Sleep in the academic sphere: identifying sleep profiles and their influencing factors using latent profile analysis in German university students.

BACKGROUND

Poor sleep quality is a prevalent issue among university students, raising concerns for both individual well-being and public health. While the heterogeneity of sleep quality has been recognized, research examining distinct sleep quality subtypes and their influencing factors is still developing. This study aimed to explore sleep quality profiles among German university students and identify socio-demographic, health-related, and academic factors associated with profile membership.

METHODS

A total of 1,526 university students from various academic disciplines participated in the study. Data were collected via an online questionnaire, including the Pittsburgh Sleep Quality Index, Perceived Stress Scale, Utrecht Work Engagement Scale for Students, and questions on socio-demographic, health-related, and academic factors. Latent profile analysis and multinomial logistic regression were used to analyze the data.

RESULTS

We identified four distinct sleep quality profiles among our participants: the Average Sleep Profile (78.5%), the Insomnia Risk Profile (8.2%), the Above Average Profile (7.2%), and the Medicated Sleepiness Profile (6.1%). Prolonged sleep onset latency and daytime sleepiness were common across most profiles, indicating that these are widespread sleep-related issues among students. Female students (OR = 2.75, p < 0.001) and those with higher stress levels (OR = 1.09, p < 0.001) were more likely to belong to the Insomnia Risk Profile. Older students (aged 23 years or above) (OR = 1.88, p = 0.030), those enrolled in State examination programs (OR = 5.73, p = 0.016) as well as students experiencing higher stress (OR = 1.08, p < 0.001) and academic workload (OR = 1.01, p = 0.022), had an increased likelihood of belonging to the Medicated Sleepiness Profile. Students reporting better subjective health status were less likely to be assigned to profiles characterized by maladaptive sleep patterns.

CONCLUSION

These findings underscore the variability in sleep quality among university students, offering insights for the development of tailored preventive interventions. Future sleep promotion programs should consider individual differences when designing strategies to address the diverse sleep challenges faced by students.