Decoding Mental Health Through Wearable Signals Using Machine Learning Approaches

Mental health conditions are widespread, yet scalable assessment remains limited, as clinical interviews are time-intensive and self-report screeners are episodic and prone to bias, motivating passive, data-driven approaches. This study investigates whether short-term physical activity patterns captured by consumer wearables can classify self-rated mental health in a large, diverse cohort. Using the All of Us Research Program’s Registered Tier Dataset v7, we aligned Fitbit activity data with self-rated mental health, binarized into positive (good/very good/excellent) and negative (poor/fair) classes. Addressing class imbalance (88.5% positive, 11.5% negative) issue through undersampling resulted in a final cohort of 2,092 participants (837 negative, 1,255 positive). In the cohort, each participant was represented by eleven seven-day physical activity features together with age. Five models, logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), and XGBoost, were trained and evaluated using AUC, accuracy, precision, recall, and F1. Result indicated that XGBoost performed best (AUC = 0.77, F1 = 0.774, recall = 0.883), followed by RF, while LR and SVM showed similar performance (AUC ≈ 0.75) and DT performed worst (AUC = 0.63). These outcomes suggest that short-term wearable activity data can inform mental health classification, with ensemble methods performing best. In practice, such systems could enable low-burden, continuous monitoring to support early identification of at-risk individuals, but should be designed as decision-support tools that complement, but not replace, clinical assessment, with careful attention to interpretability, user trust, and appropriate handling of uncertainty.