  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.
