What Effect Do Goal Setting Interventions Have on Physical Activity and Psychological Outcomes in Insufficiently Active Adults? A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
BACKGROUND: Goal setting is commonly used for promoting physical activity (PA) among insufficiently active individuals. Previous reviews have analyzed the effects of goal setting on PA, but the purpose of this systematic review was to examine the concurrent effects of goal setting on PA and psychological outcomes in insufficiently active individuals to support interventions aiming to produce sustained PA behavior change. METHODS: In this review (PROSPERO: CRD42021243970), we identified 13 studies with 1208 insufficiently active adults that reported the effects of goal-setting interventions (range 3-24 wk) on both PA and psychological outcomes (eg, self-efficacy, motivation, and affect). We used meta-analysis and narrative synthesis to analyze these effects. RESULTS: All goals used in the included studies were specific goals. Setting specific goals had a large, positive effect on PA (g [standard mean difference] = 1.11 [P < .001]; 95% confidence interval, 0.74-1.47), but only a small, positive effect on the combined psychological outcomes (g [standard mean difference] = 0.25 [P < .001]; 95% CI, 0.10-0.40). Moderator analyses revealed that interventions that did not reward participants had a significantly greater effect on PA than interventions that did provide rewards (g = 1.30 vs 0.60, respectively, P ≤ .003). No other significant moderators were found. CONCLUSION: Our review offers initial insight into the long-term effects of specific goals on PA and psychological outcomes in insufficiently active adults. Further research that examines the PA and psychological effects of goal-setting interventions and investigates a wider range of goal types could develop a stronger evidence base to inform intervention for insufficiently active individuals.
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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 | 0.021 | 0.060 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.023 | 0.037 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| 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".