Investigating the Relationship between Structural Features of Built Environment and Physical Activity Using Geographic Information Systems (GIS)
Bibliographic record
Abstract
Background: Built environment determinants of engaging in physical activity are dependent on the properties of societies. This study investigates the relationship between structural variables of the environment using geographic information systems (GIS) and the level of physical activity in 22 districts of Tehran. Methods: This cross-sectional study was based on Urban Health Equity Assessment and Response Tool (Urban HEART-2). Physical activity level was assessed via the Global Physical Activity Questionnaire (GPAQ). The characteristics of the neighborhood environment, including land use, street pattern, population density, and traffic, were determined via ArcGIS software. Walkability index (population density, street pattern, land use) was calculated to assess the effect of the main variables of living environment on physical activity level. Results: Among the built environmental variables, land use was associated with the total physical activity and travel-related physical activity level (r: 0.155, P value: 0.001, and r: 0.122, P value: 0.007, respectively). The walkability index indicated an association with the total physical activity level and travel-related physical activity level (r: 0.126, P value: 0.006, and r: 0.135, P value: 0.001, respectively). Higher levels of the walkability index were associated with an improved level of physical activity (OR: 2.04). Conclusions: Walkability index and land use positively correlate with total physical activity level, and providing action plans that improve walkability index and land use might lead to increased physical activity level.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".