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.

翻译：我们提出了PhysioLLM，这是一个交互式系统，它利用大型语言模型（LLMs），通过整合来自可穿戴设备的生理数据与情境信息，为用户提供个性化的健康理解与探索。与市面上的可穿戴设备健康应用不同，我们的系统提供了一个全面的统计分析组件，能够发现用户数据中的关联与趋势，允许用户用自然语言提问并获得生成的个性化洞察，并引导他们制定可执行的目标。作为一项案例研究，我们专注于改善睡眠质量，这既因为其可通过生理数据测量，也因其对整体健康至关重要。通过对24名Fitbit手表用户进行的用户研究，我们证明，在促进对健康数据更深入、个性化的理解以及支持实现个人健康目标的可执行步骤方面，PhysioLLM的表现优于单独使用Fitbit应用或通用的LLM聊天机器人。