Urbanization and Physical Activity in the Global Prospective Urban and Rural Epidemiology Study
Bibliographic record
Abstract
Abstract The world is rapidly urbanizing, which may influence physical activity (PA) levels - although little evidence is available for low- and middle-income countries. We evaluated associations between urbanization and total PA, as well as work-, leisure-, home-, and transport-specific PA, for 138,206 adults (35-70 years) living in 698 communities across 22 countries within the Prospective Urban and Rural Epidemiology (PURE) study. The 1-week total PA long-form International PA Questionnaire (IPAQ) was administered at baseline (2003-2015) and we used satellite-derived population density and impervious surface area to quantify levels of urbanization for 5 and 10 years prior to PA measurements. Generalized linear mixed effects models were used to examine associations between urbanization measures and PA, controlling for individual, household and community factors. Higher community baseline levels of population density (-12.4%, 95% CI: -16.0%, -8.7% per IQR) and impervious surface area (-29.2%, 95% CI: -37.5%, -19.7% per IQR), as well as 5-year population density change (-17.2%, 95% CI: -25.7%, -7.7 per IQR) was associated with lower total PA. Important differences in the associations between urbanization metrics and PA were observed between PA domains, country income levels, urban and rural status, and gender. These findings provide new information on the complex associations between urbanization and PA.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 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".