A Wearable Computing-Based Machine Learning System for Detecting PTSD Hyperarousal Events: Naturalistic Evaluation of Perceived Precision and User Acceptance

Post-Traumatic Stress Disorder (PTSD) is a prevalent and costly mental health condition characterized by symptoms such as hyperarousal, avoidance, and re-experiencing. While machine learning (ML) approaches have shown promise in detecting PTSD-related physiological patterns, most validation efforts rely on computational metrics rather than real-world user perceptions. This study evaluates the perceived precision of a smartwatch-based ML tool designed to detect PTSD hyperarousal events using heart rate and activity data. The tool, previously developed using XGBoost 1.0.0, was deployed in a 21-day naturalistic study with 12 participants diagnosed with PTSD. Quantitative results showed a median perceived precision of 65.27%, with substantial variability across participants. A Mann–Kendall trend analysis revealed a significant increase in perceived precision over time for most participants, suggesting calibration of trust. Qualitative findings indicated high usability, general trust in the system, and acceptance of false positives, though concerns about notification design and battery life were noted. The results highlight the importance of incorporating user-centered, real-world validation into the evaluation of ML-based mental health monitoring tools. This work provides preliminary evidence supporting the feasibility of wearable-based PTSD monitoring and underscores the role of perceived precision in technology adoption and sustained use.