Factors associated with poor sleep quality in the Korean general population: Providing information from the Korean version of the Pittsburgh Sleep Quality Index

Journal of Affective Disorders 271 (2020) 49–58

Contents lists available at ScienceDirect

Journal of Affective Disorders journal homepage: www.elsevier.com/locate/jad

Research paper

Factors associated with poor sleep quality in the Korean general population: Providing information from the Korean version of the Pittsburgh Sleep Quality Index

T

Soon Young Leea,b,1, , Yeong Jun Jua,b,1, Joo Eun Leea,b, Young Taek Kimc, Seung Chul Hongd, Yun Jung Choie, Min Kyoung Songe, Hye Yun Kime ⁎

a

Department of Preventive Medicine and Public Health, Ajou University School of Medicine, 206 World cup-ro, Yeongtong-gu, Suwon-si Gyeonggi-do 16499, Republic of Korea Institute of Health Services Research, Yonsei University College of Medicine, Seoul, Republic of Korea c Public Health Medical Service Office, Chungnam National University Hospital, Daejeon, Republic of Korea d Department of Psychiatry, St. Vincent's Hospital, College of Medicine, The Catholic University of Korea, Suwon, Republic of Korea e Division of Chronic Disease Control, Korea Centers for Disease Control and Prevention, Cheongju, Republic of Korea b

ARTICLE INFO

ABSTRACT

Keywords: Poor sleep quality Pittsburgh Sleep Quality Index General population Nationwide sample

Background: Recently, studies have been conducted to address the research gap in the understanding of poorquality sleep and its relationship to health outcomes, through the evaluation of sleep quality. The aim of this study was to provide information regarding poor sleep quality based on a nationwide general population sample in Korea. Methods: We conducted a cross-sectional study using data from a nationwide sample of 165,193 individuals (males: 44%) aged 19 years or older from the 2018 Korea Community Health Survey. The age range of the participants was 19–107 years (mean: 55.3 ± 17.5). The Korean version of the Pittsburgh Sleep Quality Index (PSQI) was used for assessing sleep quality. Poor sleep quality was defined as a total PSQI score of >5. Results: The overall prevalence of poor sleepers was 41.0% (males: 35.6%; females: 46.2%). Poor sociodemographic status (illiteracy, low income, and unemployment), poor health behaviors (smoking, high-risk drinking, diabetes, hypertension, non-participation in walking, and obesity), and poor mental health (perceived poor health status, stress, depressive symptoms, and subjective cognitive decline) were all associated with poor sleep quality in both males and females. Limitations: As this study relies on self-reported and cross-sectional data, causal inferences cannot be made. Conclusions: Poor sleep quality is highly prevalent in females. In addition, poor socio-demographic status, poor health behaviors, and poor mental health were associated with poor sleep quality. The mechanisms underlying sex differences in sleep quality remain to be elucidated, and further studies are required to address this.

1. Introduction Sleep health has been defined as a “multidimensional pattern of sleepwakefulness, adapted to individual, social, and environmental demands, that promotes physical and mental well-being” (Buysse, 2014). Sleep health is crucial for brain function, productivity, emotional well-being, physical health, and quality of life (Panel et al., 2015). A large number of epidemiological studies have sought to clarify the clinical consequences of inadequate sleep. In a recent meta-analysis analyzing the association between sleep duration and health outcomes, short or long sleep duration was

associated with mortality, diabetes mellitus, hypertension, cardiovascular disease, coronary heart disease, and obesity (Itani et al., 2017; Jike et al., 2018). For these reasons, adequate sleep is crucial. Recently, however, the quality and not just the quantity of sleep has been the subject of much attention and discussion. Given that sleep has a complex and multidimensional nature, it is important to consider sleep quantity as well as quality for a better understanding of sleeprelated health outcomes (Bin, 2016). The National Sleep Foundation highlighted the need for a better understanding of sleep quality through its recently published guide for the public (Ohayon et al., 2017). Some

Corresponding author. E-mail address: [email protected] (S.Y. Lee). 1 Denotes equal contribution. ⁎

https://doi.org/10.1016/j.jad.2020.03.069 Received 13 August 2019; Received in revised form 16 January 2020; Accepted 22 March 2020 Available online 08 April 2020 0165-0327/ © 2020 Elsevier B.V. All rights reserved.

Journal of Affective Disorders 271 (2020) 49–58

S.Y. Lee, et al.

studies have taken into account sleep duration and sleep quality in combination with psychological functioning, in order to better understand the factors that are associated with sleep health (Kaneita et al., 2007; Yokoyama et al., 2008). A study focusing on sleep quantity as well as quality suggested that poor sleep quality has robust associations with poor functioning, regardless of sleep quantity, in the general population (Lallukka et al., 2018). In accordance with this, recent studies have been performed to fill the research gap on the overall understanding of so-called poor-quality sleepers, and their health outcomes, through evaluation of sleep quality (Mollayeva et al., 2016). The Pittsburgh Sleep Quality Index (PSQI) (Buysse et al., 1989), developed by Buysse and colleagues in 1989, is the most commonly used self-reported instrument for assessing quality and patterns of sleep over a one-month period. Much of the published literature relating to the PSQI provides information on the psychometric properties of the PSQI, including the internal consistency, test-retest reliability, validity, and factor structure (Mollayeva et al., 2016). Several studies have provided valuable information that can be utilized as reference values for further research utilizing the PSQI (Hinz et al., 2017; Tang et al., 2017). Population-based studies have reported the prevalence of poor sleep quality in the general population to be 35.9% (males: 28.7%; females: 42.5%) in Germany (Hinz et al., 2017), 26.6% (males: 26.3%; females: 27.0%) in China (Tang et al., 2017), and 39.4% (males: 35.1%, females: 43.1%) in Hong Kong (Wong and Fielding, 2011). There is growing concern about the sleep habits and presence of sleep disorders in the Asian population, including those living in Korea and Japan (Inoue, 2016). This is in part due to individuals going to bed later and waking up earlier, resulting in a shorter nocturnal sleep duration in the general population when compared to other countries. Understanding the determinants of sleep health, and the factors that promote or disturb sleep health at the population level, is important. The provision of multidisciplinary and comprehensive information on sleep quality in a general population with a large sample size would be a significant addition to the literature and improve our understanding of this challenging issue. The aim of this study was to provide information on poor sleep quality, based on a nationwide general population sample in Korea. The objectives of this study were as follows:

