GPTCoach: Towards LLM-Based Physical Activity Coaching

要旨

Mobile health applications show promise for scalable physical activity promotion but are often insufficiently personalized. In contrast, health coaching offers highly personalized support but can be prohibitively expensive and inaccessible. This study draws inspiration from health coaching to explore how large language models (LLMs) might address personalization challenges in mobile health. We conduct formative interviews with 12 health professionals and 10 potential coaching recipients to develop design principles for an LLM-based health coach. We then built GPTCoach, a chatbot that implements the onboarding conversation from an evidence-based coaching program, uses conversational strategies from motivational interviewing, and incorporates wearable data to create personalized physical activity plans. In a lab study with 16 participants using three months of historical data, we find promising evidence that GPTCoach gathers rich qualitative information to offer personalized support, with users feeling comfortable sharing concerns. We conclude with implications for future research on LLM-based physical activity support.

著者
Matthew Jörke
Stanford University, Stanford, California, United States
Shardul Sapkota
Stanford University, Stanford, California, United States
Lyndsea Warkenthien
Stanford University, Stanford, California, United States
Niklas Vainio
Stanford University, Stanford, California, United States
Paul Schmiedmayer
Stanford University, Stanford , California, United States
Emma Brunskill
Stanford University, Stanford, California, United States
James A.. Landay
Stanford University, Stanford, California, United States
DOI

10.1145/3706598.3713819

論文URL

https://dl.acm.org/doi/10.1145/3706598.3713819

動画

会議: CHI 2025

The ACM CHI Conference on Human Factors in Computing Systems (https://chi2025.acm.org/)

セッション: Sports

G304
6 件の発表
2025-04-30 01:20:00
2025-04-30 02:50:00
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