Factors associated with acquiring exercise habits through health guidance for metabolic syndrome among middle-aged Japanese workers: A machine learning approach
Objective: Physical inactivity increases the risk of mortality and chronic morbidity. Therefore, it is crucial to establish strategies to encourage individuals to increase their physical activity and develop exercise habits. The objective of this study was to explore factors associated with acquirin...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Preventive Medicine Reports |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2211335524003309 |
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| author | Jiawei Wan Kyohsuke Wakaba Takeshi Onoue Kazuyo Tsushita Yoshio Nakata |
| author_facet | Jiawei Wan Kyohsuke Wakaba Takeshi Onoue Kazuyo Tsushita Yoshio Nakata |
| author_sort | Jiawei Wan |
| collection | DOAJ |
| description | Objective: Physical inactivity increases the risk of mortality and chronic morbidity. Therefore, it is crucial to establish strategies to encourage individuals to increase their physical activity and develop exercise habits. The objective of this study was to explore factors associated with acquiring exercise habits using machine learning algorithms. Methods: The analyzed dataset was obtained from the Specific Health Guidance for metabolic syndrome systematically implemented by the Japanese Ministry of Health, Labor, and Welfare. We selected target individuals for health guidance without exercise habits in 2017 and assessed whether the participants acquired exercise habits through health guidance in 2018. We applied ten machine learning algorithms to build prediction models for acquiring exercise habits. Results: This study included 16,471 middle-aged Japanese workers (age, 49.5 ± 6.2 years). Among the machine learning algorithms, the Boosted Generalized Linear Model was the best for predicting the acquisition of exercise habits based on the receiver operating characteristic curve on the test set (ROC-AUCtest, 0.68). According to the analyses, the following factors were associated with the acquisition of exercise habits: being in the maintenance or action stage of changing exercise and eating behaviors based on the transtheoretical model; regular physical activity or walking; normal high-density lipoprotein cholesterol; and high alcohol consumption. Conclusions: Our findings can be used to establish an efficient strategy for encouraging individuals to acquire exercise habits through Specific Health Guidance or other health guidance. However, the lower ROC-AUCtest suggests that additional variables are necessary to enhance the prediction model. |
| format | Article |
| id | doaj-art-c4c6cfa15b5243f993841324933c3bfe |
| institution | Kabale University |
| issn | 2211-3355 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Preventive Medicine Reports |
| spelling | doaj-art-c4c6cfa15b5243f993841324933c3bfe2024-12-07T08:26:12ZengElsevierPreventive Medicine Reports2211-33552024-12-0148102915Factors associated with acquiring exercise habits through health guidance for metabolic syndrome among middle-aged Japanese workers: A machine learning approachJiawei Wan0Kyohsuke Wakaba1Takeshi Onoue2Kazuyo Tsushita3Yoshio Nakata4Graduate School of Comprehensive Human Sciences, University of Tsukuba, Tsukuba 305-8574, JapanFaculty of Human Life, Jumonji University, Niiza 352-8510, JapanDepartment of Endocrinology and Diabetes, Nagoya University Graduate School of Medicine, Nagoya 464-8601, JapanFaculty of Nutrition, Kagawa Nutrition University, Sakado 350-0288, JapanInstitute of Health and Sport Sciences, University of Tsukuba, Tsukuba 305-8574, Japan; Corresponding author.Objective: Physical inactivity increases the risk of mortality and chronic morbidity. Therefore, it is crucial to establish strategies to encourage individuals to increase their physical activity and develop exercise habits. The objective of this study was to explore factors associated with acquiring exercise habits using machine learning algorithms. Methods: The analyzed dataset was obtained from the Specific Health Guidance for metabolic syndrome systematically implemented by the Japanese Ministry of Health, Labor, and Welfare. We selected target individuals for health guidance without exercise habits in 2017 and assessed whether the participants acquired exercise habits through health guidance in 2018. We applied ten machine learning algorithms to build prediction models for acquiring exercise habits. Results: This study included 16,471 middle-aged Japanese workers (age, 49.5 ± 6.2 years). Among the machine learning algorithms, the Boosted Generalized Linear Model was the best for predicting the acquisition of exercise habits based on the receiver operating characteristic curve on the test set (ROC-AUCtest, 0.68). According to the analyses, the following factors were associated with the acquisition of exercise habits: being in the maintenance or action stage of changing exercise and eating behaviors based on the transtheoretical model; regular physical activity or walking; normal high-density lipoprotein cholesterol; and high alcohol consumption. Conclusions: Our findings can be used to establish an efficient strategy for encouraging individuals to acquire exercise habits through Specific Health Guidance or other health guidance. However, the lower ROC-AUCtest suggests that additional variables are necessary to enhance the prediction model.http://www.sciencedirect.com/science/article/pii/S2211335524003309Exercise habitMetabolic syndromeHealth guidanceNational databaseMachine learning |
| spellingShingle | Jiawei Wan Kyohsuke Wakaba Takeshi Onoue Kazuyo Tsushita Yoshio Nakata Factors associated with acquiring exercise habits through health guidance for metabolic syndrome among middle-aged Japanese workers: A machine learning approach Preventive Medicine Reports Exercise habit Metabolic syndrome Health guidance National database Machine learning |
| title | Factors associated with acquiring exercise habits through health guidance for metabolic syndrome among middle-aged Japanese workers: A machine learning approach |
| title_full | Factors associated with acquiring exercise habits through health guidance for metabolic syndrome among middle-aged Japanese workers: A machine learning approach |
| title_fullStr | Factors associated with acquiring exercise habits through health guidance for metabolic syndrome among middle-aged Japanese workers: A machine learning approach |
| title_full_unstemmed | Factors associated with acquiring exercise habits through health guidance for metabolic syndrome among middle-aged Japanese workers: A machine learning approach |
| title_short | Factors associated with acquiring exercise habits through health guidance for metabolic syndrome among middle-aged Japanese workers: A machine learning approach |
| title_sort | factors associated with acquiring exercise habits through health guidance for metabolic syndrome among middle aged japanese workers a machine learning approach |
| topic | Exercise habit Metabolic syndrome Health guidance National database Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2211335524003309 |
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