Digital Biomarkers最新文献

{"title":"The Path Forward for Digital Measures: Suppressing the Desire to Compare Apples and Pineapples.","authors":"Carrie R Houts,&nbsp;Bray Patrick-Lake,&nbsp;Ieuan Clay,&nbsp;R J Wirth","doi":"10.1159/000511586","DOIUrl":"https://doi.org/10.1159/000511586","url":null,"abstract":"<p><p>Digital measures are becoming more prevalent in clinical development. Methods for robust evaluation are increasingly well defined, yet the primary barrier for digital measures to transition beyond exploratory usage often relies on a comparison to the existing standards. This article focuses on how researchers should approach the complex issue of comparing across assessment modalities. We discuss comparisons of subjective versus objective assessments, or performance-based versus behavioral measures, and we pay particular attention to the situation where the expected association may be poor or nonlinear. We propose that, rather than seeking to replace the standard, research should focus on a structured understanding of how the new measure augments established assessments, with the ultimate goal of developing a more complete understanding of what is meaningful to patients.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"4 Suppl 1","pages":"3-12"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000511586","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38816766","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Collaborative Aging Research Using Technology Initiative: An Open, Sharable, Technology-Agnostic Platform for the Research Community.","authors":"Zachary Beattie,&nbsp;Lyndsey M Miller,&nbsp;Carlos Almirola,&nbsp;Wan-Tai M Au-Yeung,&nbsp;Hannah Bernard,&nbsp;Kevin E Cosgrove,&nbsp;Hiroko H Dodge,&nbsp;Charlene J Gamboa,&nbsp;Ona Golonka,&nbsp;Sarah Gothard,&nbsp;Sam Harbison,&nbsp;Stephanie Irish,&nbsp;Judith Kornfeld,&nbsp;Jonathan Lee,&nbsp;Jennifer Marcoe,&nbsp;Nora C Mattek,&nbsp;Charlie Quinn,&nbsp;Christina Reynolds,&nbsp;Thomas Riley,&nbsp;Nathaniel Rodrigues,&nbsp;Nicole Sharma,&nbsp;Mary Alice Siqueland,&nbsp;Neil W Thomas,&nbsp;Timothy Truty,&nbsp;Rachel Wall,&nbsp;Katherine Wild,&nbsp;Chao-Yi Wu,&nbsp;Jason Karlawish,&nbsp;Nina B Silverberg,&nbsp;Lisa L Barnes,&nbsp;Sara Czaja,&nbsp;Lisa C Silbert,&nbsp;Jeffrey Kaye","doi":"10.1159/000512208","DOIUrl":"https://doi.org/10.1159/000512208","url":null,"abstract":"<p><strong>Introduction: </strong>Future digital health research hinges on methodologies to conduct remote clinical assessments and in-home monitoring. The Collaborative Aging Research Using Technology (CART) initiative was introduced to establish a digital technology research platform that could widely assess activity in the homes of diverse cohorts of older adults and detect meaningful change longitudinally. This paper reports on the built end-to-end design of the CART platform, its functionality, and the resulting research capabilities.</p><p><strong>Methods: </strong>CART platform development followed a principled design process aiming for scalability, use case flexibility, longevity, and data privacy protection while allowing sharability. The platform, comprising ambient technology, wearables, and other sensors, was deployed in participants' homes to provide continuous, long-term (months to years), and ecologically valid data. Data gathered from CART homes were sent securely to a research server for analysis and future data sharing.</p><p><strong>Results: </strong>The CART system was created, iteratively tested, and deployed to 232 homes representing four diverse cohorts (African American, Latinx, low-income, and predominantly rural-residing veterans) of older adults (<i>n</i> = 301) across the USA. Multiple measurements of wellness such as cognition (e.g., mean daily computer use time = 160-169 min), physical mobility (e.g., mean daily transitions between rooms = 96-155), sleep (e.g., mean nightly sleep duration = 6.3-7.4 h), and level of social engagement (e.g., reports of overnight visitors = 15-45%) were collected across cohorts.</p><p><strong>Conclusion: </strong>The CART initiative resulted in a minimally obtrusive digital health-enabled system that met the design principles while allowing for data capture over extended periods and can be widely used by the research community. The ability to monitor and manage health digitally within the homes of older adults is an important alternative to in-person assessments in many research contexts. Further advances will come with wider, shared use of the CART system in additional settings, within different disease contexts, and by diverse research teams.