Available via license: CC BY-NC-ND 4.0
Content may be subject to copyright.
Received: 2020.02.16
Accepted: 2020.02.25
Available online: 2020.03.03
Published: 2020.03.05
2973 4 1 38
The Effects of Social Support on Sleep Quality
of Medical Staff Treating Patients with
Coronavirus Disease 2019 (COVID-19) in
January and February 2020 in China
ABEF 1 Han Xiao*
CDF 2 Yan Zhang*
EF 2 Desheng Kong
BCD 3 Shiyue Li
ABCG 2 Ningxi Yang
* Han Xiao and Yan Zhang are both first authors and contributed equally to this study
Corresponding Author: Ningxi Yang, e-mail: yangningxi@bjmu.edu.cn
Source of support: This study was funded by the Basic Research Project of Coronavirus Disease 2019 Epidemic of Fundamental Research Funds for
the Central Universities (Harbin Engineering University)
Background: Coronavirus disease 2019 (COVID-19), formerly known as severe acute respiratory syndrome coronavirus 2
(SARS-CoV-2) and 2019 novel coronavirus (2019-nCoV), was first identified in December 2019 in Wuhan City,
China. Structural equation modeling (SEM) is a multivariate analysis method to determine the structural rela-
tionship between measured variables. This observational study aimed to use SEM to determine the effects of
social support on sleep quality and function of medical staff who treated patients with COVID-19 in January
and February 2020 in Wuhan, China.
Material/Methods: A one-month cross-sectional observational study included 180 medical staff who treated patients with COVID-19
infection. Levels of anxiety, self-efficacy, stress, sleep quality, and social support were measured using the and
the Self-Rating Anxiety Scale (SAS), the General Self-Efficacy Scale (GSES), the Stanford Acute Stress Reaction
(SASR) questionnaire, the Pittsburgh Sleep Quality Index (PSQI), and the Social Support Rate Scale (SSRS),
respectively. Pearson’s correlation analysis and SEM identified the interactions between these factors.
Results: Levels of social support for medical staff were significantly associated with self-efficacy and sleep quality and
negatively associated with the degree of anxiety and stress. Levels of anxiety were significantly associated with
the levels of stress, which negatively impacted self-efficacy and sleep quality. Anxiety, stress, and self-efficacy
were mediating variables associated with social support and sleep quality.
Conclusions: SEM showed that medical staff in China who were treating patients with COVID-19 infection during January
and February 2020 had levels of anxiety, stress, and self-efficacy that were dependent on sleep quality and so-
cial support.
MeSH Keywords: Anxiety•SARSVirus•SocialSupport•Stress,Psychological
Full-text PDF: https://www.medscimonit.com/abstract/index/idArt/923549
Authors’ Contribution:
Study Design A
Data Collection B
Statistical Analysis C
Data Interpretation D
Manuscript Preparation E
Literature Search F
Funds Collection G
1 Department of Respiration, Xuanwu Hospital Capital Medical University, Beijing,
P.R. China
2 College of Humanities and Social Sciences, Harbin Engineering University, Harbin,
Heilongjiang, P.R. China
3 School of Health Sciences, Wuhan University, Wuhan, Hubei, P.R. China
e-ISSN 1643-3750
© Med Sci Monit, 2020; 26: e923549
DOI: 10.12659/MSM.923549
e923549-1
Indexed in: [Current Contents/Clinical Medicine] [SCI Expanded] [ISI Alerting System]
[ISI Journals Master List] [Index Medicus/MEDLINE] [EMBASE/Excerpta Medica]
[Chemical Abstracts/CAS]
CLINICAL RESEARCH
This work is licensed under Creative Common Attribution-
NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Background
Coronavirus disease 2019 (COVID-19), formerly known as se-
vere acute respiratory syndrome coronavirus 2 (SARS-CoV-2)
and 2019 novel coronavirus (2019-nCoV), was first identified
in December 2019 in Wuhan City in central China [1,2]. From
the end of December 2019, COVID-19 began to spread rapid-
ly throughout Hubei Province and other areas in China [2,3].
As of February 18th, 2020, more than 72,500 people had been
diagnosed with COVID-19 in China, and more than 1,800 pa-
tients had died from this new viral infection, mainly from pneu-
monia and other respiratory complications [3].
