A Systematic Review and Meta-analysis of the Outcome Expectancy Construct in Physical Activity Research
Bibliographic record
Abstract
BACKGROUND: Cognition-based theories dominate physical activity (PA) research, and many include a construct broadly defined as "beliefs about the consequences of behavior" (e.g., outcome expectancies, perceived benefits) hereafter referred to as perceived consequences. PURPOSE: With the quantity of available research on this topic, it is important to examine whether the literature supports perceived consequences as a predictor of PA. METHODS: A meta-analysis examining longitudinal associations between perceived consequences and PA in adults was conducted. Studies were eligible if (a) perceived consequences were measured at a time point prior to PA, and (b) the target behavior was a form of PA. An omnibus meta-analysis estimating the mean effect of all included studies, and separate meta-analyses for perceived consequences content categories were conducted. RESULTS: This search yielded 6,979 articles, of these, 110 studies met inclusion criteria. Studies were published between 1989 and 2020, with sample sizes ranging from 16 to 2,824. All studies were evaluated as moderate to high quality. A small positive bivariate association was identified (r = 0.11; 95% CI [0.09, 0.13]) between perceived consequences and PA. Significant associations were identified for time, health, self-evaluative, psychological, and affective consequences. There was no association between perceived weight-related consequences and PA. CONCLUSIONS: The findings emphasize the variability with which existing studies have examined perceived consequences in the PA literature. Future research might examine whether these are important distinctions for understanding PA. Overall, the results suggest utility in examining perceived consequences as a predictor of PA, but constructs with more robust associations may require priority.
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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 | 0.028 | 0.081 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.018 | 0.024 |
| Bibliometrics | 0.016 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| 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".