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<p>Although large language models (LLMs) show promise for clinical healthcare applications, their utility for personalized health monitoring using wearable device data remains underexplored. Here we introduce the Personal Health Large Language Model (PH-LLM), designed for applications in sleep and fitness. PH-LLM is a version of the Gemini LLM that was finetuned for text understanding and reasoning when applied to aggregated daily-resolution numerical sensor data. We created three benchmark datasets to assess multiple complementary aspects of sleep and fitness: expert domain knowledge, generation of personalized insights and recommendations and prediction of self-reported sleep quality from longitudinal data. PH-LLM achieved scores that exceeded a sample of human experts on multiple-choice examinations in sleep medicine (79% versus 76%) and fitness (88% versus 71%). In a comprehensive evaluation involving 857 real-world case studies, PH-LLM performed similarly to human experts for fitness-related tasks and improved over the base Gemini model in providing personalized sleep insights. Finally, PH-LLM effectively predicted self-reported sleep quality using a multimodal encoding of wearable sensor data, further demonstrating its ability to effectively contextualize wearable modalities. This work highlights the potential of LLMs to revolutionize personal health monitoring via tailored insights and predictions from wearable data and provides datasets, rubrics and benchmark performance to further accelerate personal health-related LLM research.</p>

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A personal health large language model for sleep and fitness coaching

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年8月14日

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  • Justin Khasentino,
  • Anastasiya Belyaeva,
  • Xin Liu,
  • Zhun Yang,
  • Nicholas A. Furlotte,
  • Chace Lee,
  • Erik Schenck,
  • Yojan Patel,
  • Jian Cui,
  • Logan Douglas Schneider,
  • Robby Bryant,
  • Ryan G. Gomes,
  • Allen Jiang,
  • Roy Lee,
  • Yun Liu,
  • Javier Perez,
  • Jameson K. Rogers,
  • Cathy Speed,
  • Shyam Tailor,
  • Megan Walker,
  • Jeffrey Yu,
  • Tim Althoff,
  • Conor Heneghan,
  • John Hernandez,
  • Mark Malhotra,
  • Leor Stern,
  • Yossi Matias,
  • Greg S. Corrado,
  • Shwetak Patel,
  • Shravya Shetty,
  • Jiening Zhan,
  • Shruthi Prabhakara,
  • Daniel McDuff,
  • Cory Y. McLean

摘要

Although large language models (LLMs) show promise for clinical healthcare applications, their utility for personalized health monitoring using wearable device data remains underexplored. Here we introduce the Personal Health Large Language Model (PH-LLM), designed for applications in sleep and fitness. PH-LLM is a version of the Gemini LLM that was finetuned for text understanding and reasoning when applied to aggregated daily-resolution numerical sensor data. We created three benchmark datasets to assess multiple complementary aspects of sleep and fitness: expert domain knowledge, generation of personalized insights and recommendations and prediction of self-reported sleep quality from longitudinal data. PH-LLM achieved scores that exceeded a sample of human experts on multiple-choice examinations in sleep medicine (79% versus 76%) and fitness (88% versus 71%). In a comprehensive evaluation involving 857 real-world case studies, PH-LLM performed similarly to human experts for fitness-related tasks and improved over the base Gemini model in providing personalized sleep insights. Finally, PH-LLM effectively predicted self-reported sleep quality using a multimodal encoding of wearable sensor data, further demonstrating its ability to effectively contextualize wearable modalities. This work highlights the potential of LLMs to revolutionize personal health monitoring via tailored insights and predictions from wearable data and provides datasets, rubrics and benchmark performance to further accelerate personal health-related LLM research.