Good sleep is part of good quality of life. High-quality and efficient sleep of adequate duration helps to consolidate memory, regulate the immune system and coordinate neuroendocrine functions.Reference Magnavita and Sleep1 By contrast, poor sleep quality leads to a broad range of adverse outcomes, including cardiovascular diseases, poor mental health status, cognitive impairment and even overall mortality.Reference Grandner, Sands-Lincoln, Pak and Garland2,Reference Li, Wu, Gan, Qu and Lu3
COVID-19 may negatively affect sleep quality and other mental health outcomes among patients admitted to hospital, patients with mild and asymptomatic cases, and survivors.Reference Liu, Baumeister, Veilleux, Chen, Liu and Yue4–Reference Magnavita, Tripepi and Di Prinzio8 COVID-19 symptoms and experience during hospital stay are stressors affecting mental health status.Reference Liu, Baumeister, Veilleux, Chen, Liu and Yue4 Moreover, treatment for COVID-19 may have adverse effects on mental health and contribute to insomnia.Reference Bani-Sadr, Hentzien, Pascard, N'Guyen, Servettaz and Andreoletti9 Furthermore, SARS-Cov-2 might also infect the brain, which could directly cause adverse effects on brain functions and mental health.Reference Holmes, O'Connor, Perry, Tracey, Wessely and Arseneault5
Understanding factors associated with sleep quality among COVID-19 survivors after hospital discharge is important for intervention development and service planning to enhance well-being. Cross-sectional studies showed that 29.5% and 26% of COVID-19 survivors in Wuhan, China, were bothered by a sleeping disorder 1 month and 6 months after discharge, respectively (measured by a single item within the Patient Health Questionnaire (PHQ-9): ‘Whether you have trouble falling or staying asleep, or sleeping too much in the past two weeks’ and a self-reported symptom questionnaire: ‘What do you think about your sleeping compared with the status prior to COVID-19’).Reference Wu, Hu, Song, Yang, Xu and Cheng6 A cohort study reported that 26% of COVID-19 survivors suffered from sleep difficulties 6 months after hospital discharge.Reference Huang, Huang, Wang, Li, Ren and Gu10 Moreover, 40% of COVID-19 survivors in Italy reported insomnia (as measured by the Women's Health Initiative Insomnia Rating Scale).Reference Mazza, De Lorenzo, Conte, Poletti, Vai and Bollettini7 Only a limited range of factors associated with sleep quality have been reported. Being female, younger and having a history of psychiatric disorder were associated with poor sleep quality.Reference Mazza, De Lorenzo, Conte, Poletti, Vai and Bollettini7 Previous studies showed that sleep problems among survivors of severe acute respiratory syndrome (SARS) were likely to be persistent; 47%, 50% and 44% of survivors reported difficulty sleeping 3, 6 and 12 months after hospital discharge, respectively.Reference Tansey, Louie, Loeb, Gold, Muller and de Jager11
A systematic review suggested that there were three groups of risk factors for sleep quality among patients after hospital admission and/or critical illness: pre-hospital factors, in-hospital factors and post-hospital factors.Reference Altman, Knauert and Pisani12 Pre-hospital factors included pre-hospital sleep quality, self-reported concurrent diseases before hospital admission and smoking.Reference Altman, Knauert and Pisani12 Disease severity at hospital admission, ventilation days, intensive care unit (ICU) admission and length of hospital stay were in-hospital risk factors for sleep quality after hospital discharge.Reference Altman, Knauert and Pisani12 The main post-hospital factors associated with sleep quality were depression, anxiety and stress.Reference Altman, Knauert and Pisani12 These factors were considered in this study.
