1.  Introduction {#sec1}
===============

In December 2019, several pneumonia patients with cryptogenic etiology were reported in Wuhan, Hubei Province, China \[[@bib1]\].  A novel coronavirus was identified and subsequently named as coronavirus disease 2019 (COVID-19) by the World Health Organization (WHO) \[[@bib2],[@bib3]\].  More than 84,000 cases were confirmed with COVID-19 in China \[[@bib4]\], conservatively including 3000 doctors and nurses \[[@bib5]\].  Frontline medical workers (FMW) are exposed to high chronic stress due to high risk of infection and long work hours.  These constant stressors may negatively impact their sleep and mental health. 

Medical workers generally experience high levels of stress, irregular work schedule, and frequent work shifts, which may lead to increased sleep disturbances \[[@bib6]\].  In a meta-analysis, Qiu et al.  \[[@bib7]\], found that 39.2% of Chinese medical workers suffered from sleep disturbances, which indicated the prevalence was much higher than general population.  The outbreak of COVID-19 in China remains to be a serious challenge for FMW.  They are not only under high risk of being infected with the disease but also high mental stress, which may lead to acute sleep disturbances \[[@bib8]\].  Therefore, we conducted a cross-sectional study to evaluate the sleep disturbances, anxiety, and depression of FMW in Hubei Province, and made a comparison with non-FMW.  We hypothesized that FMW would report greater sleep disturbances, anxiety, and depression levels than non-FMW. 

2.  Methods {#sec2}
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2.1.  Study samples {#sec2.1}
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This cross-sectional study included medical workers in Hubei Province, China.  The Institutional Review Board of Chinese PLA General Hospital approved this study.  All subjects provided informed consent.  The survey was conducted anonymously.  The inclusion criteria for this study were as follows: (a) age older than 18 years old; (b) medical workers in Hubei Province, including local medical workers and supportive medical workers from other Chinese regions, and (c) volunteered to participate in the survey.  The exclusion criteria were listed as follows: (a) history of mental disorders; (b) sleep quality was affected by negative personal events; (c) taking sleep modulating medication (sedatives and hypnotics), and (d) incomplete data of the online questionnaire.  Subjects were divided into FMW and non-FMW groups according to whether directly exposed to COVID-19 patients.  FMW worked in departments specialized for the treatment of COVID-19 patients, while non-FMW worked in general departments. 

2.2.  Data collection {#sec2.2}
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The online survey was conducted in February 2020, with the contents encompassing basic information (age, gender, marriage, education level, etc.),  epidemiological investigation, Pittsburgh Sleep Quality Index (PSQI), Athens Insomnia Scale (AIS) and Visual Analogue Scale (VAS).  PSQI is a self-reported questionnaire which assesses subjective sleep quality within one month.  A subject with a total score \>6 points is defined as poor sleep quality.  The higher the PSQI, the worse the sleep quality \[[@bib9]\].  The AIS is a brief instrument to assess the severity of insomnia.  It contains eight items with each item rated from 0 to 3 points \[[@bib10]\].  A subject with a total score \>6 points screens positive for insomnia.  The higher the AIS, the more severe the insomnia \[[@bib11]\].  In this study, we used the VAS to measure anxiety and depression.  Furthermore, the VAS was presented as a horizontal graphic slider, which was divided into 10 segments from 1 (weakest) to 10 (strongest).  Higher scores indicate stronger feeling of each mental status. 

2.3.  Statistical analysis {#sec2.3}
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Categorical variables were described as percentages, and continuous variables were described as mean ± SDs.  Statistical analyses were performed using SPSS 23.0 for Windows.  The scores of PSQI, AIS, and VAS were compared by ANCOVA.  The demographic characteristics with significant differences between two groups were described as covariates and joined in the process of ANCOVA.  Comparisons of categorical variables were conducted by Chi-square test.  P value \< 0.05 was considered statistical significance. 

3.  Results {#sec3}
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3.1.  Subject demographic characteristics {#sec3.1}
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A total of 1396 medical workers answered the questionnaire.  There was no subject excluded due to exclusion criteria A, B, and C, while 90 questionnaires were excluded due to incomplete data.  Therefore, 1306 subjects (801 FMW and 505 non-FMW) with age of (33.1 ± 8.4) years old were finally enrolled, comprising 256 (19.6%) males and 1050 (80.4%) females.  There were 161 (20.1%) males and 640 (79.9%) females in FMW, while there were 95 (18.8%) males and 410 females (81.2%) in non-FMW.  There were 534 (66.7%) subjects married in FMW, whereas there were 359 (71.1%) subjects married in non-FMW.  Significant differences of age (\[32.4 ± 7.7\] vs \[34.1 ± 9.3\]; *P* = 0.016), education level (*P* = 0.002), and rank (*P* = 0.001) were found between FMW and non-FMW. 

