Transforming Wearable Data into Health Insights using Large Language Model Agents
<|reference_start|>Transforming Wearable Data into Health Insights using Large Language Model Agents: 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.<|reference_end|>
@article{merrill2024transforming,
    title={Transforming Wearable Data into Health Insights using Large Language
  Model Agents},
    author={Mike A. Merrill, Akshay Paruchuri, Naghmeh Rezaei, Geza Kovacs, Javier
  Perez, Yun Liu, Erik Schenck, Nova Hammerquist, Jake Sunshine, Shyam Tailor,
  Kumar Ayush, Hao-Wei Su, Qian He, Cory Y. McLean, Mark Malhotra, Shwetak
  Patel, Jiening Zhan, Tim Althoff, Daniel McDuff, Xin Liu},
    journal={arXiv preprint arXiv:2406.06464},
    year={2024},
    archivePrefix={arXiv},
    eprint={2406.06464},
    primaryClass={cs.AI cs.CL}
}