Mental health challenges are increasingly prevalent among university students, yet often go undetected due to reliance on traditional assessments that are subjective, infrequent, and lack behavioral context. Digital phenotyping through passively collected smartphone data offers a scalable alternative, but existing approaches often fail to integrate predictive accuracy with narrative-based insights. To overcome these limitations, we present NarrativeSense, a novel framework that combines machine learning models with narrative-based descriptions of daily life events inferred from smartphone sensing data to predict weekly affective states. The system incorporates language model components to transform behavioral patterns into contextualized, human-readable narratives that ground affective predictions in everyday experiences. This narrative layer complements structured prediction by offering intuitive, user-centered insights. Applied to longitudinal data from 58 university students over 119 days, NarrativeSense outperforms baseline machine learning models, standalone LLMs, and ensemble methods, while providing richer insights. Our findings demonstrate the potential of narrative-enhanced digital phenotyping for scalable and explainable mental health monitoring in educational and clinical settings.
Zhang et al. (Mon,) studied this question.