In addition, COVID-19 survivors may have experienced multiple stressors and traumatic events, such as having family members become infected with and/or die of COVID-19, and witnessing the painful symptoms and/or death of other patients.Reference Liu, Baumeister, Veilleux, Chen, Liu and Yue4 Corticosteroid therapy is beneficial for modulating the host inflammatory response to COVID-19.Reference Bani-Sadr, Hentzien, Pascard, N'Guyen, Servettaz and Andreoletti9 However, sleep disturbance is a commonly cited adverse effect of corticosteroid.Reference Cole13 The Chinese government has been providing psychological support for COVID-19 patients and their family members, as well as for people under quarantine, since 8 March 2020.14 Screening, prevention and treatment for mental health problems have been pro-actively provided to COVID-19 patients during their hospital stay.14 COVID-19 survivors are also encouraged to seek psychological support after hospital discharge. Community healthcare centres are coordinating with psychotherapists and social workers to provide such services to COVID-19 survivors in need.14 This study also investigated the association between receiving mental health support services during hospital stay and/or after hospital discharge with post-discharge sleep quality among COVID-19 survivors. Moreover, one study evaluating psychological interventions targeting COVID-19 patients showed that better social support predicted improvement in sleep quality.Reference Yang, Yang, Kumar, Cao, Ma and Li15 Therefore, social support after hospital discharge was also considered by this study. Furthermore, about 7–8% of COVID-19 survivors tested positive again after hospital discharge.Reference Cao, Ruan, Liu and Liao16–Reference Shui, Li, Liu, Chen and Zhang18 However, there was no consensus about the cause of such phenomena.Reference Tao, Wang, Zhang, Guo, Ma and Zhao19,Reference Wu, Zhang, Zhang, Wang and Wan20 Positive retest results may be another stressor for COVID-19 survivors.
There is a dearth of studies investigating the longer-term impact of COVID-19 on sleep quality. To address these gaps, this study investigated sleep quality among COVID-19 patients 6 months after their most recent hospital discharge. Factors associated with sleep quality were also investigated, including background characteristics and pre-hospital, in-hospital and post-hospital factors.
Method
Study design
This was a cross-sectional telephone survey among 199 COVID-19 patients 6 months after their hospital discharge. The study was conducted during August to September 2020.
Participants and data collection
Participants were recovered adult COVID-19 patients discharged between 1 February and 30 March 2020. The conveniently selected study sites included five hospitals located in five Chinese cities (i.e., Wuhan, Shenzhen, Zhuhai, Dongguan and Nanning) with high, moderate and low COVID-19 case-load. Wuhan is the capital city of Hubei Province, which is most affected by the COVID-19 pandemic in China. Shenzhen, Zhuhai and Dongguan are cities in Guangdong Province, which has the second largest number of COVID-19 cases in China. Nanning is the capital city of Guangxi Province, which has been less affected by the COVID-19 pandemic. As of 9 November 2020, the numbers of recovered COVID-19 patients in these Chinese cities were 46 475 in Wuhan, 72 in Nanning, 468 in Shenzhen, 109 in Zhuhai and 100 in Dongguan.21 COVID-19 survivors who were under 18 years old were excluded for the following reasons. First, the measurement of sleep quality (single-item Sleep Quality Scale; SQS) was not validated in a population under 18 years old.Reference Snyder, Cai, DeMuro, Morrison and Ball22 No study used this scale to measure sleep quality among children or adolescents. Second, most of the patients under 18 years old in the records kept by the collaborative hospitals were very young (5–10 years old). It would have been difficult for them to answer our questionnaire, which used relatively complicated psychosocial measurements. Asking their parents to answer the questionnaire would create additional bias.
According to the treatment guidelines in China, recovered COVID-19 patients should be quarantined for 14 days in designated facilities, followed by another 14-day home-based quarantine after hospital discharge. Their contact information is kept by the hospitals for follow-up assessments and services. Recruitment was facilitated by medical staff in the five participating hospitals, who were responsible for follow-up visits and services for recovered COVID-19 patients after hospital discharge. They contacted all discharged COVID-19 patients discharged between 1 February and 30 March 2020 who were listed in their registries. Verbal informed consent was obtained from all participants. Verbal consent was witnessed and formally recorded. The aforementioned medical staff screened prospective participants’ eligibility to join the study, briefed them about the study information and invited them to complete a telephone-based interview. Upon appointment, trained interviewers confirmed participants’ informed consent and conducted the telephone-based interview. Participants were assured that identifiable information would be kept confidential, and that withdrawal from the study would not affect their right to use any treatment services. The interview lasted for about 35 min. No incentive was given to the participants. 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. All procedures involving human subjects were approved by the Sun Yat-sen University (Shenzhen) (ref. 2020–031).
