Dataset Information


Predictors of Excessive Daytime Sleepiness in Medical Students: A Meta-Regression.

ABSTRACT: Excessive daytime sleepiness (EDS) is highly prevalent among medical students and can have serious negative outcomes for both students and their patients. Little is known about the magnitude and predictors of EDS among medical college students. A meta-regression analysis was conducted to achieve these two targets. A systematic search was performed for English-language studies that reported the prevalence of EDS among medical students using the Epworth sleepiness scale (ESS), age, sex, sleep duration and sleep quality as predictive variables. A total of nine observational studies (K = 9, N = 2587) were included in the analyses. Meta-regression analyses were performed using mean age (years), sex (proportion of male subjects), sleep duration (hours/night) and sleep quality index score (continuous scale) as moderators for EDS-with the prevalence of EDS as an outcome variable. An interaction term of sleep duration X sleep quality was created to assess if these two variables simultaneously influenced the outcome variable. Utilizing the ESS, the pooled prevalence of EDS among medical students was 34.6% (95% Confidence Interval (CI) 18.3-50.9%). Meta-regression models of age, sex, sleep duration and sleep quality alone revealed poor predictive capabilities. Meta-regression models of sleep duration-sleep quality interaction revealed results with high statistical significance. The findings from this review contribute supporting evidence for the relationship between sleep duration and sleep quality scores (i.e., sleep duration X sleep quality score) in predicting EDS in medical students.


PROVIDER: S-EPMC7445829 | BioStudies | 2019-01-01

REPOSITORIES: biostudies

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