From December 2019, medical staff in Wuhan, China, were
working with an increased workload and at risk of infection
to treat patients with COVID-19 infection. Up to February 18
th
,
more than 30,000 doctors and nurses from other provinces in
China arrived in Hubei Province to assist in the treatment of
patients. Local and national medical staff worked in isolation
wards, fever clinics, the Intensive Care Unit (ICU), and oth-
er related departments. Some medical staff became infect-
ed with COVID-19 when they were treating infected patients.
Chinese medical staff have shown professionalism and care,
but the physical and psychological health of medical staff is
at risk when working under such conditions, and anxiety and
stress can also adversely affect sleep.
Previous studies have shown that survivors of acute infectious
diseases, such as SARS, can lead to anxiety, depression, stress,
and posttraumatic stress disorder [4–6]. However, there have
been few studies on the physical and psychological effects of
outbreaks of serious infectious diseases on the medical staff,
particularly when associated with increased workload and the
stress associated with the risk of infection.
Sleep quality is a key indicator of health. For clinical staff, good
sleep quality not only helps them to work better to treat pa-
tients but also maintains optimal immune function to prevent
infection [7]. Therefore, sleep quality is an important indicator
of health. Also, psychological wellbeing and sleep are affected
by many socio-cultural factors [8]. Social support is a signif-
icant social factor. Social support refers to the care and sup-
port that people feel they get from other people [9]. Adequate
social support has previously been reported to have a posi-
tive effect on psychological health and sleep function [10,11].
Also, anxiety is a common negative emotion experienced by
medical staff during epidemics of infectious diseases [12].
The COVID-19 epidemic has become a stressor, particularly as
this is a new viral infection does not have a vaccine and can
only be treated symptomatically at present. Self-efficacy refers
to individual judgment on the ability to complete a certain be-
havior or task [13]. Self-efficacy helps medical staff cope with
high-risk and high-intensity work and help them maintain a
stable mental state. The effect of emotion such as anxiety,
stress, and self-efficacy on sleep quality has been shown by
previous studies [14,15].
These variables of anxiety, self-efficacy, stress, sleep quality, and
social support and their interactions can be analyzed by structural
equation modeling (SEM), which is a multivariate analysis meth-
od to determine the structural relationship between measured
variables. Therefore, this observational study, conducted at the
Wuhan University School of Medicine, China, aimed to use SEM
to determine the effects of social support on sleep quality and
function of medical staff who treated patients with COVID-19
in January and February 2020. The two assumptions made in
this observational study were that the social support given to
the medical staff directly affected their sleep quality, and that
social support affected sleep quality by reducing anxiety and
stress and by increasing self-efficacy as intermediate variables.
Material and Methods
Ethical approval
This study was conducted in accordance with the Declaration
of Helsinki. All participants signed the consent form. The study
was approved by the Wuhan University School of Medicine
Ethics Committee (Approval number: 20180928).
Study participants
This study included 180 medical staff from several provinces
who treated patients with COVID-19 infection in January and
February 2020. All the study participants were either doctors
or nurses who worked in departments of respiratory medicine,
fever clinics, or the intensive care unit (ICU). All study partici-
pants volunteered to participate in the study.
Study design
An observational and cross-sectional clinical study was con-
ducted that included the use of self-reported questionnaires.
Demographic and social data from the medical staff were ob-
tained. Levels of anxiety, self-efficacy, stress, sleep quality, and
social support were measured using validated clinical ques-
tionnaires and scoring systems. All questionnaires were com-
pleted anonymously by the 180 participating medical staff.
Demographic and social data
Demographic and social data from the study participants in-
cluded age, gender, education, and marital status. Professional
and work information included their title, income, role, depart-
ment, and work experience, or seniority.
e923549-2
Indexed in: [Current Contents/Clinical Medicine] [SCI Expanded] [ISI Alerting System]
[ISI Journals Master List] [Index Medicus/MEDLINE] [EMBASE/Excerpta Medica]
[Chemical Abstracts/CAS]
Xiao H. et al.:
Medical staff treating COVID-19
© Med Sci Monit, 2020; 26: e923549
CLINICAL RESEARCH
This work is licensed under Creative Common Attribution-
NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
TheSocialSupportRateScale(SSRS)[16]
The SSRS was used to measure the type and levels of social
support received by the medical staff [16]. The SSRS contained
ten items consisting of three grades, with an aggregate score
that ranged from 7–56. A higher score indicated higher levels
of social support [16]. The Cronbach’s alpha for internal con-
sistency for the use of the SSRS was 0.808.
