Digital Biomarkers最新文献

{"title":"Predicting Subjective Recovery from Lower Limb Surgery Using Consumer Wearables.","authors":"Marta Karas,&nbsp;Nikki Marinsek,&nbsp;Jörg Goldhahn,&nbsp;Luca Foschini,&nbsp;Ernesto Ramirez,&nbsp;Ieuan Clay","doi":"10.1159/000511531","DOIUrl":"https://doi.org/10.1159/000511531","url":null,"abstract":"<p><strong>Introduction: </strong>A major challenge in the monitoring of rehabilitation is the lack of long-term individual baseline data which would enable accurate and objective assessment of functional recovery. Consumer-grade wearable devices enable the tracking of individual everyday functioning prior to illness or other medical events which necessitate the monitoring of recovery trajectories.</p><p><strong>Methods: </strong>For 1,324 individuals who underwent surgery on a lower limb, we collected their Fitbit device data of steps, heart rate, and sleep from 26 weeks before to 26 weeks after the self-reported surgery date. We identified subgroups of individuals who self-reported surgeries for bone fracture repair (<i>n</i> = 355), tendon or ligament repair/reconstruction (<i>n</i> = 773), and knee or hip joint replacement (<i>n</i> = 196). We used linear mixed models to estimate the average effect of time relative to surgery on daily activity measurements while adjusting for gender, age, and the participant-specific activity baseline. We used a sub-cohort of 127 individuals with dense wearable data who underwent tendon/ligament surgery and employed XGBoost to predict the self-reported recovery time.</p><p><strong>Results: </strong>The 1,324 study individuals were all US residents, predominantly female (84%), white or Caucasian (85%), and young to middle-aged (mean age 36.2 years). We showed that 12 weeks pre- and 26 weeks post-surgery trajectories of daily behavioral measurements (steps sum, heart rate, sleep efficiency score) can capture activity changes relative to an individual's baseline. We demonstrated that the trajectories differ across surgery types, recapitulate the documented effect of age on functional recovery, and highlight differences in relative activity change across self-reported recovery time groups. Finally, using a sub-cohort of 127 individuals, we showed that long-term recovery can be accurately predicted, on an individual level, only 1 month after surgery (AUROC 0.734, AUPRC 0.8). Furthermore, we showed that predictions are most accurate when long-term, individual baseline data are available.</p><p><strong>Discussion: </strong>Leveraging long-term, passively collected wearable data promises to enable relative assessment of individual recovery and is a first step towards data-driven intervention for individuals.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"4 Suppl 1","pages":"73-86"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000511531","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38817167","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":"\"It's Not as Simple as Just Looking at One Chart\": A Qualitative Study Exploring Clinician's Opinions on Various Visualisation Strategies to Represent Longitudinal Actigraphy Data.","authors":"Alison Keogh,&nbsp;William Johnston,&nbsp;Mitchell Ashton,&nbsp;Niladri Sett,&nbsp;Ronan Mullan,&nbsp;Seamas Donnelly,&nbsp;Jonas F Dorn,&nbsp;Francesc Calvo,&nbsp;Brian Mac Namee,&nbsp;Brian Caulfield","doi":"10.1159/000512044","DOIUrl":"https://doi.org/10.1159/000512044","url":null,"abstract":"<p><strong>Background: </strong>Data derived from wearable activity trackers may provide important clinical insights into disease progression and response to intervention, but only if clinicians can interpret it in a meaningful manner. Longitudinal activity data can be visually presented in multiple ways, but research has failed to explore how clinicians interact with and interpret these visualisations. In response, this study developed a variety of visualisations to understand whether alternative data presentation strategies can provide clinicians with meaningful insights into patient's physical activity patterns.</p><p><strong>Objective: </strong>To explore clinicians' opinions on different visualisations of actigraphy data.</p><p><strong>Methods: </strong>Four visualisations (stacked bar chart, clustered bar chart, linear heatmap and radial heatmap) were created using Matplotlib and Seaborn Python libraries. A focus group was conducted with 14 clinicians across 2 hospitals. Focus groups were audio-recorded, transcribed and analysed using inductive thematic analysis.