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Theory of Planned Behavior extended model explains 29.2% of variance in physical activity behaviorNew model helps predict physical activity in young adults

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Key Takeaway
Note that the TPB extended model explains 29.2% of variance in physical activity behavior among youth.

This meta-analysis synthesized data from 31 independent samples involving 19,652 unique participants aged approximately 10 to 24 years to evaluate the Theory of Planned Behavior (TPB) extended model. The analysis focused on the predictive effects of behavioral intention, behavior plan, and perceived behavioral control (PBC) on physical activity participation.

The TPB extended model explained 54.7% of intention differences and 28.9% of behavior plan differences. The model explained 29.2% of variance in physical activity behavior. Results indicated that behavioral intention was a significant statistical mediator between the three major factors of the TPB and behavior. Additionally, perceived behavioral control showed the strongest statistical association with behavioral intention.

Several limitations were noted, including extreme heterogeneity (I2 > 90% for many pairwise associations). Because the data were derived from cross-sectional designs, the results do not allow for causal or temporal inferences regarding behavior change. High heterogeneity also limits the generalizability of pooled estimates to specific subgroups.

For clinical and program design, the findings suggest that perceived behavioral control and behavior plans may be key variables for inclusion in future intervention designs. However, the causal impact on behavior change must be established through controlled trials.

Why do some young people stay active while others struggle to start? Researchers looked at data from nearly 20,000 adolescents and college students to find out what actually drives physical activity. They used a model that looks at how people think about their goals and their ability to reach them.

The study found that a person's belief in their own control over their actions was the strongest predictor of their intention to be active. The model also showed that having a specific behavior plan was a major factor in determining whether someone actually participated in physical activity. These factors combined to explain about 29% of the differences in behavior among the participants.

While these findings are helpful for designing better programs, there are some important notes. Because the data was collected at one point in time, we cannot say for certain that these factors cause behavior change. Additionally, the wide variety in how different studies were conducted means these results might look different in different settings.

What this means for you:
Believing you can do it and having a clear plan are key factors in getting young people active.

Common questions

What factors help predict if a young person will be active?

The study found that a person's belief in their own control over their actions was the strongest link to their intention to be active. The model also showed that having a specific behavior plan was a key factor in determining whether a student actually participated in physical activity.

How much of the difference in activity did the model explain?

The model used in the study explained about 29.2% of the differences in physical activity behavior among the 19,652 participants. It also explained 28.9% of the differences in behavior plans and 54.7% of the differences in the intention to be active.

Can we say these factors definitely cause people to exercise more?

Not yet. Because the data was collected at one point in time, the study cannot prove that these factors cause a change in behavior. More controlled trials are needed to see if these specific factors directly cause people to become more active over time.

Study Details

Study typeMeta analysis
EvidenceLevel 1
PublishedSep 2026
View Original Abstract ↓
Objectives(1) To conduct a meta-analysis on the structure of the TPB extended model to examine the predictive effects of its components on physical activity participation among adolescents and college students; (2) To investigate the moderating effects of key research dimensions, including participant groups, cultural backgrounds, physical activity measurement methods, study designs, and gender composition (proportion of females), on the relationships between variables in the TPB extended model and behavioral models for adolescents and college students physical activity; (3) To test the mediating effects of the TPB extended model; (4) To examine the direct effect of perceived behavioral control on adolescents’ and college students’ behavior.MethodsA systematic literature search was conducted across multiple databases to identify studies applying the extended Theory of Planned Behavior (TPB) model to physical activity (PA) in adolescents and college students (approximately 10–24 years). A total of 28 articles (including 31 independent samples, with a total of 19,652 unique participants) met the inclusion criteria. Pairwise meta-analyses were performed using random-effects models in CMA 3.0 software. Meta-analytic structural equation modeling (MASEM) was conducted in Amos 24.0, with covariance matrix and harmonic mean constructed based on the summarized correlation matrix. Moderation analyses were conducted to examine the moderating effects of participant group, cultural background, physical activity measurement method, study design, and gender composition. Publication bias was assessed using funnel plots and Egger’s regression tests, and methodological quality was evaluated using the adapted Newcastle-Ottawa Scale (NOS-xs).ResultsThe TPB extended model explained 54.7% of intention differences, 29.2% of behavior differences, and 28.9% of behavior plan differences. Behavioral intention was found to be a significant statistical mediator variable connecting the three major factors of TPB with behavior, and additional indirect effects have been observed through the sequential pathway of behavioral intention and behavior plan. Moderator analyses revealed that the strength of several TPB relationships was contingent upon participant characteristics (adolescents and college students), cultural background, measurement methods, and study design.ConclusionThe TPB extended model demonstrates predictive utility for adolescents and college student’s physical activities at the aggregate level and can explain, on average, substantial variations in behavioral intention, behavior, and behavior plan across the diverse studies synthesized here. Nevertheless, the extreme heterogeneity (I2 > 90% for many pairwise associations) indicates that effect sizes vary considerably across populations, instruments, and study designs, limiting the generalizability of any single pooled estimate to specific subgroups or settings. In this cross-sectional synthesis, Perceived Behavioral Control (PBC) showed the strongest statistical association with behavioral intention. Behavior plan was also statistically associated with the relationship between behavioral intention and action, and may represent a potential explanatory pathway; however, the predominantly cross-sectional design precludes any causal or temporal inferences. The action path of this model is moderated by participant group, cultural background, measurement method, and study design, highlighting its contextual sensitivity. These findings underscore that PBC and behavior plan may be key variables for inclusion in future intervention designs, although their causal impact on behavior change still remains to be established via controlled trials.
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