in the Northern Border region (Rafha and Arar) and the Central region (Riyadh) in KSA using a validated questionnaire.ResultsThis study included a total of 267 participants with T2DM. Nearly all participants (99.3%) possess a cellphone, with 94.8% having daily internet access. The majority of the patients reported to have an intention to use mobile phones and the internet for managing diabetes, with 78.3% for dietary planning, 79.4% for physical activity planning, and 78.7% for text messages as reminders. Factors such as female (p = 0.008), younger age (p = 0.001), and duration of diabetes (p =
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-10T08:58:35Z
  DOI: 10.1177/20552076241308993
  Issue No: Vol. 11 (2025)
Fluctuations in mHealth engagement following receipt of goal-discrepant
feedback messages
- Authors: Lex Hurley, Nisha G O’Shea, Brooke T Nezami, Carmina G Valle, Deborah F Tate; Nisha G O’Shea, Brooke T Nezami, Carmina G Valle, Deborah F Tate
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveDigital behavior change interventions can successfully promote change in behavioral outcomes, but often suffer from steep decreases in engagement over time, which hampers their effectiveness. Providing feedback on goal performance is an established technique to promote goal attainment; however, theory indicates that sending goal-discrepant feedback messages could cause some users to respond more negatively than others. This analysis assessed whether goal-discrepant messaging was negatively associated with participant engagement, and if this relationship was exacerbated by baseline depressive symptoms within the context of a three-month weight loss pilot mHealth intervention.MethodsThis analysis applied a generalization of log-linear regression analysis with n = 52 participants (78.8% female, 61.5% white, ages 21–35) to assess the likelihood of reading consecutive program messages following receipt of messages with goal-discrepant content.ResultsReceipt of goal-discrepant messages was associated with a significantly lower likelihood (RR = 0.89) of participants reading the next program message sent, compared to receiving positive/neutral messages or no message, but these relationships were not influenced by depressive symptoms in this sample.ConclusionFeedback on goal performance remains an important behavior change technique; however, sending push messages that alert participants to their goal-discrepant status seems to reduce the likelihood that participants will read future program messages. Sending messages containing positive or neutral content does not seem to carry this negative risk among individuals in goal-discrepant states.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-09T12:59:36Z
  DOI: 10.1177/20552076241312569
  Issue No: Vol. 11 (2025)
Improving consumer trust in digital health: A mixed methods study
involving people living with chronic kidney disease
- Authors: Soraia de Camargo Catapan, Monica L Taylor, Paul Scuffham, Anthony C Smith, Jaimon T Kelly; Monica L Taylor, Paul Scuffham, Anthony C Smith, Jaimon T Kelly
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  AimTo explore preferences, experience and trust in digital health in people living with chronic kidney disease (CKD), and tailor these findings towards solutions that may enhance uptake of digital health services.MethodsMixed methods study, with cross-sectional survey and individual interviews with adults living with CKD attending specialist appointments at an Australian metropolitan hospital. Descriptive statistics and Wilcoxon matched-pairs test were used for survey responses and thematic analysis of interview transcripts, both reported on a theme-by-theme basis provided an overall understanding of trust in digital healthcare.ResultsDigital health is changing the way health services are provided, and our results demonstrate that despite limited familiarity, participants are open to learn and adapt to existing digital models of care. Limited exposure to technology may undermine trust in digital health, and telehealth can promote improvements in health literacy. Having the choice in healthcare modalities can promote trust, which can arise from trustful relationships with clinicians who demonstrate genuine interest in patient care. Participants expressed more concerns about sharing identity data than health data online and worry about fragmented information among providers. They preferred public health services due to distrust generated by the perceived risk of private sector data commercialisation. Building trust requires increasing awareness of digital health benefits, promoting positive experiences, improving digital literacy and ensuring interoperability and transparency in digital healthcare systems.ConclusionPeople with CKD want to learn and benefit from digital health. Choice and open disclosure on data management and purpose are paramount to building trust.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-09T12:58:56Z
  DOI: 10.1177/20552076241312440
  Issue No: Vol. 11 (2025)
Artificial intelligence in degenerative cervical disease: A systematic
review of MRI-based diagnostic models
- Authors: Qian Du, Xinxin Shao, Minbo Zhang, Guangru Cao; Xinxin Shao, Minbo Zhang, Guangru CaoDepartment of Orthopaedic Surgery, 604061The Second Affiliated Hospital of Zunyi Medical University, Zunyi, Guizhou, China
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveThis systematic review evaluates the performance and limitations of AI-based models for Degenerative cervical diseases (DCD) diagnosis using MRI.MethodsA comprehensive literature search was conducted in three databases—PubMed, Embase, and Web of Science—covering studies published between January 2010 and March 2024. Studies were included if they employed AI techniques for the diagnosis or prognosis of DCD using MRI. Key performance metrics, methodological details, and limitations were extracted and analyzed.ResultsEleven studies met the inclusion criteria, with AI models showing high diagnostic performance. Accuracy ranged from 81.58% to 98%, sensitivities from 84% to 98%, specificities from 90% to 100%, and AUC values reached up to 0.97. Convolutional neural networks (CNN) were the most frequently used models (four studies), followed by support vector machines (three studies). Comparative analysis revealed that CNN-based approaches showed consistently high performance in ossification of the posterior longitudinal ligament detection, while traditional machine learning methods demonstrated varying effectiveness in cervical spondylotic myelopathy classification. Sample sizes varied significantly, ranging from 28 to 900 patients. MRI protocols also differed across studies, with variations in field strengths, slice thicknesses, and sequences used. Seven studies assessed inter-rater reliability. Most studies lacked external validation, which raises concerns about the generalizability of the models. Additionally, hardware configurations were inconsistently reported, and data augmentation techniques were underutilized, limiting the robustness of the models in smaller datasets.ConclusionWhile AI models for DCD diagnosis using MRI show high diagnostic potential, methodological weaknesses such as insufficient external validation and small sample sizes hinder broader clinical adoption. Future research should focus on larger, standardized, multi-center studies to improve the robustness and clinical relevance of AI-driven tools for DCD diagnosis.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-09T12:58:26Z
  DOI: 10.1177/20552076241311939
  Issue No: Vol. 11 (2025)
Construction of a health literacy prediction model for diabetic patients:
A multicenter study
- Authors: Zepeng Wang, Junyi Shi, Fangyuan Jiang, Kui Jiang, Yalan Chen; Junyi Shi, Fangyuan Jiang, Kui Jiang, Yalan ChenDepartment of Medical Informatics, School of Medicine, 66479Nantong University, Nantong, Jiangsu, China
