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Abstract Rationale Despite its effectiveness at improving outcomes in patients with COPD, pulmonary rehabilitation (PR) attrition rates are high, often exceeding 50%. Fully remote programs, like home-based PR (HBPR), face additional challenges engaging patients in real-world settings. With the growing adoption of wearables, sensor-derived behavioral measures, like sleep patterns, may identify patients most likely to benefit from remote PR. This study evaluated whether incorporating baseline sleep measures from a wrist-worn activity monitor in machine learning (ML) models improved the prediction of 12-week engagement with HBPR in patients with COPD. Methods Among participants (n = 124), the following sleep measures were collected for 1 week before HBPR: sleep duration (min), standard deviation of sleep duration, wake-after-sleep-onset (WASO) (min), standard deviation of WASO, number of awakenings, length of awakenings (min), activity counts, movement index, movement fragmentation index, and sleep fragmentation index. Sleep measures were processed (1) using a previously validated Tudor-Locke algorithm and (2) applying partial least squares-discriminant analysis (PLS-DA) to generate a single latent variable (Composite Sleep Health Score) for inclusion in models. Engagement was defined as completion of one or more recommended activities per week for the full program duration (12 weeks). To determine if the inclusion of objective sleep measures improved the prediction of HBPR engagement, nested model comparisons for parametric (logistic regression and SVM) and non-parametric (decision tree and naïve bayes) ML models were performed. A sensitivity analysis (SA) replaced the Composite Sleep Health Score with sleep regularity (duration and WASO) measures only. Results In models adjusted for age, sex, Charlson Comorbidity Index, current smoker status, modified Medical Research Council (mMRC), and forced expiratory volume in 1 second (FEV1), the inclusion of the Composite Sleep Health Score significantly improved the prediction of 12-week engagement only in SVM models (AUC 0.755; p = 0.007). Specificity (18.2%) and accuracy (68.5%) also improved by 27.3% and 7.3%, respectively. The Composite Sleep Health Score and mMRC scores ranked most frequently as the variables of highest importance. Identical trends were observed in the SA. Conclusion Although preliminary, these findings support additional investigation into the use of wearable-derived sleep measures to improve screening for HBPR eligibility, identifying patients who will clinically benefit from fully remote PR. Building on these primary findings, future researchers should carefully select variables for model inclusion and initiate new hypotheses elucidating the link between objective sleep measures and HBPR outcomes in COPD patients. This abstract is funded by: NIH R56 HL173214, NIH R01 HL140486, and the Robert D. and Patricia E. Kern Center for the Science of Health Delivery

Introduction Despite its demonstrated effectiveness at improving outcomes, pulmonary rehabilitation (PR) for chronic obstructive pulmonary disease (COPD) is underutilized. Sensor-generated data from wearable devices have the potential to mitigate this challenge by generating digital endpoints that provide insights into patient behaviors at home; however, there is no consensus on how to measure home-based PR (HBPR) outcomes with these tools. This review aims to describe (1) the most frequent digital endpoints used in HBPR studies and (2) the devices used to capture these endpoints, summarizing gaps in their applications to HBPR for COPD patients. Methods We completed a scoping review using the PRISMA checklist across databases (Web of Science, Scopus, and OVID) from January 1, 2005 to June 1, 2025. We included peer-reviewed articles on HBPR for COPD, excluding reviews, commentaries/editorials, poster abstracts, and conference proceedings. Eligible articles included cohort studies and clinical trials of adult patients (age ≥ 18 years) with COPD participating in HBPR that include one or more digital endpoints. Results Among eligible articles ( n = 218), 13 (6.0%) met inclusion criteria, the majority of which were published after 2020 (61.5%). Most studies enrolled fewer than 100 COPD patients (76.9%) for an average monitoring period of 12.5 weeks. Activity trackers were the most commonly used device (46.2%) to capture data. The most frequently used digital endpoints were step count (84.6%), time spent active (38.5%), and time spent sedentary (30.8%). Two study designs were used: randomized controlled trial (76.9%) and observational cohort. Study designs were heterogenous with more than one-third (38.5%) presenting a lack of statistically significant results. Discussion Although we identified analogous digital endpoints in some studies, dissimilar methods and study designs remain barriers to synthesizing results generated from HBPR programs for COPD. Wearable devices have the potential to build novel PR models, but more work is needed to translate real-world data into clinically meaningful measures. Future research should elucidate which participants would benefit most from and complete HBPR to build an evidence base for the validation of HBPR-relevant digital endpoints, particularly those derived from less common sources like cardiovascular and sleep measures.

