key: cord-0825685-t04zm2cm
authors: Yang, Yu; Zhu, Jian-fu; Yang, Shu-yue; Lin, Hai-jiang; Chen, Yue; Zhao, Qi; Fu, Chao-wei
title: Prevalence and associated factors of poor sleep quality among Chinese returning workers during the COVID-19 pandemic
date: 2020-07-14
journal: Sleep Med
DOI: 10.1016/j.sleep.2020.06.034
sha: 56d864e9c0e454664be118c01eda7088ff2e0df4
doc_id: 825685
cord_uid: t04zm2cm

OBJECTIVES: The pandemic of the COVID-19 is a severe global crisis and resulted in public health problems in many aspects. This study aimed to investigate the prevalence of poor sleep quality and its related factors among employees who returned to work during the COVID-19 pandemic. METHODS: An online cross-sectional study included 2,410 participants aged ≥17 years in Deqing and Taizhou, Zhejiang Province, China from 5(th) to 14(th) March 2020. The questionnaire covered information on demographic characteristics, health status, workplace, lifestyle, attitude towards COVID-19, assessment of anxiety, depression and sleep quality. The Chinese version of the Pittsburgh Sleep Quality Index (CPSQI) was administered to measure the sleep quality. Poor sleep quality was defined as a global PSQI score>5. Factors associated with sleep quality were analyzed by logistic regression models. RESULTS: Totally, near half (49.2%) of 2,410 returning workers were females and the average year of subjects was 36.3 ± 9.1 years. The overall prevalence of poor sleep quality was 14.9% (95%CI: 13.5%-16.3%). The average score of PSQI was 3.0 ± 2.5 and average sleep duration was 7.6 ± 1.2h. Independent related factors of poor sleep quality included age older than 24 years old, higher education level, negative attitude towards COVID-19 control measures, anxiety and depression. CONCLUSIONS: Poor sleep quality was common and there was a shorter sleep duration among returning workers during the COVID-19 pandemic. Possible risk factors identified from this study can be of great importance in developing proper intervention for the targeted population to improve the sleep health during such public health emergency.

The coronavirus disease 2019 (COVID-19) pandemic is a global health crisis 28 which can damage both physical health and mental health. Previous studies have shown 29 that infectious disease epidemics like severe acute respiratory syndrome (SARS) and 30

Ebola virus disease (EVD) could cause sleep-related problems in relevant individuals 31 [1] [2] [3] . Good sleep quality is very important to health [4] . A few recent studies focused on 32 sleep quality of medical workers and individuals who self-isolated for 14 days during 33 the COVID-19 pandemic. Frontline medical workers (fMW) had worse sleep compared 34 to non-fMW [5, 6] . Anxiety was associated with stress and sleep quality in individuals 35 who self-isolated at home for 14 day [7] . 36

Since mid-February, due to the fact that the COVID-19 pandemic has largely been 37 under control in China, employees have been returning to work in many areas. The 38 returning workers experienced lifestyle changes from prolonged state of not working to 39 working again. As of March 5, it was reported that more than 100 people in 26 40 companies were infected with COVID-19, and more than 10,000 people were 41 quarantined after returning to work in China, which suggested that returning to work 42 may increase the risk of infecting COVID-19 [8]. On one hand, the fear of infection risk 43 or losing job due to economic downturn may worse the sleep quality among the 44 returning worker. On the other hand, the benefit of improving the financial status after 45 returning to work may help reduce the stress and lead to better sleep quality. So far 46 there has been no study of sleep quality and their related factors for returning workers. 47 sleep quality among Chinese who returned to work during the COVID-19 pandemic. 49

A cross-sectional study was conducted among individuals after the first 2-week of 52 returning to work in Deqing and Taizhou, Zhejiang Province, China from 5 th to 14 th 53

March 2020. Returning workers were the employees who were approved to back to 54 work and returned to work at those enterprises and/or home during 15 th Feb. to 5 th 55

March 2020. Deqing was a low-risk epidemic area with less than 10 confirmed cases of 56 COVID-19 and Taizhou was a high-risk epidemic area with more than 100 confirmed 57 cases of COVID-19 [9] . Inclusion criteria of the enterprises was that the annual business 58 turnover of 20 million RMB or above and reopened since mid-February. Exclusion 59 criteria was that the general manager or director refused to participate into this study. In 60 each area, the eligible enterprises were engaged to support this survey until recruited 61 subjects more than 900. The different kinds of enterprises were covered as many as 62 possible. Finally, 43 of 996 enterprises in Taizhou and 120 of 738 enterprises in Deqing 63 were included in this study. Inclusion criteria of returning workers were as follows: a. 64 older than 17 years old; b. full-time employees; c. who had returned to work since mid-65

