A meta-analytic review of the effect of implementation intentions on physical activity
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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 it