网络成瘾和电子屏幕时间与抑郁症状的关联性研究

杜文秀; 顾叶青; 孟革; 张卿; 刘莉; 武汉章; 牛凯军

文章摘要

杜文秀,顾叶青,孟革,张卿,刘莉,武汉章,牛凯军.网络成瘾和电子屏幕时间与抑郁症状的关联性研究[J].中华流行病学杂志,2022,43(11):1731-1738

网络成瘾和电子屏幕时间与抑郁症状的关联性研究

Associations between internet addiction, screen time and depressive symptoms

收稿日期:2022-03-30 出版日期:2022-11-22

DOI：10.3760/cma.j.cn112338-20220330-00246

中文关键词: 网络成瘾电子屏幕时间抑郁症状队列研究

英文关键词: Internet addiction Screen time Depressive symptom Cohort study

基金项目:中国队列共享平台资助项目（CCC2020003）

作者	单位	E-mail
杜文秀	天津医科大学营养流行病学研究所/公共卫生学院, 天津 300070 天津中医药大学公共卫生与健康科学学院, 天津 301617
顾叶青	中国医学科学院放射医学研究所营养与辐射流行病学研究中心, 天津 300192
孟革	天津医科大学营养流行病学研究所/公共卫生学院, 天津 300070
张卿	天津医科大学总医院健康管理中心, 天津 300052
刘莉	天津医科大学总医院健康管理中心, 天津 300052
武汉章	天津医科大学营养流行病学研究所/公共卫生学院, 天津 300070 天津中医药大学公共卫生与健康科学学院, 天津 301617
牛凯军	天津医科大学营养流行病学研究所/公共卫生学院, 天津 300070 天津中医药大学公共卫生与健康科学学院, 天津 301617 中国医学科学院放射医学研究所营养与辐射流行病学研究中心, 天津 300192 天津医科大学总医院健康管理中心, 天津 300052	nkj0809@gmail.com

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中文摘要:

目的探讨网络成瘾、电子屏幕时间与抑郁症状发生的关联。方法选取2013-2019年天津慢性低度炎症与健康（TCLSIH）队列中6 932名正常体检的60岁以下成年人为研究对象。通过自我报告问卷评估研究对象网络成瘾程度、电脑/手机使用时间和电视使用时间。采用抑郁自评量表（SDS）评估研究对象的抑郁症状。根据SDS总分分为无抑郁症状组（<45分）和有抑郁症状组（≥45分）。使用Cox比例风险回归模型评估网络成瘾和电子屏幕时间与发生抑郁症状的关联。结果调整潜在的混杂因素后，与没有网络成瘾者相比，以前有网络成瘾者、轻度网络成瘾者、中/重度网络成瘾者随访期间发生抑郁症状的HR值（95%CI）分别为0.83（0.56~1.23）、1.20（1.03~1.41）、1.48（1.16~1.89）。网络成瘾与出现抑郁症状之间的线性关联有统计学意义（趋势性P<0.001）；与使用电脑/手机0~h/d者相比，使用手机1~、3~、5~、>10 h/d者随访期间发生抑郁症状的HR值（95%CI）分别为0.59（0.40~0.88）、0.58（0.40~0.85）、0.52（0.36~0.76）、0.69（0.45~1.05）。电脑/手机使用时间与随访期间出现抑郁症状之间的关联存在“U”形趋势（趋势性P<0.001）；未调整和初步潜在混杂因素后，与每天不使用电视者相比，电视使用时间≥3 h/d者随访期间发生抑郁症状的HR值（95%CI）分别为1.36（1.09~1.69）和1.34（1.07~1.68）。电视使用时间与出现抑郁症状之间存在关联（趋势性P<0.001）。结论网络成瘾和电视使用时间与随访期间抑郁症状风险的增加有关，而电脑/手机使用时间与随访期间抑郁症状风险的降低有关。

英文摘要:

Objective To understand the associations between internet addiction, screen time (computer/mobile devices use and television watching time) and depressive symptoms in adults. Methods A total of 6 932 adults aged <60 years from the Tianjin Chronic Low-grade Sgstemic Inflammation and Health (TCLSIH) Cohort of 2013-2019 were surveyed. The information about their computer/mobile devices use and television watching time were collected by using a self-reported questionnaire. The depressive symptoms were assessed using the self-rating depression scale (SDS). The adults surveyed were divided into two groups:non-depressive symptom group (SDS score <45) and depressive symptom group (SDS score ≥ 45). The associations between internet addiction, screen time and depressive symptoms were estimated using Cox proportional hazard regression models, with adjusting for multiple confounders. Results After adjusting for confounding factors, the hazard ratios (HRs) of depressive symptom in the adults who had internet addiction before, had light internet addiction and had moderate or severe internet addiction were 0.83 (95%CI:0.56-1.23), 1.20 (95%CI:1.03-1.41) for light and 1.48 (95%CI:1.16-1.89), respectively, compared with those without internet addiction. The linear trend test results of the association between internet addiction and depressive symptoms was significant (trend P<0.001). Compared with the adults who used computer/mobile devices for <1 hour/day, the HRs of depressive symptoms in those who used computer/mobile devices for >1 hour, >3 hours, >5 hours and >10 hours were 0.59 (95%CI:0.40-0.88), 0.58 (95%CI:0.40-0.85), 0.52 (95%CI:0.36-0.76) and 0.69 (95%CI:0.45-1.05) respectively, a U-shaped association was found between computer/mobile devices use time and depressive symptoms (trend P<0.001). Compared with the adults who never watch TV, the HR of depressive symptoms was 1.36 (95%CI:1.09-1.69) for those watching TV for ≥ 3 hours/day in crude model and 1.34 (95%CI:1.07-1.68) for those watching TV for ≥ 3 hours/day in adjusted model (trend P<0.001). Conclusion Our findings suggested that internet addiction and television watching time were associated with an increased risk of depressive symptoms, while computer/mobile device use time was associated with a reduced risk of depressive symptoms.

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