Excessive daytime sleepiness and its predictors among medical and health science students of University of Gondar, Northwest Ethiopia: institution-based cross-sectional study
Abstract
Background Excessive daytime sleepiness (EDS) is a condition of sleepiness when a person would not be expected to sleep. University students are prone to EDS due to the competitive learning environment and fragmented night sleep. No study was conducted in Ethiopia on EDS. Therefore, this study aimed to determine EDS and its predictors among University of Gondar (UoG) Medical and Health Science students. Methods Institution-based cross-sectional study was carried out on 383 Medical and Health Science students of UoG who were recruited using a computer-generated simple random sampling technique. We used a validated Epworth daytime sleepiness tool to collect data. Epi-Info™ 7 and Stata 14 were used for data entry and analysis, respectively. Bivariable and multivariable binary logistic regression analyses were performed to find out predictors. Odds ratio with 95% uncertainty interval were computed. In the final model, a variable with a p < 0.05 was declared as a predictor of EDS. Results Three hundred and eighty-three students completed the questionnaire. Males were 69.97% and the mean age of participants was 20.79 (±1.83) years. In the current study, the prevalence of EDS was 31.07% (95% UI: 26.62–35.91). The odds of getting EDS was 1.83 (AOR = 1.83, 95% UI: 1.14–2.96) and 1.84 (AOR = 1.84, 95% UI: 1.13–3.00) higher among students who reported night sleep behaviour disorders and depression, respectively. Conclusion This study revealed that EDS is high and predicted by…
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Evidence Hub reads this as a cross-sectional, done in people. Its evidence level is 4 of 5 (Low · observational). Observational designs can show an association but not prove cause.
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