range of the participants was 19–107 years (mean: 55.3 ± 17.5). Among the 228,340 total participants in the study, respondents who did not provide data on the pre-specified variables were excluded from the study. After excluding participants with any missing values, a total of 165,193 eligible participants were included in the study for analysis (see details Supplementary Fig. 1). It is notable that, in most previous studies of the general population, females have reported poor sleep quality more often than males (Asghari et al., 2012; Hinz et al., 2017; Tang et al., 2017; Wong and Fielding, 2011). Sex differences in the sleep literature remain controversial, and to address this, the present study analyzed males and females separately (72,710 males; 92,483 females). 2.2. Measures 2.2.1. Sleep quality Sleep quality was measured using the 19-item self-reported PSQI questionnaire that measures the quality and patterns of sleep over a one-month duration. The PSQI has been widely used in general population-based epidemiological studies. It consists of 19 items and 7 sleep components: subjective sleep quality, sleep latency, sleep duration, sleep efficiency, sleep disturbances, use of sleep medication, and daytime dysfunction. Each component is scored on a scale ranging from 0 to 3. A global score for overall sleep quality can be calculated by adding these components together, yielding scores ranging from 0 to 21. PSQI global scores of greater than 5 are generally used to indicate poor sleep, and we adopted this same threshold in our study. The Korean version of the PSQI has shown high sensitivity and specificity, and has been validated previously (Sohn et al., 2012). The use of a cutoff point of 5 in the Korean population has also been validated in a previous study (Choi et al., 2015). 2.2.2. Instruments and variables The following instruments and variables were also used to provide information on the reference values of the PSQI: age, education level, income, marital status, employment status, smoking status, high-risk drinking, BMI, diagnosis of diabetes mellitus, diagnosis of hypertension, perceived health status, perceived stress, depressive symptoms, subjective cognitive decline, walking participation, and health-related quality of life (HRQoL). In particular, depressive symptoms were measured using the PHQ-9, which has been widely used in populationbased studies (Kroenke et al., 2001). Each item was scored on a scale from 0 to 3 (0 = not at all, 1 = several days, 2 = more than half the days, and 3 = nearly every day). Items were summed, and the presence of depressive symptoms was indicated by a score of ≥10. The Korean version of the PHQ-9 that we used in this study has been previously validated (Choi et al., 2007; Han et al., 2008). Subjective cognitive decline (SCD) was measured using the cognitive decline module of the Behavioral Risk Factor Surveillance System (Taylor et al., 2018). It was assessed based on the answer provided to a single question: “During the past 12 months, have you experienced confusion or memory loss that is happening more often or is getting worse?” Respondents who reported “yes” were classified as having SCD. In addition, HRQoL was measured using the EQ-5D-3 L index. The EQ-5D is an index of five dimensions of HRQoL, namely, mobility, self-care, usual activities, pain/discomfort, and anxiety/depression. A previous study has shown that this EQ-5D value set should be given preference for use with the South Korean population (Lee et al., 2009). EQ-5D index: 1 – [(0.05 + 0.096*Mobility level 2 (M2) + 0.418*Mobility level 3 (M3) + 0.046*Self-care level 2 (SC2) + 0.136*Self-care level 3 (SC3) + 0.051*Usual activity level 2 (UA2) + 0.208*Usual activity level 3 (UA3) + 0.037*Pain/discomfort level 2 (PD2) + 0.151*Pain/discomfort level 3 (PD3) + 0.043*Anxiety/ depression level 2 (AD2) + 0.158*Anxiety/depression level 3 (AD3) + 0.05*Only one level 3 (N3)] For example, if the mobility level was 2, “M2” was defined as 1;

(1) To provide supplemental information on the reference values for the Korean version of the PSQI, such as internal consistency; internal homogeneity; and associations between the PSQI scores and other scales, including the Patient Health Questionnaire-9 (PHQ-9) and health-related quality of life (EuroQol-five dimensions; EQ-5D). (2) To provide information on the prevalence of poor sleep quality in Korea based on a national general population sample. (3) To provide information on the PSQI component score by sociodemographic, health behavioral, and psychiatric factors; no study has reported this information to date. (4) To examine the factors associated with poor sleep quality by sociodemographic, health behavioral, and psychiatric factors. 2. Methods 2.1. Data and study population For this study, we used raw data from the 2018 Korea Community Health Survey (KCHS), conducted by the Korea Centers for Disease Control and Prevention. The KCHS is a cross-sectional survey with a study population from multistage, stratified area probability samples of civilian, non-institutionalized Korean households by geographic area, age, and gender groups. The survey is conducted annually, and collects information through in-person one-to-one interviews. Given that the population sample is extracted from the national survey data, samples are representative of the wider Korean population (Kang et al., 2015). The study included individuals aged 19 years or older. The age 50

Journal of Affective Disorders 271 (2020) 49–58

S.Y. Lee, et al.

otherwise, “M2” was defined as 0. Similarly, if the self-care level was 3, “SC3” was defined as 1; otherwise, “SC3” was 0. If all EQ-5D indices scored 1, the weighted score became 1. In addition, high-risk drinking was defined as drinking >60 g of pure alcohol per occasion for men and >40 g of pure alcohol per occasion for women, with greater than two occasions per week. With respect to BMI, height was accurately measured to 0.1 cm using a portable height measurement (InLabS50, InBody, Seoul, South Korea), and weight to 0.1 kg using an electronic scale (CAS HE-58, CAS, Seoul, South Korea).

sleep medication (r = 0.37, p < 0.01). The pattern of component-total score correlations varied according to sex. The correlation was higher in females than in males. 3.3. Associations with other psychometric scales The correlation between the PSQI and other psychometric scales is shown in Supplementary Table 1. Sleep problems were associated with depressive symptoms (PHQ-9) and low quality of life (EQ-5D). The correlation of the total score between PSQI and PHQ-9 was 0.51, and it was –0.33 between PSQI and HRQoL (EQ-5D). Regarding the role of sex, only small differences were observed.

