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

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 (SCT) model on physical activity (PA) differs across gender (boys and girls) and urbanization (urban and suburban). 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 (SEM) 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 (RMSEA) = 0.047, (root mean square residual) RMR = 0.028, goodness of fit index (GFI) = 0.974, adjusted goodness of fit index (AGFI) = 0.960, Tucker–Lewis coefficient (TLI) = 0.971, and comparative fit index (CFI) = 0.978. Regarding the subgroup analysis, social support (critical ratios [CRs] = 2.118; p < 0.001) had a more substantial impact on the PA of adolescents in suburban areas than that in urban areas, whereas self-regulation (CRs = −2.896, p < 0.001) 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.

Effectiveness of mHealth App–Based Interventions for Increasing Physical Activity and Improving Physical Fitness in Children and Adolescents: Systematic Review and Meta-Analysis

“…Subgroup analysis revealed that interventions based on SDT significantly increased TPA and MVPA in children and adolescents, while interventions based on SCT significantly reduced SB in this population. Previous studies have consistently shown that interventions based on SCT effectively improve PA among adolescents, which aligns with the findings of this study [ 54 ]. SCT is commonly used to elucidate the mechanisms underlying the improvement in PA through behavior change interventions.…”

“…Currently, mHealth app interventions employ multiple types of BCT that have been shown to be effective in influencing intervention outcomes [21]. Eight out of the 28 included studies did not employ theoretical frameworks in their mHealth app interventions [31,36,37,45,47,49,50,52] [65]. SCT is commonly used to elucidate the mechanisms underlying the improvement in PA through behavior change interventions.…”

Relationships between Perfectionism, Extra Training and Academic Performance in Chinese Collegiate Athletes: Mediating Role of Achievement Motivation

“…Finally, 243 (203 boys, 40 girls, age: 20.56 ± 1.51 years) participants completed the second-wave data collection at the 3-month follow-up in December 2021. Based on the common rule of thumb for estimating the sample size for a structural equation modeling (SEM) (i.e., the sample size is usually 10 times the number of variables) [ 35 , 36 ], the sample size of 243 was sufficient for this study. All participants were informed about the study purpose and signed the written informed consent form before the data collection.…”

Social interaction, support preferences, and the use of wearable health trackers

“…More specifically, an individual's evaluation of these factors can contribute to them establishing goals or standard expectations, which subsequently drive behavioral choices. SCT has been applied successfully in past work within the context of fitness and health (e.g., Marmo, 2013;Liu et al, 2022) and more recently, WHT and broader mHealth initiatives (Lee and Cho, 2017;Lin and Chang, 2018;Gowin et al, 2019). In each case, both personal and environmental factors have been identified as driving engagement.…”