Prevalence of Obesity in School Children and Its Relation to Lifestyle Behaviors in Al-Ahsa District of Saudi Arabia
Bibliographic record
Abstract
OBJECTIVES: To estimate obesity prevalence among children and adolescents in Al-Ahsa, Saudi Arabia for the year 2016 and to determine the related preventable risk factors.METHODS: This study was a cross-sectional study (using stratified random sampling representing different geographical areas of Al-Ahsa) through a self-administered questionnaire. It included 240 male students aged (7–15) years old from public primary and intermediate schools in Al-Ahsa governorate, Kingdom of Saudi Arabia. Anthropometric measurements of weight and height were taken for all the study participants. Body mass index (BMI) and its percentile was determined using Saudi won growth charts of the corresponding age and sex.RESULTS: The overall prevalence of overweight and obesity was 29.6% (10.8% overweight, 3.8% obese, and 15% extremely obese). The prevalence of overweight and obesity was significantly associated with early childhood obesity, parental obesity, mother's employment, family income, number of snacks and fast food consumption, physical inactivity, and time spent in watching television. Other factors (namely, eating during emotional stress, family gathering on meals, and regular eating times) were having independently significant association.CONCLUSION: There is an urgent need to spread awareness about obesity, and the prevention programs that involving schools and families are the key strategy for controlling the current epidemic of 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 | 0.001 |
| 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".