PhysioLLM: Supporting Personalized Health Insights with Wearables and Large Language Models

TL;DR

PhysioLLM is an interactive system that combines wearable physiological data with large language models to deliver personalized health insights, helping users understand and act on their health data more effectively.

Contribution

We introduce PhysioLLM, a novel system integrating physiological data and LLMs for personalized health insights, with a focus on sleep quality improvement.

Findings

PhysioLLM enables more personalized health understanding than standard apps.

Users achieved better health insights and goals with PhysioLLM.

The system outperforms generic chatbots in user studies.

Abstract

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.