Association between physical activity, socioeconomic status, blood biomarkers, and diet in lebanese adults
Bibliographic record
Abstract
BACKGROUND: Inactivity is a significant contributor to non-communicable diseases. In Lebanon, the World Health Organization reported a rising prevalence of physical inactivity among adults. Various studies highlight the benefits of physical activity (PA) on health, influenced by sociodemographic factors, gender, age, and diet. This study aims to examine PA correlates in Lebanese adults, focusing on blood biomarkers. METHODS: This cross-sectional study included 296 adults aged ≥18 years. Participants completed a brief sociodemographic and food frequency questionnaire, underwent anthropometric measurements, and provided fasting blood samples. PA was measured using the International Physical Activity Questionnaire (IPAQ) short form and was divided into two categories: low PA corresponding to any walking activity, and moderate to vigorous PA for activities requiring physical effort. Descriptive statistics were computed for sociodemographic characteristics, BMI, waist circumference, energy intake, PA levels, and blood biomarkers. Logistic regressions were used to assess PA and blood biomarkers associations, adjusted for relevant covariates. RESULTS: Gender and marital status were associated with moderate to vigorous PA levels. No association was found between PA levels, BMI, waist circumference, diet, or blood biomarkers. Multivariate binary logistic regression analyses showed that females (OR=1.96, 95% CI: 1.16-3.31) and those with LDL moderate risk (OR=1.90, 95% CI: 1.02-3.66), and high risk (OR=2.44, 95% CI: 1.08-5.55), were more likely to show moderate-to-high PA levels. CONCLUSION: PA was positively associated with gender and disease risk, particularly LDL, a biomarker known to jeopardize cardiovascular health. Disease risk appears to be a driving factor in performing physical activity among women. These results may guide early nutrition interventions endorsing physical activity as a preventive measure to decrease the prevalence of cardio metabolic disorders.
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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 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".