2.3. Statistical analyses The analyses were performed using SAS software (version 9.4; SAS Institute, Cary, NC, USA). First, Cronbach's alpha coefficient was used to confirm the internal consistency of the PSQI. In addition, Pearson's correlations between the total score and seven component scores were calculated to confirm the internal homogeneity of the PSQI. Subsequently, descriptive statistics were analyzed. The demographic characteristics and pooled responses, including the percentages, mean score, and standard deviation were examined using a Rao-Scott Chisquare test (for categorical variables) and t-test (for continuous variables). Next, we used the “PROC SURVEYMEANS” procedure to investigate how the mean PSQI component scores of the participants varied by sociodemographic, health behavioral, or psychiatric factors. The effect size (Cohen's d), calculated as the difference of the mean PSQI of sex groups divided by the weighted pooled standard deviations of these groups. Finally, logistic regression analysis was performed to examine the factors associated with poor sleep quality by sociodemographic (education, income, and employment status); health behavioral (smoking, high-risk drinking, diabetes mellitus, hypertension, BMI, and walking participation); and psychiatric factors (perceived health status, perceived stress, depressive symptoms, and subjective cognitive decline) while controlling for demographic characteristics such as age, education, and income. In addition, to produce an unbiased national estimate, a sample weight was assigned for the participating individuals to represent the Korean population (Kang et al., 2015).

3.4. PSQI total score and component scores and prevalence of poor sleep quality by sex The PSQI total score and component score by sex are shown in Table 3. Higher scores indicated that the sleep quality was more severely affected. The PSQI total score was 5.63. The values were higher in females than in males (males: 5.09; females: 6.05). The calculation of Cohen's d comparing males and females suggested medium effect sizes for PSQI mean score (d = 0.30). The mean score was the highest for sleep duration (1.58 ± 1.04) and lowest for use of sleep medication (0.10 ± 0.50). In addition, the overall mean values were higher in females than in males. The sleep latency showed the greatest difference between males and females (0.77 for males and 1.11 for females). When those with PSQI global scores of >5 were classified as having poor sleep quality, 41.0% of the total participants (35.6% males and 46.2% females) were considered poor sleepers. The distribution of the total scores for the PSQI is shown in Supplementary Fig. 2. 3.5. PSQI component score differences by sociodemographic, health behavioral, and psychiatric factors Fig. 1 shows the PSQI component score differences by sociodemographic, health behavioral, and psychiatric factors. In terms of sociodemographic factors, those with a low sociodemographic status tended to have a higher score for each PSQI component than overall scores of each component in females. The mean score was similar to or lower than the overall score, regardless of low sociodemographic characteristics in the sleep duration and daytime dysfunction components. Regarding health behavioral factors, those with poor health behavioral characteristics tended to have a higher score in each PSQI component. In particular, a high mean score was noted in females who smoked cigarettes in terms of subjective sleep quality and sleep latency. In addition, in both males and females, a high mean score was noted in those with diabetes or hypertension in the sleep disturbance component. Regarding psychiatric factors, regardless of the component types, the mean score was the highest in both males and females with depressive symptoms. In addition, the mean scores of both males and females with poor perceived health, perceived stress, or cognitive decline were also higher than the overall score.

2.4. Ethical approval The Korea Community Health Survey (KCHS) data are openly published. Participants’ data were fully anonymized prior to release. Our study was excluded from the review list pursuant to Article 2.2 of the Enforcement Rule of Bioethics and Safety Act in Korea, since the data was exempted from IRB review. 3. Results 3.1. Participant demographic characteristics In our study, a total of 165,193 participants were included (72,710 males and 92,483 females). Compared with female participants, males tended to have higher levels of education, income, job, and HRQoL, and were more likely to smoke, engage in high-risk drinking, and be overweight or obese. On the other hand, compared with male participants, females tended to have higher levels of poor perceived health status, stress, depressive symptoms, and subjective cognitive decline (Table 1).

3.6. Factors associated with poor sleep quality Figs. 2 and 3 show the results of the logistic regression analysis for the factors associated with poor sleep quality. In terms of sociodemographic factors, a gradient in poor sleep quality was found with regard to education level and income in both males and females. In addition, poor sleep quality was associated with unemployment status but not occupation type. Regarding health behavioral factors, smoking, high-risk drinking, diabetes mellitus, hypertension, and non-participation in walking were associated with poor sleep quality. Notably, taking normal weight (18.5 ≤ BMI < 25.0) as a reference, the odds ratios for the underweight (BMI < 18.5), overweight (25.0 ≤ BMI < 30.0), and obesity groups (BMI ≥ 30.0) were 1.20, 1.06, and 1.23 for males and

3.2. Internal consistency, internal homogeneity of PSQI component The internal consistency and internal homogeneity of the PSQI components are shown in Table 2. Regarding internal consistency, Cronbach's alpha coefficient was 0.79. In addition, Pearson's correlation between the total score of the PSQI and each component score was the highest for sleep latency (r = 0.70, p < 0.01), and lowest for the use of 51

Journal of Affective Disorders 271 (2020) 49–58

S.Y. Lee, et al.

