Machine Learning for Just-In-Time Adaptive Mental Health Interventions Using Smartwatch Data

Abstract

Mental health challenges among college students are a growing concern, necessitating innovative tools for early detection and intervention. This study hypothesizes that predictive modeling of mood states from multivariate time-series data collected via mobile sensors can be enhanced by leveraging sequence-aware models over non-sequential alternatives. Specifically, we compare the abilities of Long Short-Term Memory networks to those of Support Vector Machines and traditional Artificial Neural Networks for modeling temporal dependencies and predicting mood state. We further hypothesize that variations in data sampling rates will influence model performance and hypothesize that a combination of high data resolution and a focus on more recent movement will support peak performance. These hypotheses guide our exploration of data preparation techniques and model selection strategies for addressing technical challenges related to Just-In-Time Adaptive Interventions aimed at mental health monitoring. Future research directions include real-time deployment and personalized model refinement to improve predictive capabilities.