When entering the university setting, poor sleep quality is reportedly prevalent among students and has been linked to a range of adverse health outcomes, including reduced academic performance. Moreover, determinants of sleep quality are not yet fully understood. This study was designed to (1) assess the prevalence of poor sleep quality and (2) identify determinants of sleep quality in German university students. In total, 1,684 undergraduate and graduate students (50.6% female, mean age 22.87 ± 3.15 years) from multiple academic disciplines completed a cross-sectional online survey assessing socio-demographic, health, and study-related indicators and sleep quality using the Pittsburgh Sleep Quality Index (PSQI). In our sample, 820 (48.7%) met the PSQI cut-off score (>5) for poor sleep quality. Multiple regression analysis showed that older age, being a business student, lower subjective social status, poorer self-rated health, stress, exhaustion, and poor academic performance significantly predicted poor sleep quality. Our findings document a high prevalence of poor sleep quality among university students and suggest that business students, especially, might be exposed to a greater risk for poor sleep quality. Furthermore, the results of this study are valuable for academic staff to develop tailored interventions to promote healthy sleep-in 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.

Objective: This study investigates the effects of a sleep education program, "Sleep 101", on sleep hygiene practices, sleep quality, and weekday and weekend sleep behaviors among college students. Participants: A total of 338 college students from a midsized public university in the US participated, with 25 completing both the baseline and post-intervention questionnaire. Methods: The Sleep Hygiene Index (SHI) and self-reported questions were used to assess sleep behaviors and subjective sleep quality. Results: Participants who completed the study experienced a significant increase in weekday sleep duration and were less likely to engage in activities unbeneficial for sleep before bedtime. Additionally, 40% of the completers reported improvements in their sleep quality. Conclusions: These findings suggest that a brief online program like "Sleep 101" can effectively improve sleep behaviors among college students and might be a scalable approach to addressing current sleep issues and preventing future sleep problems in this population.

Objective: Distracted driving, particularly any activity requiring long glances toward a phone while driving, is common among young drivers and represents a significant road safety concern. This study used a theory-informed approach to design a questionnaire examining beliefs, intentions, and behaviors relating to distracted driving in young people. Methods: Semi-structured interviews with 20 high school students were conducted using the Integrative Model of Behavioral Prediction (IMBP) framework to explore attitudes, social norms, and perceived behavioral control. The beliefs identified were incorporated into a 38-item closed-ended questionnaire. The questionnaire was then administered to a geographically representative sample of US high school students with a driver's license. Statistical analyses included correlation analysis and linear regression to evaluate interrelationships among questionnaire items and their associations with established distracted driving measures. Results: Among the geographically representative survey sample (N = 1,126), 50.5% were 18 years of age and 50.9% were female. Participants reported spending an average of 21.1% (SD = 24.6%) of each trip glancing at their phones while driving, and participants estimated that 26.5% (SD = 28.1%) of these glances were two seconds or longer. Reliability analysis showed Cronbach's alpha ranging from 0.63 (injunctive norms) to 0.83 (negative outcome expectations). Correlation coefficients between proposed measures and Behavioral Risk Factor Surveillance System (BRFSS) questions ranged from 0.22 to 0.50, indicating weak to moderate relationships. Regression analysis demonstrated significant associations between intentions and beliefs: positive outcome expectations (b = 0.01, p < .01), negative outcome expectations (b = -0.01, p < .001), injunctive norms (b = -0.01, p < .01), descriptive norms (b = 0.03, p < .001), and perceived behavioral control (b = -0.01, p < .01). Conclusion: This study investigated the beliefs and behaviors of young drivers regarding making prolonged glances toward a phone while driving, guided by IMBP theory. Findings suggest that the Distracted Driving Belief Questionnaire (DDBQ) is a valuable tool for understanding this high-risk behavior. The DDBQ may help inform interventions to reduce phone-related distractions and enhance road safety among young drivers.

Background: Educating college students about the importance of good sleep habits could lead to the adoption of healthier sleep routines, including sufficient sleep duration and sleep quality. The current pre-post study examines the impact of completing a sleep education program called “Sleep 101” on (1) sleep hygiene practices, (2) sleep quality, and (3) weekday and weekend sleep behaviors among college students. “Sleep 101” is an interactive online course developed to educate college students about the importance of sleep by addressing basic sleep physiology, the impact of sleep on mood, academic and physical performance, the interactions between sleep and various substances, and common sleep disorders. Methods: 338 college students were recruited from a midsized public university in the US and invited to participate in “Sleep 101”, of which 25 participants completed the baseline and post-intervention questionnaire. The Sleep Hygiene Index (SHI) was utilized to evaluate sleep hygiene practices, while standard questions were employed to assess weekday and weekend sleep behaviors and overall sleep quality. Results: Those who completed the study demonstrated a significant increase in weekday sleep duration and were less likely to engage in negative prior bedtime activities. In addition, 40% of those who completed baseline and post-intervention assessments noticed improvements in their sleep quality. Conclusion: These findings suggest that a brief online program is beneficial in improving sleep behaviors among college and could be a scalable approach to addressing current sleep issues and preventing future sleep problems in college students.