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<p>Personalized, smartphone-based coaching improves physical activity but relies on static, human-crafted messages. We introduce My Heart Counts (MHC)-Coach, a large language model fine-tuned on the Transtheoretical Model of Change. MHC-Coach generates messages tailored to an individual’s psychology (their “stage of change”), providing personalized support to foster long-term physical activity behavior change. To evaluate MHC-Coach’s efficacy, 632 participants compared human-expert and MHC-Coach interventions encouraging physical activity. Among messages matched to an individual’s stage of change, 68.0% (<i>N</i> = 430) preferred MHC-Coach-generated messages (<i>P</i> &lt; 0.001). Blinded behavioral science experts (<i>N</i> = 2) rated MHC-Coach messages higher than human-expert messages for perceived effectiveness (4.4 vs. 2.8) and Transtheoretical Model alignment (4.1 vs. 3.5) on a 5-point Likert scale. This work demonstrates how language models can operationalize behavioral science frameworks for personalized health coaching, showing the potential for promoting long-term physical activity and reducing cardiovascular disease risk at scale.</p>

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Fine-tuning LLMs in behavioral psychology for scalable health coaching

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年9月22日

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  • Sriya Mantena,
  • Anders Johnson,
  • Marily Oppezzo,
  • Narayan Schütz,
  • Alexander Tolas,
  • Ritu Doijad,
  • C. Mikael Mattson,
  • Allan Lawrie,
  • Mariana Ramirez-Posada,
  • Paul Schmiedmayer,
  • Eleni Linos,
  • Abby C. King,
  • Fatima Rodriguez,
  • Daniel Seung Kim,
  • Euan A. Ashley

摘要

Personalized, smartphone-based coaching improves physical activity but relies on static, human-crafted messages. We introduce My Heart Counts (MHC)-Coach, a large language model fine-tuned on the Transtheoretical Model of Change. MHC-Coach generates messages tailored to an individual’s psychology (their “stage of change”), providing personalized support to foster long-term physical activity behavior change. To evaluate MHC-Coach’s efficacy, 632 participants compared human-expert and MHC-Coach interventions encouraging physical activity. Among messages matched to an individual’s stage of change, 68.0% (N = 430) preferred MHC-Coach-generated messages (P < 0.001). Blinded behavioral science experts (N = 2) rated MHC-Coach messages higher than human-expert messages for perceived effectiveness (4.4 vs. 2.8) and Transtheoretical Model alignment (4.1 vs. 3.5) on a 5-point Likert scale. This work demonstrates how language models can operationalize behavioral science frameworks for personalized health coaching, showing the potential for promoting long-term physical activity and reducing cardiovascular disease risk at scale.