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectivesTo achieve a rapid assessment of health literacy (HL) levels among diabetic patients.MethodsA questionnaire survey was conducted among diabetic patients from nine communities in Nantong City, Jiangsu Province, China, using convenient sampling. Based on the survey results, data from three communities were randomly selected as the test set, with the remaining data used as the training set. Feature selection was performed using recursive feature elimination. Predictive models were established and compared using logistic regression (LR), random forest (RF), and support vector machine (SVM). Calibration curves, decomposition plots, and partial dependence plots were drawn to evaluate and interpret the models.ResultsIn November 2023, a total of 802 valid questionnaires were received. Eight variables were selected for modeling: educational level, exercise habits, average monthly household income, dietary control, age, medication for blood sugar control, duration of diabetes, and number of cohabitants. The recall for LR in the three communities was 0.778, 0.800, and 0.862 [area under the curve (AUC): 0.810, 0.792, and 0.775]. For RF, the recall values were 0.879, 0.877, and 0.923 (AUC: 0.781, 0.710, and 0.710). For SVM, the recall values were 0.859, 0.862, and 0.877 (AUC: 0.813, 0.759, and 0.770). Model evaluation showed that as the data volume increased, the calibration curves became more ideal.ConclusionsAs one of the few HL prediction models for diabetic patients in mainland China that is built based on multi-center survey data and evaluated through multi-center assessment, this model can quickly identify patients with insufficient HL using a small amount of objective personal information.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-09T12:57:46Z
  DOI: 10.1177/20552076241311735
  Issue No: Vol. 11 (2025)
A qualitative exploration of the early adoption of an electronic medical
record system for type 1 diabetes management in Rwanda
- Authors: Nathalie Bille, Dirk Lund Christensen, Stine Byberg, Crispin Gishoma, Sarah Fredsted Villadsen, Michael Calopietro; Dirk Lund Christensen, Stine Byberg, Crispin Gishoma, Sarah Fredsted Villadsen, Michael Calopietro
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundDelivering adequate quality care remains a challenge in many low-and middle-income countries (LMICs), especially for people living with type 1 diabetes (T1D), requiring a complex treatment regimen. Digital health solutions, including electronic medical record (EMR) systems, have shown potential to improve delivery and quality of care but still require a successful implementation.ObjectiveTo analyze the adoption of a newly implemented EMR system in Rwanda from the perspectives of individuals with T1D, health care providers, and experts.Study designA qualitative study was conducted using the Diffusion of Innovation (DoI) theory to guide the focus of the analysis. A data triangulation strategy was used to gain multi-dimensional insights, involving in-depth interviews and participant observations with T1D individuals, EMR system users, and experts (with varying levels of acquaintance with the EMR system). Experiences of early adoption of the EMR system were explored through thematic analysis guided by the DoI theory.ResultsIn general, all the participant groups had a positive attitude towards the EMR system, believed to increase the likelihood of a successful implementation. The system was described as user-friendly and improved workflows, data quality, and patient care. The main concerns to successful adoption were related to the maintenance and long-term sustainability of the system.ConclusionThe EMR system showed great potential, was valued by users, and was positively believed to have potential to improve care and outcomes for patients. However, sustainability aspects must not be overlooked when assessing the adoption and use of an EMR system.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-09T12:57:06Z
  DOI: 10.1177/20552076241311057
  Issue No: Vol. 11 (2025)
Organizational factors impacting the implementation of a digital mental
health tool in Alberta's mental health care of youth and young adults
- Authors: Marianne Barker, Julia Hews-Girard, Karina Pinston, Sarah Daniel, Lauren Volcko, Lia Norman, Emilie Bassi, Katherine Bright, Ian Hickie, Frank Iorfino, Haley LaMonica, Karen Moskovic, Melanie Fersovitch, Jessica Bradley, Leanne Stamp, Jason Gondziola, David Johnson, Gina Dimitropoulos; Julia Hews-Girard, Karina Pinston, Sarah Daniel, Lauren Volcko, Lia Norman, Emilie Bassi, Katherine Bright, Ian Hickie, Frank Iorfino, Haley LaMonica, Karen Moskovic, Melanie Fersovitch, Jessica Bradley, Leanne Stamp, Jason Gondziola, David Johnson, Gina Dimitropoulos
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  With mental health concerns on the rise among youth and young adults (age 12–24), increased mental health options include virtual care, apps and online tools, self-management and tracking tools, and digitally-enabled coordination of care. These tools may function as alternatives or adjuncts to face-to-face models of care. Innovative solutions in the form of digital mental health (dMH) services not only provide support, resources and care, but also decrease wait times and waitlists, increase access, and empower youth. However, organizational factors may impact the extent of dMH interventions are that accepted, used, and sustained in clinical settings. This qualitative study explores organizational barriers and facilitators surrounding the implementation of a digital platform (Innowell), which uses measurement-based care (MBC) to track youth progress and outcomes. Data was collected from 154 mental health care providers participating in 23 focus groups across Alberta, drawing on school and community settings, specialized mental health services, and primary care networks. A thematic analysis revealed the following: barriers included incompatibility with current systems and workflows, lack of inter-organizational collaboration, time commitment, perceived sustainability and lack of digital literacy. Facilitators included positive attitudes towards using dMH to optimize clinical practices by empowering youth and improving continuity of care, transitions in care, and quality of care, as well as workplace culture and leadership. The study highlights a critical need for decision makers and clinical leaders to address organizational factors by integrating training and support, establishing interoperability between digitized and in-person healthcare systems, and leveraging support for MBC and youth-centred care.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-09T11:22:25Z
  DOI: 10.1177/20552076241310341
  Issue No: Vol. 11 (2025)
eHealth, digital information and technology use of men with prostate
cancer
- Authors: Stuart R Jackson, Paul Yu, Steven Sowter, Stefano Occhipinti, Suzanne Chambers, Scott Leslie, Manish I Patel; Paul Yu, Steven Sowter, Stefano Occhipinti, Suzanne Chambers, Scott Leslie, Manish I Patel
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundThe investigation of digital information sources and technologies specifically used by men with prostate cancer is scarce. This study seeks to address current gaps in the literature by investigating prostate cancer–specific internet and technology use by men with prostate cancer and factors associated with this use.MethodsCross-sectional surveys were conducted in three Australian urology clinics (local in Sydney, Western Sydney and Murrumbidgee) in 2023. Data analysis included descriptive and bivariate analysis. Chi square tests of independence, Mann–Whitney U tests and Fischer exact tests were used to assess demographic, prostate cancer-specific and psychometric variables with prostate cancer-specific usage of each website, social media and technology type.ResultsA total of 349 men responded. Mean age of respondents was 69.6 years (SD 7.8). 74.5% (n = 260) had undergone radical prostatectomy, while 10% (n = 35) reported locally advanced/metastatic disease. Information websites were used by 77.7% (n = 271) of men. Social media was used by 37% (n = 129), and total internet use was 79.1% (n = 276). Younger age, higher education and higher income were commonly associated with a greater extent of use of information source and technology types. High variability in usage and factor association was demonstrated between and within analysed group categories.ConclusionsMen with prostate cancer use a broad variety of digital information sources and technologies to access prostate cancer information at a higher rate than ever before. This work stresses the significant variability in the extent of use which men demonstrate among these resources and the factors which may play a role in this behaviour.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-09T11:21:25Z
  DOI: 10.1177/20552076241309214
  Issue No: Vol. 11 (2025)
The person-based approach to intervention development: A scoping review of
methods and applications
