Using physical literacy to predict physical activity among university students: a machine learning logistic regression model
Download this article Xiaoxi Dong *, Zengying Yan **, Jiangzhou Deng ***, Heather Johanson Desiral ****, Menglin Xu ***** and Fan Huang ******
(*) Department of Sports Teaching and Research, Lanzhou University, Lanzhou, Gansu, 730000, P. R. China
(**) School of Physical Education, Chongqing University of Posts and Telecommunications, Nan’an District, Chongqing, 400065, P.R. China
(***) chool of Economics and Management, Chongqing University of Posts and Telecommunications, Nan’an District, Chongqing, 400065, P.R. China
(****) Adrian Dominican School of Education, Barry University, 11300 NE 2nd Ave Miami, FL 33161, United States
(*****) Department of Internal Medicine, Ohio State University, 281 W Lane Ave, Columbus, OH 43120, United States
(******) Faculty of Education, University of Macau, Taipa, Macau, P.R. China
Citation
Dong, X., Yan, Z., Deng, J., Johanson Desiral, H., Xu, M., Huang, F. (2024). Using physical literacy to predict physical activity among university students: a machine learning logistic regression model. International Journal of Sport Psychology, 55(3), 280-296. doi:10.7352/IJSP.2024.55.280
Abstract
Physical inactivity has become an increasingly challenging concern for health systems and policymakers globally. The purpose of this study was to examine whether physical literacy could be used to predict the physical activity behaviors of Chinese university students in order to provide an efficient tool for encouraging public health. 1,591 Chinese university students (females = 813, males = 778) with a mean age of 19.63 years (SD = 1.36) participated in this study. Findings indicated a practical machine learning logistic regression model (LRM) for predicting PA be- haviors among university students. The accuracy on the confusion matrix was .715, and the Receiver operating characteristic curve and Concordance test indicated that the LRM is a good model. The researchers conclude that the present LRM can be a useful tool for higher education institutions to monitor and foster students’ PA behaviors to promote a healthy lifestyle and life-long participation in physical activities.
Keywords: Physical literacy, Physical activity, Logistic regression, Machine learning