TheSelf-RatingAnxietyScale(SAS)[17]
The SAS was used to measure the levels of anxiety the mood
of the medical staff [17]. The SAS questionnaire contained
20 items consisting of four grades, with questions based on
feelings of anxiety and mood in the previous seven days.
An aggregate score of 20 was then multiplied by 1.25, with
higher scores indicating more severe levels of anxiety [17].
The Cronbach’s alpha for internal consistency for the use of
the SAS was 0.821.
TheGeneralSelf-EcacyScale(GSES)[18]
The Chinese version of the GSES was used to measure the
feelings of self-efficacy by the medical staff [18]. The scale
consisted of ten items, with a score for each item of between
1–4, and the total score of 10–40, with the final points were
divided by 10. Higher scores indicated higher self-efficacy [18].
The Chinese version of the GSES has previously been shown
to have high sensitivity and validity [18]. The Cronbach’s al-
pha for internal consistency for the use of the GSES was 0.805.
TheStanfordAcuteStressReaction(SASR)[19]
The SASR questionnaire was used to measure self-reported
stress by the medical staff [19]. The SASR is a six-point Likert
scale consisting of 30 items. Each item has a score of between
0–5, with a combined score ranging from 0–150, and a high-
er score indicating higher levels of self-reported stress [19].
The Cronbach’s alpha for internal consistency for the use of
the SASR was 0.837.
ThePittsburghSleepQualityIndex(PSQI)[20]
The PSQI questionnaire was used to measure sleep quality us-
ing an 18-item scale containing seven items that included sleep
quality, sleep duration, sleep latency, habitual sleep efficiency,
sleep disturbance, use of sleeping medications, and daytime
dysfunction [20]. Each dimension scored between 0–3, with
a total score ranging from 0–21, and a higher score indicat-
ing lower sleep quality [20]. The Cronbach’s alpha for internal
consistency for the use of the PSQI was 0.811.
Statistical analysis
Data were presented as the mean ± standard deviation (SD).
The chi-squared (c2) test, Pearson’s correlation analysis, and
multivariate analysis using the structural equation model (SEM)
with path analysis were used to determine the structural re-
lationship between the measured variables. Data were ana-
lyzed using EpiData version 3.1 software (EpiData, Buenos
Aires, Argentina) and SAS version 9.4, software (SAS Institute,
Cary, NC, USA), which were used for data entry and analysis.
The intermediary effects of the variables were analyzed using
IBM SPSS AMOS version 21.0 (IBM Corp., Armonk, NY, USA).
The bootstrap number was set as 5,000. The significance of
the specific intermediary was determined using the nonpara-
metric percentile bootstrap method with deviation correc-
tion. Path analysis by the structural equation model (SEM)
was performed to measure the associations and their impor-
tance. Path analysis included the use of the goodness-of-fit
index (GFI), the adjusted goodness-of fit-index (AGFI) the in-
cremental fit index (IFI), the comparative fit index (CFI), the
Tucker-Lewis index (TLI), the normed fit index (NFI), and the
root mean square error of approximation (RMSEA). The struc-
tural equation showed and ideal fit (GFI=0.995, CFI=0.995,
TLI=0.953, IFI=0.996, NFI=0.991, AGFI=0.931, RMSEA=0.077,
c2/df=2.073). A P-value <0.05 was considered to be statisti-
cally significant.
Results
Demographic and working data of the medical staff who
treated patients with COVID-19 in January and February
2020inWuhan,China
There were 220 medical staff who were initially asked to par-
ticipate in the study, and 180 members of staff completed the
study questionnaires, indicating a study participation rate of
81.82% at our medical center. The mean age of the medical
staff was 32.31±4.88) years. The demographic and working
data of the study participants are shown in Table 1.
