What factors influence the willingness and intensity of regular mobile physical activity?— A machine learning analysis based on a sample of 290 cities in China
IntroductionThis study, based on Volunteered Geographic Information (VGI) and multi-source data, aims to construct an interpretable macro-scale analytical framework to explore the factors influencing urban physical activities. Using 290 prefecture-level cities in China as samples, it investigates th...
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1511129/full |
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| author | Hao Shen Bo Shu Jian Zhang Yaoqian Liu Ali Li |
| author_facet | Hao Shen Bo Shu Jian Zhang Yaoqian Liu Ali Li |
| author_sort | Hao Shen |
| collection | DOAJ |
| description | IntroductionThis study, based on Volunteered Geographic Information (VGI) and multi-source data, aims to construct an interpretable macro-scale analytical framework to explore the factors influencing urban physical activities. Using 290 prefecture-level cities in China as samples, it investigates the impact of socioeconomic, geographical, and built environment factors on both overall physical activity levels and specific types of mobile physical activities.MethodsMachine learning methods were employed to analyze the data systematically. Socioeconomic, geographical, and built environment indicators were used as explanatory variables to examine their influence on activity willingness and activity intensity across different types of physical activities (e.g., running, walking, cycling). Interaction effects and non-linear patterns were also assessed.ResultsThe study identified three key findings: (1) A significant difference exists between the influencing factors of activity willingness and activity intensity. Socioeconomic factors primarily drive activity willingness, whereas geographical and built environment factors have a stronger influence on activity intensity. (2) The effects of influencing factors vary significantly by activity type. Low-threshold activities (e.g., walking) tend to amplify both promotional and inhibitory effects of the factors. (3) Some influencing factors display typical non-linear effects, consistent with findings from micro-scale studies.DiscussionThe findings provide comprehensive theoretical support for understanding and optimizing physical activity among urban residents. Based on these results, the study proposes guideline-based macro-level intervention strategies aimed at improving urban physical activity through effective public resource allocation. These strategies can assist policymakers in developing more scientific and targeted approaches to promote physical activity. |
| format | Article |
| id | doaj-art-bfda0249a0224685980db63c86685363 |
| institution | Kabale University |
| issn | 2296-2565 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Public Health |
| spelling | doaj-art-bfda0249a0224685980db63c866853632025-01-23T06:56:14ZengFrontiers Media S.A.Frontiers in Public Health2296-25652025-01-011310.3389/fpubh.2025.15111291511129What factors influence the willingness and intensity of regular mobile physical activity?— A machine learning analysis based on a sample of 290 cities in ChinaHao Shen0Bo Shu1Jian Zhang2Yaoqian Liu3Ali Li4School of Architecture, Southwest Jiaotong University, Chengdu, ChinaSchool of Design, Southwest Jiaotong University, Chengdu, ChinaSchool of Design, Southwest Jiaotong University, Chengdu, ChinaSWJTU-LEEDS Joint School, Southwest Jiaotong University, Chengdu, ChinaInformation and Network Management Center, Xihua University, Chengdu, ChinaIntroductionThis study, based on Volunteered Geographic Information (VGI) and multi-source data, aims to construct an interpretable macro-scale analytical framework to explore the factors influencing urban physical activities. Using 290 prefecture-level cities in China as samples, it investigates the impact of socioeconomic, geographical, and built environment factors on both overall physical activity levels and specific types of mobile physical activities.MethodsMachine learning methods were employed to analyze the data systematically. Socioeconomic, geographical, and built environment indicators were used as explanatory variables to examine their influence on activity willingness and activity intensity across different types of physical activities (e.g., running, walking, cycling). Interaction effects and non-linear patterns were also assessed.ResultsThe study identified three key findings: (1) A significant difference exists between the influencing factors of activity willingness and activity intensity. Socioeconomic factors primarily drive activity willingness, whereas geographical and built environment factors have a stronger influence on activity intensity. (2) The effects of influencing factors vary significantly by activity type. Low-threshold activities (e.g., walking) tend to amplify both promotional and inhibitory effects of the factors. (3) Some influencing factors display typical non-linear effects, consistent with findings from micro-scale studies.DiscussionThe findings provide comprehensive theoretical support for understanding and optimizing physical activity among urban residents. Based on these results, the study proposes guideline-based macro-level intervention strategies aimed at improving urban physical activity through effective public resource allocation. These strategies can assist policymakers in developing more scientific and targeted approaches to promote physical activity.https://www.frontiersin.org/articles/10.3389/fpubh.2025.1511129/fullphysical activitywillingness and intensitysocioeconomic factorsgeographical environmental factorsbuilt environmental factorsmachine learning |
| spellingShingle | Hao Shen Bo Shu Jian Zhang Yaoqian Liu Ali Li What factors influence the willingness and intensity of regular mobile physical activity?— A machine learning analysis based on a sample of 290 cities in China Frontiers in Public Health physical activity willingness and intensity socioeconomic factors geographical environmental factors built environmental factors machine learning |
| title | What factors influence the willingness and intensity of regular mobile physical activity?— A machine learning analysis based on a sample of 290 cities in China |
| title_full | What factors influence the willingness and intensity of regular mobile physical activity?— A machine learning analysis based on a sample of 290 cities in China |
| title_fullStr | What factors influence the willingness and intensity of regular mobile physical activity?— A machine learning analysis based on a sample of 290 cities in China |
| title_full_unstemmed | What factors influence the willingness and intensity of regular mobile physical activity?— A machine learning analysis based on a sample of 290 cities in China |
| title_short | What factors influence the willingness and intensity of regular mobile physical activity?— A machine learning analysis based on a sample of 290 cities in China |
| title_sort | what factors influence the willingness and intensity of regular mobile physical activity a machine learning analysis based on a sample of 290 cities in china |
| topic | physical activity willingness and intensity socioeconomic factors geographical environmental factors built environmental factors machine learning |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2025.1511129/full |
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