Direct and Indirect Relationships Between the Built Environment and Individual-Level Perceptions of Physical Activity: A Systematic Review
Bibliographic record
Abstract
BACKGROUND: Socioecological models highlight the potential direct and indirect effects of multiple levels of influence in explaining physical activity (PA). Social cognitive theories, however, position individual cognitions as the mediator of external factors such as the built environment when explaining PA. PURPOSE: To appraise the evidence for direct and indirect associations between the built environment and social cognition to predict PA. METHODS: Literature searches were concluded in February 2019 using five common databases. Eligible studies were in the English language that included any direct and indirect tests of individual perceptions and the built environment with PA. RESULTS: The initial search yielded 18,521 hits, which was reduced to 46 independent studies of primarily medium quality after screening for eligibility criteria. Findings were grouped by type of PA then grouped by the type of individual and built environment constructs within the model, and subdivided by adult and youth samples. There was evidence that self-efficacy/perceived control accounted for the covariance between environmental accessibility/convenience and total PA, while habit accounted for the covariance in this relationship for transport PA, particularly in adult samples. There was no evidence that the built environment had a direct association with PA after controlling for individual-level factors. CONCLUSIONS: The results provide initial support for the mediation tenet in social cognition models for the relationship between individual, built environment, and PA. In practice, these findings highlight the need for coordinated interventions of individual and environmental change.
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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 | 0.006 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.009 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".