- Authors: Lydia Holt, Sarah Denford, Hannah Bowers, Paula Kuberka, Ingrid Muller, Richard Amlôt, Lucy Yardley; Sarah Denford, Hannah Bowers, Paula Kuberka, Ingrid Muller, Richard Amlôt, Lucy Yardley
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundThe person-based approach (PBA) has emerged as a prominent methodology guiding the development of digital and hybrid health behaviour change interventions over the last decade, and there is a salient need to understand its utilization.ObjectiveThis study aims to describe which elements of the PBA have been utilised in intervention development research, for which populations, and how this has been reported.MethodsA search for intervention development papers published between 2015 and 2023 using forward citation searches was undertaken in Scopus, using two seed articles. Results are presented using frequency counts, and qualitative data were summarised using content analysis.ResultsThe review encompasses 239 papers. The PBA has frequently been applied in early stage development of digital interventions for adult populations, prioritising the use of qualitative methods. It has been used globally to develop, adapt, optimise and evaluate digital, hybrid and offline interventions for a wide range of contexts including primary and secondary healthcare, educational, community, and public health settings. Researchers value it as a proven method to identify user needs and preferences in order to create persuasive content.ConclusionThe PBA is most frequently linked to research undertaken to understand target populations and iteratively design content in early development phases. The PBA provides guidance on combining evidence-, theory- and person-based research, but these three elements are not always evident in the literature. Training focused on these elements, plus exemplar studies and use of reporting guidelines, could make this integrative work more visible in future papers.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-09T11:20:45Z
  DOI: 10.1177/20552076241305934
  Issue No: Vol. 11 (2025)
Linking multiple-channel information seeking and lifestyle among Chinese
older adults: A moderation and mediation analysis
- Authors: Qingrui Li, Yifang Wu, Xinshu Zhao; Yifang Wu, Xinshu Zhao
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundHealthy lifestyle improvement of older Chinese adults has drawn a lot of attention due to an exceeding ageing population in mainland China. The current study aims to investigate the beneficial functions of the multi-channel health information seeking on elders’ lifestyle self-management.ObjectiveWe conducted a mediation analysis to test the association between multi-channel information seeking behavior and lifestyle self-management, which mediates by perceived self-management competence. Meanwhile, we also test the moderation effect of perceived self-management competence on lifestyle management with motivation for health promotion and prevention as the moderator.MethodsTo examine this mediation and moderation effects, we conducted a quota sampling online survey in mainland China from June 11 to October 12, 2023. The final sample size was 898 Chinese respondents aged 60 or above, with 54.5% male.ResultsHealth information seeking using the mHealth app (bp = .03, 95% CI: [.005, .055]) and social media (bp = .06, 95% CI: [.031, .086]) is positively associated with lifestyle self-management through perceived self-management competence. While broadcast media (bp = .01, 95% CI: [−.015, .040]), print media (bp = .01, 95% CI: [−.015, .026]), and search engine (bp = .02, 95% CI: [−.006, .043]) show no significant impact on lifestyle management. Furthermore, health promotion and prevention motivation shows a positive moderation effect on perceived self-management competence and lifestyle management.ConclusionsOur findings show evidence supporting a cognitive mechanism of moderated mediation that links seeking health information to improve LSM in older Chinese adults. It is essential for health self-education and health promotion among older Chinese adults.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-09T11:20:07Z
  DOI: 10.1177/20552076241305481
  Issue No: Vol. 11 (2025)
An immersive virtual reality exergame as a patient education approach in
fibromyalgia: Pilot study
- Authors: Cassandra Lucia Bardelli, Luca Chittaro, Simone Longhino, Luca Quartuccio; Luca Chittaro, Simone Longhino, Luca Quartuccio
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundImmersive Virtual Reality (VR) has been applied in pain management for various conditions, but its use in fibromyalgia (FM) remains underexplored. While physical activity plays a role in treating FM, patients’ low tolerance often limits its effectiveness. After reviewing the literature on VR and games for FM, we designed a novel VR exergame to assist FM patients in performing physical activity, and evaluate its feasibility.Materials and MethodsThis pilot study involved three female subjects with FM and four healthy female volunteers. The main outcomes included qualitative assessments of exertion, pain levels, psychological states experienced during the VR session, but also device comfort.ResultsImprovements in perceived exertion and pain intensity were observed during the VR exergame session in comparison to pre-exergame levels, along with a reduction in depression, stress and anxiety levels while using the VR immersive system. Most participants experienced also increase of relaxation and positive emotions during the exergame. Only one participant was not able to complete all levels of the exergame due to musculoskeletal pain exacerbation; nevertheless, this patient reported an improvement in motivation and enjoyment during the gameplay. Many participants expressed a greater motivation to perform the exercises in the VR environment compared to traditional training methods.ConclusionThe proposed VR exergame is a feasible system that might reduce depression, stress and anxiety, while boosting motivation and relaxation in both healthy and FM subjects. A calibration protocol is required to tailor the system to each user's pain levels and physical abilities.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-09T11:19:26Z
  DOI: 10.1177/20552076241304904
  Issue No: Vol. 11 (2025)
Impact of AI and big data analytics on healthcare outcomes: An empirical
study in Jordanian healthcare institutions
- Authors: Rand Al-Dmour, Hani Al-Dmour, Eatedal Basheer Amin, Ahmed Al-Dmour; Hani Al-Dmour, Eatedal Basheer Amin, Ahmed Al-Dmour
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  Artificial intelligence (AI) and big data analytics are transforming healthcare globally and in Jordan. This study investigates the effects of AI and big data analytics on healthcare outcomes in Jordanian healthcare institutions. A comprehensive model is proposed to understand the antecedents of healthcare outcomes, including the impact of perceived ease of use, perceived usefulness, and organizational capabilities. Data were collected from 400 structured questionnaires, with a final sample size of 288 respondents, and analyzed using partial least squares structural equation modeling. The findings reveal that AI technologies significantly improve diagnostic accuracy and treatment planning, while big data analytics enhances operational efficiency and patient care. However, the comparative influence of AI on different healthcare processes was less significant. Additionally, robust organizational capabilities effectively enhanced the adoption and impact of AI and big data analytics. The study highlights the mediating roles of perceived ease of use and usefulness in the relationship between technology adoption and healthcare outcomes. Understanding the interplay between AI, big data analytics, and healthcare delivery is crucial for policymakers, healthcare administrators, and technology developers to develop effective strategies that improve patient care and operational efficiency. This study recommends investing in user-friendly AI and big data analytics tools, enhancing organizational capabilities, and providing comprehensive training for healthcare professionals. Future research should extend this study to different cultural contexts to validate the findings and contribute further to the literature on AI and healthcare.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-07T09:34:45Z
  DOI: 10.1177/20552076241311051
  Issue No: Vol. 11 (2025)
Impact of digital device utilization on public health surveillance to
