Is neighbourhood walkability related to body mass index among different age groups? A cross-sectional study of Canadian urban areas
Bibliographic record
Abstract
BACKGROUND: Studies of neighbourhood walkability and body mass index (BMI) have shown mixed results, possibly due to biases from self-reported outcomes or differential effects across age groups. Our objective was to examine relationships between walkability and objectively measured BMI in various age groups, in a nationally representative population. METHODS: The study population came from the 2007-2011 Canadian Health Measures Survey, a cross-sectional survey of a nationally representative Canadian population. In our covariate-adjusted analyses, we included survey respondents aged 6-79 who were not pregnant, did not live in rural areas, were not missing data and were not thin/underweight. We used objectively measured height and weight to calculate BMI among adults aged 18-79 and zBMI among children aged 6-17. We categorised respondents into walkability quintiles based on their residential Street Smart Walk Score values. We performed linear regression to estimate differences between walkability quintiles in BMI and zBMI. We analysed adults and children overall; age subgroups 6-11, 12-17, 18-29, 30-44, 45-64 and 65-79; and sex subgroups. RESULTS: The covariate-adjusted models included 9265 respondents overall. After adjustment, differences between walkability quintiles in BMI and zBMI were small and not statistically significant, except for males aged 6-17 in the second-highest walkability quintile who had significantly lower zBMIs than those in the lowest quintile. CONCLUSION: After accounting for confounding factors, we did not find evidence of a relationship between walkability and BMI in children or adults overall, or in any age subgroup with sexes combined. However, post hoc analysis by sex suggested males aged 6-17 in more walkable areas may have lower zBMIs.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.004 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".