Background: Although levels of physical activity (PA) have been researched, no information on how university students organize their PA across different life domains is available. The purpose of this study is to explore if and how students organize their PA across transport and recreational domains, and to identify the psychosocial factors related to these patterns. Methods: Students from 31 Irish universities completed a supervised online survey measuring participant characteristics, psychosocial factors, and PA. Two-step cluster analysis was used to identify specific PA patterns in students. Binary logistic regressions identified factors associated with cluster membership while controlling for age, sex, household income, and perceived travel time to a university. Results: Analysis was performed on 6951 students (50.7% male; 21.51 [5.55] y). One Low Active cluster emerged. Four clusters containing a form of PA emerged including Active Commuters, Active in University, Active Outside University, and High Active. Increases in motivation and planning improved the likelihood of students being categorized in a cluster containing PA. Conclusion: One size does not fit all when it comes to students PA engagement, with 5 patterns identified. Health professionals are advised to incorporate strategies for increasing students' motivation, action planning, and coping planning into future PA promotion efforts.
Background: Although levels of physical activity (PA) have been researched, no information on how university students organize their PA across different life domains is available. The purpose of this study is to explore if and how students organize their PA across transport and recreational domains, and to identify the psychosocial factors related to these patterns. Methods: Students from 31 Irish universities completed a supervised online survey measuring participant characteristics, psychosocial factors, and PA. Two-step cluster analysis was used to identify specific PA patterns in students. Binary logistic regressions identified factors associated with cluster membership while controlling for age, sex, household income, and perceived travel time to a university. Results: Analysis was performed on 6951 students (50.7% male; 21.51 [5.55] y). One Low Active cluster emerged. Four clusters containing a form of PA emerged including Active Commuters, Active in University, Active Outside University, and High Active. Increases in motivation and planning improved the likelihood of students being categorized in a cluster containing PA. Conclusion: One size does not fit all when it comes to students PA engagement, with 5 patterns identified. Health professionals are advised to incorporate strategies for increasing students' motivation, action planning, and coping planning into future PA promotion efforts.
Entities:
Keywords:
active commuting; cluster analysis; psychology; youth
Authors: Jindong Chang; Liming Yong; Yali Yi; Xiaolei Liu; Hanbing Song; Yan Li; Ming Yang; Lei Yao; Naiqing Song Journal: Front Public Health Date: 2021-04-22
Authors: Javier Molina-García; Cristina Menescardi; Isaac Estevan; Vladimir Martínez-Bello; Ana Queralt Journal: Int J Environ Res Public Health Date: 2019-08-31 Impact factor: 3.390
Authors: Antonio Castillo-Paredes; Natalia Inostroza Jiménez; Maribel Parra-Saldías; Ximena Palma-Leal; José Luis Felipe; Itziar Págola Aldazabal; Ximena Díaz-Martínez; Fernando Rodríguez-Rodríguez Journal: Int J Environ Res Public Health Date: 2021-02-13 Impact factor: 3.390