Table. 1 General characteristics of the sample. Variables Age group 19–29 30–39 40–49 50–59 60–69 ≥70 Education Uneducated Elementary school Middle school High school College and over Income Low Middle low Middle high High Marital status Married Divorced, widowed, or separated Never married Job classification White-collar worker Pink-collar worker Blue-collar worker Unemployed Smoking status Yes No High risk drinking Yes No Objectively measured body mass index Underweight (BMI < 18.5) Normal (18.5 ≤ BMI < 25.0) Overweight (25.0 ≤ BMI < 30) Obesity (BMI ≥ 30.0) Diabetes mellitus Yes No Hypertension Yes No Perceived health status Good Bad Perceived stress status Yes No Depressive symptoms Yes (PHQ9 score ≥ 10) No (PHQ9 score 0–9) Subjective cognitive decline Yes No Walking participation Yes No EQ-5D (mean ± SD)

Total (n = 165,193) N %*

Male (n = 72,710) N %*

Female (n = 92,483) N %*

P Value**

16,219 19,331 25,350 31,669 32,024 40,600

17.0 16.6 19.4 19.9 13.6 13.6

7794 9037 11,889 13,989 14,140 15,861

18.3 17.2 20.2 20.1 12.5 11.7

8425 10,294 13,461 17,680 17,884 24,739

15.8 16.0 18.6 19.7 14.6 15.4

19,180 27,803 18,664 52,736 46,810

4.9 9.5 8.6 37.2 39.8

3194 10,234 8621 26,689 23,972

1.8 6.9 8.0 40.0 43.3

15,986 17,569 10,043 26,047 22,838

7.9 12.0 9.1 34.6 36.4

40,417 41,239 41,287 42,250

13.4 21.6 29.1 35.9

14,422 18,859 19,602 19,827

11.0 21.4 30.4 37.2

25,995 22,380 21,685 22,423

15.7 21.8 27.8 34.6

110,864 30,831 23,498

64.9 12.8 22.3

52,994 6400 13,316

66.4 6.6 27.0

57,870 24,431 10,182

63.4 18.7 17.9

29,588 19,647 51,554 64,404

25.9 13.1 23.9 37.1

15,327 6719 31,322 19,342

29.4 11.3 34.5 24.9

14,261 12,928 20,232 45,062

22.6 14.9 13.7 48.8

27,713 137,480

19.9 80.1

24,997 47,713

37.0 63.0

2716 89,767

3.4 96.6

19,381 145,812

13.8 86.2

15,394 57,316

22.5 77.5

3987 88,496

5.5 94.5

7037 100,515 49,392 8249

4.6 61.8 28.6 5.0

2285 41,861 24,849 3715

2.7 56.6 34.9 5.9

4752 58,654 24,543 4534

6.4 66.8 22.7 4.2

18,918 146,275

8.5 91.5

8840 63,870

9.1 90.9

10,078 82,405

7.8 92.2

48,693 116,500

21.1 78.9

20,465 52,245

21.2 78.8

28,228 64,255

20.9 79.1

56,792 108,401

38.6 61.4

28,936 43,774

43.0 57.0

27,856 64,627

34.4 65.6

37,801 127,392

25.4 74.6

15,832 56,878

25.0 75.0

21,969 70,514

25.8 74.2

6273 158,920

3.6 96.4

1921 70,789

2.5 97.5

4352 88,131

4.6 95.4

31,797 133,396

16.3 83.7

11,273 61,437

12.6 87.4

20,524 71,959

19.8 80.2

73,402 91,791 95.20 ± 9.64

49.4 50.6

33,331 39,379 96.54 ± 8.26

50.0 50.0

40,071 52,412 93.92 ± 10.65

48.9 51.1

<0.001

<0.001

<0.001

<0.001

<0.001

<0.001 <0.001 <0.001

<0.001 0.268 <0.001 0.006 <0.001 <0.001 0.002 <0.001

*Weighted estimates of proportions were calculated. **P value for the comparison between male and female groups, using the Rao-Scott chi-square test.

1.06, 1.07, and 1.24 for females, respectively. Regarding psychiatric factors, among males and females, those who had experienced poor perceived health status, stress, depressive symptoms, or subjective

cognitive decline showed a trend toward greater odds of poor sleep quality, when compared to other characteristics such as sociodemographic and health behavioral factors.

52

Journal of Affective Disorders 271 (2020) 49–58

S.Y. Lee, et al.

4.1. Internal consistency, internal homogeneity of PSQI components, and associations with other scales

Table. 2 Correlation between the total score of the PSQI and each component score.

C1 C2 C3 C4 C5 C6 C7

Subjective sleep quality Sleep latency Sleep duration Habitual sleep efficiency Sleep disturbance Use of sleep medication Daytime dysfunction

Total

Male

Female

0.69* 0.70* 0.66* 0.67* 0.49* 0.37* 0.46*

0.65* 0.66* 0.64* 0.64* 0.46* 0.34* 0.47*

0.71* 0.71* 0.68* 0.69* 0.51* 0.38* 0.46*

The internal consistency of the present study was reasonable (Cronbach's alpha = 0.79), and comparable to the values obtained from previous epidemiological studies. The Cronbach's alpha ranged from 0.70 to 0.83 among 15 previous studies evaluating the internal consistency of the PSQI (Mollayeva et al., 2016). Through exploring the correlation between the total PSQI score and each of the component scores, we confirmed that all components contributed to the total score. Considering that absolute value correlations between 0.3 and 0.7 were considered moderate (Lohr, 2002), our correlation values show reasonable internal homogeneity (in order of the seven components: 0.69, 0.70, 0.66, 0.67, 0.49, 0.37, and 0.46). Regarding the correlation between PSQI and other scales, a moderate association was found between the PSQI total score and PHQ-9 total score (r = 0.51) and EQ-5D total score (r = –0.33). This correlation is consistent with previous research (Jones et al., 2018; Qiu et al., 2016).

