Emotion regulation interventions on physical activity: a systematic review and meta-analysis
Bibliographic record
Abstract
The purpose of this meta-analysis was to examine the effectiveness of emotion regulation (ER; i.e. intra-psychic tactic(s) to manage one’s emotions) interventions to change physical activity and to explore potential moderators of the findings. Eligible studies were published in a peer-reviewed journal in English, included an experimental design with physical activity as the dependent variable and ER as the independent variable, among adults (>18 yrs.). A literature search completed in March 2025, using six common databases, yielded 30 independent effect sizes. Random-effects meta-analysis, after removing four outliers (N = 1934), showed positive changes in physical activity favoring the intervention over the control group g = 0.24 (95% CI = 0.12 to 0.36). The point estimate, however, showed significant (Q = 40.32, p = 0.03) heterogeneity, and follow-up moderator analyses found that studies with smaller sample sizes, all-female samples, validated self-report of physical activity, feasibility designs, shorter interventions, and shorter follow-up assessments of physical activity reported significantly larger effect sizes when compared to larger samples, mixed gender samples, direct physical activity assessments, and effectiveness trials with longer durations and follow-up periods. Overall, the findings show that physical activity may change as a result of experimental intervention using ER approaches, but estimates are biased by preliminary phase studies.
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.
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 | 0.015 | 0.032 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.036 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 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".