Effect of a sports-medicine–guided football program on physical fitness in adolescents: A controlled school-based trial

Integrating artificial intelligence (AI) with sports medicine principles into school-based physical education may enhance physical fitness and reduce injury risk among adolescents. This study aimed to evaluate whether a 10-week sports-medicine-guided football curriculum augmented with explainable machine-learning feedback produces greater improvements in physical fitness and injury-prevention outcomes than a standard physical-education curriculum. A total of 195 healthy junior high school students (mean age 14.1 ± 0.7 years) participated in a controlled school-based intervention and were allocated to either an AI-supported sports-medicine curriculum (intervention) or a standard curriculum (control). Physical fitness outcomes, including lung capacity, estimated maximal oxygen uptake (VO2max), sprint performance, standing long jump, and middle-distance running performance (800 m girls/1000 m boys), were assessed before and after the 10-week program. Outcomes were analyzed using 2-way mixed analysis of variance to test Group × Time interactions. Injury risk prediction was supported by an explainable machine-learning ensemble model (CatBoost + Gradient Boosting) interpreted using SHAPley Additive explanations. Significant Group × Time interactions were observed for lung capacity (P < .01) and VO2max (P < .01), indicating greater improvements in the intervention group compared with controls. The intervention group also demonstrated superior improvements in sprint performance, middle-distance running, and lower-limb power (P < .05), while no significant between-group differences were observed for upper-body strength (P > .05). A school-based football curriculum integrating sports-medicine principles with explainable AI-driven feedback leads to significantly greater improvements in key physical-fitness outcomes than a standard physical-education curriculum. This approach directly supports individualized training adaptation and injury-risk awareness, offering a scalable model for enhancing adolescent health and safety in school physical education.

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Publication Details

Journal
Medicine
Published
2026-05-22
DOI
https://doi.org/10.1097/md.0000000000048649
Primary Topic
Cardiovascular Effects of Exercise
Type
article
Field-Weighted Citation Impact
0.00
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article

Effect of a sports-medicine–guided football program on physical fitness in adolescents: A controlled school-based trial

Liuxiang Wei, Lin Wang, Tianlun Zheng, Yuelong Ye et al.
Medicine
Cardiovascular Effects of Exercise
article

Effect of a sports-medicine–guided football program on physical fitness in adolescents: A controlled school-based trial

Liuxiang Wei, Lin Wang, Tianlun Zheng, Yuelong Ye, Kaiyue Tang, Ke Shi, Jing Bin
article en

Abstract

Integrating artificial intelligence (AI) with sports medicine principles into school-based physical education may enhance physical fitness and reduce injury risk among adolescents. This study aimed to evaluate whether a 10-week sports-medicine-guided football curriculum augmented with explainable machine-learning feedback produces greater improvements in physical fitness and injury-prevention outcomes than a standard physical-education curriculum. A total of 195 healthy junior high school students (mean age 14.1 ± 0.7 years) participated in a controlled school-based intervention and were allocated to either an AI-supported sports-medicine curriculum (intervention) or a standard curriculum (control). Physical fitness outcomes, including lung capacity, estimated maximal oxygen uptake (VO2max), sprint performance, standing long jump, and middle-distance running performance (800 m girls/1000 m boys), were assessed before and after the 10-week program. Outcomes were analyzed using 2-way mixed analysis of variance to test Group × Time interactions. Injury risk prediction was supported by an explainable machine-learning ensemble model (CatBoost + Gradient Boosting) interpreted using SHAPley Additive explanations. Significant Group × Time interactions were observed for lung capacity (P < .01) and VO2max (P < .01), indicating greater improvements in the intervention group compared with controls. The intervention group also demonstrated superior improvements in sprint performance, middle-distance running, and lower-limb power (P < .05), while no significant between-group differences were observed for upper-body strength (P > .05). A school-based football curriculum integrating sports-medicine principles with explainable AI-driven feedback leads to significantly greater improvements in key physical-fitness outcomes than a standard physical-education curriculum. This approach directly supports individualized training adaptation and injury-risk awareness, offering a scalable model for enhancing adolescent health and safety in school physical education.

MedicineVol. 105(21)
Guilin Medical University (CN), Guilin University of Aerospace Technology (CN), Guilin University of Technology (CN), Guangzhou Vocational College of Science and Technology (CN), Russian State University for Physical Education Sport Youth and Tourism (RU), Guangdong Baiyun University (CN), Guilin University of Electronic Technology (CN), Southeast University (CN)
Quality Education
Openalex Percentile: Top 5%
Cardiovascular Effects of Exercise
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