Predicting Daily Depression Scores Using Passive Sensing: A Behaviour-Aware Approach to Missing Value Handling

Depression is a serious mental health issue among college students, yet early detection and continuous monitoring remain challenging due to reliance on infrequent and subjective self-reports. Recent developments in mobile and wearable sensor technologies provide a promising alternative by using passive, real-time monitoring of behavioural patterns. However, such data are often missing, and traditional imputation methods typically assume data are missing (completely) at random. In this study, we introduced a sensor-aware preprocessing pipeline that adjusts specific missing values when the absence of data may reflect meaningful user behaviour rather than sensor failure. The pipeline is applied to longitudinal mobile sensing data and weekly Patient Health Questionnaire-4 (PHQ-4) depression scores from college students over three academic years. By interpolating PHQ-4 scores to generate daily labels, we train and evaluate regression models to predict depression scores. Our sensor-aware adjustment approach improved model performance compared to standard preprocessing methods. These findings show the value of including contextual knowledge in missing data handling and support the feasibility of passive, continuous mental health monitoring in student populations.