A meta-analytic review of the effect of implementation intentions on physical activity
Bibliographic record
Abstract
Implementation intentions are a powerful strategy to promote health-related behaviours, but mixed results are observed regarding physical activity. The primary aim of this study was to systematically and quantitatively review the literature on the effectiveness of implementation intentions on physical activity. The second aim was to identify conditions under which effectiveness is optimal. A literature search was performed in several databases for published and non-published reports. The inverse variance method with random effect model was used for the meta-analysis of results. Effect sizes were reported as standard mean differences. Twenty-six independent studies were included in the systematic review. The overall effect size of implementation intentions was 0.31, 95% confidence intervals (CI) [0.11, 0.51] at post-intervention and 0.24, 95% CI [0.13, 0.35] at follow-up. The duration of follow-up had no significant effect on effect size (F(1, 18) = 0.21, p=0.66. This strategy was more effective among student and clinical samples, and when barrier management was part of implementation intentions. The present meta-analysis provides support for the use of implementation intentions to promote physical activity, even though the effect size is small to medium.
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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 | 0.026 | 0.077 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.018 | 0.025 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| 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".