</p><p><strong>Results: </strong>Three major themes were identified: (1) the importance of context, (2) interpreting the visualisations and (3) applying visualisations to clinical practice. Although clinicians saw the potential value in the visualisations, they expressed a need for further contextual information to gain clinical benefits from them. Allied health professionals preferred more granular, temporal information compared to doctors. Specifically, physiotherapists favoured heatmaps, whereas the remaining members of the team favoured stacked bar charts. Overall, heatmaps were considered more difficult to interpret.</p><p><strong>Conclusion: </strong>The current lack of contextual data provided by wearables hampers their use in clinical practice. Clinicians favour data presented in a familiar format and yet desire multi-faceted filtering. Future research should implement user-centred design processes to identify ways in which all clinical needs can be met, potentially using an interactive system that caters for multiple levels of granularity. Irrespective of how data is displayed, unless clinicians can apply it in a manner that best supports their role, the potential of this data cannot be fully realised.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"4 Suppl 1","pages":"87-99"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000512044","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38817168","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":"Assessment of Fatigue Using Wearable Sensors: A Pilot Study.","authors":"Hongyu Luo,&nbsp;Pierre-Alexandre Lee,&nbsp;Ieuan Clay,&nbsp;Martin Jaggi,&nbsp;Valeria De Luca","doi":"10.1159/000512166","DOIUrl":"https://doi.org/10.1159/000512166","url":null,"abstract":"<p><strong>Background: </strong>Fatigue is a broad, multifactorial concept encompassing feelings of reduced physical and mental energy levels. Fatigue strongly impacts patient health-related quality of life across a huge range of conditions, yet, to date, tools available to understand fatigue are severely limited.</p><p><strong>Methods: </strong>After using a recurrent neural network-based algorithm to impute missing time series data form a multisensor wearable device, we compared supervised and unsupervised machine learning approaches to gain insights on the relationship between self-reported non-pathological fatigue and multimodal sensor data.</p><p><strong>Results: </strong>A total of 27 healthy subjects and 405 recording days were analyzed. Recorded data included continuous multimodal wearable sensor time series on physical activity, vital signs, and other physiological parameters, and daily questionnaires on fatigue. The best results were obtained when using the causal convolutional neural network model for unsupervised representation learning of multivariate sensor data, and random forest as a classifier trained on subject-reported physical fatigue labels (weighted precision of 0.70 ± 0.03 and recall of 0.73 ± 0.03). When using manually engineered features on sensor data to train our random forest (weighted precision of 0.70 ± 0.05 and recall of 0.72 ± 0.01), both physical activity (energy expenditure, activity counts, and steps) and vital signs (heart rate, heart rate variability, and respiratory rate) were important parameters to measure. Furthermore, vital signs contributed the most as top features for predicting mental fatigue compared to physical ones. These results support the idea that fatigue is a highly multimodal concept. Analysis of clusters from sensor data highlighted a digital phenotype indicating the presence of fatigue (95% of observations) characterized by a high intensity of physical activity. Mental fatigue followed similar trends but was less predictable. Potential future directions could focus on anomaly detection assuming longer individual monitoring periods.</p><p><strong>Conclusion: </strong>Taken together, these results are the first demonstration that multimodal digital data can be used to inform, quantify, and augment subjectively captured non-pathological fatigue measures.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"4 Suppl 1","pages":"59-72"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000512166","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38817165","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":"Defining and Developing the Workforce Needed for Success in the Digital Era of Medicine.","authors":"Jennifer C Goldsack,&nbsp;Cole A Zanetti","doi":"10.1159/000512382","DOIUrl":"https://doi.org/10.1159/000512382","url":null,"abstract":"<p><p>Artificial intelligence offers the promise of transforming biomedical research and helping clinicians put the \"care\" back in healthcare. Digital medicine is on its way to becoming just plain medicine. But who will digitize how we define health and disease? And who will deploy this knowledge to improve the lives