Combined associations of takeaway food availability and walkability with adiposity: Cross-sectional and longitudinal analyses
Bibliographic record
Abstract
BACKGROUND: Diet and physical activity are important determinants of energy balance, body weight and chronic health conditions. Peoples' health and behaviour are shaped by their environment. For example, the availability of unhealthy takeaway food in residential neighbourhoods and the ability to easily walk to a range of local destinations (high "walkability") influence diets and physical activity levels. Most existing evidence on the associations between residential neighbourhood and adiposity is cross-sectional and examines either walkability or takeaway availability, but not both in combination.We examined the cross-sectional and longitudinal associations of residential neighbourhood walkability and takeaway food availability with markers of adiposity separately and combined. METHODS: With data from the Fenland Study (Cambridgeshire, UK; n = 12,435), we used linear regression to estimate associations for walkability and takeaway availability separately and in mutually adjusted models, in addition to combining both into a measure of neighbourhood supportiveness for active living and healthy eating. Objective measures of BMI were examined cross-sectionally at baseline (2005-2015) and as change between baseline and follow-up (2014-2020). Additional outcomes (percentage body fat, waist circumference and hip circumference) were also examined both cross-sectionally and longitudinally. RESULTS: (95% CI = 0.58 to 1.39) respectively. These associations were more consistent when both neighbourhood measures were included in mutually adjusted models. The combined supportiveness measure was associated with lower BMI. High walkability and low takeaway availability were also associated with lower body fat percentage, waist circumference and hip circumference. CONCLUSIONS: These findings are consistent with the residential environment having a role in shaping people's health and behaviour. Living in an area that supports walking and cycling and affords less access to unhealthy food may support people to maintain a healthy lifestyle. It was important to consider walkability and takeaway food availability together because to examine them separately risks unobserved confounding by the other. Future research could incorporate additional environmental measures, especially those likely to be correlated.
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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 | 0.005 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".