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Digital Biomarkers
Number of Followers: 0  

  This is an Open Access Journal Open Access journal
ISSN (Online) 2504-110X
Published by Karger Homepage  [121 journals]
  • The Digital Outcome Measure

    • Abstract: Improving clinical outcomes remains the gold standard in advancing healthcare. Focusing on outcomes holds the potential to unite all clinical stakeholders including payers, industry, providers, and patients. Yet, the dominant ways in which outcomes are captured, provider-collected or patient-reported, have significant limitations. The emerging field of biosensors and wearables, which aims to capture many types of health data, holds promise to specifically capture outcomes while complementing existing outcome collection methods. A digital outcome measure, unlike a traditional provider-collected or patient-reported outcome measure, depends less on active patient or provider participation. Thus, digital outcome measures may be more amenable to standardization as well as greater collection consistency, frequency, and accuracy.
      Digit Biomark 2018;2:94–105
  • Illuminating the True Nature of Disease

    • Abstract:
      Digit Biomark 2018;2:90–93
  • Continuous Monitoring of Patient Mobility for 18 Months Using Inertial
           Sensors following Traumatic Knee Injury: A Case Study

    • Abstract: Continuous patient activity monitoring during rehabilitation, enabled by digital technologies, will allow the objective capture of real-world mobility and aligning treatment to each individual’s recovery trajectory in real time. To explore the feasibility and added value of such approaches, we present a case study of a 36-year-old male participant monitored continuously for activity levels and gait parameters using a waist-worn inertial sensor following a tibial plateau fracture on the right side, sustained as a result of a high-energy trauma during a sporting accident. During rehabilitation, data were collected for a period of 553 days, with #x3e; 80% daytime compliance, until the participant returned to near full mobility. The participant completed a daily diary with the annotation of major events (falls, near falls, cycling periods, or physiotherapy sessions) and key dates in the patient’s recovery, including medical interventions, transitioning off crutches, and returning to work. We demonstrate the feasibility of collecting, storing, and mining of continuous digital mobility data and show that such data can detect changes in mobility and provide insights into long-term rehabilitation. We make both raw data and annotations available as a resource with the aspiration that further methods and insights will be built on this initial exploration of added value and continue to demonstrate that continuous monitoring can be deployed to aid rehabilitation.
      Digit Biomark 2018;2:79–89
  • Validation of a Wearable Sensor for Measuring Running Biomechanics

    • Abstract: Background: Running biomechanics have traditionally been analyzed in laboratory settings, but this may not reflect natural running gait. Wearable sensors may offer an alternative. Methods: A concurrent validation study to determine agreement between the RunScribeTM wearable sensor (triaxial accelerometer and gyroscope) and the 3D motion capture system was conducted. Twelve injury-free participants (6 males, 6 females; age = 23.1 ± 5.5 years, weekly mileage = 16.1 ± 9.3) ran 1.5 miles on a treadmill. Ten consecutive strides from each limb were collected, and the mean values were analyzed. Pronation excursion, maximum pronation velocity, contact time, and cycle time were compared between measurement platforms using intraclass correlation coefficients (ICC) and Bland-Altman analyses. Results: Excellent ICC estimates were found for maximum pronation velocity, contact time, and cycle time. Pronation excursion demonstrated fair ICC estimates. The mean differences between platforms were small with limits of agreement clustered around zero, except for contact time measures which were consistently higher with the RunScribe compared to the camera-based system. Conclusion: Our study revealed that the RunScribe wearable device showed good to excellent concurrent validity for maximum pronation velocity, contact time, and cycle time; however, direct comparisons or results between the two platforms should not be used.
      Digit Biomark 2018;2:74–78
  • The Best Digital Biomarkers Papers of 2017

    • Abstract: The use and evaluation of digital biomarkers, objective and quantifiable measures of biology, and health collected through digital devices is growing rapidly. To highlight some of the most promising work in the field, we have compiled a list of the top digital biomarkers papers from the past year. Eligible papers reported on original research that evaluated a digital sensor (e.g., smartphone, wearable sensor, implantable device) in humans and was published in a peer-reviewed journal in 2017. Nominations were solicited from the editorial board of Digital Biomarkers and supplemented by papers the editorial team identified from Web of Science, Google Scholar, and PubMed. The editorial board then selected up to ten papers to be recognized among 28 nominations. Here, we present all of the nominated papers and profile the eight that received the most votes. The top eight papers evaluated 1,290 individuals with digital pills, smartwatches, wearable devices, and electronic inhalers in disease states ranging from dementia to diabetes and from Parkinson disease to pain.
      Digit Biomark 2018;2:64–73
  • Free-Living Physical Activity Monitoring in Adult US Patients with
           Multiple Sclerosis Using a Consumer Wearable Device