of patients that medicine - and digital medicine - exists to serve? Here we define the emerging field of digital medicine and identify the disciplines and skills needed for success. We examine the current and projected skills gaps. We also consider the impact of the culture clash that occurs at the intersection of healthcare and technology, and the lack of diversity in the workforce of both of these fields. We conclude by describing the requirements for the skills pivot needed to ensure that the digital transformation of healthcare is successful: (1) big tent thinking to recognize the critical importance of new technical skills alongside more traditional clinical disciplines, (2) the integration of clinical and technical skill sets within educational curricula, companies, and professional institutions, and (3) a commitment to diversity that goes beyond lip service.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"4 Suppl 1","pages":"136-142"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000512382","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38817111","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":"Precompetitive Consensus Building to Facilitate the Use of Digital Health Technologies to Support Parkinson Disease Drug Development through Regulatory Science.","authors":"Diane Stephenson, Robert Alexander, Varun Aggarwal, Reham Badawy, Lisa Bain, Roopal Bhatnagar, Bastiaan R Bloem, Babak Boroojerdi, Jackson Burton, Jesse M Cedarbaum, Josh Cosman, David T Dexter, Marissa Dockendorf, E Ray Dorsey, Ariel V Dowling, Luc J W Evers, Katherine Fisher, Mark Frasier, Luis Garcia-Gancedo, Jennifer C Goldsack, Derek Hill, Janice Hitchcock, Michele T Hu, Michael P Lawton, Susan J Lee, Michael Lindemann, Ken Marek, Nitin Mehrotra, Marjan J Meinders, Michael Minchik, Lauren Oliva, Klaus Romero, George Roussos, Robert Rubens, Sakshi Sadar, Joseph Scheeren, Eiichi Sengoku, Tanya Simuni, Glenn Stebbins, Kirsten I Taylor, Beatrice Yang, Neta Zach","doi":"10.1159/000512500","DOIUrl":"10.1159/000512500","url":null,"abstract":"<p><p>Innovative tools are urgently needed to accelerate the evaluation and subsequent approval of novel treatments that may slow, halt, or reverse the relentless progression of Parkinson disease (PD). Therapies that intervene early in the disease continuum are a priority for the many candidates in the drug development pipeline. There is a paucity of sensitive and objective, yet clinically interpretable, measures that can capture meaningful aspects of the disease. This poses a major challenge for the development of new therapies and is compounded by the considerable heterogeneity in clinical manifestations across patients and the fluctuating nature of many signs and symptoms of PD. Digital health technologies (DHT), such as smartphone applications, wearable sensors, and digital diaries, have the potential to address many of these gaps by enabling the objective, remote, and frequent measurement of PD signs and symptoms in natural living environments. The current climate of the COVID-19 pandemic creates a heightened sense of urgency for effective implementation of such strategies. In order for these technologies to be adopted in drug development studies, a regulatory-aligned consensus on best practices in implementing appropriate technologies, including the collection, processing, and interpretation of digital sensor data, is required. A growing number of collaborative initiatives are being launched to identify effective ways to advance the use of DHT in PD clinical trials. The Critical Path for Parkinson's Consortium of the Critical Path Institute is highlighted as a case example where stakeholders collectively engaged regulatory agencies on the effective use of DHT in PD clinical trials. Global regulatory agencies, including the US Food and Drug Administration and the European Medicines Agency, are encouraging the efficiencies of data-driven engagements through multistakeholder consortia. To this end, we review how the advancement of DHT can be most effectively achieved by aligning knowledge, expertise, and data sharing in ways that maximize efficiencies.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"4 Suppl 1","pages":"28-49"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7768153/pdf/dib-0004-0028.