Objective: To explore the impact of sleep education programs on freshman sleep time, knowledge, attitudes, behavior and daytime sleepiness, so as to provide a reference for further improving the sleep status of college students. Methods: By using the method of cluster sampling, freshmen were invited from a university in Beijing (288 at baseline, 187 at posttest and 108 at follow up for experimental group (EG); 207 at baseline and 105 at posttest for control group (CG). The sleep education content was embedded into other courses and implemented in a blending learning mode for EG, lasting 10 weeks, while the CG received no intervention. Both groups were assessed using questionnaires at both baseline and 2 weeks after the intervention, and reassessed at 9 months follow up after posttest for EG. Results: After intervention, compared with CG, the weekday and weekend bedtime in EG was 21 min and 17 min earlier than that in CG respectively ( t=-2.78, -2.15, P <0.05). The sleep duration at night on weekday in EG was 19 min longer than that in CG ( t=3.51, P<0.01). In EG, the phenomenon of going to bed with electronic products before sleep, no delay in falling asleep, sleep knowledge, sleep attitude, sleep habit and daytime sleepiness were significantly better than those in CG ( χ 2/t =9.15, 2.82, 5.71, 3.98, 2.41, -4.90, P <0.05). After intervention, comparing with that at baseline, the weekday and weekend bedtime in EG were significantly earlier by 11 min and 17 min respectively ( t=3.50, 3.67, P <0.01), the sleep duration at nights on weekdays and weekend increased by 13 min and 18 min, respectively ( t=-3.01, -3.67, P <0.05), and the daytime sleepiness, going to bed with electronic products before sleep, no delay in falling asleep, sleep knowledge, sleep attitude and sleep habit were significantly improved ( χ 2/t =4.64, 15.19, -2.08, -9.31, -3.28, -2.14, P<0.05). At the 9 months follow up after the posttest, the bedtime on working day was significantly advanced by 8 min ( t =2.00), the sleep duration at night on working day was prolonged by 9 min ( t =-2.15), and the phenomenon of going to bed with electronic products before sleep and sleep knowledge were still significantly improved( χ 2/t =21.50, -6.26)( P <0.05). Conclusion Sleep education programs embedded in other courses and implemented in a blending learning mode can improve students sleep knowledge, sleep attitude and some habits, and reduce daytime sleepiness.

Objective: To explore the relationship between daytime or nighttime screen time, sleep duration, bedtime, sleep quality and depressive symptoms, so as to provide reference for preventing depression symptoms in college students. Methods: A total of 1 259 college students in one university in Beijing were recruited by using a cluster random sampling method for online and offline questionnaire surveys in October 2022 and April to May 2023. The sleeping quality, depression symptoms and screen time of participants were measured with the Pittsburgh Sleep Quality Index(PSQI), Chinese Version of the Beck Depression Inventory-II (BDI-II-C) and Screen Time Questionnaire. Logistic ordered regression and multiple linear regression were used to analyze the correlation among screen time, sleep parameters and depressive symptoms. Results: The prevalence of depressive symptoms was 24.9 %. There was no significant correlation between daytime screen time and depressive symptoms for a week after controlling for night screen time in a week, gender and age ( OR= 1.00 , 95%CI=1.00-1.01, P >0.05). There was a significant correlation between night screen time and depressive symptoms for a week ( OR=1.05, 95%CI=1.03-1.06, P <0.01) after controlling for daytime screen time in a week, gender and age. However, after controlling for the weekday sleep duration, weekend bedtime, and sleep quality step by step, there was no significant correlation between the night screen time for a week and the depressive symptoms ( OR=1.01, 95%CI= 0.99 -1.02, P >0.05). After adjusting for gender and age, multiple linear regression analysis found that the duration of one week s night vision screen had statistical significance in predicting weekday sleep duration, weekend sleep time and sleep quality ( β=-0.29, 0.45, 0.26, P <0.05). There were positive correlation between the duration of sleep on study days, the duration of sleep on rest days, and the quality of sleep with depressive symptoms（ OR =1.27,1.39,1.45， P <0.01）. Conclusions Excessive night screen time has a greater impact on sleep problems and depressive symptoms. Reducing nighttime video and improving sleep habits are potential intervention goals for reducing depression symptoms in college students.