The interplay between air pollution, built environment, and physical activity: perceptions of children and youth in rural and urban India
Bibliographic record
Abstract
ABSTRACT The role of physical inactivity as a contributor to non-communicable diseases (NCDs) risk in children and youth is widely recognized. Air pollution and built environment can limit participation in physical activity and exacerbate NCD risk; however, the relationships between perceptions of air pollution, built environment, and health behaviors are not fully understood, particularly among children and youth in low and middle-income countries. Currently, there are no studies capturing how child and youth perceptions of air pollution and built environment influence physical activity in India, thus, this study investigates the association between perceived air pollution and built environmental factors on moderate-to-vigorous physical activity (MVPA) levels of children and youth in both rural and urban India. Online surveys captured MVPA, perception of air pollution and built environment factors, as well as relevant sociodemographic characteristics from parents and children aged 5 to 17 years in partnership with 41 schools across 28 urban and rural locations during the Coronavirus disease lockdowns in 2021. After adjusting for age, gender, and location, a significant association was found between the perception of air pollution and MVPA levels (β = −18.365, p < 0.001). Similarly, the perception of a high crime rate was associated with lower MVPA levels (β = −23.383, p = 0.002). Reporting the presence of zebra crossings and pedestrian signals or attractive natural sightings was associated with higher MVPA levels; however, this association varied across sociodemographic groups. These findings emphasize the importance of addressing air pollution and improving the built environment to facilitate outdoor active living, including active transportation – solutions that are particularly relevant not only for NCD risk mitigation, but also for climate change adaptation.
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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 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".