Correlation between the findings from the self-reported
questionnairesonanxiety,self-ecacy,stress,sleep
quality,andsocialsupport
The levels of anxiety, self-efficacy, stress, sleep quality, and
social support were measured using the Self-Rating Anxiety
Scale (SAS), the General Self-Efficacy Scale (GSES), the Stanford
Acute Stress Reaction (SASR) questionnaire, the Pittsburgh
Sleep Quality Index (PSQI), and the Social Support Rate Scale
(SSRS), respectively. Pearson’s correlation analysis was used
to identify the correlations between the results from the re-
sponses of the medical staff.
e923549-3
Indexed in: [Current Contents/Clinical Medicine] [SCI Expanded] [ISI Alerting System]
[ISI Journals Master List] [Index Medicus/MEDLINE] [EMBASE/Excerpta Medica]
[Chemical Abstracts/CAS]
Xiao H. et al.:
Medical staff treating COVID-19
© Med Sci Monit, 2020; 26: e923549
CLINICAL RESEARCH
This work is licensed under Creative Common Attribution-
NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
There was a significant positive correlation between the SSRS
scores and the GSES scores (r=0.405, P<0.01), and a nega-
tive correlation between the SSRS scores and the SAS scores
(r=–0.565, P<0.01), the SASR scores (r=–0.391, P <0.01), and
the PSQI scores (r=–0.413, P<0.01). There was a negative asso-
ciation between the GSES scores and the SAS scores (r=–0.351,
P<0.01), and the SASR scores (r=–0.277, P<0.01), and the PSQI
scores (r=–0.483, P <0.01). There was a significant positive cor-
relation between the SAS scores and the SASR scores (r=0.397,
P<0.01), and the PSQI scores (r=0.489, P<0.01). There was a sig-
nificant positive association between the SASR scores and the
PSQI scores (r=0.457, P<0.01). Table 2 summarizes these results.
Path analysis of the effects of social support (from the
SSRS)onsleepquality(fromthePSQI)
Path analysis by the structural equation model (SEM) was per-
formed to measure the associations and importance of so-
cial support on sleep quality in the medical staff. The struc-
tural equation showed and ideal fit (GFI=0.995, CFI=0.995,
TLI=0.953, IFI=0.996, NFI=0.991, AGFI=0.931, RMSEA=0.077,
c2/df=2.073). The path analysis obtained from the SEM of the
relationships from the results of the SSRS, the GSES, the SAS,
the SASR, and the PSQI of medical staff (with standardized
beta weighting) is shown in Figure 1.
Variable Number %
Gender
Male 51 28.3
Female 129 71.7
Education
College degree or below 34 18.9
Bachelor’s degree 82 45.6
Master’s degree or above 64 35.5
Marital status
Unmarried 41 22.8
Married 122 67.8
Divorced or widowed 17 9.4
Seniority
Primary 36 20.0
Intermediate 108 60.0
Senior 36 20.0
Table 1. Demographic and working data of the medical staff treating patients with COVID-19 in January and February 2020 in Wuhan,
China.
Mean Standard
deviation SSRS GSES SAS SASR PSQI
SSRS 34.172 10.263 1
GSES 2.267 0.767 .405** 1
SAS 55.256 14.183 –.565** –.351** 1
SASR 77.589 29.525 –.391** –.277** .397** 1
PSQI 8.583 4.567 –.413** –.483** .489** .457** 1
Table 2. The relationships between the Social Support Rate Scale (SSRS), the General Self-Efficacy Scale (GSES), the Stanford Acute
Stress Reaction (SASR) questionnaire, the Self-Rating Anxiety Scale (SAS), and the Pittsburgh Sleep Quality Index (PSQI) of the
medical staff treating patients with COVID-19.