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38817164","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 Role of Digital Navigators in Promoting Clinical Care and Technology Integration into Practice.","authors":"Hannah Wisniewski, Tristan Gorrindo, Natali Rauseo-Ricupero, Don Hilty, John Torous","doi":"10.1159/000510144","DOIUrl":"10.1159/000510144","url":null,"abstract":"<p><p>As the role of technology expands in healthcare, so does the need to support its implementation and integration into the clinic. The concept of a new team member, the digital navigator, able to assume this role is introduced as a solution. With a digital navigator, any clinic today can take advantage of digital health and smartphone tools to augment and expand existing telehealth and face to face care. The role of a digital navigator is suitable as an entry level healthcare role, additional training for an experienced clinician, and well suited to peer specialists. To facilitate the training of digital navigators, we draw upon our experience in creating the role and across health education to introduce a 10-h curriculum designed to train digital navigators across 5 domains: (1) core smartphone skills, (2) basic technology troubleshooting, (3) app evaluation, (4) clinical terminology and data, and (5) engagement techniques. This paper outlines the curricular content, skills, and modules for this training and features a rich online supplementary Appendix with step by step instructions and resources.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"4 Suppl 1","pages":"119-135"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7768140/pdf/dib-0004-0119.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38817169","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 Roadmap to Inform Development, Validation and Approval of Digital Mobility Outcomes: The Mobilise-D Approach.","authors":"Lynn Rochester,&nbsp;Claudia Mazzà,&nbsp;Arne Mueller,&nbsp;Brian Caulfield,&nbsp;Marie McCarthy,&nbsp;Clemens Becker,&nbsp;Ram Miller,&nbsp;Paolo Piraino,&nbsp;Marco Viceconti,&nbsp;Wilhelmus P Dartee,&nbsp;Judith Garcia-Aymerich,&nbsp;Aida A Aydemir,&nbsp;Beatrix Vereijken,&nbsp;Valdo Arnera,&nbsp;Nadir Ammour,&nbsp;Michael Jackson,&nbsp;Tilo Hache,&nbsp;Ronenn Roubenoff","doi":"10.1159/000512513","DOIUrl":"https://doi.org/10.1159/000512513","url":null,"abstract":"<p><p>Health care has had to adapt rapidly to COVID-19, and this in turn has highlighted a pressing need for tools to facilitate remote visits and monitoring. Digital health technology, including body-worn devices, offers a solution using digital outcomes to measure and monitor disease status and provide outcomes meaningful to both patients and health care professionals. Remote monitoring of physical mobility is a prime example, because mobility is among the most advanced modalities that can be assessed digitally and remotely. Loss of mobility is also an important feature of many health conditions, providing a read-out of health as well as a target for intervention. Real-world, continuous digital measures of mobility (digital mobility outcomes or DMOs) provide an opportunity for novel insights into health care conditions complementing existing mobility measures. Accepted and approved DMOs are not yet widely available. The need for large collaborative efforts to tackle the critical steps to adoption is widely recognised. Mobilise-D is an example. It is a multidisciplinary consortium of 34 institutions from academia and industry funded through the European Innovative Medicines Initiative 2 Joint Undertaking. Members of Mobilise-D are collaborating to address the critical steps for DMOs to be adopted in clinical trials and ultimately health care. To achieve this, the consortium has developed a roadmap to inform the development, validation and approval of DMOs in Parkinson's disease, multiple sclerosis, chronic obstructive pulmonary disease and recovery from proximal femoral fracture. Here we aim to describe the proposed approach and provide a high-level view of the ongoing and planned work of the Mobilise-D consortium. Ultimately, Mobilise-D aims to stimulate widespread adoption of DMOs through the provision of device agnostic software, standards and robust validation in order to bring digital outcomes from concept to use in clinical trials and health care.</p>","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"4 Suppl 1","pages":"13-27"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000512513","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38817162","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 Future of Digital Health.","authors":"Ieuan Clay","doi":"10.1159/000511705","DOIUrl":"https://doi.org/10.1159/000511705","url":null,"abstract":"","PeriodicalId":11242,"journal":{"name":"Digital Biomarkers","volume":"4 Suppl 1","pages":"1-2"},"PeriodicalIF":0.0,"publicationDate":"2020-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1159/000511705","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"38816764","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 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}