Intervention effects on physical activity identity: a systematic review and meta-analysis
Bibliographic record
Abstract
Physical activity (PA) identity (i.e., categorisation of oneself in a particular role) has been linked to PA behaviour in observational research, yet experimental research has seen less attention. The purpose of this meta-analysis was to examine the effectiveness of interventions to change identity and subsequent PA. Eligible studies were published in a peer-reviewed journal in English, included an experimental or quasi-experimental design in the PA domain with a measure of identity as the dependent variable, among an adult (>18 yrs.) sample. A literature search was completed in March 2024 using five common databases. The search yielded 40 independent effect sizes, representing 4939 participants. Random-effects meta-analysis showed positive changes in identity favouring the intervention over the control group g = 0.18 (95% CI = 0.11–0.24) and positive changes in a sub-sample (k = 30) of these studies that also measured PA g = 0.61 (95% CI = 0.41–0.81). Changes in identity did not have significant (Q = 43.08, p = 0.30) heterogeneity, yet changes in PA showed heterogeneity (Q = 204.62, p < .001) and follow-up moderator analyses found potential publication bias, and differences by methods (comparison group, length of intervention) and theoretical approach. Overall, PA identity can change as a result of interventions, but the effect may be smaller than changes in behaviour in these interventions.
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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 | 0.013 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.022 | 0.036 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".