| Title: | Exploring Remote Monitoring of Poststroke Mood With Digital Sensors by Assessment of Depression Phenotypes and Accelerometer Data in UK Biobank: Cross-Sectional Analysis |
| Journal: | JMIR Neurotechnology |
| Published: | 10 Jan 2025 |
| DOI: | https://doi.org/10.2196/56679 |
Publication 13646
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Abstract
Abstract Background Interest in using digital sensors to monitor patients with prior stroke for depression, a risk factor for poor outcomes, has grown rapidly; however, little is known about behavioral phenotypes related to future mood symptoms and if patients with and without previously diagnosed depression experience similar phenotypes. Objective This study aimed to assess the feasibility of using digital sensors to monitor mood in patients with prior stroke with a prestroke depression diagnosis (DD) and controls. We examined relationships between physical activity behaviors and self-reported depression frequency. Methods In the UK Biobank wearable accelerometer cohort, we retrospectively identified patients who had previously suffered a stroke (N=1603) and conducted cross-sectional analyses with those who completed a subsequent depression survey follow-up. Sensitivity analyses assessed a general population cohort excluding previous stroke participants and 2 incident cohorts: incident stroke (IS) and incident cerebrovascular disease (IC). Results In controls, the odds of being in a higher depressed mood frequency category decreased by 23% for each minute spent in moderate-to-vigorous physical activity (odds ratio 0.77, 95% CI 0.69-0.87; P <.001). This association persisted in both general cohorts and in the IC control cohort. Conclusions Although moderate-to-vigorous physical activity was linked with less frequent depressed mood in patients with prior stroke without DD, this finding did not persist in DDs. Thus, accelerometer-mood monitoring may provide clinically useful insights about future mood in patients with prior stroke without DDs. Considering the finding in the IC cohort and the lack of findings in the IS cohorts, accelerometer-mood monitoring may also be appropriately applied to observing broader cerebrovascular disease pathogenesis. </p>
5 Authors
- Stephanie J Zawada
- Ali Ganjizadeh
- Gian Marco Conte
- Bart M Demaerschalk
- Bradley J Erickson
1 Application
| Application ID | Title |
|---|---|
| 90018 | Environment, genotype, AI-derived phenotype, and the risk of cardiovascular diseases, metabolic diseases, and chronic kidney disease |
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