enhance city resilience during the public health emergency response: A
case study of SARS-CoV-2 response in Thailand (2020–2023)
- Authors: Watcharaporn Chutarong, Roongaroon Thammalikhit, Rungwasun Kraiklang, Anurak Sawangwong, Orachorn Saechang, Yuqian Guo, Weiwen Zhang; Roongaroon Thammalikhit, Rungwasun Kraiklang, Anurak Sawangwong, Orachorn Saechang, Yuqian Guo, Weiwen Zhang
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveThis study aims to examine the impact of digital devices on public health surveillance, the impact of public health surveillance on resilient cities, and the impact of digital devices on resilient cities.MethodsQuestionnaires were issued to residents of Thailand during the severe acute respiratory syndrome coronavirus 2 response (2020–2023). In total, 1025 valid responses were recorded from Thai nationals and expatriates. Exploratory factor analysis, confirmatory factor analysis, and structural equation modeling were used to assess the model through IBM SPSS 23 and AMOS 23.ResultsDigital devices have a strong positive direct effect on public health surveillance (β = 0.73, p ≤ .001), public health surveillance has a strong positive direct effect on resilient cities (β = 0.79, p ≤ .001), and digital devices have a low positive direct and a moderate indirect effect on resilient cities (β = 0.13, p ≤ .001, and β = 0.58, p ≤ .001, respectively). The use of digital devices in data collection, analysis, and dissemination, positively impacted public health surveillance, considering five dimensions: medical and vaccine, individual, health care, epidemiological, and disease. Meanwhile, using digital devices in public health surveillance positively impacted the resilience of cities, considering three dimensions: socioeconomic, institutional, and living. The causal relationship model of the digital device utilization on public health surveillance enhancing the resilience of the cities met all the necessary criteria: X2/df = 2.802, comparative fit index = 0.953, goodness of fit index = 0.901, normed fit index = 0.935, Tucker–Lewis index = 0.935, root mean square of approximation = 0.048, and root of mean square residual = 0.043. This indicates the model fits the empirical data.ConclusionDigital devices are vital tools in collecting, analyzing, and disseminating public health surveillance-related data during the public health emergency. This, in turn, can improve medical and vaccine, individual, health care, epidemiological, and disease surveillance, and also enhance cities’ socioeconomic, institutional, and living resilience.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-07T09:34:06Z
  DOI: 10.1177/20552076241304070
  Issue No: Vol. 11 (2025)
A comprehensive review of digital twin in healthcare in the scope of
simulative health-monitoring
- Authors: Mubaris Nadeem, Sascha Kostic, Mareike Dornhöfer, Christian Weber, Madjid Fathi; Sascha Kostic, Mareike Dornhöfer, Christian Weber, Madjid FathiFaculty IV: School of Science Knowledge Management, University of Siegen, Siegen, Germany
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveDigital twins (DTs) emerged in the wake of Industry 4.0 and the creation of cyber-physical systems, motivated by the increased availability and variability of machine and sensor data. DTs are a concept to create a digital representation of a physical entity and imitate its behavior, while feeding real-world data to the digital counterpart, thus allowing enabling digital simulations related to the real-world entity. The availability of new data sources raises the potential for developing structured approaches for prediction and analysis. Similarly, in the field of medicine and digital healthcare, the collection of patient-focused data is rising. Medical DTs, a new concept of structured, exchangeable representations of knowledge, are increasingly used for capturing personal health, targeting specific illnesses, or addressing complex healthcare scenarios in hospitals.MethodsThis article surveys the current state-of-the-art in applying DTs in healthcare, and how these twins are generated to support smart, personalized medicine. These concepts are applied to a DT for a simulated health-monitoring scenario.ResultsThe DT use case is implemented using AnyLogic multi-agent simulation, monitoring the patient’s personal health indicators and their development.ConclusionThe results indicate both possibilities and challenges and provide important insights for future DT implementations in healthcare. They have the potential to optimize healthcare in various ways, such as providing patient-centered health-monitoring.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-07T09:05:06Z
  DOI: 10.1177/20552076241304078
  Issue No: Vol. 11 (2025)
Applicability of the decomposed theory of planned behavior for the
evaluation of community-dwelling older adults’ acceptance in continuous
usage of robot-assisted board games for cognitive training
- Authors: Chiu-Mieh Huang, Su-Fei Huang, Yu-Ting Chen, Ching-Hao Chang, Hsiu-Chun Chien, Ying-Jie Chang, Kuei-Yu Huang, Jong-Long Guo; Su-Fei Huang, Yu-Ting Chen, Ching-Hao Chang, Hsiu-Chun Chien, Ying-Jie Chang, Kuei-Yu Huang, Jong-Long Guo
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundImproving cognitive function in healthy older adults is a global concern. Cognitive training delays mental deterioration. The utilization of robots and board games for aiding older adults in cognitive training represents a prominent technological trend and is a subject of meriting investigation.ObjectiveThis study evaluates the acceptability and factors influencing the continuous usage intention of a robot-assisted board game (RABG) for cognitive training in community-dwelling older adults based on the decomposed theory of planned behavior (DTPB).MethodsIn this explanatory study, we developed an RABG with six educational modules. The experiences of 126 older adults recruited from northern Taiwan who completed the program were assessed using a DTPB-based questionnaire. Partial least-squares structural equation modeling was used to examine the correlations.ResultsThe results demonstrate the DTPB's sufficient fitness and 79.9% explanatory power for the continuous usage intention of the RABG, confirming the effectiveness of the proposed structural model. Perceived usefulness positively affected attitude and continuous usage intention, indicating that perceived usefulness is critical in promoting older adults’ continuous usage intention. The interpersonal influence was a major antecedent of subjective norms. Self-efficacy affects perceived behavioral control. Attitudes and perceived behavioral control affected users’ intentions to use the RABG.ConclusionsOur findings support the applicability of the DTPB in evaluating RABGs for cognitive training in older adults, suggesting its potential integration in future interventions.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-07T07:15:07Z
  DOI: 10.1177/20552076241312576
  Issue No: Vol. 11 (2025)
Prediction of acute kidney injury in intensive care unit patients based on
interpretable machine learning
- Authors: Li Zhang, Mingyu Li, Chengcheng Wang, Chi Zhang, Hong Wu; Mingyu Li, Chengcheng Wang, Chi Zhang, Hong Wu
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveAcute kidney injury (AKI) poses a lethal risk in intensive care unit (ICU) patients, where early detection is challenging. This study was to establish a prediction model for AKI 24 hours in advance for ICU patients and to help clinicians monitor patients at an early stage by key features.MethodsIn this study, the Medical Information Mart for Intensive Care (MIMIC) databases were used to construct a dataset of critically ill patients. Predictive models were constructed using five machine learning algorithms based on MIMIC-IV data, and the best predictive model was selected by multiple model evaluation metrics. MIMIC-III data were used for external validation. We conducted an interpretability analysis of the model using SHapley Additive exPlanations (SHAP) to clarify key features and decision-making mechanisms.ResultsA total of 18,186 patient data were included in this study. The analysis combining calibration and decision curves demonstrated that the eXtreme Gradient Boosting (XGBoost) exhibited superior performance among the five algorithms, achieving an area under the receiver operating characteristic curve of 0.88. Interpretability analysis based on the XGBoost model showed diuretic use, mechanical ventilation, vasopressor use, age, and antibiotic use as the most important decision factors of the model. The SHAP summary plot was used to illustrate the effects of the top 19 features attributed to the XGBoost.ConclusionsThe XGBoost algorithm can predict the occurrence of AKI more accurately. Interpretative analysis of the model reveals the mechanisms of key features, and reflects the individual differences between patients, providing an important clinical reference.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-07T07:14:06Z