** P<0.01. The data shown represent the scores of the questionnaires.
Variable Number %
Monthly income
<6000 yuan 33 18.2
6000–10000 yuan 110 70.6
>10000 yuan 37 11.2
Profession
Doctor 82 45.6
Nurse 98 54.4
Department
Fever clinic or respiratory clinic 86 47.8
COVID-19 pneumonia isolation
Hospital ward 52 28.9
Intensive Care Unit (ICU) 42 23.3
Working experience
<2 years 26 14.4
2–5 years 33 18.3
>5 years 121 67.2
e923549-4
Indexed in: [Current Contents/Clinical Medicine] [SCI Expanded] [ISI Alerting System]
[ISI Journals Master List] [Index Medicus/MEDLINE] [EMBASE/Excerpta Medica]
[Chemical Abstracts/CAS]
Xiao H. et al.:
Medical staff treating COVID-19
© Med Sci Monit, 2020; 26: e923549
CLINICAL RESEARCH
This work is licensed under Creative Common Attribution-
NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
Table 3 shows the normalized path coefficient. The SSRS
scores of the medical staff negatively affected the SAS scores
(b=–0.565, P<0.0001) and the SASR scores (b=–0.245, P=0.003)
significantly and positively affected the GSES scores (b=0.304,
P<0.001). The SAS scores positively affected the SASR scores
(b=0.259, P=0.001) and the PSQI scores (b=0.257, P<0.001),
and negatively affected the GSES scores (b=–0.179, P=0.029).
The SASR scores significantly affected the PSQI scores (b=0.255,
P<0.001). The GSES score negatively affected the PSQI scores
(b=–0.308, P<0.001). However, the effect of the SSRS scores
on the PSQI scores was not significant (b=–0.046, P=0.538).
Hypothesis-testing using SEM
The two hypotheses tested in this study were hypothesis 1,
that the social support given to the medical staff directly af-
fected their sleep quality, and hypothesis 2, that social sup-
port affected sleep quality by reducing anxiety and stress and
by increasing self-efficacy as intermediate variables. Based on
the scores from the self-reported SSRS, SAS, GSES, SASR, and
PSQI questionnaires, the results showed that the social support
given to the medical staff negatively affected (reduced) their
anxiety and stress levels, and positively affected their self-ef-
ficacy, but did not directly affect their sleep quality. The lev-
els of staff anxiety significantly affected their levels of stress
and significantly reduced their self-efficacy and sleep quality.
Therefore, the hypothesis 1 was not supported and hypothe-
sis 2 was confirmed.
Bootstrap indirect effects analysis of the intermediary
effects of the variables
The intermediary effects of the variables were analyzed with
the bootstrap number set as 5,000. The significance of the
specific intermediary was determined using the nonpara-
metric percentile bootstrap method with deviation correc-
tion. The path of SSRS®SAS®PSQI, when the confidence in-
terval was not 0, showed that the SAS score had a significant
effect between the SSRS score and the PSQI score (b=0.157,
P=0.002). The path of SSRS®SASR®PSQI, when the confi-
dence interval was not 0, showed that the SAS score had a
e1
.30
.26
.26
.25
–.57
–.31
–.05
–.24
–.18
GSES
SAS
SASR
SSRS PSQI
e3
e2 e4
Figure 1. The path analysis obtained from the structural
equation model (SEM) of the relationships from
the results of the Social Support Rate Scale (SSRS),
the General Self-Efficacy Scale (GSES), the Self-Rating
Anxiety Scale (SAS), the Stanford Acute Stress Reaction
(SASR), and the Pittsburgh Sleep Quality Index (PSQI)
of medical staff (with standardized beta weighting).
Path Standardization
coefficient
Unstandardized
coefficient
Standard
error
Critical
ratio P-value
SAS <--- SSRS –0.565 –0.781 0.085 –9.165 ***
SASR <--- SSRS –0.245 –0.704 0.233 –3.015 0.003
GSES <--- SSRS 0.304 0.023 0.006 3.723 ***
SASR <--- SAS 0.259 0.539 0.169 3.189 0.001
GSES <--- SAS –0.179 –0.010 0.004 –2.184 0.029
PSQI <--- SAS 0.257 0.082 0.023 3.514 ***
PSQI <--- SASR 0.255 0.039 0.010 3.937 ***
PSQI <--- SSRS –0.046 –0.020 0.033 –0.616 0.538
PSQI <--- GSES –0.308 –1.820 0.380 –4.788 ***
Table 3. The normalized path coefficients for the Social Support Rate Scale (SSRS), the General Self-Efficacy Scale (GSES), the Stanford
Acute Stress Reaction (SASR) questionnaire, the Self-Rating Anxiety Scale (SAS), and the Pittsburgh Sleep Quality Index (PSQI)
of the medical staff treating patients with COVID-19.
