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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".