* P Value < 0.001. 0 ≤ r < 0.3: weak relationship. 0.3 ≤ r < 0.7: moderate relationship. r ≥ 0.7: strong relationship. Table. 3 PSQI component and total scores by sex. Variables

Total

Male

Female

P Value*

Subjective sleep quality, M ± SD Very good Fairly good Fairly bad Very bad Sleep latency, M ± SD ≤15 min,% 16–30 min,% 31–60 min,% >60 min,% Sleep duration, M ± SD >7 h,% 6.1–7 h,% 5.1–6 h,% ≤5 h,% Habitual sleep efficiency, M ± SD ≥85,% 75–84,% 65–74,% <65,% Sleep disturbance, M ± SD Use of sleep medication Never,% 5),%

1.14 ± 0.69 1.04 ± 0.65 1.21 ± 0.72 <0.001

4.2. PSQI scores and prevalence of poor sleep quality by sex

13.8 15.5 12.1 63.2 66.3 60.3 19.9 16.2 23.4 3.1 2.0 4.2 0.96 ± 1.02 0.77 ± 0.94 1.11 ± 1.06 <0.001 44.6 51.3 38.3 30.6 29.9 31.3 13.8 11.2 16.3 10.9 7.6 14.1 1.58 ± 1.04 1.52 ± 0.39 1.63 ± 1.05 <0.001 16.8 16.7 16.9 31.2 32.5 29.9 29.3 30.7 28.0 22.7 20.1 25.2 0.48 ± 0.88 0.39 ± 0.80 0.55 ± 0.94 <0.001

The total PSQI mean score of this study was 5.63. The values were higher in females than in males (males: 5.09; females: 6.05). The total mean score was similar to or slightly higher than that observed in other recent studies; for example, a total mean score of 5.00 (males: 4.38; females: 5.54) for a German community sample (Hinz et al., 2017); 4.26 (males: 4.21; females: 4.32) for a Chinese sample (Tang et al., 2017); and 5.30 (males: 4.91; females: 5.63) for a general population sample from Hong Kong (Wong and Fielding, 2011). In addition, when considering PSQI scores of >5 as an indication of poor sleep quality, 41.0% (males: 35.6%; females: 46.0%) of the present study sample met this criterion. Community- or population-based studies have reported the prevalence of poor sleep quality in the general population to be 35.9% (males: 28.7%; females: 42.5%) in Germany (Hinz et al., 2017), 26.6% (males: 26.3%; females: 27.0%) in China (Tang et al., 2017), and 39.4% (males: 35.1%, females: 43.1%) in Hong Kong (Wong and Fielding, 2011). Of note, among the seven dimensions of the PSQI, the sleep duration component had a markedly higher mean score (M = 1.58) compared with the results of previous studies involving a general population. In the German and Chinese studies mentioned above, the mean score of the sleep duration component was 0.58. A total of 20% of participants had a sleep duration of less than 5 h, resulting in a high mean score of this component.

74.8 14.0 5.7 5.5 0.92 ± 0.52

78.0 13.0 4.8 4.2 0.86 ± 0.51

71.7 14.9 6.6 6.8 0.97 ± 0.52 <0.001

0.10 ± 0.50 96.3 1.0 0.8 1.9 0.45 ± 0.74

0.07 ± 0.42 97.6 0.6 0.5 1.3 0.44 ± 0.72

0.12 ± 0.55 <0.001 95.1 1.4 1.0 2.5 0.46 ± 0.75 <0.001

5.63 ± 3.24 5.09 ± 2.91 6.05 ± 3.42 <0.001 41.0

35.6

4.3. PSQI component score differences by sociodemographic, health behavioral, and psychiatric factors

46.2

In general, those with poor sociodemographic, health behavioral, or psychiatric status tended to have a higher mean score than the overall mean scores in each component, with a few exceptions. In particular, in the sleep duration and daytime dysfunction components, the mean scores were similar to or lower than the overall score in males with poor sociodemographic status or health behavior, whereas those with poor psychiatric status tended to have a higher mean score than the overall score in males. To the best of our knowledge, our study is the first to provide mean scores for the seven PSQI components across sociodemographic status, health behavioral, or psychiatric characteristics. Hence, there is a limit in the ability to compare it with findings from previous studies. In addition, careful interpretation is necessary because it is a value of one component of the PSQI, not an objectively measured value. Further studies are needed to evaluate this issue.

*P Value comparing the male and female groups using the t-test. Note: Each component was scored on a scale from 0 to 3. Higher scores correspond to lower sleep quality.

4. Discussion Sleep problems in modern society have become more pronounced and visible. Despite emerging evidence highlighting the importance of sleep quality (Bin, 2016; Ohayon et al., 2017), there remains a significant research gap in the understanding of the factors influencing sleep quality. This nationwide population-based study with a large sample size is, to the best of our knowledge, the first to provide a variety of information on the reference values of the PSQI in South Korea. Our study was carried out using national data, and the findings can in theory be generalized to the South Korean population and other similar Asian populations with similar characteristics.

4.4. Factors associated with poor sleep quality In terms of sociodemographic factors, our logistic regression 53

Journal of Affective Disorders 271 (2020) 49–58

S.Y. Lee, et al.

(caption on next page)

54

Journal of Affective Disorders 271 (2020) 49–58

S.Y. Lee, et al.

Fig. 1. PSQI component score differences by factors. The scores ranged between 0 and 3 in each PSQI component. Higher scores indicated more severe sleep problems. (A) PSQI component score differences by poor sociodemographic characteristics including illiteracy, low income, and unemployed status (B) PSQI component score differences by poor health behavioral characteristics including smoking, high-risk drinking, diabetes, hypertension, and non-participation of walking (C) PSQI component score differences by poor mental health characteristics including poor perceived health, stress, depressive symptoms, and subjective cognitive decline.

