{"title":"Using Large Language Models to Enhance Exercise Recommendations and Physical Activity in Clinical and Healthy Populations: Scoping Review.","authors":"Xiangxun Lai, Jiacheng Chen, Yue Lai, Shengqi Huang, Yongdong Cai, Zhifeng Sun, Xueding Wang, Kaijiang Pan, Qi Gao, Caihua Huang","doi":"10.2196/59309","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Regular exercise recommendations (ERs) and physical activity (PA) are crucial for the prevention and management of chronic diseases. However, creating effective exercise programs demand substantial time and specialized expertise from both medical and sports professionals. Large language models (LLMs), such as ChatGPT, offer a promising solution by helping create personalized ERs. While LLMs show potential, their use in exercise planning remains in its early stages and requires further exploration.</p><p><strong>Objectives: </strong>This study aims to systematically review and classify the applications of LLMs in ERs and PA. It also seeks to identify existing gaps and provide insights into future research directions for optimizing LLM integration in personalized health interventions.</p><p><strong>Methods: </strong>A scoping review methodology was used to identify studies related to LLM applications in ERs and PA. Literature searches were conducted in Web of Science, PubMed, IEEE, and arXiv for English language papers published up to March 21, 2024. Keywords included LLMs, chatbots, ERs, PA, fitness plan, and related terms. Two independent reviewers (XL and CH) screened and selected studies based on predefined inclusion criteria. Thematic analysis was used to synthesize findings, which were presented narratively.</p><p><strong>Results: </strong>An initial search identified 598 papers, of which 1.8% (11/598) of studies were included after screening and applying selection criteria. Of these, ChatGPT-based models were used in 55% (6/11) of the studies. In addition, 73% (8/11) of the studies used expert evaluations and user feedback to assess model usability, and 45% (5/11) of the studies used experimental designs to evaluate LLM interventions in ERs and PA. Key findings indicated that LLMs can generate tailored ERs, save time in clinical practice, and enhance safety by incorporating patient-specific data. They also increased engagement and supported behavior change. This made PA guidance more accessible, especially in remote or underserved communities.</p><p><strong>Conclusions: </strong>This review highlights the promising applications of LLMs in ERs and PA but emphasizes that they remain a supplement to human expertise. Expert validation is essential to ensure safety and mitigate risks. Future research should prioritize pilot testing, clinician training programs, and large-scale clinical trials to enhance feasibility, transparency, and ethical integration.</p>","PeriodicalId":56334,"journal":{"name":"JMIR Medical Informatics","volume":"13 ","pages":"e59309"},"PeriodicalIF":3.1000,"publicationDate":"2025-05-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"JMIR Medical Informatics","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.2196/59309","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"MEDICAL INFORMATICS","Score":null,"Total":0}
引用次数: 0
Abstract
Background: Regular exercise recommendations (ERs) and physical activity (PA) are crucial for the prevention and management of chronic diseases. However, creating effective exercise programs demand substantial time and specialized expertise from both medical and sports professionals. Large language models (LLMs), such as ChatGPT, offer a promising solution by helping create personalized ERs. While LLMs show potential, their use in exercise planning remains in its early stages and requires further exploration.
Objectives: This study aims to systematically review and classify the applications of LLMs in ERs and PA. It also seeks to identify existing gaps and provide insights into future research directions for optimizing LLM integration in personalized health interventions.
Methods: A scoping review methodology was used to identify studies related to LLM applications in ERs and PA. Literature searches were conducted in Web of Science, PubMed, IEEE, and arXiv for English language papers published up to March 21, 2024. Keywords included LLMs, chatbots, ERs, PA, fitness plan, and related terms. Two independent reviewers (XL and CH) screened and selected studies based on predefined inclusion criteria. Thematic analysis was used to synthesize findings, which were presented narratively.
Results: An initial search identified 598 papers, of which 1.8% (11/598) of studies were included after screening and applying selection criteria. Of these, ChatGPT-based models were used in 55% (6/11) of the studies. In addition, 73% (8/11) of the studies used expert evaluations and user feedback to assess model usability, and 45% (5/11) of the studies used experimental designs to evaluate LLM interventions in ERs and PA. Key findings indicated that LLMs can generate tailored ERs, save time in clinical practice, and enhance safety by incorporating patient-specific data. They also increased engagement and supported behavior change. This made PA guidance more accessible, especially in remote or underserved communities.
Conclusions: This review highlights the promising applications of LLMs in ERs and PA but emphasizes that they remain a supplement to human expertise. Expert validation is essential to ensure safety and mitigate risks. Future research should prioritize pilot testing, clinician training programs, and large-scale clinical trials to enhance feasibility, transparency, and ethical integration.
背景:定期运动建议(ERs)和身体活动(PA)对于预防和管理慢性疾病至关重要。然而,制定有效的锻炼计划需要大量的时间和医学和体育专业人士的专业知识。大型语言模型(llm),如ChatGPT,通过帮助创建个性化的er提供了一个很有前途的解决方案。虽然法学硕士显示出潜力,但它们在运动规划中的应用仍处于早期阶段,需要进一步探索。目的:本研究旨在系统回顾和分类法学硕士在er和PA中的应用。它还寻求确定现有的差距,并为优化LLM整合个性化健康干预的未来研究方向提供见解。方法:采用范围审查方法确定与法学硕士在er和PA中的应用相关的研究。文献检索在Web of Science、PubMed、IEEE和arXiv中进行,检索截止到2024年3月21日发表的英文论文。关键词包括llm、聊天机器人、er、PA、健身计划和相关术语。两位独立审稿人(XL和CH)根据预先确定的纳入标准筛选和选择研究。主题分析用于综合研究结果,并以叙事方式呈现。结果:初步检索到598篇论文,经筛选和应用选择标准后纳入1.8%(11/598)的研究。其中,55%(6/11)的研究使用了基于chatgpt的模型。此外,73%(8/11)的研究采用专家评价和用户反馈来评估模型可用性,45%(5/11)的研究采用实验设计来评估法学硕士对急诊室和PA的干预。主要研究结果表明,llm可以产生量身定制的急诊室,节省临床实践时间,并通过纳入患者特定数据提高安全性。他们还增加了参与度,支持行为改变。这使得PA的指导更容易获得,特别是在偏远或服务不足的社区。结论:这篇综述强调了法学硕士在急诊室和PA中的应用前景,但强调它们仍然是人类专业知识的补充。专家验证对于确保安全性和降低风险至关重要。未来的研究应优先考虑试点试验、临床医生培训计划和大规模临床试验,以提高可行性、透明度和伦理整合。
期刊介绍:
JMIR Medical Informatics (JMI, ISSN 2291-9694) is a top-rated, tier A journal which focuses on clinical informatics, big data in health and health care, decision support for health professionals, electronic health records, ehealth infrastructures and implementation. It has a focus on applied, translational research, with a broad readership including clinicians, CIOs, engineers, industry and health informatics professionals.
Published by JMIR Publications, publisher of the Journal of Medical Internet Research (JMIR), the leading eHealth/mHealth journal (Impact Factor 2016: 5.175), JMIR Med Inform has a slightly different scope (emphasizing more on applications for clinicians and health professionals rather than consumers/citizens, which is the focus of JMIR), publishes even faster, and also allows papers which are more technical or more formative than what would be published in the Journal of Medical Internet Research.