The association between overweight/obesity and psychological distress: A population based cross-sectional study in Saudi Arabia
Bibliographic record
Abstract
OBJECTIVES: The objective of this study was to compare the association between mental well-being between obese (classes 1 and 2), over-weight and non-obese population-based individuals. METHODS: A population-based cross-sectional study was conducted in Al-Kharj, Saudi Arabia. A total of 1019 Saudi nationals aged ≥ 18 years participated in the survey. BMI scores were used to categorize participants into three groups: Obese, overweighted and non-obese/non-overweight. Mental well-being was evaluated by using the validated Arabic version of the General Health Questionnaire version 12 (GHQ-12). RESULTS: We used total GHQ score (Mean=12; SD=5.23) to compare mental well-being between the four BMI class categories. The overall one-way ANOVA model was statistically significant (F = 7.018, d = 6, P < 0.001). In multivariate analysis, after adjusting for sociodemographic variables, diabetes and smoking statuses we found that higher psychological distress (as evident by a higher total GHQ score) was associated with higher BMI. The unstandardized Beta regression coefficient = 2.627; P = 0.034). Females were more likely to have higher psychological distress than males (unstandardized Beta = 1.466, P = 0.003). Job status whether being unemployed or 'civilian' (civil worker) was significantly associated with higher psychological distress (unstandardized Beta = 1.405, P = 0.041). Being diabetic has a 1.6 times higher risk of psychological distress (unstandardized Beta = 1.604, P = 0.027). CONCLUSION: The study highlights the public health implications of psychological distress amongst individuals with overweight and obesity in Saudi Arabia. Future longitudinal studies should explore the temporality of this relationship.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".