Food outlet visits, physical activity and body weight: variations by gender and race–ethnicity
Bibliographic record
Abstract
PURPOSE: Recent evidence documents significant associations between community design, physical activity and obesity when adjusting for demographic covariates. Yet it is well understood that energy imbalance and weight gain are also a function of dietary patterns, and perhaps the degree of access to healthy food choices. METHODS: The current study builds upon the Atlanta-based SMARTRAQ study of over 10,000 respondents and reports an integrated assessment of obesity impacts of physical activity and food outlet visitation. Respondents in the SMARTRAQ survey aged 25-65 provided BMI, self-reported physical activity levels (IPAQ), demographic factors, and where they went for food over a 2 day period. RESULTS: The relative effect of physical activity, neighbourhood walkability, and food outlet visitation on BMI differed significantly across gender and ethnicity. BMI in females increased with fast food and decreased with grocery store visitation and physical activity, but not with walkability or walking. BMI in males was not related to where they went for food but decreased with walking and overall physical activity and with walkability. Fast food visitation was associated with increased BMI in white respondents and grocery store visitation with decreased BMI in black respondents. Meeting moderate activity guidelines was associated with lower BMI in both black and white respondents, yet walking was only significant in predicting reduced BMI in white respondents. CONCLUSION: Obesity influences of physical activity, walkability, and where people go for food differ significantly across gender and ethnicity and offer important policy implications and insights for future research.
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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 | 0.002 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".