  DOI: 10.1177/20552076241311173
  Issue No: Vol. 11 (2025)
Engaging and supporting young adults in smoking cessation: Insights from a
mobile-based cessation program in China
- Authors: Xue Weng, Hongcui Yang, Chuyu Song, Jiayu Tu, Kefeng Liu, Man Ping Wang; Hongcui Yang, Chuyu Song, Jiayu Tu, Kefeng Liu, Man Ping Wang
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveEvidence-based smoking cessation treatments are underutilized by young adult smokers. This study explored young smokers’ experiences with a mobile-based smoking cessation program that included a Quit & Win contest at a university in Zhuhai, China, aiming to identify key engagement and cessation mechanisms.MethodsTwenty participants (aged 18–25 years) were selected through purposive sampling. Semistructured interviews explored participants’ experiences, motivations, and perceptions of the intervention. Thematic analysis was used to identify major themes from interview transcripts.FindingsTwo main themes emerged: (1) strategies for enhancing program engagement, including the influence of campus-wide smoke-free environments, the appeal of the Quit & Win contest, and the use of peer counselors for recruitment; and (2) strategies for supporting smoking cessation, emphasizing the importance of a multifaceted approach. This approach included financial incentives as a motivator for quitting, sustaining cessation efforts with online group support, and building a therapeutic alliance with cessation counselors.ConclusionMobile-based interventions, combined with peer support, financial incentives, and campus-wide smoke-free activities, can effectively engage young adults in smoking cessation. Future interventions should implement comprehensive programs that incorporate these elements to address the unique challenges faced by this population.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-07T07:13:43Z
  DOI: 10.1177/20552076241311055
  Issue No: Vol. 11 (2025)
The causal effect of Internet use on rural middle-aged and older adults’
depression: A propensity score matching analysis
- Authors: Junqi Ma; 66530Chongqing Technology Business University, Chongqing, China
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveDepression among older adults is increasingly becoming a global public health issue. Along with the rapid development of digital information technology, the Internet has profoundly changed the lifestyle of older adults. However, few studies have focused on the mental health of rural middle-aged and older adult populations, and this study aims to explore the impact of Internet use on depressive symptoms among rural middle-aged and older adults.MethodsOur study is based on 10,946 Chinese rural participants aged 45 and above in the 2018 China Health and Retirement Longitudinal Study (CHARLS). Depression is measured by a 10-item Centre for Epidemiologic Studies (CES-D10), and multiple linear regression and the propensity score matching (PSM) method are used to examine the effect of Internet use on depression in Chinese rural middle-aged and older adults.ResultsInternet use significantly reduced depression in rural middle-aged and older adults. The mechanism was that Internet use improved mental health by improving social interaction and enhancing social support. Furthermore, desk computer, laptop computer, and cellphone use were all significantly associated with lower depression scores compared to non-Internet users. And the more the content of Internet use, the significantly lower the level of depression in rural middle-aged and older adults. Heterogeneity analysis showed that Internet use reduced depression more pronounced in the groups of males, those in elementary and secondary education, low-medium income, and aged under 75.ConclusionThe paper confirms that Internet use significantly reduces depression, with social interaction and social support playing a mediating role. The results of the study show that strengthening rural Internet infrastructure can promote healthy aging in rural areas.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-07T07:13:03Z
  DOI: 10.1177/20552076241310041
  Issue No: Vol. 11 (2025)
Refining breast cancer classification: Customized attention integration
approaches with dense and residual networks for enhanced detection
- Authors: Mohammad Sakif Alam, Anwar Hossain Efat, SM Mahedy Hasan, Md Palash Uddin; Anwar Hossain Efat, SM Mahedy Hasan, Md Palash Uddin
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveBreast cancer detection is critical for timely and effective treatment, and automatic detection systems can significantly reduce human error and improve diagnosis speed. This study aims to develop an accurate and robust framework for classifying breast cancer into benign and malignant categories using a novel machine learning architecture.MethodsWe propose a dense-ResNet attention integration (DRAI) architecture that combines DenseNet and ResNet models with three attention mechanisms to enhance feature extraction from the BreakHis dataset. The attention mechanisms focus on regionally important features, improving classification accuracy. A triple-level ensemble (TLE) method combines the performance of multiple models, further enhancing prediction accuracy.ResultsThe proposed DRAI architecture with TLE achieves an accuracy of 99.58% in classifying breast cancer into benign and malignant categories, surpassing existing methodologies. This high accuracy demonstrates the effectiveness of the fusion architecture and its ability to reduce manual errors in breast cancer diagnosis.ConclusionThe DRAI architecture with TLE provides a robust, automated framework for breast cancer classification. Its exceptional accuracy lays a solid foundation for future advancements in automated diagnostics and offers a reliable method for aiding early breast cancer detection.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-07T07:12:03Z
  DOI: 10.1177/20552076241309947
  Issue No: Vol. 11 (2025)
Health recommender systems to facilitate collaborative decision-making in
chronic disease management: A scoping review
- Authors: Antonia Barbaric, Kenneth Christofferson, Susanne M Benseler, Chitra Lalloo, Alex Mariakakis, Quynh Pham, Joost F Swart, Rae S M Yeung, Joseph A Cafazzo; Kenneth Christofferson, Susanne M Benseler, Chitra Lalloo, Alex Mariakakis, Quynh Pham, Joost F Swart, Rae S M Yeung, Joseph A Cafazzo
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveHealth recommender systems (HRSs) are increasingly used to complement existing clinical decision-making processes, but their use for chronic diseases remains underexplored. Recognizing the importance of collaborative decision making (CDM) and patient engagement in chronic disease treatment, this review explored how HRSs support patients in managing their illness.MethodsA scoping review was conducted using the framework proposed by Arksey and O’Malley, advanced by Levac et al., in line with the PRISMA-ScR checklist. Quantitative (descriptive numerical summary) and qualitative (inductive content analysis) methods wered used to synthesize the data. ResultsForty-five articles were included in the final review, most commonly covering diabetes (9/45, 20%), mental health (9/45, 20.0%), and tobacco dependence (7/45, 15.6%). Behavior change theories (10/45, 22.2%) and authoritative sources (10/45, 22.2%) were the most commonly referenced sources for design and development work. From the thematic analysis, we conclude: (a) the main goal of HRSs is to induce behavior change, but limited research investigates their effectiveness in achieving this aim; (b) studies acknowledge that theories, models, frameworks, and/or guidelines help design HRSs to elicit specific behavior change, but they do not implement them; (c) connections between CDM and HRS purpose should be more explicit; and (d) HRSs can often offer other self-management services, such as progress tracking and chatbots.ConclusionsWe recommend a greater emphasis on evaluation outcomes beyond algorithmic performance to determine HRS effectiveness and the