Associations between Sleep Quality and Different Measures of Obesity in Saudi Adults
Bibliographic record
Abstract
The prevalence's of overweight and obesity have grown to epidemic proportions in Saudi Arabia the past few years, epidemiological studies have suggested that sleeping for less than seven hours/day is associated with increased morbidity in terms of the development of obesity. The aim of this study was to examine the association between sleep quality and different measures of obesity (body mass index, waist circumference and body fat percentage) and some lifestyle habits among female university students. A cross-sectional study targeted a convenience sample of 233 undergraduate female students at King Faisal University (KFU) in AL-Hasa, Saudi Arabia. The results indicated that poor sleep quality was common in students (54%) with mean total sleeping hours of five hours/day. Poor sleep quality was associated with overweight/obese (OR 4.210, P=0.000), at risk waist circumference (OR 2.005, P=0.009), moderate/high body fat percentage (OR 1.058, P=0.025), low physical active (OR 2.045, P=0.037), and skipping breakfast (OR 2.710, P=0.003). In conclusion, the present study highlights the prevalence of poor sleep quality among female university students in Saudi Arabia, and they support previously published studies indicating that poor sleep quality was associated with different measures of obesity and some of lifestyle habits. Sleep quality is an untraditional approach that might be used to prevent or treat overweight and obesity.
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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 | 0.000 | 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".