Of the 317 recovered COVID-19 patients discharged from these hospitals, 27 were under 18 years old (the scale used in this study was not applicable to these patients), 22 had changed their telephone number and one had died in a car accident. The remaining 267 eligible patients were contacted by the research team; 68 eligible patients refused to participate in the study. A total of 199 eligible patients provided consent and completed the telephone-based interview. The response rate was 74.5% (Wuhan: 31/49, 63.3%; Nanning: 56/72, 77.8%; Shenzhen: 38/50, 76.0%; Zhuhai: 39/51, 76.5%; and Dongguan: 35/45, 77.8%).
Measures
Development of the questionnaire
A panel consisting of one epidemiologist, two public health researchers, a health psychologist and a clinician was formed to develop the questionnaire used in the current study.
Background characteristics
Participants were asked to report on sociodemographic characteristics, including age, gender, permanent residency status, highest educational level, relationship status, monthly personal income, employment status and whether they had children.
Sleep quality
A single-item SQS was used to measure global sleep quality over a 7 day recall period with a rating ranging from 0 to 10. A higher score indicated better sleep quality.Reference Snyder, Cai, DeMuro, Morrison and Ball22 Cut-off scores of 1, 4, 7, and 10 were used to define poor, fair, good and excellent sleep quality, respectively.Reference Snyder, Cai, DeMuro, Morrison and Ball22 The single-item SQS uses more suitable measurement characteristics to assess sleep quality compared with lengthier sleep questionnaires such as the Pittsburgh Sleep Quality Index (PSQI) and the Morning Questionnaire-insomnia.Reference Snyder, Cai, DeMuro, Morrison and Ball22 This instrument has been demonstrated to be a reliable and valid measure that does not significantly increase the burden on respondents and has been used in a number of published studies,Reference Serlachius, Schache, Boggiss, Lim, Wallace-Boyd and Brenton-Peters23–Reference Shrestha, Ghimire, Acharya, Prabhat, Singh and Sharma27 including studies conducted in China.Reference Pan, Xin, Zhang, Dong, Fang and Wu24 In addition, participants were asked to compare their current sleep quality with that in the time before COVID-19 (response categories: 1 = got worse, 2 = no change, 3 = got better). One item extracted from the PHQ-9 scale was used to assess whether participants were bothered by sleep disorder: ‘Whether you have trouble falling or staying asleep, or sleeping too much in the past two weeks’ (response categories: not at all, several days, more than half the days and nearly every day).Reference Wu, Hu, Song, Yang, Xu and Cheng6
Pre-hospital factors
Participants were asked to report whether they had received a diagnosis of insomnia or other chronic diseases (i.e., hypertension, diabetes mellitus, cancers, and other chronic heart/lung/liver/renal diseases) before COVID-19. Cigarette smoking within 1 year prior to COVID-19 was also recorded.
In-hospital factors
Information about severity level of COVID-19 at hospital admission, days in hospital, ICU admission, and the use of invasive ventilation and corticosteroid therapy was extracted from participants’ medical records. Participants were asked whether they had a family member or members with COVID-19 or who had died of COVID-19, and whether they had witnessed the suffering and death of other COVID-19 patients. In addition, participants were asked whether they had received any mental health support services during their hospital stay.
Post-hospital factors
Participants reported whether they had received positive SARS-Cov-2 nucleic acid testing results and any mental health support services after hospital discharge.
Depressive symptoms were measured by a validated Chinese version of the PHQ-9 scale, which has been widely used for screening for depression in the Chinese population.Reference Pan, Xin, Zhang, Dong, Fang and Wu24,Reference Wang, Bian, Zhao, Li, Wang and Du28,Reference Pan, Fang, Xin, Dong, Zhou and Hou29 The Cronbach's alpha of the PHQ-9 was 0.91; one factor was identified by exploratory factor analysis, explaining 59.1% of the total variance.
