A Test of the Theory of Planned Behavior to Predict Physical Activity in an Overweight/Obese Population Sample of Adolescents From Alberta, Canada
Bibliographic record
Abstract
PURPOSE: To examine the utility of the theory of planned behavior (TPB) for explaining physical activity (PA) intention and behavior among a large population sample of overweight and obese adolescents (Alberta, Canada), using a web-based survey. Secondary objectives were to examine the mediating effects of the TPB constructs and moderating effects of weight status. METHODS: A subsample of 427 overweight and 133 obese participants (n = 560), completed a self-administered web-based questionnaire on health and PA behaviors, including assessment of attitude, subjective norm, perceived behavioral control (PBC), and intention to participate in regular PA. Structural equation models were examined using AMOS 17.0. RESULTS: Overall, 62% of the variance in intention was accounted for by attitude, subjective norm, and PBC; whereas 44% of the variance in PA behavior was explained by PBC and intention. When examining the TPB separately in overweight and obese subsamples, 66% and 56% of the variance for PA intention was explained for overweight and obese subsamples, respectively; and 38% and 56% of the variance in PA behavior were accounted for in the overweight and obese subsamples. Overall, attitude was the strongest predictor of PA intention, whereas PBC was the strongest predictor for PA behavior. Intention was not predictive of PA behavior. CONCLUSIONS: These results provide partial support for the utility of TPB in explaining PA behavior in a sample of overweight and obese adolescents. In particular, strong associations regarding attitude and PBC were evident across each subsample. These findings have implications for tailoring PA programs in this population.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".