One small step for man, one giant leap for men’s health: a meta-analysis of behaviour change interventions to increase men’s physical activity
Bibliographic record
Abstract
OBJECTIVE: To determine the effects of behaviour change interventions on men's physical activity (postintervention), sustained change in physical activity behaviour (≥12 months postintervention) and to identify variations in effects due to potential moderating variables (eg, theoretical underpinning, gender-tailored, contact frequency). DESIGN: . DATA SOURCES: Medline, EMBASE, CINAHL, SportDiscus and Web of Science to April 2019. ELIGIBILITY CRITERIA FOR SELECTED STUDIES: Randomised control trials of behaviour change interventions in men (≥18 years) where physical activity was an outcome and data were from men-only studies or disaggregated by sex. RESULTS: Twenty-six articles described 24 eligible studies. The overall mean intervention effect on men's physical activity was 0.35 (SE=0.05; 95% CI 0.26 to 0.45; p<0.001). This effect size is consistent with an increase of approximately 97 min of total physical activity per week or 980 steps per day. Intervention moderators associated with greater increases in physical activity included objective physical activity outcome measures, a gender-tailored design, use of a theoretical framework, shorter length programmes (≤12 weeks), using four or more types of behaviour change techniques and frequent contact with participants (≥1 contact per week). 12 studies included additional follow-up assessments (≥12 months postintervention) and the overall mean effect was 0.32 (SE=0.09; 95% CI 0.15 to 0.48; p<0.001) for that sustained increase in physical activity. SUMMARY: Behaviour change interventions targeting men's physical activity can be effective. Moderator analyses are preliminary and suggest research directions.
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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 | 0.054 | 0.104 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.028 | 0.056 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".