Sugar-Sweetened Beverage Consumption and Associated Health Risks Awareness Among University Students in Kuwait: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Excessive consumption of sugar-sweetened beverages (SSBs) is linked to various health risks, including obesity, type 2 diabetes, and cardiovascular diseases. University students are particularly vulnerable due to lifestyle factors and high consumption patterns. Objective: This cross-sectional survey examined SSB consumption patterns, sugar intake, and awareness of health risks among Kuwait University students. Methods: Statistical analyses, including chi-square tests and logistic regression models, were conducted using SPSS. Results: Findings revealed a high prevalence of SSB consumption, with significant associations between intake levels and demographic characteristics. Regular soda was the most consumed SSB, with 42% of students drinking it 5–6 times per week and 32% consuming it daily. The median daily sugar intake from soda alone was 38 g, approaching or exceeding recommended limits. Overall, 34% of students were classified as high-sugar consumers. Males had a higher total sugar intake, while females consumed SSBs more frequently. Greater health awareness was associated with lower sugar consumption, such as obesity (OR = 0.142, 95% CI = 0.046–0.435, p < 0.001), whereas students who were aware of the sugar content in SSBs and who preferred unsweetened fruit juices had significantly lower sugar intake from SSBs (OR = 0.653, 95% CI = 0.435–0.980, p = 0.040; OR = 0.447, 95% CI = 0.295, 0.675; p < 0.001, respectively). Conclusions: The findings underscore the urgent need for targeted interventions—such as educational campaigns and policy measures—to reduce SSB consumption and promote healthier dietary habits among young adults in Kuwait.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".