Abstract
Health behaviors such as physical activity and sleep affect mental health, but the effect of each health behavior varies substantially across individuals, limiting the usefulness of generic behavioral recommendations. We collected one year of continuous wearable and ecological momentary assessment data from 3,139 participants in the Intern Health Study (2018-2023), and examined individual-level associations between wearable-derived features and mood across the internship year. The behaviors associated with mood were highly heterogeneous between individuals: the two most prevalent drivers of mood were wake-up time (the strongest driver for 34.0% of subjects) and step count (10.6% of subjects). The correlation directionality remained largely stable despite fluctuations in strength. Interestingly, 20.3% of subjects showed no significant correlations. These findings highlight the limitations of population-level recommendations and the critical need for personalized, data-driven approaches to mental health assessment and intervention. To translate these personalized insights into actionable support, we developed MoodDriver, a large language models (LLM)-powered system that generates tailored feedback emails based on each participant's behavioral and physiological patterns. This work demonstrates the feasibility of combining digital phenotyping with large language models to advance precision digital mental health for high-risk populations.