Despite the proliferation of wearable health trackers and the importance of sleep and exercise to health, deriving actionable personalized insights from wearable data remains a challenge because doing so requires non-trivial open-ended analysis of these data. The recent rise of large language model (LLM) agents, which can use tools to reason about and interact with the world, presents a promising opportunity to enable such personalized analysis at scale. Yet, the application of LLM agents in analyzing personal health is still largely untapped. In this paper, we introduce the Personal Health Insights Agent (PHIA), an agent system that leverages state-of-the-art code generation and information retrieval tools to analyze and interpret behavioral health data from wearables. We curate two benchmark question-answering datasets of over 4000 health insights questions. Based on 650 hours of human and expert evaluation we find that PHIA can accurately address over 84% of factual numerical questions and more than 83% of crowd-sourced open-ended questions. This work has implications for advancing behavioral health across the population, potentially enabling individuals to interpret their own wearable data, and paving the way for a new era of accessible, personalized wellness regimens that are informed by data-driven insights.

翻译：尽管可穿戴健康追踪设备日益普及，且睡眠与运动对健康至关重要，但要从可穿戴数据中提炼出可操作的个性化洞见仍面临挑战，因为这需要对这些数据进行复杂的开放式分析。近期大型语言模型（LLM）代理的兴起——它们能够运用工具进行推理并与世界交互——为实现这种大规模个性化分析提供了契机。然而，LLM代理在个人健康分析中的应用仍尚待充分开发。本文介绍了个人健康洞见代理（PHIA），这是一种利用先进代码生成与信息检索工具，分析并解读可穿戴设备行为健康数据的代理系统。我们构建了两个包含4000余个健康洞见问题的基准问答数据集。基于650小时的人工与专家评估，我们发现PHIA能准确回答超过84%的事实性数值问题及83%以上的众包开放式问题。本研究对推动群体行为健康进步具有深远意义，有望帮助个体解读自身可穿戴数据，并为开创由数据驱动洞见支撑的、个性化且可普及的健康管理模式奠定基础。