Association Between Sleep Quality and Cognitive Function Among University Students in Dhaka: A Cross-Sectional Study
Bibliographic record
Abstract
Background: Sleep quality is an essential determinant of cognitive performance, especially among university students who are often exposed to academic stress, irregular routines, and excessive screen time. Despite its importance, few studies in Bangladesh have explored the relationship between sleep quality and cognitive function in this population. Objectives: To assess the association between sleep quality and cognitive function among university students in Dhaka. Methods: A cross-sectional study was conducted from January to December 2024 among 300 undergraduate students from three private universities in Khulna and Dhaka. Data were collected using two validated tools: the Pittsburgh Sleep Quality Index (PSQI) to measure sleep quality and the Montreal Cognitive Assessment (MoCA) to assess cognitive function. Descriptive statistics, Chi-square test, Pearson correlation, and multivariate linear regression were used for data analysis. Results: The mean PSQI score was 7.32 ± 2.45, with 66% of students classified as having poor sleep quality. The mean MoCA score was 24.7 ± 3.1, and 62.7% of participants showed signs of mild cognitive impairment. A significant association was found between poor sleep quality and impaired cognitive function (χ² = 42.89, p < 0.001). A moderate negative correlation was observed between PSQI and MoCA scores (r = -0.53, p < 0.001). In multivariate analysis, poor sleep quality remained a significant predictor of lower cognitive performance (β = -0.71, p < 0.001), even after adjusting for age, gender, screen time, and physical activity. Conclusion: Poor sleep quality is highly prevalent among university students and is significantly associated with reduced cognitive function. These findings highlight the urgent need for sleep hygiene awareness and supportive interventions to improve cognitive health and academic performance in student populations.
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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 | 0.002 |
| 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".