analysis showed that low levels of education, low income, and unemployment status were associated with poor sleep quality in both males and females. These associations have been well-established in previous studies (Hinz et al., 2017; Wong and Fielding, 2011; Zhang et al., 2017). The possible reasons are that lower sociodemographic status may indicate low socioeconomic status, which is seemingly related to poor quality of life, greater vulnerability to distress from life stress, and poor sleep (Grandner et al., 2016; Johnson et al., 2016). Regarding health behavior factors, cigarette smoking and high-risk drinking were associated with poor sleep quality, and this association was more prominent in females. Current smokers reported worse sleep quality than non-smokers, and this finding has been well-documented in many previous studies. For example, current smokers reported more sleep disturbance, including less total sleep time, longer sleep onset latency, increased difficulty falling asleep, and higher prevalence of poor sleep quality (McNamara et al., 2014; Purani et al., 2019). However, the effect of sex differences in the association between smoking status and sleep quality was unclear. Further studies are needed to determine why female current smokers tend to report poor sleep quality, because evidence on the sex differences in this association is scarce. Considering that previous studies have reported that females who currently smoke are more likely to report having a psychiatric problem compared to males (Kim et al., 2016), sleep problems may be reported more in females who are more vulnerable to psychiatric distress than males. Regarding alcohol consumption, a study based on healthy adults offers potential explanations for this finding. In terms of pharmacokinetics, the study found that sleep disruption caused by alcohol is objectively more evident in females than in males at an equally high peak breath alcohol concentration (Arnedt et al., 2011). In addition, we found that individuals with diabetes mellitus or hypertension had higher odds of poor sleep quality. Studies have shown that these chronic diseases resulted in a greater risk of poor sleep quality (Lo et al., 2018; Surani et al., 2015). Notably, we found that not only obesity (BMI ≥ 30.0) but also being underweight (BMI < 18.5) were associated with poor sleep quality in males, but not in females. Although the relationship between obesity and sleep quality has been well-established in previous studies (Hung et al., 2013; Logue et al., 2014), most studies on sleep quality did not consider the underweight BMI category, and therefore, the relationship between being underweight and subsequent sleep quality was rarely reported. In addition, little to no research is available that assesses the association between BMI and poor sleep quality, focusing on sex differences. Possible explanations for the observed sex differences are unclear. We think that being underweight may have a potential relationship with sleep quality in males, which needs further study. In terms of psychiatric factors, perceived health status, stress, depressive symptoms, or subjective cognitive decline was strongly associated with poor sleep quality in both males and females. Studies have shown that sleep disturbances are highly prevalent in individuals with poor mental health (Augner, 2011; Baglioni et al., 2016).

reported poor sleep quality more than males (Asghari et al., 2012; Hinz et al., 2017; Tang et al., 2017; Wong and Fielding, 2011). While sex differences have been discussed across the sleep literature, a precise understanding of the mechanisms underlying sex differences remains unclear (Suh et al., 2018). The impact of sex differences on sleep problems is not likely to be explained by a single factor, because sleep is regulated by various (including physiological or psychological) factors. For example, objective polysomnographic measures of sleep architecture performed in the general population showed objectively better sleep quality in females in areas such as longer total sleep time, shorter sleep onset latency, and better sleep efficiency, compared to males (Bixler et al., 2009; Lauderdale et al., 2006)—which is contradictory to the higher prevalence of subjective poor sleep quality in females. A previously published study suggested that under-recognition of sleep disorders may result in misdiagnosis and inappropriate treatment, which may further exacerbate sex differences. For example, the male:female ratio of insomnia complaints was 1:2 in the community versus 1:5 in sleep clinics (Hale et al., 2009). In terms of physiological factors, hormonal changes during the menstrual cycle, menopause, pregnancy, and postpartum in females affect circadian rhythms and sleep architecture, which cause frequent sleep disturbances and exacerbate poor sleep quality (Nowakowski et al., 2013). In addition, these hormonal changes were associated with depression, anxiety, and irritability, which can contribute to the deterioration of sleep quality in females (Baker et al., 2018). With regard to psychosocial factors, considering that stressful life events contribute to the development of sleep problems including insomnia, sleep problems may be reported more by females who are more vulnerable to distress caused by a stressful life, compared to males (Altemus et al., 2014; Gobinath et al., 2015). As with previous studies, our study also failed to explore the exact mechanisms underlying the sex differences in sleep problems. However, we suggest that sex differences in poor sleep quality can be influenced by a variety of factors; therefore, it should be analyzed in many ways rather than from a single point of view (Mollayeva et al., 2016; Suh et al., 2018). This study has some strengths compared to previous studies. We used a nationwide general population sample to provide enriched information on the existing reference values of the PSQI. Our findings from the nationwide sample should be of significant benefit to the literature on sleep quality. However, there were some limitations. First, objective methods of measuring sleep quality (e.g., polysomnography) would be ideal; however, we had no choice but to measure self-reported sleep quality based on interviews. Objectively measuring the quality of sleep for the general population nationwide has never been attempted previously, and is logistically very difficult. Second, as this study was cross-sectional in design, causal inferences cannot be made on the relationship between poor sleep quality and associated factors including psychiatric factors. Hence, it is not possible to conclude whether poor mental health is associated with poor sleep quality or vice versa.

4.5. Sex differences in sleep quality

The authors assert that all procedures contributing to this work comply with the ethical standards of the relevant national and institutional committees on human experimentation and with the Helsinki Declaration of 1975, as revised in 2008.

5. Ethical standards

Generally, in most previous studies of the general population, as well as in our study, females had a higher mean PSQI score and

55

Journal of Affective Disorders 271 (2020) 49–58

S.Y. Lee, et al.

Fig. 2. Factors associated with poor sleep quality (PSQI > 5) in sociodemographic and health behavior characteristics. Adjusted odds ratios were calculated using logistic regression analysis and adjusted for age, education level, and income.

56

Journal of Affective Disorders 271 (2020) 49–58

S.Y. Lee, et al.

Fig. 3. Factors associated with poor sleep quality (PSQI > 5) in mental health characteristics. Adjusted odds ratios were calculated using logistic regression analysis and adjusted for age, education level, and income.