creation of an evidence-driven, methodological approach to creating HRSs to optimize their use in enhancing patient care.Lay summaryOur work aims to provide a summary of the current landscape of health recommender system (HRS) use for chronic disease management. HRSs are digital tools designed to help people manage their health by providing personalized recommendations based on their health history, behaviors, and preferences, enabling them to make more informed health decisions. Given the increased use of these tools for personalized care, and especially with advancements in generative artificial intelligence, understanding the current methods and evaluation processes used is integral to optimizing their effectiveness. Our findings show that HRSs are most used for diabetes, mental health, and tobacco dependence, but only a small percentage of publications directly reference and/or use relevant frameworks to help guide their design and evaluation processes. Furthermore, the goal for most of these HRSs is to induce behavior change, but there is limited research investigating how effective they are in accomplishing this. Given these findings, we recommend that evaluations shift their focus from algorithms to more holistic approaches and to be more intentional about the processes used when designing the tool to support an evidence-driven approach and ultimately create more effective and useful HRSs for chronic disease management.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-07T07:11:03Z
  DOI: 10.1177/20552076241309386
  Issue No: Vol. 11 (2025)
The benefits and safety of a virtual reality intervention in patients
suffering from acute and chronic pain: A pilot study
- Authors: Bingjie Ma, Libo Zhang, Yun Ji, Xuehua Huang, Luandi Yao, Wei Cheng, Li Hu, Xuejing Lu, Ke Ma; Libo Zhang, Yun Ji, Xuehua Huang, Luandi Yao, Wei Cheng, Li Hu, Xuejing Lu, Ke Ma
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundTo overcome the challenge of psychotherapist scarcity in applying pain psychotherapy in clinical practice, we developed a virtual reality (VR) program delivering weeks of pain psychotherapy without psychotherapists, with a focus on minimizing the risk of motion sickness.ObjectivesWe conducted a single-arm pilot study to assess the efficacy and motion sickness associated with a VR session delivering guided imagery and breathing techniques selected from the initial course of our VR program, involving patients suffering from various acute and chronic pain.MethodsPatients underwent a 15-min VR session. Pain-related and anxiety ratings using a 0–10 numerical rating scale were collected pre-, during-, post-VR and in 6-h follow-up. Motion sickness symptoms were assessed using Simulator Sickness Questionnaire pre- and post-VR.ResultsPatients (n = 73) reported their perceived pain intensity and anxiety reduced significantly from pre- to post-VR by 22.9% and 45.0% (all p
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-07T07:10:05Z
  DOI: 10.1177/20552076241308703
  Issue No: Vol. 11 (2025)
Opportunities and barriers for reimbursement of digital therapeutics in
Austria: Findings From expert interviews
- Authors: Mahdi Sareban, Gunnar Treff, Jan David Smeddinck, Rada Hussein, Josef Niebauer; Gunnar Treff, Jan David Smeddinck, Rada Hussein, Josef Niebauer
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundDigital therapeutics (DTx) are software-based interventions that aim to prevent or treat especially non-communicable diseases. Currently, no framework for reimbursement of DTx exists in Austria. The aim of this study was to gather a comprehensive perspective on regulatory considerations of Austrian stakeholders with regard to reimbursement of DTx and to outline strategies for establishing a national reimbursement framework.MethodsBased on a stakeholder analysis, seven semi-structured interviews with Austrian experts from the digital health-related fields medicine, public health, health technology assessment, health industry and social security providers were conducted. Interviews were analyzed according to seven predefined themes.ResultsOverall, participants agreed that reimbursement of DTx by the public insurance in Austria is desirable. Prerequisites are (i) a high user and prescriber acceptance of DTx, which must be integrated into a transparent and interoperable Austrian e-Health infrastructure, (ii) a sufficient, risk-based level of evidence for clinical effectiveness, (iii) national authorities that transparently provide evidence-base, indications, contra-indications and potential risks, (iv) adopting European regulations about data security, secondary use of data and use of artificial intelligence and (v) a health-economical evaluation of DTx.ConclusionA comprehensive national strategy for reimbursing DTx will need to consider technical, scientific and socio-economical requirements, patient safety and liability, secure health data handling and use of artificial intelligence in order to establish a sound framework with equitable access also for socioeconomically disadvantaged persons in order to address the growing burden of non-communicable diseases.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-07T07:09:33Z
  DOI: 10.1177/20552076241299062
  Issue No: Vol. 11 (2025)
Occupational health professionals’ and HR specialists’ perceptions of
telemental health services in occupational health care settings: A
qualitative study
- Authors: Elina Kervinen, Lauri Vähätalo, Anna Siukola, Tiia Reho, Klas Winell, Riitta Sauni; Lauri Vähätalo, Anna Siukola, Tiia Reho, Klas Winell, Riitta Sauni
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveThe rise in mental health-related work disability pensions highlights the need for more research on how occupational health care (OHC) can support mental health, including the use of telehealth (TH) services in mental health care.MethodsThe research, employing a descriptive qualitative approach through interviews (n = 42), focused on experiences of professionals from a private OHC service provider in Finland and human resource representatives (HRRs) of OHC client companies. Inductive content analysis was used to analyze the data.ResultsOur research suggests that TH services provided by OHC can enhance access to care and expedite the initiation of work ability support, particularly in mental health cases. However, potential challenges include a perceived sense of distance, superficiality in interactions, and difficulty in forming a comprehensive understanding due to few non-verbal cues.ConclusionA combined approach of TH and face-to-face services is suggested to provide flexible, and individualized support. Further studies on remote low-threshold discussion mental health services and studies comparing TH and face-to-face services are advised.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-07T07:09:03Z
  DOI: 10.1177/20552076241297409
  Issue No: Vol. 11 (2025)
Demand, utilization, and supply of community smart senior care services
for older people in China
- Authors: Ruobing Fa, Shengxuan Jin, Peng Fan, Fengyuan Tang, Qian Jin, Changqing Wang; Shengxuan Jin, Peng Fan, Fengyuan Tang, Qian Jin, Changqing Wang
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveAlthough smart senior care services offer numerous benefits, they have not yet gained widespread acceptance among the general populace, particularly seniors. Numerous related issues have surfaced, with the structural imbalance between supply and demand being most prominent. Currently, there is a lack of research distinguishing between the various categories of demand for smart ageing services and the associated behaviors of older individuals. In this study, we aimed to identify the types of demand and utilization of smart senior care services among Chinese older adults to understand their diverse characteristics and the factors that facilitate certain behaviors. We also analyzed the imbalance between supply and demand for smart senior care services and explored the factors influencing it, thereby providing a reference for optimizing smart senior services.MethodsWe conducted a cross-sectional study from January to March 2023 and analyzed 1037 valid questionnaires. Three types of smart senior care services were investigated: intelligent information, intelligent consultation, and intelligent monitoring. We identified the demand, utilization, and supply of these services among older individuals. Latent class analysis (LCA) was used to differentiate the heterogeneity of older adults in terms of service demand and utilization. Factors influencing service preferences were analyzed using binary logistic regression based on Andersen's behavioral model.ResultsBased on the LCA findings, service demand, and utilization were divided into two categories: positive demand (desire to use the services) or negative demand, and taking action or negative action to use the services. The persons with high demand but low utilization comprised the largest number of older people in this study (69.35%). The results indicated that the number of children (odds ratio (OR) = 1.491), community-provided smart devices (OR = 1.700), number of chronic diseases (OR = 1.218), and self-care capacity (OR = 0.214) are associated with positive demand. Meanwhile, pre-retirement employment, income sources, community device provided, community promotion, region, and self-care ability were significant predictors (p
