The intention-behaviour gap in physical activity: a systematic review and meta-analysis of the action control framework
Bibliographic record
Abstract
OBJECTIVE: Intention is the proximal antecedent of physical activity in many popular psychological models. Despite the utility of these models, the discrepancy between intention and actual behaviour, known as the intention-behaviour gap, is a central topic of current basic and applied research. The purpose of this meta-analysis was to quantify intention-behaviour profiles and the intention-behaviour gap. DESIGN: Systematic review and meta-analysis. DATA SOURCES: Literature search was conducted in June 2022 and updated in February 2023 in five databases. ELIGIBILITY CRITERIA FOR SELECTING STUDIES: Eligible studies included a measure of physical activity, an assessment of physical activity intention and the employment of the intention-behaviour relationship into profile quadrants. Only papers published in the English language and in peer-reviewed journals were considered. Screening was assisted by the artificial intelligence tool ASReview. RESULTS: Twenty-five independent samples were selected from 22 articles including a total of N=29 600. Random-effects meta-analysis revealed that 26.0% of all participants were non-intenders not exceeding their intentions, 4.2% were non-intenders who exceeded their intentions, 33.0% were unsuccessful intenders and 38.7% were successful intenders. Based on the proportion of unsuccessful intenders to all intenders, the overall intention-behaviour gap was 47.6%. CONCLUSION: The findings underscore that intention is a necessary, yet insufficient antecedent of physical activity for many. Successful translation of a positive intention into behaviour is nearly at chance. Incorporating mechanisms to overcome the intention-behaviour gap are recommended for clinical practice.
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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 | 0.050 | 0.118 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.035 |
| Bibliometrics | 0.014 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.002 |
| 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".