3.2.  Comparison of scores and prevalence {#sec3.2}
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The scores of PSQI, AIS, and VAS are presented in [Table 1](#tbl1){ref-type="table"} .  Compared to non-FMW, FMW had significantly higher scores of PSQI (9.3 ± 3.8 vs 7.5 ± 3.7; *P* \< 0.001; Cohen\'s *d* = 0.47), AIS (6.9 ± 4.3 vs 5.3 ± 3.8; *P* \< 0.001; Cohen\'s *d* = 0.38), anxiety (4.9 ± 2.7 vs 4.3 ± 2.6; *P* \< 0.001; Cohen\'s *d* = 0.22), and depression (4.1 ± 2.5 vs 3.6 ± 2.4; *P* = 0.001; Cohen\'s *d* = 0.21) ([Fig.  1](#fig1){ref-type="fig"} ).Table 1Comparison of PSQI, AIS, VAS, and sleep disturbances prevalence. Table 1Overall (n = 1306)FMW (n = 801)Non-FMW (n = 505)Effect sizeP valueScoresCohen\'s *d* PSQI8.6 ± 3.99.3 ± 3.87.5 ± 3.70.47\<0.001 AIS6.3 ± 4.26.9 ± 4.35.3 ± 3.80.38\<0.001 Anxiety VAS4.7 ± 2.74.9 ± 2.74.3 ± 2.60.22\<0.001 Depression VAS3.9 ± 2.44.1 ± 2.53.6 ± 2.40.210.001PrevalenceRR PSQI\>6936 (71.7%)628 (78.4%)308 (61.0%)1.29\<0.001 AIS\>6594 (45.5%)414 (51.7%)180 (35.6%)1.45\<0.001[^1]Fig.  1**The boxplots for PSQI, AIS, Anxiety and Depression VAS scores of FMW and non-FMW (**A) The FMW had significantly higher scores of PSQI (*P* \< 0.001) than non-FMW. ( B) The FMW had significantly higher scores of AIS (*P* \< 0.001) than non-FMW. ( C) The FMW had significantly higher scores of anxiety VAS (*P* \< 0.001) than non-FMW. ( D) The FMW had significantly higher scores of depression VAS (*P* = 0.001) than non-FMW.  FMW = frontline medical workers, PSQI= Pittsburgh Sleep Quality Index, AIS = Athens Insomnia Scale, VAS= Visual Analogue Scale. Fig.  1

Overall, 936 (71.7%) subjects had poor sleep quality according to PSQI\> 6 points, while 594 (45.5%) subjects had insomnia according to AIS\> 6 points.  There were 628 (78.4%) FMW and 308 (61.0%) non-FMW with PSQI\> 6 points.  Meanwhile, AIS \>6 points were observed in 414 (51.7%) FMW and 180 (35.6%) non-FMW.  Compared to non-FMW, FMW had significantly higher prevalence of sleep disturbances, according to PSQI \>6 points (78.4% vs 61.0%; *P* \< 0.001) and AIS \> 6 points (51.7% vs 35.6%; *P* \< 0.001).  FMW were 29% more likely to endorse poor sleep quality relative to non-FMW (relative risk \[RR\]: 1.29), and had 45% increased risk of insomnia (RR: 1.45) ([Table 1](#tbl1){ref-type="table"}).  Additionally, there were 670 (83.6%) FMW and 353 (69.9%) non-FMW with PSQI\> 5 points, and 538 (67.2%) FMW and 241 (47.7%) non-FMW with PSQI \> 7 points.  In subgroup analysis, 516 (80.6%) female FMW and 112 (69.6%) male FMW had PSQI \> 6 points.  Compared to male FMW, female FMW had significantly higher prevalence of sleep disturbances (80.6% vs 69.6%; *P* = 0.002) according to PSQI\>6 points. 

4.  Discussion {#sec4}
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The rapid spread of the disease and inadequate early realization toward COVID-19 challenged the health institutions in many countries.  In addition, the mortality of COVID-19 was 2.3%, and medical workers accounted for 3.8% of confirmed cases as reported by the latest epidemiological study \[[@bib5]\].  Previous studies have indicated medical workers were vulnerable to sleep disturbances in ordinary times \[[@bib6],[@bib7]\].  Therefore, we hypothesized that FMW under the outbreak of COVID-19 may be more susceptible to sleep disturbances.  To the best of our knowledge, this is the first study to evaluate the sleep disturbances of FMW under the outbreak of COVID-19. 

