Presentation
Machine Learning–Enabled Tracking of Nonlinear Hyperarousal and Mental-Health Symptom Trajectories During a Veteran Endurance-Cycling Intervention
SessionHC3: AI Collaborations
DescriptionPost-traumatic stress disorder (PTSD) in veterans is characterized by persistent hyperarousal and comorbid anxiety and depressive symptoms that are difficult to monitor and manage outside clinical settings. This pilot study evaluated a wearable-integrated digital mental health system embedded within a real-world endurance cycling intervention to examine longitudinal hyperarousal dynamics, symptom trajectories, and user-centered performance of a machine learning (ML)–based detection system. Fourteen veterans participating in a Project Hero cycling event in Texas were randomized in a naturalistic setting to two arms: (1) digital intervention plus physical activity, or (2) physical activity only, plus a third at-home monitoring control cohort. Continuous smartwatch sensing combined heart rate and accelerometer features to detect hyperarousal events, which were confirmed in real time by participants. Weekly self-report measures of anxiety, depression, and PTSD severity were collected. Generalized additive mixed models characterized nonlinear trajectories over time. Baseline-normalized hyperarousal trajectories differed significantly across conditions, with the digital intervention group showing structured stabilization compared to late-study escalation in the physical-only group. Together, these results suggest that coupling wearable detection with digital self-management tools may support stabilization of hyperarousal and symptom improvement while emphasizing the importance of personalization and human-centered design in wearable mental health systems.
Event Type
Lecture
TimeTuesday, October 20th1:50pm - 2:10pm PDT
Location
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