*** P<0.001 The data shown represent the scores of the questionnaires.
e923549-5
Indexed in: [Current Contents/Clinical Medicine] [SCI Expanded] [ISI Alerting System]
[ISI Journals Master List] [Index Medicus/MEDLINE] [EMBASE/Excerpta Medica]
[Chemical Abstracts/CAS]
Xiao H. et al.:
Medical staff treating COVID-19
© Med Sci Monit, 2020; 26: e923549
CLINICAL RESEARCH
This work is licensed under Creative Common Attribution-
NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
significant effect between the SSRS score and the PSQI score
(b=0.159, P<0.0001). Table 4 summarizes the findings of the
bootstrap indirect effect analysis of the scores for the self-re-
porting questionnaires.
Discussion
This study used the structural equation model (SEM) to deter-
mine the effects of social support on sleep quality and func-
tion of medical staff who treated patients with coronavirus
disease 2019 (COVID-19) [1–3] in January and February 2020
in Wuhan, China. The levels of anxiety, self-efficacy, stress,
sleep quality, and social support were measured using the
Self-Rating Anxiety Scale (SAS), the General Self-Efficacy Scale
(GSES), the Stanford Acute Stress Reaction (SASR) question-
naire, the Pittsburgh Sleep Quality Index (PSQI), and the Social
Support Rate Scale (SSRS), respectively. The findings from this
study showed that the sleep quality of the medical staff was
low with a mean PSQI score of 8.583. Compared with the nor-
mal Chinese PSQI score of 7 points, the sleep quality of medi-
cal staff who treated COVID-19 was relatively low. There were
several factors that may have resulted in reduced sleep quality
in the medical staff. Doctors and nurses had to wear protec-
tive clothing every day, including hazardous materials (HazMat)
suits. The staff worked continuously in the isolation wards with
high work intensity and under pressure. Also, some of the pa-
tients could not be cured, and infection with COVID-19 is as-
sociated with patient mortality [1–3].
The findings from this study showed that social support of the
medical staff did not directly affect their sleep quality, but had
an indirect through several paths or steps. Firstly, social support
reduces anxiety and stress, and improves self-efficacy. Social
support can help medical staff reduce anxiety levels, as friends
or family members provide social and emotional support and
share empathy [21]. Social interactions reduce negative emo-
tions such as anxiety and can improve mood [22]. Currently,
with the increase in the number of cases of COVID-19 infec-
tion in China, front-line medical staff are required to wear pro-
tective masks and protective clothing, which may cause added
stress. When medical staff have a wide social network, social
support can help to reduce stress by reducing the perception
of the threat of stressful events and the physiological response
and inappropriate behavior that can result from stress [23].
Social support contributes to improving self-efficacy, leading to
more understanding, respect, encouragement, courage, and a
sense of professional achievement [24]. Self-efficacy results in
increased confidence to do the job well, and when combined
with social support, members of the medical profession suffer
less from loneliness and might be more optimistic, which im-
proves coping mechanisms when under stress [25,26].
Secondly, the combination of anxiety, stress, and self-effica-
cy of medical staff act on their sleep quality. Anxiety affects
sleep quality because anxious people often find it difficult to
fall asleep and may wake up frequently during sleep [27]. Also,
the combination of anxiety with sleep disorders may make it
difficult to fall asleep [28]. The fact that stress is closely related
to sleep quality has been confirmed by a previous study [29].
Increased stress can increase the levels of vigilance regarding
the environment, which will reduce sleep quality [30]. However,
self-efficacy is a positive mental state that may enhance sleep
quality [31]. People with high self-efficacy can maintain rela-
tively stable emotions even under pressure, and they may ex-
perience fewer episodes of night waking, sleep anxiety, and
sleep onset delay [32]. Self-efficacy also increases concentra-
tion and self-control [33]. Even though all medical staff expe-
rience pressure at work, people who have high self-efficacy
are able to control their emotions and try to sleep regular-
ly after work. Therefore, with high self-efficacy, medical staff
may have good sleep quality. Anxiety has been shown to in-
crease sensitivity to work pressure and the working environ-
ment and has a negative effect on self-efficacy because it re-
duces positive behaviors and initiative [34,35].
