What Predicts the Physical Activity Intention–Behavior Gap? A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Intention is theorized as the proximal determinant of behavior in many leading theories and yet intention-behavior discordance is prevalent. PURPOSE: To theme and appraise the variables that have been evaluated as potential moderators of the intention-physical activity (I-PA) relationship using the capability-opportunity-motivation- behavior model as an organizational frame. METHODS: Literature searches were concluded in August 2020 using seven common databases. Eligible studies were selected from English language peer-reviewed journals and had to report an empirical test of moderation of I-PA with a third variable. Findings were grouped by the moderator variable for the main analysis, and population sample, study design, type of PA, and study quality were explored in subanalyses. RESULTS: The search yielded 1,197 hits, which was reduced to 129 independent studies (138 independent samples) of primarily moderate quality after screening for eligibility criteria. Moderators of the I-PA relationship were present among select variables within sociodemographic (employment status) and personality (conscientiousness) categories. Physical capability, and social and environmental opportunity did not show evidence of interacting with I-PA relations, while psychological capability had inconclusive findings. By contrast, key factors underlying reflective (intention stability, intention commitment, low goal conflict, affective attitude, anticipated regret, perceived behavioral control/self-efficacy) and automatic (identity) motivation were moderators of I-PA relations. Findings were generally invariant to study characteristics. CONCLUSIONS: Traditional intention theories may need to better account for key I-PA moderators. Action control theories that include these moderators may identify individuals at risk for not realizing their PA intentions. Prospero # CRD42020142629.
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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 | 0.014 | 0.086 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".