Relationships Between Measures of the Physical Activity-Related Built Environment and Excess Weight in Preschoolers: A Retrospective, Population-Level Cohort Study
Bibliographic record
Abstract
Background: The built environment can impact health outcomes. Our purpose was to examine relationships between built environment variables related to physical activity and excess weight in preschoolers. Methods: In this retrospective, population-level study of 4- to 6-year-olds, anthropometric measurements were taken between 2009 and 2017 in Calgary and Edmonton, Alberta, Canada. Based on BMI z-scores (BMIz), children were classified as normal weight (−2 ≤ BMIz <1) or excess weight (BMIz ≥1; overweight and obesity). Physical activity-related built environment variables were calculated (distances to nearest playground, major park, school; street intersection density; number of playgrounds and major parks within an 800 m buffer zone). Binomial logistic regression models estimated associations between physical activity-related built environment variables and excess weight. Results: Our analysis included 140,368 participants (females: n = 69,454; Calgary: n = 84,101). For Calgary, adjusted odds ratios (aORs) showed the odds of excess weight increased 1% for every 100-intersection increase [1.010 (1.006–1.015); p < 0.0001] and 13.6% when there were ≥4 playgrounds (vs. 0 or 1) within an 800 m buffer zone [1.136 (1.037–1.243); p = 0.0059]. For Edmonton, aORs revealed lower odds of excess weight for every 100 m increase in distances between residences to nearest major park [0.991 (0.986–0.996); p = 0.0005] and school [0.992 (0.990–0.995); p < 0.0001]. The odds of excess weight decreased as the number of major parks within the 800 m buffer zone increased from 0 to 1 [0.943 (0.896–0.992); p = 0.023] and from 0 to ≥3 [0.879 (0.773–0.999); p = 0.048]. Conclusion: The physical activity-related built environment was associated with excess weight in preschoolers, although relationships varied between cities that differed demographically and geographically.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".