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-07T07:08:33Z
  DOI: 10.1177/20552076241293641
  Issue No: Vol. 11 (2025)
Consumer opinion on the use of machine learning in healthcare settings: A
qualitative systematic review
- Authors: Jacqueline H Stephens, Celine Northcott, Brianna F Poirier, Trent Lewis; Celine Northcott, Brianna F Poirier, Trent Lewis
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  IntroductionGiven the increasing number of artificial intelligence and machine learning (AI/ML) tools in healthcare, we aimed to gain an understanding of consumer perspectives on the use of AI/ML tools for healthcare diagnostics.MethodsWe conducted a qualitative systematic review, following established standardized methods, of the existing literature indexed in the following databases up to 4 April 2022: OVID MEDLINE, OVID EMBASE, Scopus and Web of Science.ResultsFourteen studies were identified as appropriate for inclusion in the meta-synthesis and systematic review. Most studies (n = 12) were conducted in high-income countries, with data extracted from both mixed methods (42.9%) and qualitative (57.1%) studies. The meta-synthesis identified four overarching themes across the included studies: (1) Trust, fear, and uncertainty; (2) Data privacy and ML governance; (3) Impact on healthcare delivery and access; and (4) Consumers want to be engaged.ConclusionThe current evidence demonstrates consumers’ understandings of AI/ML for medical diagnosis are complex. Consumers express a complex combination of both hesitancy and support towards AI/ML in healthcare diagnosis. Importantly, their views of the use of AI/ML in medical diagnosis are influenced by the perceived trustworthiness of their healthcare providers who use these AI/ML tools. Consumers recognize the potential for AI/ML tools to improve diagnostic accuracy, efficiency and access, and express a strong interest to be engaged in the development and implementation process of AI/ML into routine healthcare.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-07T07:02:04Z
  DOI: 10.1177/20552076241288631
  Issue No: Vol. 11 (2025)
Artificial intelligence in precision medicine for lung cancer: A
bibliometric analysis
- Authors: Yuchai Wang, Weilong Zhang, Xiang Liu, Li Tian, Wenjiao Li, Peng He, Sheng Huang, Fuyuan He, Xue Pan; Weilong Zhang, Xiang Liu, Li Tian, Wenjiao Li, Peng He, Sheng Huang, Fuyuan He, Xue Pan
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundThe increasing body of evidence has been stimulating the application of artificial intelligence (AI) in precision medicine research for lung cancer. This trend necessitates a comprehensive overview of the growing number of publications to facilitate researchers’ understanding of this field.MethodThe bibliometric data for the current analysis was extracted from the Web of Science Core Collection database, CiteSpace, VOSviewer ,and an online website were applied to the analysis.ResultsAfter the data were filtered, this search yielded 4062 manuscripts. And 92.27% of the papers were published from 2014 onwards. The main contributing countries were China, the United States, India, Japan, and Korea. These publications were mainly published in the following scientific disciplines, including Radiology Nuclear Medicine, Medical Imaging, Oncology, and Computer Science Notably, Li Weimin and Aerts Hugo J. W. L. stand out as leading authorities in this domain. In the keyword co-occurrence and co-citation cluster analysis of the publication, the knowledge base was divided into four clusters that are more easily understood, including screening, diagnosis, treatment, and prognosis.ConclusionThis bibliometric study reveals deep learning frameworks and AI-based radiomics are receiving attention. High-quality and standardized data have the potential to revolutionize lung cancer screening and diagnosis in the era of precision medicine. However, the importance of high-quality clinical datasets, the development of new and combined AI models, and their consistent assessment for advancing research on AI applications in lung cancer are highlighted before current research can be effectively applied in clinical practice.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-03T08:38:08Z
  DOI: 10.1177/20552076241300229
  Issue No: Vol. 11 (2025)
Digital assessment of muscle adaptation in obese patients with
osteoarthritis: Insights from surface electromyography (sEMG)
- Authors: Xinran Luo, Qiaojie Wang, Hongyu Tan, Wenbo Zhao, Yifei Yao, Shengdi Lu; Qiaojie Wang, Hongyu Tan, Wenbo Zhao, Yifei Yao, Shengdi Lu
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundObesity and severe knee osteoarthritis (KOA) lead to significant gait and muscle adaptations. However, the relationship between core muscle strength and the severity of KOA in obese patients remains unclear. This study aimed to determine the association between muscle strength adaptation and the severity of KOA in obese individuals.MethodsWe recruited 119 obese participants with unilateral KOA from January 2021 to December 2023, all classified with mild to moderate KOA grades. KOA severity was assessed using the Western Ontario and McMaster University Osteoarthritis Index (WOMAC), which categorized participants into two groups based on disease severity. Electromyographic data from the psoas, gluteus medius, vastus lateralis, vastus medialis, rectus femoris, medial gastrocnemius, lateral gastrocnemius, tibialis anterior, and biceps femoris muscles were collected during isometric and dynamic knee extension.ResultsSignificant differences were observed in all selected muscles between the affected knee joint and the contralateral side during both dynamic and isometric knee extensions. The difference in electromyographic data—including mean absolute value (MAV), root mean square (RMS), and center frequency (CF)—was significantly different across groups categorized by KOA severity. Notably, the MAV values of the vastus medialis, lateral gastrocnemius, and biceps femoris, as well as the CF values of the medial and lateral gastrocnemius, showed no significant differences in some instances during both dynamic and isometric extensions.ConclusionThis study indicates that obese individuals with KOA exhibit lower muscle intensity and higher fatigability in comparison to the contralateral side during both isometric and dynamic knee extensions. Furthermore, significant reductions in muscle intensity were observed in the psoas, gluteus medius, vastus lateralis, rectus femoris, medial gastrocnemius, and tibialis anterior muscles, correlating with the advanced severity of KOA.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-03T08:18:18Z
  DOI: 10.1177/20552076241311940
  Issue No: Vol. 11 (2025)
Feasibility and psychometric quality of smartphone administered cognitive
ecological momentary assessments in women with metastatic breast cancer
- Authors: Ashley M Henneghan, Emily W Paolillo, Kathleen M Van Dyk, Oscar Y Franco-Rocha, Soyeong Bang, Rebecca Tasker, Tara Kaufmann, Darren Haywood, Nicolas H Hart, Raeanne C Moore; Emily W Paolillo, Kathleen M Van Dyk, Oscar Y Franco-Rocha, Soyeong Bang, Rebecca Tasker, Tara Kaufmann, Darren Haywood, Nicolas H Hart, Raeanne C Moore
