Mechanism by Which BMI Influences Leisure‐time Physical Activity Behavior
Bibliographic record
Abstract
OBJECTIVE: The objective of this prospective study was to clarify the mechanism by which BMI influences leisure-time physical activity. This was achieved in accordance with the assumptions underlying the Theory of Planned Behavior (TPB), considered as one of the most useful theories to predict behavior adoption. METHODS AND PROCEDURES: At baseline, a sample of 1,530 respondents completed a short questionnaire to measure intention and perceived behavioral control (PBC), the two proximal determinants of behavior of TPB. Past behavior, sociodemographic variables, and weight and height were also assessed. The dependent variable, leisure-time physical activity was assessed 3 months later. RESULTS: Hierarchical multiple regression analyses revealed that BMI is a direct predictor of future leisure-time physical activity, not mediated by the variables of TPB. Additional hierarchical analyses indicated that BMI was not a moderator of the intention-behavior and PBC-behavior relationships. DISCUSSION: The results of this study suggest that high BMI is a significant negative determinant of leisure-time physical activity. This observation reinforces the importance of preventing weight gain as a health promotion strategy for avoiding a sedentary lifestyle.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".