Applying Social Cognitive Theory in Predicting Physical Activity Among Chinese Adolescents: A Cross-Sectional Study With Multigroup Structural Equation Model

Frontiers in Psychology 12 (2022)
  Copy   BIBTEX

Abstract

This cross-sectional study aimed to assess the applicability of social cognitive determinants among the Chinese adolescents and examine whether the predictability of the social cognitive theory model on physical activity differs across gender and urbanization. A total of 3,000 Chinese adolescents ranging between the ages of 12–15 years were randomly selected to complete a set of questionnaires. Structural equation modeling was applied to investigate the relationships between social cognitive variables and PA in the urbanization and gender subgroups. The overall model explained 38.9% of the variance in PA. Fit indices indicated that the structural model of SCT was good: root mean square error of approximation = 0.047, RMR = 0.028, goodness of fit index = 0.974, adjusted goodness of fit index = 0.960, Tucker–Lewis coefficient = 0.971, and comparative fit index = 0.978. Regarding the subgroup analysis, social support had a more substantial impact on the PA of adolescents in suburban areas than that in urban areas, whereas self-regulation had a more substantial impact on the PA of adolescents in urban areas than in suburban areas. The results indicate that the SCT model predicts the PA of Chinese adolescents substantially. An SCT model could apply over a range of subgroups to predict the PA behavior and should be considered comprehensively when designing interventions. These findings would benefit PA among the Chinese adolescents, especially across genders and urbanization.

Other Versions

No versions found

Links

PhilArchive



    Upload a copy of this work     Papers currently archived: 97,244

External links

Setup an account with your affiliations in order to access resources via your University's proxy server

Through your library

Similar books and articles

Analytics

Added to PP
2022-04-09

Downloads
12 (#1,258,301)

6 months
7 (#960,159)

Historical graph of downloads
How can I increase my downloads?