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveMetastatic breast cancer (MBC) is associated with burdensome side effects, including cognitive changes that require ongoing monitoring. Cognitive ecological momentary assessments (EMAs) allow for assessment of individual cognitive functioning in natural environments and can be administered via smartphones. Accordingly, we sought to establish the feasibility, reliability, and validity of a commercially available cognitive EMA platform.MethodsUsing a prospective design, clinical cognitive and psychosocial assessments (cognitive batteries; patient reported outcomes) were collected at baseline, followed by a 28-day daily EMA protocol that included self-ratings for symptoms and mobile cognitive tests (memory, executive functioning, working memory, processing speed). Satisfaction and feedback questions were included in follow-up data collection. Feasibility data were analyzed using mixed descriptive methods. Test-retest reliability was examined using intraclass correlation coefficients (ICCs) for each EMA, and Pearson's correlation were used to evaluate convergent validity between cognitive EMAs and baseline clinical cognitive and psychosocial variables.ResultsFifty-one women with MBC (n = 51) completed this EMA study. High satisfaction (median 90%), low burden (median 19%), high adherence rates (mean 94%), and 100% retention rate were observed. ICCs for cognitive tests of working memory, executive function, and processing speed were robust (>0.90) and ICC for memory tests acceptable (>0.66). Other correlational findings indicated strong convergent validity for all cognitive and psychosocial EMAs.ConclusionCognitive EMA monitoring for 28 days is feasible and acceptable in women with MBC, with specific cognitive EMAs (mobile cognitive tests; cognitive function self-ratings) demonstrating strong reliability and validity.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-03T08:17:47Z
  DOI: 10.1177/20552076241310474
  Issue No: Vol. 11 (2025)
Integrating telerehabilitation and serious gaming during home-based
exercise intervention after stroke: A randomized controlled pilot trial of
the DISKO-tool
- Authors: Elisabet Åkesson, Maria Bergqvist, Maja Eder, Nanna Bäckström, Erika Franzén, Jörgen Borg, Susanne Palmcrantz; Maria Bergqvist, Maja Eder, Nanna Bäckström, Erika Franzén, Jörgen Borg, Susanne Palmcrantz
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  BackgroundTo support recovery after stroke, rehabilitative actions and innovations are needed in resource-limited health care and geographically distant regions.ObjectiveThe first objective was to explore the feasibility of performing home-based training using the novel DISKO-tool including both telerehabilitation and serious gaming customized to target dynamic balance poststroke. The second objective was to assess the outcome using the Balance Evaluation Systems Test as the primary outcome.MethodsThis randomized controlled pilot trial, included ambulatory patients ≥18 years of age with physical impairments 3–6 months poststroke. During primary care rehabilitation, patients were randomized to conventional and 6 weeks of DISKO-tool training in the home (n = 10) or conventional training only (n = 11). Feasibility was assessed with process, resource, management, and scientific perspectives using questionnaires, logbooks, DISKO-tool data and clinical assessments.ResultsThe study design was feasible including safety, resource capacity, a retention rate of 87%, high compliance to the protocol (mean 30 training days), and highly rated experience of the tool (median 10 of 10) despite some technical issues. The recruitment rate was low. The DISKO-group presented improved balance, especially in anticipatory postural adjustment compared to the conventional group (p
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-03T08:17:07Z
  DOI: 10.1177/20552076241308614
  Issue No: Vol. 11 (2025)
Corrigendum to “Navigating the crossroads of aging, caregiving and
technology: Insights from southern Spain about video-based technology in
the care context”
- Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-03T05:45:47Z
  DOI: 10.1177/20552076241310293
  Issue No: Vol. 11 (2025)
Google Trends applications for COVID-19 pandemic: A bibliometric analysis
- Authors: Hao Li, Ning Zhang, Xingxing Ma, Yuqing Wang, Feixiang Yang, Wanrong Wang, Yuxi Huang, Yinyin Xie, Yinan Du; Ning Zhang, Xingxing Ma, Yuqing Wang, Feixiang Yang, Wanrong Wang, Yuxi Huang, Yinyin Xie, Yinan Du
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  IntroductionCOVID-19 is one of the most severe global health events in recent years. Google Trends provides a comprehensive analysis of the search frequency for specific terms on Google, reflecting the public's areas of interest. As of now, there has been no bibliometric study on COVID-19 and Google Trends. Therefore, the aim of this study is to perform a comprehensive bibliometric analysis of existing Google Trends research related to COVID-19.MethodsWe retrieved 467 records from the Web of Science™ Core Collection, covering the period from January 1, 2020, to December 31, 2023. We then conducted scientific metric analyses using CiteSpace, VOSviewer, and the Bibliometrix package in R-software to explore the temporal and spatial distribution, author distribution, thematic categories, references, and keywords related to these records.ResultsA total of 467 valid records, comprising 418 articles and 49 reviews, were collected for analysis. Over the 4 years, the highest number of publications occurred in 2021. The United States had the most published papers, followed by China. Notably, the United States and China had the closest collaborative relationship. Harvard University ranked as the institution with the highest number of published papers. However, there appeared to be a lack of collaboration between institutions. The research hotspots related to COVID-19 in Google Trends encompassed “outbreak,” “epidemic,” “air pollution,” “internet,” “time series,” and “public interest.”ConclusionThis study provides a valuable overview of the directions in which Google Trends is being utilized for studying infectious diseases, particularly COVID-19.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-03T05:43:41Z
  DOI: 10.1177/20552076241310055
  Issue No: Vol. 11 (2025)
Transcreating BMT4me: A protocol for adapting an mobile health medication
adherence app for Spanish-speaking caregivers in pediatric hematopoietic
stem cell transplant
- Authors: Ashley Benhayoun, Mark Wang, Melissa Beauchemin, Emre Sezgin, Micah A. Skeens; Mark Wang, Melissa Beauchemin, Emre Sezgin, Micah A. Skeens
  Abstract: DIGITAL HEALTH, Volume 11, Issue , January-December 2025.
  ObjectiveHematopoietic stem cell transplant is a life-saving procedure that treats patients with various conditions by transplanting hematopoietic stem cells from bone marrow. Mobile health apps could be useful in closing the digital divide and improving health equity among Spanish-speaking caregivers of children who undergo pediatric hematopoietic stem cell transplant. This study aims to transcreate the BMT4me adherence app originally designed for English-speaking caregivers for Spanish-speaking caregivers and evaluate the feasibility and usability.MethodsThis study consists of two phases. Phase 1 transcreates the existing BMT4me app for Spanish-speaking populations. App feedback is collected from three community advisory board focus groups (n = 10; each meeting will include the same 10 community advisory board members). Groups consist of members connected to the local Spanish-speaking population and participating in the Community Engagement Program at The Ohio State University Center for Clinical and Translation Science. Phase 2 tests the feasibility and usability of the Spanish BMT4me app with child–caregiver dyads (n = 30; 15 at site 1, n = 15 at site 2) whose primary language is Spanish. This phase is mixed methods and incorporates both a qualitative approach (caregiver interviews) and quantitative measures (system usability scale). It is expected that app users in phase 2 will report above average system usability scale scores (>68%). It is also expected that>75% of families approached in phase 2 will enroll and complete the surveys in our study.ConclusionThis protocol paper details the transcreation process of the BMT4me app into a Spanish version. The findings of the study will demonstrate the level of acceptability of the Spanish BMT4me app with participants whose primary language is Spanish. As a digital health intervention for an underrepresented population that is increasingly online yet historically underserved, this app can overcome health barriers and disparities and improve overall health equity.
  Citation: DIGITAL HEALTH
  PubDate: 2025-01-03T05:40:48Z
  DOI: 10.1177/20552076241297218
  Issue No: Vol. 11 (2025)

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