Analysis of the mechanism of physical activity enhancing well-being among college students using artificial neural network
Abstract This study explores the impact mechanism of college students’ sports behavior on their well-being by constructing an Artificial Neural Network (ANN) model. The study employs an ANN architecture that combines a Long Short-Term Memory (LSTM) network and a Convolutional Neural Network (CNN). A...
Saved in:
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-07-01
|
| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-11269-3 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849345151892193280 |
|---|---|
| author | Yuxin Cong Roxana Dev Omar Dev Shamsulariffin Bin Samsudin Kaihao Yu |
| author_facet | Yuxin Cong Roxana Dev Omar Dev Shamsulariffin Bin Samsudin Kaihao Yu |
| author_sort | Yuxin Cong |
| collection | DOAJ |
| description | Abstract This study explores the impact mechanism of college students’ sports behavior on their well-being by constructing an Artificial Neural Network (ANN) model. The study employs an ANN architecture that combines a Long Short-Term Memory (LSTM) network and a Convolutional Neural Network (CNN). A prediction model is established based on the characteristics of sports behavior and psychological indices of well-being, such as psychological resilience, self-efficacy, and subjective well-being. The results show that the proposed LSTM + CNN model has achieved significant improvement on the test set. Its mean absolute error is only 0.072, the mean square error is 0.00596, and the root mean square error is 0.077, which is remarkably superior to traditional machine learning methods such as random forest and support vector regression. The innovative advantages of the proposed model in capturing the nonlinear relationships and deep characteristics of psychological and behavioral data is proved. The analysis of Shapley Additive Explanations (SHAP) values reveals three key factors significantly influencing well-being improvement. These impactful factors include the high-frequency exercise days per week (≥ 4), sustained morning exercise duration, and participation levels in group sports activities. The analysis of the dynamic threshold effect reveals that the critical points of distinct characteristic values exhibit substantial variations in their impact on well-being. Concurrently, the regulatory influence of sports behavior demonstrates differing intensities across diverse conditions. This study provides a new theoretical basis for designing personalized sports interventions and improves the accuracy of predicting psychological measurement data. Thus, it demonstrates the potential of sports behavior in promoting the mental health and well-being of college students. |
| format | Article |
| id | doaj-art-df9adce685bc4a51a45086cd18da6c1f |
| institution | Kabale University |
| issn | 2045-2322 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Nature Portfolio |
| record_format | Article |
| series | Scientific Reports |
| spelling | doaj-art-df9adce685bc4a51a45086cd18da6c1f2025-08-20T03:42:31ZengNature PortfolioScientific Reports2045-23222025-07-0115111510.1038/s41598-025-11269-3Analysis of the mechanism of physical activity enhancing well-being among college students using artificial neural networkYuxin Cong0Roxana Dev Omar Dev1Shamsulariffin Bin Samsudin2Kaihao Yu3Department of Sports Studies, Faculty of Educational Studies, Universiti Putra MalaysiaDepartment of Sports Studies, Faculty of Educational Studies, Universiti Putra MalaysiaDepartment of Sports Studies, Faculty of Educational Studies, Universiti Putra MalaysiaDepartment of Sports Studies, Faculty of Educational Studies, Universiti Putra MalaysiaAbstract This study explores the impact mechanism of college students’ sports behavior on their well-being by constructing an Artificial Neural Network (ANN) model. The study employs an ANN architecture that combines a Long Short-Term Memory (LSTM) network and a Convolutional Neural Network (CNN). A prediction model is established based on the characteristics of sports behavior and psychological indices of well-being, such as psychological resilience, self-efficacy, and subjective well-being. The results show that the proposed LSTM + CNN model has achieved significant improvement on the test set. Its mean absolute error is only 0.072, the mean square error is 0.00596, and the root mean square error is 0.077, which is remarkably superior to traditional machine learning methods such as random forest and support vector regression. The innovative advantages of the proposed model in capturing the nonlinear relationships and deep characteristics of psychological and behavioral data is proved. The analysis of Shapley Additive Explanations (SHAP) values reveals three key factors significantly influencing well-being improvement. These impactful factors include the high-frequency exercise days per week (≥ 4), sustained morning exercise duration, and participation levels in group sports activities. The analysis of the dynamic threshold effect reveals that the critical points of distinct characteristic values exhibit substantial variations in their impact on well-being. Concurrently, the regulatory influence of sports behavior demonstrates differing intensities across diverse conditions. This study provides a new theoretical basis for designing personalized sports interventions and improves the accuracy of predicting psychological measurement data. Thus, it demonstrates the potential of sports behavior in promoting the mental health and well-being of college students.https://doi.org/10.1038/s41598-025-11269-3Artificial neural networkPsychological resilienceSHAP value analysisSports behaviorWell-being |
| spellingShingle | Yuxin Cong Roxana Dev Omar Dev Shamsulariffin Bin Samsudin Kaihao Yu Analysis of the mechanism of physical activity enhancing well-being among college students using artificial neural network Scientific Reports Artificial neural network Psychological resilience SHAP value analysis Sports behavior Well-being |
| title | Analysis of the mechanism of physical activity enhancing well-being among college students using artificial neural network |
| title_full | Analysis of the mechanism of physical activity enhancing well-being among college students using artificial neural network |
| title_fullStr | Analysis of the mechanism of physical activity enhancing well-being among college students using artificial neural network |
| title_full_unstemmed | Analysis of the mechanism of physical activity enhancing well-being among college students using artificial neural network |
| title_short | Analysis of the mechanism of physical activity enhancing well-being among college students using artificial neural network |
| title_sort | analysis of the mechanism of physical activity enhancing well being among college students using artificial neural network |
| topic | Artificial neural network Psychological resilience SHAP value analysis Sports behavior Well-being |
| url | https://doi.org/10.1038/s41598-025-11269-3 |
| work_keys_str_mv | AT yuxincong analysisofthemechanismofphysicalactivityenhancingwellbeingamongcollegestudentsusingartificialneuralnetwork AT roxanadevomardev analysisofthemechanismofphysicalactivityenhancingwellbeingamongcollegestudentsusingartificialneuralnetwork AT shamsulariffinbinsamsudin analysisofthemechanismofphysicalactivityenhancingwellbeingamongcollegestudentsusingartificialneuralnetwork AT kaihaoyu analysisofthemechanismofphysicalactivityenhancingwellbeingamongcollegestudentsusingartificialneuralnetwork |