The pooled prevalence of sleep disturbances for Chinese healthcare professionals is 39.2% \[[@bib7]\].  In comparison, the present study demonstrated that medical workers under COVID-19 outbreak suffered from elevated sleep disturbances, especially for FMW.  Our study showed that 78.4% of FMW had poor sleep quality and 51.7% of them had insomnia.  The high prevalence of sleep disturbances for FMW should be put much emphasize, since sleep disturbances influence not only medical workers' health but also their professional performance \[[@bib12]\].  The study also demonstrated FMW had significantly higher prevalence of sleep disturbances than non-FMW.  Change of work environment may underpin the more severe sleep disturbances of FMW.  With the direct contact with COVID-19 patients, FMW are inclined to emerge anxiety and worries of being infected.  The rapid spread of COVID-19 boomed the medical demands, aggravated the shortage of medical resources, and increased work stress for FMW, especially for those with continuous work and frequent day--night shifts.  By comparison, non-FMW work in a relatively mild environment and have less possibility of being infected, thus having fewer sleep disturbances.  Our study also found female FMW had worse sleep quality than male FMW.  Zhang et al.  \[[@bib13]\], calculated a hazard ratio of 1.41 for female versus male toward insomnia.  In general, females have inferior symptom bearing and greater bodily vigilance \[[@bib14]\], which potentially exaggerates the severity and prevalence of sleep disturbances. 

The study has several limitations that should be noted.  First, this is a cross-sectional study.  All subjects volunteered to participate in the survey, so there may be subject selection bias.  Second, we did not measure COVID-19 exposure, infection, or attitudes in subjects, which could influence sleep quality.  Third, our questionnaires did not contain sufficient items to explore the potential risk factors for sleep disturbances.  Finally, our study lacked of follow-ups, which may show the following changes of sleep disturbances for FMW. 

5.  Conclusion {#sec5}
=============

In conclusion, FMW have higher prevalence of sleep disturbances and worse sleep quality than non-FMW.  And female FMW report more sleep disturbances than male FMW.  Further interventions should be administrated for FMW, aiming to maintain their healthy condition and guarantee their professional performance in the battle against COVID-19. 

CRediT authorship contribution statement {#sec6}
========================================

**Jing Qi:** Conceptualization, Investigation, Data curation, Formal analysis, Methodology, Software, Writing - original draft. ** Jing Xu:** Conceptualization, Investigation, Data curation, Formal analysis, Methodology, Software. ** Bo-Zhi Li:** Investigation, Data curation, Formal analysis, Methodology, Software. ** Jin-Sha Huang:** Investigation, Resources, Data curation. ** Yuan Yang:** Investigation, Resources, Data curation. ** Zhen-Tao Zhang:** Investigation, Resources, Data curation. ** Dong-Ai Yao:** Investigation, Resources, Data curation. ** Qun-Hui Liu:** Investigation, Resources, Data curation. ** Min Jia:** Investigation, Resources, Data curation. ** Dao-Kai Gong:** Investigation, Resources, Data curation. ** Xiao-Hong Ni:** Investigation, Resources, Data curation. ** Qi-Mei Zhang:** Investigation, Resources, Data curation. ** Fu-Rong Shang:** Investigation, Resources, Data curation. ** Nian Xiong:** Investigation, Resources, Data curation. ** Chun-Li Zhu:** Investigation, Resources, Data curation. ** Tao Wang:** Conceptualization, Investigation, Resources, Data curation, Methodology, Writing - review & editing. ** Xi Zhang:** Conceptualization, Data curation, Funding acquisition, Methodology, Project administration, Supervision, Writing - review & editing. 

Conflict of interest {#appsec1}
====================

The following is the supplementary data to this article:Multimedia component 1Multimedia component 1

This study was supported by National Military Science Foundation of China \[grant number AWS14J011\].  The funding agency did not take part in the design of the study and collection, analysis, and interpretation of data and in writing the manuscript. 

None. 

The ICMJE Uniform Disclosure Form for Potential Conflicts of Interest associated with this article can be viewed by clicking on the following link: <https://doi.org/10.1016/j.sleep.2020.05.023>. 

[^1]: FMW = frontline medical workers, PSQI= Pittsburgh Sleep Quality Index, AIS = Athens Insomnia Scale, VAS= Visual Analogue Scale, RR = relative risk.