Moderators of the intention-behaviour and perceived behavioural control-behaviour relationships for leisure-time physical activity
Bibliographic record
Abstract
BACKGROUND: Intention is a key determinant of action. However, there is a gap between intention and behavioural performance that remains to be explained. Therefore, the aim of this study was to identify moderators of the intention-behaviour and perceived behavioural control (PBC)- behaviour relationships for leisure-time physical activity. METHOD: This was tested in reference to Ajzen's Theory of Planned Behaviour. A sample of 300 volunteers, 192 women and 108 men, aged 18 to 55, participated in the study. At baseline, the participants completed a self-administrated psychosocial questionnaire assessing Ajzen's theory variables (i.e., intention and perceived behavioural control). The behavioural measure was obtained by mail three months later. RESULTS: Multiple hierarchical regression analyses indicated that age and annual income moderated the intention-behaviour and PBC-behaviour relationships. However, in the final model predicting behaviour (R2 = .46), only the interaction term of PBC by annual income (beta = .24, p = 0.0003) significantly contributed to the prediction of behaviour along with intention (beta = .49, p = 0.0009) and past behaviour (beta = .44, p < 0.0001). CONCLUSION: Physical activity promotion programs would benefit not only from focusing on increasing the intention of low intenders, but also from targeting factors that moderate the perceived behavioural control-behaviour relationships.
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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 | 0.005 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".