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"4 Suppl 1","pages":"100-118"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000512208","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38817110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Evaluation of Speech-Based Digital Biomarkers: Review and Recommendations.","authors":"Jessica Robin,&nbsp;John E Harrison,&nbsp;Liam D Kaufman,&nbsp;Frank Rudzicz,&nbsp;William Simpson,&nbsp;Maria Yancheva","doi":"10.1159/000510820","DOIUrl":"https://doi.org/10.1159/000510820","url":null,"abstract":"<p><p>Speech represents a promising novel biomarker by providing a window into brain health, as shown by its disruption in various neurological and psychiatric diseases. As with many novel digital biomarkers, however, rigorous evaluation is currently lacking and is required for these measures to be used effectively and safely. This paper outlines and provides examples from the literature of evaluation steps for speech-based digital biomarkers, based on the recent V3 framework (Goldsack et al., 2020). The V3 framework describes 3 components of evaluation for digital biomarkers: verification, analytical validation, and clinical validation. Verification includes assessing the quality of speech recordings and comparing the effects of hardware and recording conditions on the integrity of the recordings. Analytical validation includes checking the accuracy and reliability of data processing and computed measures, including understanding test-retest reliability, demographic variability, and comparing measures to reference standards. Clinical validity involves verifying the correspondence of a measure to clinical outcomes which can include diagnosis, disease progression, or response to treatment. For each of these sections, we provide recommendations for the types of evaluation necessary for speech-based biomarkers and review published examples. The examples in this paper focus on speech-based biomarkers, but they can be used as a template for digital biomarker development more generally.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"4 3","pages":"99-108"},"PeriodicalIF":0.0,"publicationDate":"2020-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000510820","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38315669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A COVID-19 Multipurpose Platform.","authors":"Nikos Petrellis","doi":"10.1159/000511704","DOIUrl":"10.1159/000511704","url":null,"abstract":"<p><strong>Background: </strong>Contactless symptom tracking is essential for the diagnosis of COVID-19 cases that need hospitalization. Indications from sensors and user descriptions have to be combined in order to make the right decisions.</p><p><strong>Methods: </strong>The proposed multipurpose platform Coronario combines sensory information from different sources for a valid diagnosis following a dynamically adaptable protocol. The information exchanged can also be exploited for the advancement of research on COVID-19. The platform consists of mobile and desktop applications, sensor infrastructure, and cloud services. It may be used by patients in pre- and post-hospitalization stages, vulnerable populations, medical practitioners, and researchers.</p><p><strong>Results: </strong>The supported audio processing is used to demonstrate how the Coronario platform can assist research on the nature of COVID-19. Cough sounds are classified as a case study, with 90% accuracy.</p><p><strong>Discussion/conclusions: </strong>The dynamic adaptation to new medical protocols is one of the main advantages of the developed platform, making it particularly useful for several target groups of patients that require different screening methods. A medical protocol determines the structure of the questionnaires, the medical sensor sampling strategy and, the alert rules.