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

•Artificial intelligence (AI) models were generally effective for physical activity (PA) promotion (16 studies), outcome prediction (7 studies), and pattern recognition (1 study).•Twelve studies found AI-driven interventions, such as mobile apps, recommendation systems, and chatbots improved PA outc...

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Published in:Journal of sport and health science Vol. 13; no. 3; pp. 428 - 441
Main Authors: An, Ruopeng, Shen, Jing, Wang, Junjie, Yang, Yuyi
Format: Journal Article
Language:English
Published: China Elsevier B.V 01.05.2024
上海体育大学
Brown School,Washington University,St.Louis,MO 63130,USA%Department of Physical Education,China University of Geosciences Beijing,Beijing 100083,China%School of Kinesiology and Health Promotion,Dalian University of Technology,Dalian 116024,China%Brown School,Washington University,St.Louis,MO 63130,USA
Division of Computational and Data Sciences,Washington University,St.Louis,MO 63130,USA
Elsevier
Subjects:
Algorithms
Artificial Intelligence
Deep Learning
Exercise
Health Promotion - methods
Humans
Intervention
Machine Learning
Neural network
Physical activity
Intervention
Neural network
Physical activity
Artificial intelligence
Machine learning
ISSN:2095-2546, 2213-2961, 2213-2961
Online Access:Get full text
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Summary:•Artificial intelligence (AI) models were generally effective for physical activity (PA) promotion (16 studies), outcome prediction (7 studies), and pattern recognition (1 study).•Twelve studies found AI-driven interventions, such as mobile apps, recommendation systems, and chatbots improved PA outcomes compared to traditional approaches.•An increasing trend was observed of adopting state-of-the-art deep learning and reinforcement learning models over standard machine learning.•Six key areas were identified for future AI adoption: personalized interventions, real-time monitoring and adaptation, multimodal data integration, evaluating effectiveness, expanding access, and preventing injuries.•Exploring emerging AI-driven strategies is essential for optimizing PA interventions and promoting public health. 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. [Display omitted]
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ISSN:2095-2546
2213-2961
2213-2961
DOI:10.1016/j.jshs.2023.09.010