  We present PhysioLLM, an interactive system that leverages large language
models (LLMs) to provide personalized health understanding and exploration by
integrating physiological data from wearables with contextual information.
Unlike commercial health apps for wearables, our system offers a comprehensive
statistical analysis component that discovers correlations and trends in user
data, allowing users to ask questions in natural language and receive generated
personalized insights, and guides them to develop actionable goals. As a case
study, we focus on improving sleep quality, given its measurability through
physiological data and its importance to general well-being. Through a user
study with 24 Fitbit watch users, we demonstrate that PhysioLLM outperforms
both the Fitbit App alone and a generic LLM chatbot in facilitating a deeper,
personalized understanding of health data and supporting actionable steps
toward personal health goals.