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"4 3","pages":"89-98"},"PeriodicalIF":0.0,"publicationDate":"2020-10-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7588689/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38588391","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Thorough Examination of Morning Activity Patterns in Adults with Arthritis and Healthy Controls Using Actigraphy Data.","authors":"Alison Keogh,&nbsp;Niladri Sett,&nbsp;Seamas Donnelly,&nbsp;Ronan Mullan,&nbsp;Diana Gheta,&nbsp;Martina Maher-Donnelly,&nbsp;Vittorio Illiano,&nbsp;Francesc Calvo,&nbsp;Jonas F Dorn,&nbsp;Brian Mac Namee,&nbsp;Brian Caulfield","doi":"10.1159/000509724","DOIUrl":"https://doi.org/10.1159/000509724","url":null,"abstract":"<p><strong>Background: </strong>Wearable sensors allow researchers to remotely capture digital health data, including physical activity, which may identify digital biomarkers to differentiate healthy and clinical cohorts. To date, research has focused on high-level data (e.g., overall step counts) which may limit our insights to <i>whether</i> people move differently, rather than <i>how</i> they move differently.</p><p><strong>Objective: </strong>This study therefore aimed to use actigraphy data to thoroughly examine activity patterns during the first hours following waking in arthritis patients (<i>n</i> = 45) and healthy controls (<i>n</i> = 30).</p><p><strong>Methods: </strong>Participants wore an Actigraph GT9X Link for 28 days. Activity counts were analysed and compared over varying epochs, ranging from 15 min to 4 h, starting with waking in the morning. The sum, and a measure of rate of change of cumulative activity in the period immediately after waking (area under the curve [AUC]) for each time period, was calculated for each participant, each day, and individual and group means were calculated. Two-tailed independent <i>t</i> tests determined differences between the groups.</p><p><strong>Results: </strong>No differences were seen for summed activity counts across any time period studied. However, differences were noted in the AUC analysis for the discrete measures of relative activity. Specifically, within the first 15, 30, 45, and 60 min following waking, the AUC for activity counts was significantly higher in arthritis patients compared to controls, particularly at the 30 min period (<i>t</i> = -4.24, <i>p</i> = 0.0002). Thus, while both cohorts moved the same amount, the way in which they moved was different.</p><p><strong>Conclusion: </strong>This study is the first to show that a detailed analysis of actigraphy variables could identify activity pattern changes associated with arthritis, where the high-level daily summaries did not. Results suggest discrete variables derived from raw data may be useful to help identify clinical cohorts and should be explored further to determine if they may be effective clinical biomarkers.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"4 3","pages":"78-88"},"PeriodicalIF":0.0,"publicationDate":"2020-09-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000509724","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38591452","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Digital Measures That Matter to Patients: A Framework to Guide the Selection and Development of Digital Measures of Health.","authors":"Christine Manta,&nbsp;Bray Patrick-Lake,&nbsp;Jennifer C Goldsack","doi":"10.1159/000509725","DOIUrl":"https://doi.org/10.1159/000509725","url":null,"abstract":"<p><strong>Background: </strong>With the rise of connected sensor technologies, there are seemingly endless possibilities for new ways to measure health. These technologies offer researchers and clinicians opportunities to go beyond brief snapshots of data captured by traditional in-clinic assessments, to redefine health and disease. Given the myriad opportunities for measurement, how do research or clinical teams know what they <i>should</i> be measuring? Patient engagement, early and often, is paramount to thoughtfully selecting what is most important. Regulators encourage stakeholders to have a patient focus but actionable steps for continuous engagement are not well defined. Without patient-focused measurement, stakeholders risk entrenching digital versions of poor traditional assessments and proliferating low-value tools that are ineffective, burdensome, and reduce both quality and efficiency in clinical care and research.</p><p><strong>Summary: </strong>This article synthesizes and defines a sequential framework of core principles for selecting and developing measurements in research and clinical care that are meaningful for patients. We propose next steps to drive forward the science of high-quality patient engagement in support of measures of health that matter in the era of digital medicine.