Socio-Demographic and Lifestyle Factors Predict 5-Year Changes in Adiposity among a Group of Black South African Adults
Bibliographic record
Abstract
The rising prevalence of obesity and excessive adiposity are global public health concerns. Understanding determinants of changes in adiposity over time is critical for informing effective evidence-based prevention or treatment. However, limited information is available to achieve this objective. Cultural, demographic, environmental, and behavioral factors including socio-economic status (SES) likely account for obesity development. To this end, we related these variables to anthropometric measures in 1058 black adult Tswana-speaking South Africans who were HIV negative in a prospective study over five years. Body mass index (BMI) and waist circumference increased in both sexes, whereas triceps skinfold thickness remained the same. Over the five years, women moved to higher BMI categories and more were diagnosed with central obesity. Age correlated negatively, whereas SES, physical activity, energy, and fat intake correlated positively with adiposity markers in women. In men, SES, marital status, physical activity, and being urban predicted increases in adiposity. For women, SES and urbanicity increased, whereas menopause and smoking decreased adiposity. Among men, smokers had less change in BMI than those that never smoked over five years. Our findings suggest that interventions, focusing on the urban living, the married and those with the highest SES-the high-risk groups identified herein-are of primary importance to contain morbidity and premature mortality due to obesity in black South Africans.
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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 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".