NarrativeSense: Predicting Affective States in University Students through Smartphone Sensing and Contextual Narratives

Abstract

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 tr..