A scoping review of methodologies for applying artificial intelligence to physical activity interventions  

作　　者：Ruopeng An Jing Shen Junjie Wang Yuyi Yang 

机构地区：[1]Brown School,Washington University,St.Louis,MO 63130,USA [2]Department of Physical Education,China University of Geosciences Beijing,Beijing 100083,China [3]School of Kinesiology and Health Promotion,Dalian University of Technology,Dalian 116024,China [4]Division of Computational and Data Sciences,Washington University,St.Louis,MO 63130,USA

出　　处：《Journal of Sport and Health Science》2024年第3期428-441,共14页运动与健康科学（英文）

摘　　要：Purpose This scoping review aimed to offer researchers and practitioners an understanding of artificial intelligence(AI)applications in physical activity(PA)interventions;introduce them to prevalent machine learning(ML),deep learning(DL),and reinforcement learning(RL)algorithms;and encourage the adoption of AI methodologies.Methods A scoping review was performed in PubMed,Web of Science,Cochrane Library,and EBSCO focusing on AI applications for promoting PA or predicting related behavioral or health outcomes.AI methodologies were summarized and categorized to identify synergies,patterns,and trends informing future research.Additionally,a concise primer on predominant AI methodologies within the realm of PA was provided to bolster understanding and broader application.Results The review included 24 studies that met the predetermined eligibility criteria.AI models were found effective in detecting significant patterns of PA behavior and associations between specific factors and intervention outcomes.Most studies comparing AI models to traditional statistical approaches reported higher prediction accuracy for AI models on test data.Comparisons of different AI models yielded mixed results,likely due to model performance being highly dependent on the dataset and task.An increasing trend of adopting state-of-the-art DL and RL models over standard ML was observed,addressing complex human–machine communication,behavior modification,and decision-making tasks.Six key areas for future AI adoption in PA interventions emerged:personalized PA interventions,real-time monitoring and adaptation,integration of multimodal data sources,evaluation of intervention effectiveness,expanding access to PA interventions,and predicting and preventing injuries.Conclusion The scoping review highlights the potential of AI methodologies for advancing PA interventions.As the field progresses,staying informed and exploring emerging AI-driven strategies is essential for achieving significant improvements in PA interventions and fostering overall

关 键 词：Artificial intelligence INTERVENTION Machine learning Neural network Physical activity 

分 类 号：G80[文化科学—运动人体科学]