Toward precision rehabilitation in adolescent mild traumatic brain injury: leveraging physiologic data from commercially available smartwatches to identify patient subgroups
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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 ( P <0.01). The Active subgroup took a high number of steps per day, had lower sedentary time, and had the highest mean SPM in QI. The Sedentary subgroup took fewer steps per day than the Active subgroup, had high sedentary time, and showed the highest resting heart rate. The AEHR subgroup took fewer steps per day than the Active subgroup, had high sedentary time, and spent a higher percentage of time with an atypically high heart rate response to low levels of activity than the other subgroups.
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.