Generalised anxiety disorder was measured by a validated Chinese version of the Generalized Anxiety Disorder Scale (GAD-7).Reference Tong, An, McGonigal, Park and Zhou30 The Cronbach's alpha of the GAD-7 Scale was 0.92; one factor was identified by exploratory factor analysis, explaining 68.1% of the total variance.
Post-traumatic stress disorder (PTSD) was measured by the eight-item Post-Traumatic Stress Disorder scale (PTSD-8).Reference Hansen, Andersen, Armour, Elklit, Palic and Mackrill31 The items correspond to the DSM-IV criteria for PTSD. They are answered on a four-point Likert scale, ranging from 1 (not at all) to 5 (all the time). The summed score provides a score for symptom severity. The internal consistency as measured by Cronbach's alpha (0.89) was good in the current sample.
Four items were used to measure perceived emotional and instrumental support from family members and friends with a rating ranging from 0 (not at all) to 10 (very much). The Perceived Social Support Scale was formed by summing up individual item scores, with higher scores indicating perceived higher social support. The Cronbach's alpha of the Social Support Scale was 0.80; one factor was identified by exploratory factor analysis, explaining 62.8% of the total variance.
Statistical analysis
The score on the single-item SQS was used as the dependent variable. The associations between independent variables of interest (pre-hospital factors, in-hospital factors and post-hospital factors) and the dependent variable were tested using multivariate linear regression models, after adjusting for significant background characteristics. Adjusted unstandardised coefficients (B) and 95% CIs were reported. SPSS version 24.0 (IBM Corporation) was used to conduct all analyses, with a P-value less than 0.05 indicating statistical significance. This was a secondary analysis of a study investigating behaviours and mental health of COVID-19 survivors in China.
Results
Background characteristics
Over half of the participants were aged 50 years or younger (63.8%, n = 127), female (53.3%, n = 106), were married or cohabiting with a partner (81.9%, n = 163), had not completed any tertiary education (55.3%, n = 110), did not have permanent residency of the city where they lived (73.4%, n = 146), had a personal income of less than RMB 6000 (approximately USD 900) per month (74.4%, n = 148), did not have a full-time job (59.8%, n = 119) and had at least one child (80.4%, n = 160) (Table 1).
Table 1 Sociodemographic characteristics of participants (n = 199)
Sleep quality
Among the participants, 10.1% (n = 20) reported terrible or poor sleep quality, and 26.6% (n = 53) reported fair sleep quality. The mean score on the single-item SQS was 6.9 (s.d. 2.3). About 30% of the participants (26.1%, n = 52) reported worse sleep quality when comparing their current status with the time before COVID-19. Over one-third of the participants had been bothered by a sleeping disorder in the past 2 weeks (33.7%, n = 67).
Pre-hospital, in-hospital and post-hospital factors
Prior to COVID-19, none of the participants had been diagnosed with insomnia, whereas 33.7% (n = 67) had other chronic conditions. The most commonly reported chronic conditions were hypertension (n = 18, 9.5%) and diabetes mellitus (n = 14, 7.0%). One-ninth of the participants (n = 22, 11.1%) had smoked in the year prior to COVID-19.
Based on their medical records, the majority of the participants had a moderate level of severity at hospital admission (55.8%, n = 111), spent no more than 28 days in hospital (57.3%, n = 114), were not admitted to the ICU (97.5%, n = 194), and did not undergo invasive ventilation (96.5%, n = 192) or corticosteroid therapy (87.9%, n = 175). Among the participants, 26.1% (n = 52) and 13.1% (n = 26) had witnessed the suffering and death of other COVID-19 patients, 42.7% (n = 85) had received mental health support during their hospital stay, 44.7% (n = 89) reported having at least one family member infected with COVID-19 and 1.5% (n = 3) had a family member who had died of COVID-19.
