Prevalence of insomnia among university students in Saudi Arabia: a systematic review and meta‑analysis
Bibliographic record
Abstract
Abstract Background Insomnia is a prevalent sleep disorder affecting cognitive functions critical to academic performance. University students, particularly in high-stress academic settings, are highly vulnerable. Despite its significant impact on students' health and education, there is limited research on the prevalence of insomnia among university students in Saudi Arabia. This systematic review and meta-analysis aim to assess the prevalence of insomnia among university students in Saudi Arabia, focusing on demographic variations and academic settings, to provide evidence for targeted interventions. Methods A comprehensive literature search was conducted across databases including PubMed, Scopus, and Web of Science, with additional manual searches. Inclusion criteria were cross-sectional studies addressing insomnia prevalence among Saudi university students, using standard diagnostic criteria. A total of 11 studies met the inclusion criteria, comprising data from diverse faculties, including medical and non-medical disciplines. Quality assessment was conducted using the Newcastle-Ottawa Scale. Statistical analyses were performed using a random-effects model to account for heterogeneity. Results Eleven studies, involving a total of 8297 university students, were included in the analysis. Insomnia prevalence varied widely, ranging from 19.3% to 98.7%, with a pooled prevalence of 43.3% (95% CI 28.9–58.2%). Subgroup analyses showed a prevalence of 38.6% among medical students and 38.7% among female students. The analysis revealed high heterogeneity ( I 2 = 99.17%), indicating significant variability in study designs, populations, and diagnostic methods. Conclusions Insomnia is highly prevalent among university students in Saudi Arabia, with significant variations across demographics and academic contexts. The findings underscore the urgent need for targeted interventions, including stress management, improved sleep hygiene education, and support systems to mitigate the impact of insomnia on academic performance and overall health. Future research should explore the longitudinal impacts of insomnia and the efficacy of tailored interventions in this population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.012 | 0.024 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.027 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".