The effectiveness of multi-component goal setting interventions for changing physical activity behaviour: a systematic review and meta-analysis
Bibliographic record
Abstract
Drawing from goal setting theory (Latham & Locke, 1991; Locke & Latham, 2002; Locke et al., 1981), the purpose of this study was to conduct a systematic review and meta-analysis of multi-component goal setting interventions for changing physical activity (PA) behaviour. A literature search returned 41,038 potential articles. Included studies consisted of controlled experimental trials wherein participants in the intervention conditions set PA goals and their PA behaviour was compared to participants in a control group who did not set goals. A meta-analysis was ultimately carried out across 45 articles (comprising 52 interventions, 126 effect sizes, n = 5912) that met eligibility criteria using a random-effects model. Overall, a medium, positive effect (Cohen's d(SE) = .552(.06), 95% CI = .43-.67, Z = 9.03, p < .001) of goal setting interventions in relation to PA behaviour was found. Moderator analyses across 20 variables revealed several noteworthy results with regard to features of the study, sample characteristics, PA goal content, and additional goal-related behaviour change techniques. In conclusion, multi-component goal setting interventions represent an effective method of fostering PA across a diverse range of populations and settings. Implications for effective goal setting interventions are discussed.
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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 | 0.015 | 0.038 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.015 | 0.019 |
| Bibliometrics | 0.010 | 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) | 0.003 | 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".