After hospital discharge, 3.5% (n = 7) received positive SARS-Cov-2 nucleic acid testing results, and 22.1% (n = 44) received mental health support services. The mean scores on the PHQ-9 scale, the GAD-7 scale, the PTSD-8 scale and the Social Support Scale are shown in Table 2.
Table 2 Sleep quality and its potential correlates among participants (n = 199)
a. PHQ-9, nine items, Cronbach's alpha: 0.91; one factor was identified by exploratory factor analysis, explaining 59.1% of total variance.
b. GAD-7, seven items, Cronbach's alpha: 0.92; one factor was identified by exploratory factor analysis, explaining 68.1% of total variance.
c. PTSD-8, eight items, Cronbach's alpha: 0.89; one factor was identified by exploratory factor analysis, explaining 57.6% of total variance.
d. Social Support Scale, four items, Cronbach's alpha: 0.80; one factor was identified by exploratory factor analysis, explaining 62.8% of total variance.
Factors associated with sleep quality
Older participants (51–60 years: B = −1.12, 95% CI = −2.21, −0.03, P = 0.04; >60 years: B = −1.37, 95% CI = −2.42, −0.32, P = 0.01; reference group: 18–30 years) and those who were retired (B = −1.20, 95% CI = −1.97, −0.42, P = 0.003; reference group: full-time employment) reported poorer sleep quality. Participants who had a higher monthly income (RMB 3000–5999: B = 0.83, 95% CI = 0.01, 1.65, P = 0.048; reference group: no fixed income) and lived in Nanning (B = 1.14, 95% CI = 0.17, 2.10, P = 0.02; reference group: Dongguan) reported better sleep quality (Table 3).
Table 3 Correlates of sleep quality (n = 199)
a. Unadjusted B: unstandardised coefficient.
After controlling for these significant background characteristics, participants who had witnessed the suffering (adjusted B = −1.15, 95% CI = −1.70, −0.33, P = 0.006) or death (adjusted B = −1.55, 95% CI = −2.62, −0.49, P = 0.04) of other COVID-19 patients had poorer sleep quality. Regarding post-hospital factors, more severe depressive symptoms (adjusted B = −0.26, 95% CI = −0.31, −0.20, P < 0.001), anxiety symptoms (adjusted B = −0.25, 95% CI = −0.33, −0.17, P < 0.001) and PTSD (adjusted B = −0.16, 95% CI = −0.22, −0.10) were associated with poorer sleep quality, whereas a higher level of social support (adjusted B = 0.07, 95% CI = 0.04, 0.10, P < 0.001) was associated with better sleep quality. The association between receiving positive SARS-Cov-2 nucleic acid testing results after hospital discharge and the dependent variable was of marginal statistical significance (adjusted B = −1.56, 95% CI = −3.36, 0.24, P = 0.09) (Table 4).
Table 4 Correlates of sleep quality among participants (n = 199)
Discussion
In our study, the proportion of participants having had sleep disorders in the past 2 weeks (33.7%) was slightly higher than that of a group of Chinese COVID-19 survivors 1 month after hospital discharge (29.2%).Reference Wu, Hu, Song, Yang, Xu and Cheng6 Compared with a previous survey using the same one-item SQS to measure sleep quality among healthy Chinese factory workers in March 2020, the prevalence of terrible or poor sleep quality was much higher in our study (10% v. 3.7%).Reference Pan, Xin, Zhang, Dong, Fang and Wu24 These findings point to the necessity of interventions and services aimed at improving sleep quality among COVID-19 survivors.
