Feasibility of Observing Cerebrovascular Disease Phenotypes with Smartphone Monitoring: Study Design Considerations for Real-World Studies.

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.

Accelerometer-Measured Behavior Patterns in Incident Cerebrovascular Disease: Insights for Preventative Monitoring From the UK Biobank.

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.

Visualizing Clinical Data Retrieval and Curation in Multimodal Healthcare AI Research: A Technical Note on RIL-workflow.

Curating and integrating data from sources are bottlenecks to procuring robust training datasets for artificial intelligence (AI) models in healthcare. While numerous applications can process discrete types of clinical data, it is still time-consuming to integrate heterogenous data types. Therefore, there exists a need for more efficient retrieval and storage of curated patient data from dissimilar sources, such as biobanks, health records, and sensors. We describe a customizable, modular data retrieval application (RIL-workflow), which integrates clinical notes, images, and prescription data, and show its feasibility applied to research at our institution. It uses the workflow automation platform Camunda (Camunda Services GmbH, Berlin, Germany) to collect internal data from Fast Healthcare Interoperability Resources (FHIR) and Digital Imaging and Communications in Medicine (DICOM) sources. Using the web-based graphical user interface (GUI), the workflow runs tasks to completion according to visual representation, retrieving and storing results for patients meeting study inclusion criteria while segregating errors for human review. We showcase RIL-workflow with its library of ready-to-use modules, enabling researchers to specify human input or automation at fixed steps. We validated our workflow by demonstrating its capability to aggregate, curate, and handle errors related to data from multiple sources to generate a multimodal database for clinical AI research. Further, we solicited user feedback to highlight the pros and cons associated with RIL-workflow. The source code is available at github.com/magnooj/RIL-workflow.

Why U.S. Patients Declined Hospital-at-Home during the COVID-19 Public Health Emergency: An Exploratory Mixed Methods Study.

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.