Post-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. Thirteen veterans participating in a Project Hero cycling event in Texas were randomized by computer-generated sequence 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 consisting of 7 veterans selected from the broader Project Hero veteran community. 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 (n=7) showing structured stabilization compared to late-study escalation in the physical-only group (n=3). Both cycling groups exhibited acute symptom improvements during the endurance event; however, the digital intervention group demonstrated a higher overall maintenance of gains. The at-home control group (n=4) showed gradual symptom declines. Perceived precision of ML detections varied substantially across individuals and was positively associated with symptom severity, with higher-severity participants confirming a greater proportion of detected events. 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.

翻译：创伤后应激障碍（PTSD）在退伍军人中的特征表现为持续性过度警觉，以及难以在临床环境外监测和管理的共病性焦虑与抑郁症状。本研究在自然情境下，通过计算机生成序列将参与德克萨斯州"英雄计划"骑行活动的13名退伍军人随机分为两组：（1）数字干预联合体力活动组；（2）纯体力活动组，并设置由从"英雄计划"退伍军人群体中选取的7名退伍军人组成的居家监测对照组。连续智能手表传感系统融合心率和加速度计特征以检测过度警觉事件，并由参与者实时确认。每周收集焦虑、抑郁及PTSD严重程度的自我报告量表，采用广义加性混合模型刻画非线性时间轨迹。各组基线归一化的过度警觉轨迹存在显著差异：数字干预组（n=7）呈现结构化稳定趋势，而纯体力活动组（n=3）在后期研究阶段表现出症状恶化。两个骑行组在耐力活动期间均出现急性症状改善，但数字干预组展现出更高的总体疗效维持水平。居家对照组（n=4）表现为渐进式症状缓解。机器学习检测的感知精度存在显著的个体差异，且与症状严重程度呈正相关——高严重程度参与者确认的检测事件比例更高。研究结果表明，将可穿戴检测与数字自我管理工具相结合，可能有助于稳定过度警觉并促进症状改善，同时凸显了在可穿戴心理健康系统中实施个性化与人本化设计的重要性。