</p><p><strong>Key messages: </strong>All measures of health should be meaningful, regardless of the product's regulatory classification, type of measure, or context of use. To evaluate meaningfulness of signals derived from digital sensors, the following four-level framework is useful: Meaningful Aspect of Health, Concept of Interest, Outcome to be measured, and Endpoint (exclusive to research). Incorporating patient input is a dynamic process that requires more than a single, transactional touch point but rather should be conducted continuously throughout the measurement selection process. We recommend that developers, clinicians, and researchers reevaluate processes for more continuous patient engagement in the development, deployment, and interpretation of digital measures of health.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"4 3","pages":"69-77"},"PeriodicalIF":0.0,"publicationDate":"2020-09-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000509725","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38511099","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Device- and Analytics-Agnostic Infrastructure for Continuous Inpatient Monitoring: A Technical Note.","authors":"Noé Brasier,&nbsp;Lukas Geissmann,&nbsp;Miro Käch,&nbsp;Markus Mutke,&nbsp;Bianca Hoelz,&nbsp;Fiorangelo De Ieso,&nbsp;Jens Eckstein","doi":"10.1159/000509279","DOIUrl":"https://doi.org/10.1159/000509279","url":null,"abstract":"<p><p>The internet of healthcare things aims at connecting biosensors, clinical information systems and electronic health dossiers. The resulting data expands traditionally available diagnostics with digital biomarkers. In this technical note, we report the implementation and pilot operation of a device- and analytics-agnostic automated monitoring platform for in-house patients at hospitals. Any available sensor, as well as any analytics tool can be integrated if the application programming interface is made available. The platform consists of a network of Bluetooth gateways communicating via the hospital's secure Wi-Fi network, a server application (Device Hub) and associated databases. Already existing access points or low-cost hardware can be used to run the gateway software. The platform can be extended to a remote patient monitoring solution to close the gap between in-house treatments and follow-up patient monitoring.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"4 2","pages":"62-68"},"PeriodicalIF":0.0,"publicationDate":"2020-08-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000509279","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38511517","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Book Review","authors":"R. Kapur","doi":"10.1159/000508200","DOIUrl":"https://doi.org/10.1159/000508200","url":null,"abstract":"","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"4 1","pages":"60 - 61"},"PeriodicalIF":0.0,"publicationDate":"2020-07-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000508200","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48081621","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Real-Life Multimarker Monitoring in Patients with Heart Failure: Continuous Remote Monitoring of Mobility and Patient-Reported Outcomes as Digital End Points in Future Heart-Failure Trials.","authors":"Frank Kramer,&nbsp;Javed Butler,&nbsp;Sanjiv J Shah,&nbsp;Christian Jung,&nbsp;Savina Nodari,&nbsp;Stephan Rosenkranz,&nbsp;Michele Senni,&nbsp;Luke Bamber,&nbsp;Stephan Cichos,&nbsp;Chrysanthi Dori,&nbsp;Toeresin Karakoyun,&nbsp;Gabriele Jenny Köhler,&nbsp;Kinjal Patel,&nbsp;Paolo Piraino,&nbsp;Thomas Viethen,&nbsp;Praneeth Chennuru,&nbsp;Ayse Paydar,&nbsp;Jason Sims,&nbsp;Richard Clark,&nbsp;Rob van Lummel,&nbsp;Alexandra Müller,&nbsp;Chad Gwaltney,&nbsp;Salko Smajlovic,&nbsp;Hans-Dirk Düngen,&nbsp;Wilfried Dinh","doi":"10.1159/000507696","DOIUrl":"https://doi.org/10.1159/000507696","url":null,"abstract":"<p><strong>Aims: </strong>Heart failure (HF) affects approximately 26 million people worldwide. With an aging global population, innovative approaches to HF evaluation and management are needed to cope with the worsening HF epidemic. The aim of the Real-Life Multimarker Monitoring in Patients with Heart Failure (REALIsM-HF) study (NCT03507439) is to evaluate a composite instrument comprising remote, real-time, activity-monitoring devices combined with daily electronic patient-reported outcome (ePRO) items in patients who have been hospitalized for HF and are undergoing standard HF assessment (e.g., 6-min walking distance [6MWD], blood biomarkers, Kansas City Cardiomyopathy Questionnaire [KCCQ], and echocardiography).