Association between sleep quality and depressive symptoms

萧条(经济学) 医学 联想(心理学) 睡眠(系统调用) 心理学 精神科 临床心理学 抑郁症状 睡眠质量 失眠症 心理治疗师 焦虑 计算机科学 操作系统 宏观经济学 经济
作者
Hye Jin Joo,Kyung A Kwon,Jaeyong Shin,Sohee Park,Sung‐In Jang
出处
期刊:Journal of Affective Disorders [Elsevier BV]
卷期号:310: 258-265 被引量:56
标识
DOI:10.1016/j.jad.2022.05.004
摘要

Depression is a common mental health disorder. Despite sleep disturbance being associated with depression, limited data regarding the association of sleep quality with depression exists. We aimed to investigate the association between sleep quality and depressive symptoms in the South Korean population.This cross-sectional study used data from the 2018 Korean Community Health Survey, a nationwide representative survey conducted annually at national public health centers since 2008. The study population comprised 176,794 individuals (78,356 male and 98,438 female) aged 19 years and over. Sleep quality was measured using the Korean version of Pittsburgh Sleep Quality Index and depressive symptoms with the Patient Health Questionnaire-9. Data were analyzed using multiple logistic regression.The average PSQI score was 5.03 for men and 5.98 for women. Individuals of both sexes with poor sleep quality were more likely to be depressed (men: odds ratio (OR) = 7.02 [95% confidence interval (CI) = 6.17-7.99]). In subgroup analysis stratified by independent variables, participants with the following characteristics had greater association between poor sleep quality and depressive symptoms: unmarried, college or higher education, white-collar occupation, current smoker, frequent drinker, walking physical activity, and no-stress.Limitations included the cross-sectional nature of the study, use of only secondary data and a self-rated questionnaire for evaluating depressive symptoms, and inherent limitations in the PSQI.Poor sleep quality may contribute to depressive symptoms among Korean adults. Screening for poor sleep quality and implementing measures to improve sleep behaviors may prevent the onset of depression.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
猪皮恶人发布了新的文献求助10
刚刚
刚刚
henjie发布了新的文献求助10
刚刚
又鸟关注了科研通微信公众号
2秒前
2秒前
ha发布了新的文献求助10
3秒前
重要尔曼发布了新的文献求助30
4秒前
jessia发布了新的文献求助10
4秒前
科研通AI6.4应助ccccc采纳,获得10
5秒前
5秒前
小马甲应助丢一池月光采纳,获得10
5秒前
cyc发布了新的文献求助10
6秒前
7秒前
晴空发布了新的文献求助50
8秒前
Hello应助隐形的长颈鹿采纳,获得10
8秒前
9秒前
9秒前
9秒前
9秒前
upup发布了新的文献求助10
9秒前
fengzi151发布了新的文献求助10
11秒前
苏幕遮发布了新的文献求助10
12秒前
JamesPei应助张先生采纳,获得10
13秒前
搜集达人应助流云采纳,获得10
13秒前
hewd3完成签到,获得积分10
14秒前
魔力瓶发布了新的文献求助10
15秒前
深情安青应助15采纳,获得30
15秒前
16秒前
laoshi完成签到,获得积分10
17秒前
18秒前
18秒前
20秒前
yu发布了新的文献求助10
21秒前
学术猩猩发布了新的文献求助10
21秒前
22秒前
22秒前
22秒前
sakura发布了新的文献求助10
23秒前
共享精神应助小晴天采纳,获得10
23秒前
24秒前
高分求助中
(应助此贴封号)【重要!!请各用户(尤其是新用户)详细阅读】【科研通的精品贴汇总】 10000
Römisch-Germanische Forschungen 1000
China Pluperfect I: Epistemology of Past and Outside in Chinese Art 520
Matrix Methods in Data Mining and Pattern Recognition Second Edition 510
The fast track to determining transfer functions of linear circuits: The student guide 500
The Analytical and Numerical Solution of Electric and Magnetic Fields 500
Green Fire Retardants for Polymeric Materials 400
热门求助领域 (近24小时)
化学 材料科学 医学 生物 纳米技术 工程类 有机化学 化学工程 生物化学 计算机科学 内科学 物理 复合材料 催化作用 细胞生物学 无机化学 光电子学 物理化学 电极 基因
热门帖子
关注 科研通微信公众号,转发送积分 7616781
求助须知:如何正确求助?哪些是违规求助? 9192100
关于积分的说明 19699051
捐赠科研通 7189301
什么是DOI,文献DOI怎么找? 3271910
关于科研通互助平台的介绍 2434670
邀请新用户注册赠送积分活动 2266901