    • Abstract: Introduction: Wearable devices have been used to characterize physical activity in multiple sclerosis (MS). The objectives of this study were to advance the literature on the utility of free-living physical activity tracking from secondary analyses of a pilot study in MS patients. Method: The original observational study was conducted in participants with MS at PatientsLikeMe (, an online network of patients with chronic diseases. Participants completed a baseline self-assessment, and received a Fitbit OneTM wearable device with instructions to upload data. Eligible participants (1) self-reported MS, (2) logged on to the PatientsLikeMe website 90 days prior to enrollment, and (3) consented to participate electronically. Participants (1) #x3c; 18 years, (2) living outside the United States, and (3) requiring wheelchair assistance for most daily activities were excluded. The secondary analyses were limited to participants with complete data on MS type, disease duration, and Multiple Sclerosis Rating Scale (MSRS) and at least 7 days of wearable data. Step count was used as a measure of physical activity. Results: The analysis cohort of 114 participants uploaded a mean of 20.1 days of wearable data over the 23-day study (87% adherence); participants averaged 4,393 steps per day. The mean age of participants was 52 years, predominantly female (75%), relapsing-remitting type (79%), with mean disease duration of 16 years. Mean MSRS score within 30-day of baseline was 32; 72% reported mild-moderate walking disability. The reliability of step count measured by intraclass correlation was 0.55 for a single day, ≥0.7 for 2-day average, and ≥0.9 for 7-day average. After controlling for covariates, self-reported disease severity (MSRS quartile) was an independent predictor of step count (p #x3c; 0.001). Least square means (LS means) for participants that were least disabled (lowest quartile) was 5,937 steps, which was significantly higher than participants in the second, third, and fourth quartiles (4,570, 3,490, and 3,272, respectively). Similarly, LS means of participants with no ambulatory disability (measured by MSRS walk component) was 6,931 steps, significantly higher than participants with greater disability (4,743, 4,394, 2,727 steps for symptomatic, mild, and moderate disability, respectively, p #x3c; 0.001). Discussion: Using an interactive platform, this study captured free-living mobility data in MS patients. Important metrics such as the use of a minimum of 2-day estimates and self-reported disability were found to be robust indicators and correlates, respectively, of participant activity levels. Further triangulation of such metrics may reduce the burden on patients, clinicians, and researchers when monitoring clinical status.
      Digit Biomark 2018;2:47–63
  • Harnessing the Digital Exhaust: Incorporating Wellness into the Pharma

    • Abstract: The increasing availability of devices capable of tracking biomarkers presents major opportunities in contemporary healthcare. Herein we advocate a new role for the pharmaceutical industry to capitalize on these opportunities and, in doing so, incorporate wellness and patient engagement programs into their standard business models. Medical-grade decision-making using diagnostic, prognostic, and monitoring biomarkers will require coordinated approaches between the pharmaceutical and technology industries and the careful design of longitudinal clinical studies to validate their efficacy. These studies will also require data capture, archiving, curating, and sharing on a previously unprecedented scale, and raise additional concerns with regard to data security and ownership. Concurrently, systems-based approaches to the capture and interpretation of a new class of digital biomarkers are emerging, and they hold promise for heightened levels of patient engagement and remote sensing. Collectively, if these new opportunities are approached within the context of the patient-provider ecosystem, major repositioning of the pharmaceutical industry may be possible in the near future.
      Digit Biomark 2018;2:31–46
  • Use of Mobile Devices to Measure Outcomes in Clinical Research,
           2010–2016: A Systematic Literature Review

    • Abstract: Background: The use of mobile devices in clinical research has advanced substantially in recent years due to the rapid pace of technology development. With an overall aim of informing the future use of mobile devices in interventional clinical research to measure primary outcomes, we conducted a systematic review of the use of and clinical outcomes measured by mobile devices (mobile outcomes) in observational and interventional clinical research. Method: We conducted a PubMed search using a range of search terms to retrieve peer-reviewed articles on clinical research published between January 2010 and May 2016 in which mobile devices were used to measure study outcomes. We screened each publication for specific inclusion and exclusion criteria. We then identified and qualitatively summarized the use of mobile outcome assessments in clinical research, including the type and design of the study, therapeutic focus, type of mobile device(s) used, and specific mobile outcomes reported. Results: The search retrieved 2,530 potential articles of interest. After screening, 88 publications remained. Twenty-five percent of the publications (n = 22) described mobile outcomes used in interventional research, and the rest (n = 66) described observational clinical research. Thirteen therapeutic areas were represented. Five categories of mobile devices were identified: (1) inertial sensors, (2) biosensors, (3) pressure sensors and walkways, (4) medication adherence monitors, and (5) location monitors; inertial sensors/accelerometers were most common (reported in 86% of the publications). Among the variety of mobile outcomes, various assessments of physical activity were most common (reported in 74% of the publications). Other mobile outcomes included assessments of sleep, mobility, and pill adherence, as well as biomarkers assessed using a mobile device, including cardiac measures, glucose, gastric reflux, respiratory measures, and intensity of head-related injury. Conclusion: Mobile devices are being widely used in clinical research to assess outcomes, although their use in interventional research to assess therapeutic effectiveness is limited. For mobile devices to be used more frequently in pivotal interventional research – such as trials informing regulatory decision-making – more focus should be placed on: (1) consolidating the evidence supporting the clinical meaningfulness of specific mobile outcomes, and (2) standardizing the use of mobile devices in clinical research to measure specific mobile outcomes (e.g., data capture frequencies, placement of device). To that aim, this manuscript offers a broad overview of the various mobile outcome assessments currently used in observational and interventional research, and categorizes and consolidates this information for researchers interested in using mobile devices to assess outcomes in interventional research.
      Digit Biomark 2018;2:11–30
  • Assessment of Postural Sway in Individuals with Multiple Sclerosis Using a
           Novel Wearable Inertial Sensor

