Relationship among screen time, depressive symptoms and sleep parameters among college students
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摘要:
目的 探讨大学生白天和夜晚视屏时长、睡眠时长、就寝时间、睡眠质量与抑郁症状间的关联，为预防大学生抑郁症状提供参考。 方法 于2022年10月和2023年4—5月，采用整群随机抽样方法选取北京市某高校1 259名大学生进行线上和线下问卷调查，使用匹兹堡睡眠质量指数(PSQI)、中国版贝克抑郁量表第2版(BDI-II-C)和视屏时长问卷测量睡眠参数、抑郁症状和视屏时间。采用Logistic有序回归和多元线性回归分析视屏时长、睡眠参数与抑郁症状的相关性。 结果 大学生抑郁症状的检出率为24.9%。控制一周夜晚视屏时长、性别和年龄后，一周白天视屏时长与抑郁症状风险相关无统计学意义(OR=1.00，95%CI=1.00~1.01，P>0.05)。控制一周白天视屏时长、性别和年龄后，一周夜晚视屏时长与抑郁症状风险升高有统计学意义(OR=1.05，95%CI=1.03~1.06，P < 0.01)，但相继控制了学习日睡眠时长、休息日就寝时间和睡眠质量后，一周夜晚视屏时长与抑郁症状风险升高相关无统计学意义(OR=1.01，95%CI=0.99~1.02，P>0.05)。校正性别和年龄后，经多元线性回归分析发现，一周夜晚视屏时长对学习日睡眠时长、休息日就寝时间和睡眠质量的预测均有统计学意义(β值分别为-0.29，0.45，0.26，P值均 < 0.05)，学习日睡眠时长、休息日就寝时间及睡眠质量与抑郁症状均呈正相关(OR值分别为1.27，1.39，1.45，P值均 < 0.01)。 结论 过度夜间视屏对睡眠问题和抑郁症状风险的影响更大。减少夜间视屏、改善睡眠习惯是减少大学生抑郁症状的潜在干预目标。 Abstract: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. -
Key words:
- Fixation, ocular /
- Depression /
- Sleep /
- Mental health /
- Regression analysis /
- Students
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表 1 有无抑郁症状组大学生视屏时间比较(x±s, h/d)
Table 1. Comparison of screen time between groups with or without depressive symptoms (x±s, h/d)
抑郁症状 人数 学习日 休息日 一周 夜晚 白天 夜晚 白天 夜晚 白天 无 946 2.97±1.29 4.38±2.33 3.95±1.51 5.42±2.59 23.20±8.61 33.19±15.57 有 313 3.55±1.72 4.89±2.63 4.83±2.17 6.19±3.03 27.68±11.94 37.20±17.69 t值 -5.48 -3.04 -6.72 -4.01 -6.14 -3.58 P值 ＜0.01 ＜0.01 ＜0.01 ＜0.01 ＜0.01 ＜0.01 
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表 2 有无抑郁症状组大学生睡眠情况比较(x±s)
Table 2. Comparison of sleep variables between groups with or without depressive symptoms(x±s)
抑郁症状 人数 睡眠时长/(h·d-1) 就寝时间 PSQI得分 学习日 休息日 日均 学习日 休息日 无 946 7.93±1.02 10.03±1.60 8.53±0.98 11:27±0.78 12:01±1.18 3.95±2.58 有 313 7.49±1.12 9.51±2.09 8.07±1.14 11:50±0.96 12:56±1.61 6.92±3.04 t值 6.08 4.02 6.41 -6.43 -9.37 -15.54 P值 ＜0.01 ＜0.01 ＜0.01 ＜0.01 ＜0.01 ＜0.01
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表 3 大学生一周夜晚视屏时长和抑郁症状的Logistic有序回归模型分析(n=1 259)
Table 3. Logistic ordered regression analysis for night screen time in a week and depressive symptoms in college students (n=1 259)
预测变量 模型1 模型2 模型3 OR值(95%CI) P值 OR值(95%CI) P值 OR值(95%CI) P值 一周夜晚视屏时长 1.04(1.02~1.05) ＜0.01 1.02(1.00~1.03) 0.04 1.01(0.99~1.02) 0.55 性别 1.13(0.84~1.52) 0.43 1.03(0.76~1.39) 0.86 1.27(0.92~1.75) 0.14 年龄 1.06(0.96~1.17) 0.24 1.06(0.96~1.17) 0.25 0.99(0.89~1.10) 0.81 学习日睡眠时长 0.75(0.66~0.85) ＜0.01 0.87(0.76~0.99) 0.04 1.27(1.10~1.47) ＜0.01 休息日就寝时间 1.50(1.34~1.67) ＜0.01 1.39(1.24~1.56) ＜0.01 睡眠质量 1.45(1.37~1.53) ＜0.01
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表 4 大学生视屏时段与睡眠参数的多元线性回归分析(n=1 259)
Table 4. Multiple linear regression analysis of screen time and sleep parameters in college students(n=1 259)
自变量 学习日睡眠时长 休息日就寝时间 睡眠质量 β值 t值 P值 β值 t值 P值 β值 t值 P值 一周夜视屏时长 -0.29 -10.18 ＜0.01 0.45 16.55 ＜0.01 0.26 8.90 ＜0.01 一周白天视屏时长 -0.03 -1.13 0.26 0.02 0.70 0.48 0.04 1.26 0.21 性别 0.03 0.92 0.36 -0.07 -2.55 0.01 0.06 2.32 0.02 年龄 -0.05 -1.96 0.05 0.03 0.99 0.32 0.12 4.19 ＜0.01
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