The Association between Sweet Sugar Beverage Intakes and the Quality of Sleep in Working Age Adults
Bibliographic record
Abstract
Background: This cross-sectional study investigates the relationship between daily sugar intake from sugar-sweetened beverages (SSBs) and sleep quality in working adults aged 18–45 years who do not have chronic illnesses or diagnosed sleep disorders. The primary objective is to determine whether high sugar consumption increases the risk of poor sleep when other risk factors are considered simultaneously. This study is significant as it adds to the growing body of evidence regarding the impact of dietary factors on sleep quality, highlighting the potential health implications of sugar consumption. Methods: Participants were selected using consecutive sampling, and the study was conducted at Jomtien Hospital in Chonburi Province, Thailand. Data collection involved three key components: general demographic and health information records, a validated Thai questionnaire assessing sweetened beverage consumption, and the Thai version of the Pittsburgh Sleep Quality Index (PSQI). Sugar intake from SSBs was categorized into two groups: high (≥ 50 grams/day) and low (< 50 grams/day). Sleep quality was classified as poor (PSQI > 5) or good (PSQI ≤ 5). The relationship between sugar intake and sleep quality was analyzed using logistic regression, and the results were presented as odds ratios (ORs) with corresponding 95% confidence intervals (CIs). Results: The study included 300 participants, 68% of whom were women, with an average age of 30 ± 7.6 years. A total of 58% of participants were found to have poor sleep quality. Those with poor sleep quality consumed an average of 131.9 ± 102.9 grams of sugar per day, compared to 99.8 ± 86.3 grams for those with good sleep quality, a difference that was statistically significant (p = 0.005). When sugar consumption was categorized, 79.3% of individuals in the high sugar consumption group (≥ 50 grams/day) had poor sleep quality, compared to 65.1% with good sleep quality in the same group. In contrast, 20.7% of individuals in the low sugar consumption group (< 50 grams/day) had poor sleep quality, compared to 34.9% with good sleep quality, also showing a statistically significant difference (p = 0.008). The crude odds ratio for high sugar consumption associated with poor sleep quality was 2.06 (95% CI: 1.22-3.45, p = 0.006). After adjusting for other variables, the odds ratio remained significant, with an adjusted OR of 2.02 (95% CI: 1.05-3.92, p = 0.036). Conclusion: The findings indicate that high sugar consumption from sugar-sweetened beverages is significantly associated with an increased risk of poor sleep quality (adjusted OR: 2.02, 95% CI: 1.05–3.92, p = 0.036). Based on these results, it is recommended that individuals experiencing sleep issues consider reducing their sugar intake. Future research should explore the mechanisms underlying this relationship and evaluate public health strategies aimed at reducing high sugar consumption.
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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".