Environmental attributes and sedentary behaviours among Canadian adults
Bibliographic record
Abstract
Abstract The potential of the neighbourhood built environment for reducing sedentary behaviour has been highlighted in the recent research building on the socio-ecological models. Nevertheless, few studies have investigated the associations between objectively-measured environmental attributes and domain-specific sedentary behaviours in different geographical locations. Notably, high-quality environmental measures that are less data-dependent and are replicable in and comparable across different contexts are needed to expand the evidence on urban design and public health. We examined associations of environmental attributes and Space Syntax Walkability (SSW) with leisure screen time and car driving in a sample of Canadian adults. A total of 2006 Calgarian adults completed a survey that captured their leisure screen time and car driving. Environmental attributes were population density, intersection density, availability of sidewalks, availability of destinations, and SSW using geographic information systems. Adjusting for covariates, a one standard deviation increase in SSW was associated with 0.43 (95% CI −0.85, −0.02) hours/week decrease in leisure screen time. No other environmental attributes were significantly associated with leisure screen time. All environmental attributes (except the availability of sidewalks) were negatively associated with car driving. The strongest association was observed between SSW with car driving—a one standard deviation increase in SSW was associated with 0.77 (95% CI −0.85, −0.02) hours/week decrease in the car driving. Those who lived in highly populated and more connected areas with a variety of destinations nearby spent less time driving their cars. Further, our findings highlight that the composite measure of SSW is associated with both leisure screen time and car driving. Focusing on a novel environmental aspect (SSW) and an emerging health risk factor (sedentary behaviour) among a relatively large sample of Canadian adults, our study provides unique insights into environmental health research.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".