Wearables may flag PTSD flare-ups before symptoms escalate

Post-traumatic stress disorder (PTSD) symptoms can fluctuate substantially over short periods, yet routine screening typically relies on infrequent self-report. Wearable sensors provide continuous behavioural and physiological signals that may help identify periods of elevated risk. This study aimed to evaluate whether combining wearable sensor features with daily self-report data could identify short-term PTSD symptom increases among recently discharged veterans. Seventy-four veterans wore commercial activity trackers and completed brief daily questionnaires over 87 days. For each participant, we defined an individual baseline by using the first 14 days of PTSD scores. Wearable variables were transformed into baseline-referenced deviation features to capture departures from personal norms. Missing data were addressed with multiple imputation by chained equations. Candidate predictors were prioritised with least absolute shrinkage and selection operator regression, and a set of machine-learning classifiers was evaluated. Primary performance was assessed by using the area under the precision-recall curve (PR AUC). Across feature set sizes (k = 1-25), performance peaked at k = 17. At this iteration, LightGBM achieved the strongest discrimination (PR AUC 0.86 (s.d. 0.07); area under the receiver-operating characteristic curve 0.89 (s.d. 0.04)) with a precision of 0.67 (s.d. 0.08), recall of 0.64 (s.d. 0.08) and F1 of 0.65 (s.d. 0.07). Key predictors reflected a multimodal profile, combining self-reported affect and perceived stress with wearable indicators of sleep continuity, activity variability and autonomic regulation. Baseline-referenced wearable features combined with daily self-report may help identify near-term PTSD symptom increases among recently discharged veterans with elevated PTSD symptoms and problematic cannabis use. Future work should validate performance in broader PTSD populations, including samples without problematic cannabis use.