Access to Healthy Built and Natural Environments and Physical Activity and Screen Time in New Zealand Adolescents: A Geospatial Cross‐Sectional Study
Bibliographic record
Abstract
The built and natural environment can facilitate (un)healthy behaviors in adolescence. However, most previous studies have focused on examining associations between singular aspects of the environment. This study examined the association between the mixture of health-promoting and health-constraining environmental features in a Healthy Location Index (HLI) and physical activity and screen time among adolescents. This cross-sectional study used data from the Built Environment and Active Transport to School (BEATS) Research Program based in Dunedin, New Zealand. Data from 1,162 adolescents with complete demographic, health behavior and spatial records were included in the analysis. The environment was defined using the HLI based on access to health-promoting (e.g., greenspace, blue space, physical activity facility) and health-constraining (e.g., fast-food outlets) features and their mixture. Quantile g-computation and multilevel mixed effects models, with adolescents nested within schools, examined associations between the environment and the two health behaviors. A positive association existed between meeting physical activity guidelines and access to health-promoting environments (OR = 1.23 [95% CI 1.03; 1.47]), particularly blue space. In addition, the mixture of health-promoting and health-constraining environments was also important (ψ = 1.18 [1.01; 1.37]). However, there was limited evidence of an association between the environment and screen time. We provide evidence of a relationship between health-promoting environments, the mix of health-promoting and health-constraining environments, and physical activity among adolescents. The findings support the hypothesis that the environment has the potential to positively influence healthy behaviors of youth, particularly when it comes to physical activity.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".