To examine the association between post-stroke depression (PSD) and macrostructural and microstructural brain measures, and to explore whether changes in accelerometer-measured physical activity (PA) are associated with PSD, we conducted an exploratory study in UK Biobank with dementia-free participants diagnosed with at least one prior stroke. Eligible participants (n = 1186) completed an MRI scan. Depression was classified based on positive depression screening scores (PHQ-2 ≥ 3). Multivariate linear regression models assessed the relationships between depression and structural and diffusion measures generated from brain MRI scans. Logistic regression models were used to examine the relationship between accelerometer-measured daily PA and future depression (n = 367). Depression was positively associated with total white matter hyperintensities (WMHs) volume (standardized β [95% CI]—0.1339 [0.012, 0.256]; FDR-adjusted p-value—0.039), periventricular WMHs volume (standardized β [95% CI]—0.1351 [0.020, 0.250]; FDR-adjusted p-value—0.027), and reduced MD for commissural fibers (standardized β [95% CI]—−0.139 [−0.255, −0.024]; adjusted p-value—0.045). The odds of depression decreased by 0.3% for each daily minute spent in objectively measured light PA, while each minute spent in sleep from midnight to 6:00 AM was associated with a 0.9% decrease in the odds of depression. This early-stage analysis using a population cohort offers a scientific rationale for researchers using multimodal data sources to investigate the heterogenous nature of PSD and, potentially, identify stroke patients at risk of poor outcomes.

Background: Although no tests are currently available to predict post-stroke depression (PSD), a risk factor for diminished quality of life and stroke-related death, emerging evidence suggests that ischemic stroke (IS) may induce behavioral changes by damaging connections throughout the brain rather than via localized injury. Hypothesis: We sought to determine if active (surveys) and passive (accelerometer, GPS, and other sensors) smartphone data could quantify exploratory behavioral proxies of PSD. Methods: Thirty-four adults with a recent IS (n = 12) or transient ischemic attack (TIA) (n = 22) diagnosis were followed for 3 months. Participants used the Beiwe app to complete a weekly Patient Health Questionnaire-8 (PHQ-8) survey and were evaluated every 30 days with the Montgomery-Asberg Depression Rating Scale (MADRS). Results: Preliminary analysis of app survey compliance was adequate (86%), and the quantity of data collected was greater for Android than iOS phones. PHQ-8 indicated depression (moderate or severe) in 20% of participants. MADRS highly correlated with the most recent PHQ-8 ( r = 0.92, 95% CI (0.19, 0.99), P = 0.028). Several exploratory passive physical activity and social measures showed statistically significant change over time and/or associations with PHQ-8 and MADRS. Conclusion: Low-burden smartphone data holds promise for developing novel IS and TIA outcome measures as well as identifying patients most at risk of PSD.

Accelerated by the adoption of remote monitoring during the COVID-19 pandemic, interest in using digitally captured behavioral data to predict patient outcomes has grown; however, it is unclear how feasible digital phenotyping studies may be in patients with recent ischemic stroke or transient ischemic attack. In this perspective, we present participant feedback and relevant smartphone data metrics suggesting that digital phenotyping of post-stroke depression is feasible. Additionally, we proffer thoughtful considerations for designing feasible real-world study protocols tracking cerebrovascular dysfunction with smartphone sensors.