The findings provide some empirical insights that could inform intervention development and service planning, and suggest the need to tailor such interventions and services to specific groups. Older participants were more likely to have poor sleep quality after hospital discharge. Previous studies have shown changes in the sleep cycles of elderly people, for instance, decreased total sleep time, increased time spent falling asleep at sleep onset, sleep fragmentation and daytime sleep.Reference Chen, Hsu, McHugh, Campbell and Tzeng32 They should receive more attention in future programmes. Participants without a fixed income were more likely to have poor sleep quality. Although COVID-19 treatment is free in China, hospital admission and subsequent quarantine may affect the work of the survivors and may increase financial difficulties, especially among those with low or no fixed income. Financial difficulties represent a risk factor for sleep quality.Reference Hall, Buysse, Nofzinger, Reynolds, Thompson and Mazumdar33 Future programmes should consider providing financial assistance to COVID-19 survivors who have financial difficulties. Participants in Nanning reported better sleep quality than those in the other cities. Compared with the other four study sites, Nanning has been less affected by COVID-19. The COVID-19 situation in the city may be a structural factor influencing people's sleep quality.
In contrast to our hypothesis, pre-hospital factors such as presence of insomnia, other concurrent chronic conditions and smoking were not significantly associated with sleep quality in this study. Witnessing painful symptoms and deaths of other COVID-19 patients were significant in-hospital factors associated with poor sleep quality. These experiences are major psychological traumas for COVID-19 survivors, which may result in PTSD, a mental disorder leading to serious distress and disability.Reference Liu, Baumeister, Veilleux, Chen, Liu and Yue4,Reference Xiao, Luo and Xiao34 In line with other studies targeting COVID-19 survivors,Reference Mazza, De Lorenzo, Conte, Poletti, Vai and Bollettini7 our study found a high level of PTSD in this population. PTSD was negatively associated with sleep quality.Reference Altman, Knauert and Pisani12,Reference Mantua, Helms, Weymann, Capaldi and Lim35 Moreover, PTSD is long-lasting among survivors of infectious disease outbreaks (e.g., SARS). The median time taken for PTSD to remit is about 36 months for individuals who have sought mental health support services, and can be as long as 64 months for those who have never sought such services.Reference Kessler, Chiu, Demler, Merikangas and Walters36 A meta-analysis suggested that early intervention could lead to durable effects on PTSD severity.Reference Oosterbaan, Covers, Bicanic, Huntjens and de Jongh37 However, as the majority of participants had been discharged from hospitals before the government started providing mental health support services to all COVID-19 patients,14 only 42.7% received such services during their hospital stay. It is also unclear whether such mental health support services targeted traumatic experience, as the results showed a non-significant association between in-hospital mental health support service utilisation and sleep quality. In contrast to our hypothesis, ICU admission and the use of invasive ventilation and corticosteroids were not associated with post-discharge sleep quality, probably owing to the very small number of COVID-19 survivors who had such experiences (2.5–12.1%).
Similar to previous findings, depressive symptoms and anxiety symptoms were significant post-hospital factors that were associated with poorer sleep quality.Reference Altman, Knauert and Pisani12 The associations between depressive and anxiety symptoms and sleep quality were bidirectional. Poor sleep quality might increase the risk of depression and anxiety, whereas presence of depressive symptoms or anxiety symptoms would increase the risk of sleep disturbance.Reference Altman, Knauert and Pisani12,Reference Orwelius, Nordlund, Nordlund, Edéll-Gustafsson and Sjöberg38 One recent study suggested that sleep is a mediator of the relationship between stress and mental disorders in people infected with SARS-Cov-2.Reference Magnavita, Tripepi and Di Prinzio8 Such findings highlight the need to provide mental health support services to COVID-19 survivors after hospital discharge. Currently, these mental health support services are provided to COVID-19 survivors upon their request,14 and utilisation is relatively low (22.1%). Future programmes should actively reach out to COVID-19 survivors to increase coverage, and the services should be tailored to the needs of COVID-19 survivors. Increasing social support may be a useful strategy to improve post-discharge sleep quality. It is important to provide COVID-19-related health education to the general public and to family members of survivors, so as to reduce unrealistic fears and stigma towards COVID-19 survivors. These approaches may improve social support for COVID-19 survivors. In addition, having a positive SARS-Cov-2 retest result was marginally associated with poor sleep quality. As the cause of this phenomenon is still under investigation,Reference Tao, Wang, Zhang, Guo, Ma and Zhao19,Reference Wu, Zhang, Zhang, Wang and Wan20 receiving a positive retest result may trigger fear, worry and other negative emotional responses among COVID-19 survivors. In the future, longitudinal studies should be conducted to explore whether COVID-19 survivors with sleep disorders have worse recovery than those without.
