Association between quality of sleep and health‐related quality of life in persons with diabetes mellitus type 2
Bibliographic record
Abstract
AIM AND OBJECTIVES: To estimate the relationship of sleep quality with health-related quality of life (HRQOL) in persons with diabetes mellitus type 2 (DMT2) living in the United Arab Emirates (UAE). BACKGROUND: DMT2 is an epidemic health condition in the UAE that has enormous impacts on heath, and consequent effects on HRQOL. However, because of an absence of screening for quality of sleep, people with DMT2 who experience poor sleep are likely to go untreated, which may compound the distressing impacts of DMT2 on their HRQOL. DESIGN: This is a cross-sectional quantitative research design. METHODS: A sample of 268 participants with DMT2 were recruited from community healthcare settings in the UAE using cluster sampling. Participants completed questionnaires, including the Pittsburgh Sleep Quality Index (PSQI) and the World Health Organization HRQOL. Data analysis used descriptive and correlational statistics. RESULTS: Of the 268 participants, 34% identified as "poor sleepers" and 55% had poor HRQOL. Poor sleepers showed significantly lower scores for HRQOL than good sleepers. The global PSQI scores were found to be independently predictive of global HRQOL. Subjective perceptions of sleep quality, the use of sleep medications and impaired daytime functioning were the variables found to have the highest correlations with global HRQOL and its four domains. CONCLUSIONS: This study found that people with DMT2 who indicate experiencing poor quality sleep are more likely to show a negative correlation with HRQOL. Additional research is needed to investigate how poor sleep may impact the health of people with DMT2. RELEVANCE TO CLINICAL PRACTICE: Findings suggest that assessment of sleep quality should be an essential component of diabetes care. Understanding sleep practices may aid public health practitioners and other healthcare providers in the design of culturally appropriate interventions to improve sleep quality in persons with DMT2.
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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 | 0.008 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| 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".