ArticlemedRxiv : the preprint server for health sciences2026
Toward precision rehabilitation in adolescent mild traumatic brain injury: leveraging physiologic data from commercially available smartwatches to identify patient subgroups.
Article in medRxiv : the preprint server for health sciences, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Objective: To identify meaningful subgroups of adolescents with mTBI using physiologic and physical activity data obtained via consumer-grade smartwatches. Setting: Specialty concussion clinic. Participants: Eighty participants aged 13-18 within six months of mTBI diagnosis were enrolled. Sixty-one participants were included in the analysis. Design: Prospective longitudinal cohort study. Participants wore a Fitbit Sense 2. Heart rate and step count data collected within fourteen days of enrollment were included. Main measures: A group-based steps per minute (SPM) threshold (25th percentile; 10 SPM) and individualized heart rate threshold (20% heart rate reserve (HRR)) were used to classify each minute of active daytime data into quadrants: SPM>10 & HRR>20% (QI), SPM≤10 & HRR>20% (QII), SPM≤10 & HRR≤20% (QIII), and SPM>10 & HRR≤20% (QIV). Percentage of minutes in QI, QII, and QIV, mean steps per day, percentage of minutes with zero steps, mean SPM in QI, and resting heart rate were included in a k-means clustering algorithm. Subgroup differences by clustering variables were evaluated using Kruskal-Wallis tests. Results: Three subgroups emerged: Sedentary (n=12), Active (n=23), and Atypically Elevated Heart Rate (AEHR; n=26). Subgroups varied significantly on all clustering variables ( Conclusions: Data from wearable devices can identify subgroups of adolescents with mTBI with distinct physiologic/physical activity profiles, which may ultimately be used to inform personalized activity prescriptions.
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