</p><p><strong>Methods: </strong>REALIsM-HF is an ongoing, 12-week, observational study enrolling 80-100 patients aged ≥45 years with HF with preserved ejection fraction (HFpEF; EF ≥45%) or reduced EF (HFrEF; EF ≤35%). Statistical analyses will include examining the association between data from wearables (the AVIVO^© mobile patient management patch or VitalPatch^© biosensor, and the DynaPort MoveMonitor^©), daily ePROs, and conventional HF metrics (e.g., serum/plasma biomarkers, 6MWD, KCCQ, and echocardiographic parameters). The feasibility of and patient compliance with at-home devices will be documented, and the data captured for the purpose of establishing reference values in patients with HFpEF or HFrEF will be summarized.</p><p><strong>Conclusions: </strong>The REALIsM-HF study is to evaluate the longitudinal daily activity profiles of patients with HF and correlate these with changes in serum/plasma biomarker profiles, symptoms, quality of life, and cardiac function and morphology to inform the use of wearable activity monitors for developing novel therapies and managing patients.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"4 2","pages":"45-59"},"PeriodicalIF":0.0,"publicationDate":"2020-06-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000507696","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38511097","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing Smartphone-Based Objective Assessments of Physical Function in Rheumatoid Arthritis Patients: The PARADE Study.","authors":"Valentin Hamy,&nbsp;Luis Garcia-Gancedo,&nbsp;Andrew Pollard,&nbsp;Anniek Myatt,&nbsp;Jingshu Liu,&nbsp;Andrew Howland,&nbsp;Philip Beineke,&nbsp;Emilia Quattrocchi,&nbsp;Rachel Williams,&nbsp;Michelle Crouthamel","doi":"10.1159/000506860","DOIUrl":"https://doi.org/10.1159/000506860","url":null,"abstract":"<p><strong>Background: </strong>Digital biomarkers that measure physical activity and mobility are of great interest in the assessment of chronic diseases such as rheumatoid arthritis, as it provides insights on patients' quality of life that can be reliably compared across a whole population.</p><p><strong>Objective: </strong>To investigate the feasibility of analyzing iPhone sensor data collected remotely by means of a mobile software application in order to derive meaningful information on functional ability in rheumatoid arthritis patients.</p><p><strong>Methods: </strong>Two objective, active tasks were made available to the study participants: a wrist joint motion test and a walk test, both performed remotely and without any medical supervision. During these tasks, gyroscope and accelerometer time-series data were captured. Processing schemes were developed using machine learning techniques such as logistic regression as well as explicitly programmed algorithms to assess data quality in both tasks. Motion-specific features including wrist joint range of motion (ROM) in flexion-extension (for the wrist motion test) and gait parameters (for the walk test) were extracted from high quality data and compared with subjective pain and mobility parameters, separately captured via the application.</p><p><strong>Results: </strong>Out of 646 wrist joint motion samples collected, 289 (45%) were high quality. Data collected for the walk test included 2,583 samples (through 867 executions of the test) from which 651 (25%) were high quality. Further analysis of high-quality data highlighted links between reduced mobility and increased symptom severity. ANOVA testing showed statistically significant differences in wrist joint ROM between groups with light-moderate (220 participants) versus severe (36 participants) wrist pain (<i>p</i> < 0.001) as well as in average step times between groups with slight versus moderate problems walking about (<i>p</i> < 0.03).</p><p><strong>Conclusion: </strong>These findings demonstrate the potential to capture and quantify meaningful objective clinical information remotely using iPhone sensors and represent an early step towards the development of patient-centric digital endpoints for clinical trials in rheumatoid arthritis.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":" ","pages":"26-43"},"PeriodicalIF":0.0,"publicationDate":"2020-04-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000506860","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38023748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}