Examining the state, quality and strength of the evidence in the research on built environments and physical activity among adults: An overview of reviews from high income countries
Bibliographic record
Abstract
BACKGROUND: Built environments have been implicated in the development of chronic disease, with physical activity (PA) considered one of the critical mechanisms for this relationship. Substantial growth in research on built environments and PA makes navigating the available evidence challenging. OBJECTIVE: To examine and describe the current state, strength and quality of research investigating the associations between built environments and PA domains of active living (i.e., leisure, transportation, occupational) and total PA among adults (≥18 years) from high-income OECD countries. METHODS: We conducted an overview of systematic reviews. A systematic search of six bibliographic databases and grey literature from January 2000 to May 2020. Review quality was assessed with the AMSTAR2. Results by age group were synthesized narratively and direction of association displayed using harvest plots. Certainty of the evidence was assessed using a modified GRADE approach. RESULTS: The overview included 116 systematic reviews. Most evidence was cross-sectional and of low-to-very low quality. Moderate-to-high certainty of evidence supported positive associations between environments that support active transportation (e.g., walkability, walking infrastructure, street connectivity, land-use mix) and transportation PA among adults/working-aged adults. Across all age groups, there was very low-to-moderate certainty for consistent positive associations between point of decision prompts (e.g., signs in stairwells and along paths) and all PA. Evidence from older adults was of very low certainty and largely equivocal. There was little-to-no evidence for young and middle-aged adults and occupational PA. DISCUSSION: While there has been an increase in evidence from observational and natural experiment studies, most has been related to active transportation infrastructure and point of decision prompts. There remains a need for these studies to evaluate built environments for leisure and occupational PA and among younger and older adults, and for high quality reviews to summarize this evidence. Interventions that target changes to the built environment show promise for promoting PA among adults, providing an important means to combat the global physical inactivity crisis.
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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 | 0.039 | 0.190 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.012 | 0.012 |
| Bibliometrics | 0.033 | 0.029 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.004 | 0.002 |
| 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".