Background The goal was to compare patterns of physical activity (PA) behaviors (sedentary behavior [SB], light PA, moderate‐to‐vigorous PA [MVPA], and sleep) measured via accelerometers for 7 days between patients with incident cerebrovascular disease (CeVD) (n=2141) and controls (n=73 938). Methods and Results In multivariate models, cases spent 3.7% less time in MVPA (incidence rate ratio [IRR], 0.963 [95% CI, 0.929–0.998]) and 1.0% more time in SB (IRR, 1.010 [95% CI, 1.001–1.018]). Between 12 and 24 months before diagnosis, cases spent more time in SB (IRR, 1.028 [95% CI, 1.001–1.057]). Within the year before diagnosis, cases spent less time in MVPA (IRR, 0.861 [95% CI, 0.771–0.964]). Although SB time was not associated with CeVD risk, MVPA time, both total min/d (hazard ratio [HR], 0.998 [95% CI, 0.997–0.999]) and guideline threshold adherence (≥150 min/wk) (HR, 0.909 [95% CI, 0.827–0.998]), was associated with decreased CeVD risk. Comorbid burden had a significant partial mediation effect on the relationship between MVPA and CeVD. Cases slept more during 12:00 to 17:59 hours (IRR, 1.091 [95% CI, 1.002–1.191]) but less during 0:00 to 5:59 hours (IRR, 0.984 [95% CI, 0.977–0.992]). No between‐group differences were significant at subgroup analysis. Conclusions Daily behavior patterns were significantly different in patients before CeVD. Although SB was not associated with CeVD risk, the association between MVPA and CeVD risk is partially mediated by comorbid burden. This study has implications for understanding observable behavior patterns in cerebrovascular dysfunction and may help in developing remote monitoring strategies to prevent or reduce cerebrovascular decline.

To understand why US patients refused participation in hospital-at-home (H@H) during the coronavirus disease 2019 Public Health Emergency, eligible adult patients seen at 2 Mayo Clinic sites, Mayo Clinic Health System—Northwest Wisconsin region (NWWI) and Mayo Clinic Florida (MCF), from August 2021 through March 2022, were invited to participate in a convergent-parallel study. Quantitative associations between H@H participation status and patient baseline data at hospital admission were investigated. H@H patients were more likely to have a Mayo Clinic patient portal at baseline ( P-value: .014), indicating a familiarity with telehealth. Patients who refused were more likely to be from NWWI ( P-value < .001) and have a higher Epic Deterioration Index score ( P-value: .004). The groups also had different quarters (in terms of fiscal calendar) of admission ( P-value: .040). Analyzing qualitative interviews (n = 13) about refusal reasons, 2 themes portraying the quantitative associations emerged: lack of clarity about H@H and perceived domestic challenges. To improve access to H@H and increase patient recruitment, improved education about the dynamics of H@H, for both hospital staff and patients, and inclusive strategies for navigating domestic barriers and diagnostic challenges are needed.

Technology-enhanced hospital-at-home (H@H), commonly referred to as hybrid H@H, became more widely adopted during the COVID-19 pandemic. We conducted focus group interviews with Mayo Clinic staff members (n = 14) delivering hybrid H@H in three separate locations—a rural community health system (Northwest Wisconsin), the nation’s largest city by area (Jacksonville, FL), and a desert metropolitan area (Scottsdale, AZ)—to understand staff experiences with implementing a new care delivery model and using new technology to monitor patients at home during the pandemic. Using a grounded theory lens, transcripts were analyzed to identify themes. Staff reported that hybrid H@H is a complex care coordination and communication initiative, that hybrid H@H faces site-specific challenges modulated by population density and state policies, and that many patients are receiving uniquely high-quality care through hybrid H@H, partly enabled by advances in technology. Participant responses amplify the need for additional qualitative research with hybrid H@H staff to identify areas for improvement in the deployment of new models of care enabled by modern technology.