Optimizing physical education schedules for long-term health benefits
IntroductionPhysical education (PE) plays a vital role in promoting long-term health and wellness among students. Effective scheduling of PE classes is essential for maximizing fitness improvements across diverse populations. However, traditional approaches to optimizing PE schedules may not adequat...
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2025-06-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1555977/full |
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| author | Liang Tan Qin Chen Jianwei Wu Mingbang Li Tianyu Liu |
| author_facet | Liang Tan Qin Chen Jianwei Wu Mingbang Li Tianyu Liu |
| author_sort | Liang Tan |
| collection | DOAJ |
| description | IntroductionPhysical education (PE) plays a vital role in promoting long-term health and wellness among students. Effective scheduling of PE classes is essential for maximizing fitness improvements across diverse populations. However, traditional approaches to optimizing PE schedules may not adequately account for individual differences in demographics and activity patterns.MethodsThis study proposes an efficient method for optimizing PE schedules using deep learning (DL) techniques. The developed DL model integrates convolutional neural network (CNN) layers to capture spatial features and long short-term memory (LSTM) layers to extract temporal patterns from demographic and activity-related variables. These features are combined through a fusion layer, and a customized loss function is employed to accurately predict fitness scores.ResultsExtensive experimental evaluation demonstrates that the proposed model consistently outperforms competitive baseline models. Specifically, the model achieved notable improvements in mean squared error (MSE) by 1.35%, R-squared R2 by 1.18%, and mean absolute error (MAE) by 1.22% compared to existing approaches.DiscussionThe findings indicate that the DL-based approach provides an effective method for optimizing PE schedules; resulting in increased fitness levels and potential long-term health benefits. This model can assist educational institutions and policymakers in designing and implementing effective PE programs personalized to diverse student populations. |
| format | Article |
| id | doaj-art-08a52cb28d5249dfa03e8b1d7d8ac533 |
| institution | Kabale University |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj-art-08a52cb28d5249dfa03e8b1d7d8ac5332025-08-20T03:31:20ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-06-011310.3389/fpubh.2025.15559771555977Optimizing physical education schedules for long-term health benefitsLiang Tan0Qin Chen1Jianwei Wu2Mingbang Li3Tianyu Liu4School of Sport and Health, Chengdu University of Traditional Chinese Medicine, Chengdu, ChinaSchool of Sport, Hunan University of Humanities, Science and Technology, Loudi, ChinaSchool of Sport and Health, Chengdu University of Traditional Chinese Medicine, Chengdu, ChinaCollege of Physical Education and Health, Geely University, Chengdu, ChinaSchool of Sport and Health, Chengdu University of Traditional Chinese Medicine, Chengdu, ChinaIntroductionPhysical education (PE) plays a vital role in promoting long-term health and wellness among students. Effective scheduling of PE classes is essential for maximizing fitness improvements across diverse populations. However, traditional approaches to optimizing PE schedules may not adequately account for individual differences in demographics and activity patterns.MethodsThis study proposes an efficient method for optimizing PE schedules using deep learning (DL) techniques. The developed DL model integrates convolutional neural network (CNN) layers to capture spatial features and long short-term memory (LSTM) layers to extract temporal patterns from demographic and activity-related variables. These features are combined through a fusion layer, and a customized loss function is employed to accurately predict fitness scores.ResultsExtensive experimental evaluation demonstrates that the proposed model consistently outperforms competitive baseline models. Specifically, the model achieved notable improvements in mean squared error (MSE) by 1.35%, R-squared R2 by 1.18%, and mean absolute error (MAE) by 1.22% compared to existing approaches.DiscussionThe findings indicate that the DL-based approach provides an effective method for optimizing PE schedules; resulting in increased fitness levels and potential long-term health benefits. This model can assist educational institutions and policymakers in designing and implementing effective PE programs personalized to diverse student populations.https://www.frontiersin.org/articles/10.3389/fpubh.2025.1555977/fullfitness score predictionlong-term health benefitsdata-driven public healthhealth promotion strategiesphysical education |
| spellingShingle | Liang Tan Qin Chen Jianwei Wu Mingbang Li Tianyu Liu Optimizing physical education schedules for long-term health benefits Frontiers in Public Health fitness score prediction long-term health benefits data-driven public health health promotion strategies physical education |
| title | Optimizing physical education schedules for long-term health benefits |
| title_full | Optimizing physical education schedules for long-term health benefits |
| title_fullStr | Optimizing physical education schedules for long-term health benefits |
| title_full_unstemmed | Optimizing physical education schedules for long-term health benefits |
| title_short | Optimizing physical education schedules for long-term health benefits |
| title_sort | optimizing physical education schedules for long term health benefits |
| topic | fitness score prediction long-term health benefits data-driven public health health promotion strategies physical education |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1555977/full |
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