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.

翻译：移动健康应用在可扩展的体育活动促进方面展现出潜力，但其个性化程度往往不足。相比之下，健康指导能提供高度个性化的支持，但其成本可能过高且难以获取。本研究从健康指导中汲取灵感，探索大语言模型如何应对移动健康领域的个性化挑战。我们通过对12名健康专业人士和10名潜在指导对象进行形成性访谈，制定了基于大语言模型的健康指导系统的设计原则。随后，我们开发了GPTCoach——一个聊天机器人，它实现了基于循证指导项目中的初始对话流程，运用了动机性访谈中的对话策略，并结合可穿戴设备数据来创建个性化的体育活动计划。在一项涉及16名参与者并使用其三个月历史数据的实验室研究中，我们发现了有希望的证据：GPTCoach能够收集丰富的定性信息以提供个性化支持，且用户乐于分享他们的顾虑。最后，我们讨论了未来基于大语言模型的体育活动支持研究的意义。