Ethics approval

Asghari, A., Farhadi, M., Kamrava, S.K., Ghalehbaghi, B., 2012. Subjective sleep quality in urban population. Arch. Iran. Med. 15 (2), 95. Augner, C., 2011. Associations of subjective sleep quality with depression score, anxiety, physical symptoms and sleep onset latency in students. Cent. Eur. J. Public Health 19 (2), 115. Baglioni, C., Nanovska, S., Regen, W., Spiegelhalder, K., Feige, B., Nissen, C., Reynolds III, C.F., Riemann, D., 2016. Sleep and mental disorders: a meta-analysis of polysomnographic research. Psychol Bull 142 (9), 969. Baker, F.C., de Zambotti, M., Colrain, I.M., Bei, B., 2018. Sleep problems during the menopausal transition: prevalence, impact, and management challenges. Nat. Sci. Sleep 10, 73. Bin, Y.S., 2016. Is sleep quality more important than sleep duration for public health? Sleep 39 (9), 1629–1630. Bixler, E.O., Papaliaga, M.N., Vgontzas, A.N., Lin, H.M., Pejovic, S., Karataraki, M., Vela‐Bueno, A., Chrousos, G.P., 2009. Women sleep objectively better than men and the sleep of young women is more resilient to external stressors: effects of age and menopause. J. Sleep Res. 18 (2), 221–228. Buysse, D.J., 2014. Sleep health: can we define it? Does it matter? Sleep 37 (1), 9–17. Buysse, D.J., Reynolds III, C.F., Monk, T.H., Berman, S.R., Kupfer, D.J., 1989. The Pittsburgh Sleep Quality Index: a new instrument for psychiatric practice and research. Psychiatry Res. 28 (2), 193–213. Choi, H., Kim, S., Kim, B., Kim, I., 2015. Psychometric properties of the Korean versions of three sleep evaluation questionnaires. Clin. Nurs. Res. 24 (5), 526–538. Choi, H.S., Choi, J.H., Park, K.H., Joo, K.J., Ga, H., Ko, H.J., Kim, S.R., 2007. Standardization of the Korean version of patient health questionnaire-9 as a screening instrument for major depressive disorder. J. Korean Acad. Fam. Med. 28 (2), 114–119. Gobinath, A.R., Mahmoud, R., Galea, L.A., 2015. Influence of sex and stress exposure across the lifespan on endophenotypes of depression: focus on behavior, glucocorticoids, and hippocampus. Front. Neurosci. 8, 420. Grandner, M.A., Williams, N.J., Knutson, K.L., Roberts, D., Jean-Louis, G., 2016. Sleep disparity, race/ethnicity, and socioeconomic position. Sleep Med. 18, 7–18. Hale, L., Do, D.P., Basurto-Davila, R., Heron, M., Finch, B.K., Dubowitz, T., Lurie, N., Bird, C.E., 2009. Does mental health history explain gender disparities in insomnia symptoms among young adults? Sleep Med. 10 (10), 1118–1123. Han, C., Jo, S.A., Kwak, J.-H., Pae, C.-U., Steffens, D., Jo, I., Park, M.H., 2008. Validation of the patient health questionnaire-9 Korean version in the elderly population: the Ansan geriatric study. Compr. Psychiatry 49 (2), 218–223. Hinz, A., Glaesmer, H., Brähler, E., Löffler, M., Engel, C., Enzenbach, C., Hegerl, U., Sander, C., 2017. Sleep quality in the general population: psychometric properties of the Pittsburgh Sleep Quality Index, derived from a German community sample of 9284 people. Sleep Med. 30, 57–63. Hung, H.C., Yang, Y.C., Ou, H.Y., Wu, J.S., Lu, F.H., Chang, C.J., 2013. The association between self‐reported sleep quality and overweight in a Chinese population. Obesity 21 (3), 486–492. Inoue, Y., 2016. Asian specific feature of sleep habits and sleep disorders. Sleep Biol. Rhythms 14 (2), 123–124. Itani, O., Jike, M., Watanabe, N., Kaneita, Y., 2017. Short sleep duration and health outcomes: a systematic review, meta-analysis, and meta-regression. Sleep Med. 32, 246–256.

The Korea Community Health Survey (KCHS) data are openly published. Participants’ data were fully anonymized prior to release. Our study was excluded from the review list pursuant to Article 2.2 of the Enforcement Rule of Bioethics and Safety Act in Korea, since the data was exempted from IRB review. CRediT authorship contribution statement Soon Young Lee: Supervision, Project administration, Validation. Yeong Jun Ju: Conceptualization, Methodology, Formal analysis, Investigation, Data curation, Writing - original draft, Writing - review & editing, Visualization, Validation. Joo Eun Lee: Investigation, Data curation, Writing - review & editing. Young Taek Kim: Resources. Seung Chul Hong: Writing - review & editing. Yun Jung Choi: Data curation. Min Kyoung Song: Data curation. Hye Yun Kim: Data curation. Declaration of Competing Interest None. Acknowledgments None. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.jad.2020.03.069. References Altemus, M., Sarvaiya, N., Epperson, C.N., 2014. Sex differences in anxiety and depression clinical perspectives. Front. Neuroendocrinol. 35 (3), 320–330. Arnedt, J.T., Rohsenow, D.J., Almeida, A.B., Hunt, S.K., Gokhale, M., Gottlieb, D.J., Howland, J., 2011. Sleep following alcohol intoxication in healthy, young adults: effects of sex and family history of alcoholism. Alcoholism 35 (5), 870–878.