This study had the strengths of recruiting survivors in multiple Chinese cities and considering pre-hospital, in-hospital and post-hospital factors. Although several studies have applied the single-item SQS in various Chinese populations,Reference Pan, Xin, Zhang, Dong, Fang and Wu24,Reference Zhao, Song, Brytek-Matera, Zhang and He39 there has been no validation study conducted in a Chinese population. This was a weakness of this study. The study had some other limitations. First, there was no appropriate control group (e.g., patients who were admitted to hospital for reasons other than COVID-19 during the same period). Second, as compared with longer sleep questionnaires (e.g., PSQI), the single-item SQS was not able to cover important information such as sleep latency, sleep duration or habitual sleep efficiency. It merely reflected subjective sleep quality. Similar to some published cross-sectional studies, we measured self-reported changes in sleep quality before and after the COVID-19 outbreak.Reference Wu, Hu, Song, Yang, Xu and Cheng6 However, such measurements are susceptible to strong recall bias. Therefore, we were not able to conclude that our participants had a decrease in sleep quality. Third, the sample size of this study was relatively small. The effect sizes of the non-significant associations were quite small, and the non-significance cannot be fully explained by the relatively small sample size. Fourth, COVID-19 survivors were recruited in five Chinese cities; generalisations should be made cautiously to other geographic locations in China. Fifth, we were not able to collect information from survivors who refused to participate in the study. Therefore, we could compare characteristics between participants and those who refused to complete the survey and hence could not estimate the impact of refusals on study outcomes. Selection bias existed. Sixth, parts of the data were self-reported, and verification was not feasible. Recall bias may have occurred, and we were not able to estimate the impact of recall bias on study outcomes. Moreover, we did not collect information about history of psychiatric disorders, which is a significant factor in post-discharge sleep quality. Furthermore, this was a cross-sectional study and could not establish causal relationships.
Among the COVID-19 survivors, a significant proportion were bothered by sleep disorders, and many reported terrible or poor sleep quality 6 months after hospital discharge. Witnessing the suffering or death of other COVID-19 patients was a significant in-hospital factor associated with poor sleep quality after hospital discharge. Depressive symptoms, anxiety symptoms, PTSD and social support were significant post-hospital factors influencing sleep quality. Interventions and support services to improve sleep quality should be provided to COVID-19 survivors.
Data availability
The data that support the findings of this study are available from the corresponding author, H.Z., upon reasonable request.
Acknowledgements
We thank Dr Yusheng Jie from the Third Affiliated Hospital of Sun Yat-sen University for his support.
Author contributions
H.Z., Z.W. and F.X. conceived the study and designed the protocol with L.F. and Y.F. L.F., Y.F., D.L. and B.W. conducted this study. L.F., Y.F., D.L. and B.W. contributed to statistical analysis and interpretation of data. L.F., Y.F., D.L., B.W., F.X., Z.W. and H.Z. drafted the manuscript with all authors critically revising the manuscript. All authors have read and approved the final version of the manuscript.
Funding
This study was supported by the Natural Science Foundation of China Excellent Young Scientists Fund (82022064), Natural Science Foundation of China International/Regional Research Collaboration Project (72061137001), Natural Science Foundation of China Young Scientist Fund (81703278), the Australian National Health and Medical Research Commission Early Career Fellowship (APP1092621), the National Science and Technology Major Project of China (2018ZX10721102), the Sanming Project of Medicine in Shenzhen (SZSM201811071), the High Level Project of Medicine in Longhua, Shenzhen (HLPM201907020105), the National Key Research and Development Program of China (2020YFC0840900) and the Guangxi Medical and Health Appropriate Technology Development and Application Project (S2020124). None of the funding parties had any role in the design of the study or in the explanation of the data.
Declaration of interest
None.
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