    • Abstract: Balance impairment is common in individuals with multiple sclerosis (MS). However, objective assessment of balance usually requires clinical expertise and/or the use of expensive and obtrusive measuring equipment. These barriers to the objective assessment of balance may be overcome with the development of a lightweight inertial sensor system. In this study, we examined the concurrent validity of a novel wireless, skin-mounted inertial sensor system (BioStamp®, MC10 Inc.) to measure postural sway in individuals with MS by comparing measurement agreement between this novel sensor and gold standard measurement tools (force plate and externally validated inertial sensor). A total of 39 individuals with MS and 15 healthy controls participated in the study. Participants with MS were divided into groups based on the amount of impairment (MSMild: EDSS 2–4, n = 19; MSSevere: EDSS ≥6, n = 20). The balance assessment consisted of two 30-s quiet standing trials in each of three conditions: eyes open/firm surface, eyes closed/firm surface, and eyes open/foam surface. For each trial, postural sway was recorded with a force plate (Bertec) and simultaneously using two accelerometers (BioStamp and Xsens) mounted on the participant’s posterior trunk at L5. Sway metrics (sway area, sway path length, root mean square amplitude, mean velocity, JERK, and total power) were derived to compare the measurement agreement among the measurement devices. Excellent agreement (intraclass correlation coefficients #x3e;0.9) between sway metrics derived from the BioStamp and the MTx sensors were observed across all conditions and groups. Good to excellent correlations (r #x3e;0.7) between devices were observed in all sway metrics and conditions. Additionally, the acceleration sway metrics were nearly as effective as the force plate sway metrics in differentiating individuals with poor balance from healthy controls. Overall, the BioStamp sensor is a valid and objective measurement tool for postural sway assessment. This novel, lightweight and portable sensor may offer unique advantages in tracking patient’s postural performance.
      Digit Biomark 2018;2:1–10
  • A Pilot Study Evaluating the Physiological Parameters of
           Performance-Induced Stress in Undergraduate Music Students

    • Abstract: Music performance anxiety (MPA) is a specific condition for musicians. Although it can have a negative influence on their music careers, little attention is paid to this phenomenon both in the professional environment and in stress research. In the current pilot study, insight was gained into the physiology of the autonomic stress response related to anxiety in musicians when performing on stage by using a wearable biosensor patch for registration of a range of physiological parameters. Also, the validity of two different psychometric questionnaires in objectifying the stress response on stage to predict the individual stress response was explored. The autonomic physiological parameters (heart rate, respiratory rate, skin temperature) of 11 violists and violinists were collected while performing on stage and in resting state using the VitalConnect HealthPatch®. In addition, scores on validated questionnaires in research on MPA (State Anxiety Inventory, Kenny Music Performance Anxiety Inventory, Short Form Health Survey) were collected in order to try to objectify the magnitude of the subjective level of both MPA and experienced stress. The registration of the autonomic parameters showed a significant increase in heart rate, respiratory rate, and stress level from resting state measurements during stage performance. Analysis of heart rate variability showed a shift from indices of parasympathetic nervous system activity during baseline measurements towards indices of sympathetic nervous system activity during stress measurements. Surprisingly, none of the questionnaires was correlated to the physiological stress parameters on stage. In conclusion, the wearable biosensor patch proved to be an adequate tool to assess physiological stress parameters on stage. The different questionnaires did not contribute to the prediction of its occurrence in a group of musicians.
      Digit Biomark 2017;1:118–125
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
Tel: +00 44 (0)131 4513762
Fax: +00 44 (0)131 4513327
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