57

Journal of Affective Disorders 271 (2020) 49–58

S.Y. Lee, et al. Jike, M., Itani, O., Watanabe, N., Buysse, D.J., Kaneita, Y., 2018. Long sleep duration and health outcomes: a systematic review, meta-analysis and meta-regression. Sleep Med. Rev. 39, 25–36. Johnson, D.A., Lisabeth, L., Lewis, T.T., Sims, M., Hickson, D.A., Samdarshi, T., Taylor, H., Roux, A.V.D., 2016. The contribution of psychosocial stressors to sleep among African Americans in the Jackson Heart Study. Sleep 39 (7), 1411–1419. Jones, S., Nagel, C., McSweeney, J., Curran, G., 2018. Prevalence and correlates of psychiatric symptoms among first responders in a Southern State. Arch. Psychiatr. Nurs. 32 (6), 828–835. Kaneita, Y., Ohida, T., Osaki, Y., Tanihata, T., Minowa, M., Suzuki, K., Wada, K., Kanda, H., Hayashi, K., 2007. Association between mental health status and sleep status among adolescents in Japan: a nationwide cross-sectional survey. J. Clin. Psychiatry 68 (9), 1426–1435. Kang, Y.W., Ko, Y.S., Kim, Y.J., Sung, K.M., Kim, H.J., Choi, H.Y., Sung, C., Jeong, E., 2015. Korea community health survey data profiles. Osong Public Health Res. Perspect. 6 (3), 211–217. Kim, S.M., Jung, J.-W., Park, I.-W., Ahn, C.M., Kim, Y.-I., Yoo, K.-H., Chun, E.M., Jung, J.Y., Park, Y.S., Park, J.-H., 2016. Gender differences in relations of smoking status, depression, and suicidality in Korea: findings from the Korea National Health and nutrition examination survey 2008-2012. Psychiatry Investig. 13 (2), 239. Kroenke, K., Spitzer, R.L., Williams, J.B., 2001. The PHQ‐9: validity of a brief depression severity measure. J. Gen. Intern. Med. 16 (9), 606–613. Lallukka, T., Sivertsen, B., Kronholm, E., Bin, Y.S., Øverland, S., Glozier, N., 2018. Association of sleep duration and sleep quality with the physical, social, and emotional functioning among Australian adults. Sleep Health 4 (2), 194–200. Lauderdale, D.S., Knutson, K.L., Yan, L.L., Rathouz, P.J., Hulley, S.B., Sidney, S., Liu, K., 2006. Objectively measured sleep characteristics among early-middle-aged adults: the Cardia study. Am. J. Epidemiol. 164 (1), 5–16. Lee, Y.-K., Nam, H.-S., Chuang, L.-H., Kim, K.-Y., Yang, H.-K., Kwon, I.-S., Kind, P., Kweon, S.-S., Kim, Y.-T., 2009. South Korean time trade-off values for Eq-5d health states: modeling with observed values for 101 health states. Value Health 12 (8), 1187–1193. Lo, K., Woo, B., Wong, M., Tam, W., 2018. Subjective sleep quality, blood pressure, and hypertension: a meta‐analysis. J. Clin. Hypertens. 20 (3), 592–605. Logue, E.E., Scott, E.D., Palmieri, P.A., Dudley, P., 2014. Sleep duration, quality, or stability and obesity in an urban family medicine center. J. Clin. Sleep Med. 10 (02), 177–182. Lohr, K.N., 2002. Assessing health status and quality-of-life instruments: attributes and review criteria. Qual. Life Res. 11 (3), 193–205. McNamara, J.P., Wang, J., Holiday, D.B., Warren, J.Y., Paradoa, M., Balkhi, A.M., Fernandez-Baca, J., McCrae, C.S., 2014. Sleep disturbances associated with cigarette

smoking. Psychol. Health Med. 19 (4), 410–419. Mollayeva, T., Thurairajah, P., Burton, K., Mollayeva, S., Shapiro, C.M., Colantonio, A., 2016. The Pittsburgh sleep quality index as a screening tool for sleep dysfunction in clinical and non-clinical samples: a systematic review and meta-analysis. Sleep Med. Rev. 25, 52–73. Nowakowski, S., Meers, J., Heimbach, E., 2013. Sleep and women's health. Sleep Med. Res. 4 (1), 1. Ohayon, M., Wickwire, E.M., Hirshkowitz, M., Albert, S.M., Avidan, A., Daly, F.J., Dauvilliers, Y., Ferri, R., Fung, C., Gozal, D., 2017. National Sleep Foundation's sleep quality recommendations: first report. Sleep Health 3 (1), 6–19. Panel, C.C., Watson, N.F., Badr, M.S., Belenky, G., Bliwise, D.L., Buxton, O.M., Buysse, D., Dinges, D.F., Gangwisch, J., Grandner, M.A., 2015. Joint consensus statement of the American Academy of Sleep Medicine and Sleep Research Society on the recommended amount of sleep for a healthy adult: methodology and discussion. Sleep 38 (8), 1161–1183. Purani, H., Friedrichsen, S., Allen, A.M., 2019. Sleep quality in cigarette smokers: associations with smoking-related outcomes and exercise. Addict. Behav. 90, 71–76. Qiu, C., Gelaye, B., Zhong, Q.-.Y., Enquobahrie, D.A., Frederick, I.O., Williams, M.A., 2016. Construct validity and factor structure of the Pittsburgh Sleep Quality Index among pregnant women in a Pacific-Northwest cohort. Sleep Breath. 20 (1), 293–301. Sohn, S.I., Kim, D.H., Lee, M.Y., Cho, Y.W., 2012. The reliability and validity of the Korean version of the Pittsburgh Sleep Quality Index. Sleep Breath. 16 (3), 803–812. Suh, S., Cho, N., Zhang, J., 2018. Sex differences in insomnia: from epidemiology and etiology to intervention. Curr. Psychiatry Rep. 20 (9), 69. Surani, S., Brito, V., Surani, A., Ghamande, S., 2015. Effect of diabetes mellitus on sleep quality. World J. Diabetes 6 (6), 868. Tang, J., Liao, Y., Kelly, B.C., Xie, L., Xiang, Y.-T., Qi, C., Pan, C., Hao, W., Liu, T., Zhang, F., 2017. Gender and regional differences in sleep quality and insomnia: a general population-based study in Hunan Province of China. Sci. Rep. 7, 43690. Taylor, C.A., Bouldin, E.D., McGuire, L.C., 2018. Subjective cognitive decline among adults aged≥ 45 years—United States, 2015–2016. Morb. Mortal. Wkly. Rep. 67 (27), 753. Wong, W.S., Fielding, R., 2011. Prevalence of insomnia among Chinese adults in Hong Kong: a population‐based study. J. Sleep Res. 20 (1pt1), 117–126. Yokoyama, E., Saito, Y., Kaneita, Y., Ohida, T., Harano, S., Tamaki, T., Ibuka, E., Kaneko, A., Nakajima, H., Takeda, F., 2008. Association between subjective well-being and sleep among the elderly in Japan. Sleep Med. 9 (2), 157–164. Zhang, H.-S., Li, Y., Mo, H.-Y., Qiu, D.-X., Zhao, J., Luo, J.-L., Lin, W.-Q., Wang, J.-J., Wang, P.-X., 2017. A community-based cross-sectional study of sleep quality in middle-aged and older adults. Qual. Life Res. 26 (4), 923–933.

58