The built environment, purpose-specific walking behaviour and overweight: evidence from Wuhan metropolis in central China
Bibliographic record
Abstract
The impact of objective and subjective environmental factors on health outcomes has been a topic of significant debate, with a growing body of research acknowledging the role of a physically active lifestyle in promoting health. However, consensus regarding their precise influence remains elusive. This study contributes to these discussions by exploring how individual health outcomes correlate with transport and leisure walking behaviours, set against both the objective and subjective aspects of environmental influences in the context of Wuhan, an inland Chinese megacity. Street view images, multi-source geospatial data and a questionnaire survey were employed to characterise the "5D + Greenery" objective and perceived characteristics of the neighbourhood environment. Multi-group structural equation modelling was utilised to unravel the complex relationship and gender heterogeneity among environmental factors, purpose-specific walking, and overweight. Our results suggest that both objective land use diversity and perceived convenience are significantly associated with overweight. The accessibility of local service facilities and visible greenery promote both transport and leisure walking. While perceived neighbourhood safety encourages transport walking, perceived walkability is positively correlated with leisure walking. Notably, leisure walking, usually considered beneficial, presents a positive association with overweight conditions, acting as a mediation. Gender disparities exist in pathways between the environment and purpose-specific walking, as well as weight. The findings lend support to the planning of an activity-supporting built environment as a crucial strategy for obesity prevention.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".