A Review and Meta-Analysis of Affective Judgments and Physical Activity in Adult Populations
Bibliographic record
Abstract
BACKGROUND: Popular theories of health behavior have often been criticized for neglecting an affective component to behavioral engagement. PURPOSE: This study reviewed affective judgment (AJ) constructs employed in physical activity research to assess the relationship with behavior. Studies were eligible if they included: (a) a measure of physical activity; (b) a distinct measure of AJ (e.g., affective attitude, enjoyment, intrinsic motivation); and (c) involved participants with a mean age of 18 years or older. METHODS: Literature searches were concluded in September, 2009 among five key search engines. This search yielded a total of 10,631 potentially relevant records; of these, 102 passed the eligibility criteria. Random effects meta-analysis procedures with correction for sampling and measurement bias were employed in the analysis. RESULTS: Articles were published between 1989 and 2009, with sample sizes ranging from 15 to 6,739. Of the studies included, 82 were correlational and 20 were experimental, yielding 114 independent samples. The majority of the correlational samples reported a significant positive correlation between AJ and physical activity (83 out of 85), with a summary r of 0.42 (95% CI 0.37 to 0.46) that was invariant across the measures employed, study quality, population sampled and cultural variables. Experimental studies demonstrated that persuasive, information-based, and self-regulatory interventions failed to change AJ; by contrast, environmental and experiential interventions showed promise in their capability to influence AJ. CONCLUSIONS: The results point to a medium-effect size relationship between AJ and physical activity. Interventions that change AJ are scarce despite their potential for changing physical activity. Future experimental work designed to evaluate the causal impact ofAJ on physical activity is required.
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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 | 0.022 | 0.068 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.020 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| 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".