Associations between active travel and adiposity in rural India and Bangladesh: a cross-sectional study
Bibliographic record
Abstract
BACKGROUND: Data on use and health benefits of active travel in rural low- and middle- income country settings are sparse. We aimed to examine correlates of active travel, and its association with adiposity, in rural India and Bangladesh. METHODS: Cross sectional study of 2,122 adults (≥18 years) sampled in 2011-13 from two rural sites in India (Goa and Chennai) and one in Bangladesh (Matlab). Logistic regression was used to examine whether ≥150 min/week of active travel was associated with socio-demographic indices, smoking, oil/butter consumption, and additional physical activity. Adjusting for these same factors, associations between active travel and BMI, waist circumference and waist-to-hip ratio were examined using linear and logistic regression. RESULTS: Forty-six percent of the sample achieved recommended levels of physical activity (≥150 min/week) through active travel alone (range: 33.1 % in Matlab to 54.8 % in Goa). This was more frequent among smokers (adjusted odds ratio 1.36, 95 % confidence interval 1.07-1.72; p = 0.011) and those that spent ≥150 min/week in work-based physical activity (OR 1.71, 1.35-2.16; p < 0.001), but less frequent among females than males (OR 0.25, 0.20-0.31; p < 0.001). In fully adjusted analyses, ≥150 min/week of active travel was associated with lower BMI (adjusted coefficient -0.39 kg/m(2), -0.77 to -0.02; p = 0.037) and a lower likelihood of high waist circumference (OR 0.77, 0.63-0.96; p = 0.018) and high waist-to-hip ratio (OR 0.72, 0.58-0.89; p = 0.002). CONCLUSIONS: Use of active travel for ≥150 min/week was associated with being male, smoking, and higher levels of work-based physical activity. It was associated with lower BMI, and lower risk of a high waist circumference or high waist-to-hip ratio. Promotion of active travel is an important component of strategies to address the growing prevalence of overweight in rural low- and middle- income country settings.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".