Sleep disorders in a sample of students in Taif University, Saudi Arabia: The role of obesity, insulin resistance, anemia and high altitude

Prevalence and factors associated with excessive and severe daytime sleepiness among healthcare university students in the Brazilian Midwest

The present study aimed to determine the prevalence of, and factors associated with excessive and severe daytime sleepiness in healthcare university students. A cross-sectional university-based study was conducted with 1,779 students from a university located in the Brazilian Midwest State of Goiás, Brazil, in 2018. Daytime sleepiness was assessed using the Epworth Sleepiness Scale (ESS) and classified as excessive daytime sleepiness (EDS; cut-off ESS score ≥10) and severe EDS (S-EDS; cut-off ESS score ≥16). Associated factors included sociodemographic, behavioural, academic, nutritional status, and sleep-related and perceived health characteristics. Poisson regression was used for the data analysis. The mean (SD) age of the sample was 22.5 (3.84) years. The prevalence of EDS was 54.4% (95% confidence interval [CI] 51.9-56.1) and S-EDS was 10.0% (95% CI 9.2-11.7). After adjustment, a higher probability of occurrence of EDS was found among women (prevalence ratio [PR] 1.37, 95% CI 1.24-1.53), younger students (PR 1.23, 95% CI 1.07-1.42), those who were studying medicine (PR 1.14, 95% CI 1.02-1.28), with poor sleep quality (PR 1.29, 95% CI 1.17-1.43), and among those who reported constant loss of sleep due to internet use (PR 1.14, 95% CI 1.02-1.27). After adjustment, the highest probability of occurrence of S-EDS was found among women (PR 1.72, 95% CI 1.22-2.43), among those with poor sleep quality (PR 2.17, 95% CI 1.54-3.08), and medical students (PR 1.39, 95% CI 1.01-1.90). In conclusion, there was a high prevalence of daytime sleepiness among healthcare university students, especially among medical students and women.

Analysis of Obesity among Malaysian University Students: A Combination Study with the Application of Bayesian Structural Equation Modelling and Pearson Correlation

In obesity modelling studies, researchers have been seeking to identify the effective indicators of obesity by using appropriate statistical or mathematical techniques. The main objective of the present study is addressed in three stages. First, a new framework for modelling obesity in university students is introduced. The second stage involves data analysis based on Bayesian Structural Equation Modelling (BSEM) for estimating the Body Mass Index (BMI) (representative of the obesity level) of students at three university levels: Bachelor, Master and PhD. In the third stage, the highest significant correlation is determined between the BMI and other variables in the research model that were found significant through the second phase. The data for this study were collected from students at selected Malaysian universities. The results indicate that unhealthy food intake (fast food and soft drinks), social media use and stress exhibit the highest weightage contributing to overweight and obesity issues for Malaysian university students.