Predictors of School Students’ Leisure-Time Physical Activity: An Extended Trans-Contextual Model Using Bayesian Path Analysis

Abstract

Background:The trans-contextual model (TCM) has been applied to identify the determinants of leisure-time physical activity participation in secondary school students. In the current study, the TCM was extended to include additional constructs that represent non-conscious, implicit processes that lead to leisure-time physical activity participation alongside the motivational and social cognition constructs from the TCM. The current study used baseline and follow-up data from an intervention study to test the extended TCM. Methods:The current study adopted a two-wave prospective design. Secondary-school students (N = 502) completed measures of perceived autonomy support from physical education (PE) teachers, autonomous motivation in PE and leisure-time contexts, social cognition constructs (attitudes, subjective norms, perceived behavioral control), intentions, trait self-control, habits, and past behavior all in a leisure-time physical activity context. Five weeks later, students (N = 298) completed a follow-up self-report measure of their leisure-time physical activity. Results:Bayesian path analyses supported two key premises of the model: perceived autonomous support predicted autonomous motivation in PE, and autonomous motivation in PE predicted autonomous motivation in leisure time. However, while both forms of autonomous motivation predicted social cognition constructs and intentions, they did not predict leisure-time physical activity participation.Trait self-control, attitudes, and past behavior were direct predictors of intentions and leisure-time physical activity participation. Specifying informative priors for key model relations using Bayesian analysis yielded greater precision for some of the estimates. Conclusions:Findings raise some questions on the efficacy of the original TCM, but highlight the value of extending the TCM to incorporate additional constructs representing non-conscious processes. Note. †Parameters with informative priors. Model 1 = Bayesian path model with non-informative priors; Model 2 = Bayesian path model including informative priors; H = Hypothesis; β = Parameter estimate; 95% CrI = 95% credibility interval of path coefficient; %diff = Percent difference in 95% credibility interval of path coefficients of path analysis including informative priors for specified model relationships compared to analysis using non-informative priors (negative numbers indicate a