February. The returning workers whose completion time on survey was more than 30 66 minutes or less than 2 minutes or those who were diagnosed with COVID-19, had 67 family members being diagnosed, or had close contact with confirmed cases of COVID-68 was used to measure the depressive symptom with a summed score ranging from 0 to 27 85 for the 9 items, and mild depression was defined as a PHQ-9 score≥5 in this study 86

[15]. Cronbach's alpha for the internal consistency reliability and 2-week test-retest 87 reliability of the Chinese version of the PHQ-9 were 0.86 [16] . The 7-item Generalized 88

Anxiety Disorder (GAD-7) was used to measure anxiety symptom with a summed score 89 ranging from 0 to 21, and mild anxiety was defined as a GAD-7 score ≥5 in this 90 

The study was approved by the Institutional Review Board of the Fudan University 104

School of Public Health (IRB#2020040817). All the participants gave an online 105 informed consent. 106

Data were analyzed using SPSS version 24.0 (IBM Corp, Armonk, New York, 108 USA). Continuous variables were described as means ± standard deviations (SD) and 109 categorical variables as frequencies and percentages. Chi-square was occupied to 110 compare the distributions of categorical variables between good sleep quality group and 111 poor sleep quality group, or sex groups, or age groups. Mean differences of continuous 112 variables between different sex groups was tested by t-test or Mann-Whitney U test, 113 and those between different age groups were tested by one way analysis of variance 114 (ANOVA) or Kruskal-Wallis test. Univariate and multivariate logistic regression 115 models were used to identify factors associated with poor sleep quality. The univariate 116 OR, multivariate OR and their corresponding 95% confidence intervals (CI) were 117 estimated. Two-sided P value＜0.05 was considered to be statistically significant. 118

The social-demographic and other characteristics of 2,410 subjects according to 121 sleep quality were shown in Table 1 . The average age was 36.3 ± 9.1 years, and sex 122 ratio was about 1:1. Near 60% of subjects were from Deqing. Most of them worked 123 only at enterprise (90.4%). 40% of subjects had the quarantine experience during the 124 COVID-19 pandemic. There were significant differences in age, education level, 125 alcohol drinking, regular physical exercise, being negative for the COVID-19 control, 126 anxiety and depression between subjects with and without poor sleep (P＜0.05). 127

The subjects had an average sleep duration of 7.6 ± 1.2h. The overall mean score 129 (± SD) was 0.6 (± 0.7) for subjective sleep quality, 0.7 (± 0.7) for sleep latency, 0.2 (± 130 0.5) for sleep duration, 0.5 (± 0.8) for sleep efficiency, 0.6 (± 0.5) for sleep disturbance, 131 0.0 (± 0.1) use of sleeping medication and 0.5 (± 0.7) for daytime dysfunction. For these 132 PSQI domain scores, sleep latency was the highest, and the use of sleeping medication 133 was the lowest. The average score of PSQI was 3.0 ± 2.4 and was higher in males than 134 females (3.2 ± 2.5 vs 2.9 ± 2.4, P＜0.001). The detailed sex-and age-specific domain 135 scores of sleep quality and PSQI score were presented in 19.6%). The sex-and age-specific prevalence of poor sleep quality was shown in 148 Note: * Significant difference over different age groups (P value＜0.05). The error bars mean 95% 152 confidence intervals.

As presented in Tables 2, after the adjustment of covariates, the independent 155 factors of poor sleep quality were age, education levels, being negative for the COVID-156 19 control, anxiety and depression. The risk of poor sleep quality increased with age. 157

Higher education level was associated with a higher risk of poor sleep quality (aOR: 158 pandemic. There were several limitations for the study. Firstly, this study did not 218 include small enterprises. As a consequence, the sleep quality could be underestimated 219 because workers in small or private enterprises tended to have higher risk jobs, which 220 was a risk factor of insomnia [37] . Secondly, the cross-sectional design cannot infer a 221 causal relationship between associated factors and poor sleep quality. In addition, the 222 assessment of sleep quality with PSQI questionnaire was more subjective compared 223 with polysomnography. 224

among employees who just returned to work. Employees who were older than 24 years 227 old, had higher education level or negative attitude towards the COVID-19 control, or 228 had anxiety and depressive symptoms tended to have a higher risk of poor sleep quality. 229

These findings suggested that the proper measures should be taken to reduce the 230 negative attitude and to care psychological symptoms of anxiety and depression for the 231 targeted population to improve the sleep health during the COVID-19 pandemic. 232

The authors declare no potential conflicts of interest. 

It is firstly reported that poor sleep quality was common among Chinese returning workers during the COVID-19 pandemic.

It is firstly reported that poor sleep duration was shorten among Chinese among returning workers.

Possible risk factors identified from this study can be of great importance in developing psychological intervention and health education for the targeted population during the public health emergency.

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