Smartphone-Based Digital Phenotyping for Identifying Elevated Depressive Symptom Levels Using Machine Learning

초록

Featured Application The proposed smartphone-based digital phenotyping framework may serve as a component of future mobile mental health monitoring systems by enabling passive assessment of behavioral patterns associated with depressive symptom risk. Because the framework relies solely on data collected from built-in smartphone sensors without requiring additional wearable devices, it offers potential applicability for continuous behavioral monitoring. Although further validation in larger and more diverse populations is required, the proposed approach demonstrates the potential of smartphone-based digital phenotyping as a supportive tool for depressive symptom risk screening and personalized mental health monitoring.Abstract Depressive symptoms among university students are a growing public health concern, motivating unobtrusive monitoring with smartphone-based digital phenotyping. This study examined whether passively collected behavioral features can identify elevated depressive symptom risk. We collected smartphone sensing data from 36 university students over two weeks, yielding 551 participant-days. We extracted 21 digital phenotyping features, including 14 additional behavioral features and 7 baseline features. Multiple machine learning models were evaluated using repeated bootstrap validation, and participant-independent generalization was further assessed with Leave-One-Subject-Out (LOSO) validation. We also examined PHQ-9 thresholds, class-imbalance mitigation, and feature importance using SHAP. Tree-based ensemble models achieved the best performance under bootstrap validation, and the proposed feature set consistently outperformed the baseline set. However, performance dropped substantially under LOSO validation, indicating that participant-independent generalization remains challenging. Class weighting and SMOTE did not meaningfully improve performance. SHAP analysis identified home-stay behavior and time-of-day smartphone use as the most influential predictors. These findings highlight that participant-independent prediction remains challenging and underscore the importance of rigorous participant-level validation when developing smartphone-based digital phenotyping models.

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