Brown School, Washington University, St. Louis, MO 63130, USA.
Department of Physical Education, China University of Geosciences Beijing, Beijing 100083, China.
J Sport Health Sci. 2024 May;13(3):428-441. doi: 10.1016/j.jshs.2023.09.010. Epub 2023 Sep 29.
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.
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.
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.
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 well-being.
本范围综述旨在帮助研究人员和从业者了解人工智能（AI）在体育活动（PA）干预中的应用；介绍常用的机器学习（ML）、深度学习（DL）和强化学习（RL）算法；并鼓励采用 AI 方法。
在 PubMed、Web of Science、Cochrane 图书馆和 EBSCO 中进行了范围综述，重点关注 AI 应用于促进 PA 或预测相关行为或健康结果。总结和分类 AI 方法，以确定协同作用、模式和趋势，为未来的研究提供信息。此外，还提供了一个关于 PA 领域主要 AI 方法的简明入门，以增强理解和更广泛的应用。
综述共纳入 24 项符合预定纳入标准的研究。AI 模型在检测 PA 行为的显著模式以及特定因素与干预结果之间的关联方面被证明是有效的。大多数将 AI 模型与传统统计方法进行比较的研究报告称，AI 模型在测试数据上的预测准确性更高。不同 AI 模型的比较结果喜忧参半，这可能是由于模型性能高度依赖于数据集和任务。观察到采用最先进的 DL 和 RL 模型而不是标准 ML 模型的趋势不断增加，解决了复杂的人机通信、行为修改和决策任务。未来在 PA 干预中采用 AI 的六个关键领域出现：个性化 PA 干预、实时监测和调整、多模态数据源的整合、干预效果的评估、扩大 PA 干预的可及性以及预测和预防伤害。
本范围综述强调了 AI 方法在推进 PA 干预方面的潜力。随着该领域的发展，及时了解和探索新兴的 AI 驱动策略对于在 PA 干预中取得显著进展和促进整体健康至关重要。