Mediation effect path Standardization
coefficient
Unstandardized
coefficients
Standard
error
95% CI P-value
Lower Upper
SSRS®SAS®PSQI –0.145 –0.064 0.019 –0.107 –0.031 0.000
SSRS®GSES®PSQI –0.094 –0.041 0.014 –0.075 –0.018 0.000
SSRS®SASR®PSQI –0.062 –0.028 0.012 –0.056 –0.009 0.002
SSRS®SAS®GSES®PSQI –0.031 –0.014 0.007 –0.030 –0.002 0.025
SSRS®SAS®SASR®PSQI –0.037 –0.016 0.008 –0.037 –0.005 0.002
Table 4. Result of the bootstrap indirect effects analysis of the Social Support Rate Scale (SSRS), the General Self-Efficacy Scale (GSES),
the Stanford Acute Stress Reaction (SASR) questionnaire, the Self-Rating Anxiety Scale (SAS), and the Pittsburgh Sleep Quality
Index (PSQI) of the medical staff treating patients with COVID-19.
e923549-6
Indexed in: [Current Contents/Clinical Medicine] [SCI Expanded] [ISI Alerting System]
[ISI Journals Master List] [Index Medicus/MEDLINE] [EMBASE/Excerpta Medica]
[Chemical Abstracts/CAS]
Xiao H. et al.:
Medical staff treating COVID-19
© Med Sci Monit, 2020; 26: e923549
CLINICAL RESEARCH
This work is licensed under Creative Common Attribution-
NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
The findings from this study may provide support for the imple-
mentation of measures to improve the social support of med-
ical staff during increased demands associated with COVID-19
infection at this time. For example, professional psychother-
apy teams should take the initiative to support the mental
health of medical staff and provide individually targeted in-
terventions. Hospital managers should provide logistic sup-
port for medical staff, and support groups for medical staff
should be established. However, this study had several limi-
tations. Firstly, this was a cross-sectional study with a small
sample size, and definitive causal relationships remain to be
established. For example, anxiety may increase stress, and
stress may increase anxiety [36]. However, anxiety has been
shown to result in impaired sleep, and poor sleep quality in-
creases anxiety [37]. Therefore, cohort studies with larger sam-
ples are needed to investigate the effects of social support on
sleep quality and function of medical staff who are working
with increased levels of stress and increased workloads, as
with the COVID-19 infection epidemic in Wuhan, China. Also,
this study used subjective self-reported questionnaires to ob-
tain the data. Future studies should include objective indica-
tors of stress, such as measurements of serum cortisol level
with the questionnaire [38].
Conclusions
This observational cross-sectional clinical study aimed to use
SEM to determine the effects of social support on sleep qual-
ity and function of medical staff who treated patients with
coronavirus disease 2019 (COVID-19) in January and February
2020 in Wuhan, China. Structural equation modeling (SEM)
showed that medical staff had increased levels of anxiety,
stress, and self-efficacy that were dependent on sleep quali-
ty and social support.
Acknowledgments
The authors thank the medical staff who participated in the
study.
Conflict of interest
None.
References:
1. World Health Organization. Novel Coronavirus (2019-nCoV) technical guid-
ance. https://www.who.int/emergencies/diseases/novel-coronavirus-2019/
technical-guidance
2. Centers for Disease Control and Prevention. 2019 Novel coronavirus,
Wuhan, China. Information for Healthcare Professionals. https://www.cdc.
gov/coronavirus/2019-nCoV/hcp/index.html
3. World Health Organisation (WHO). February 18
th
2020. Coronavirus disease
2019 (COVID-19) Situation Report-29. https://www.who.int/docs/default-
source/coronaviruse/situation-reports/20200218-sitrep-29-covid-19.pdf
4. Wu KK, Chan SK, Ma TM: Posttraumatic stress, anxiety, and depression in
survivors of severe acute respiratory syndrome (SARS). J Trauma Stress,
2005; 18: 39–42
5. Hawryluck L, Gold WL, Robinson S et al: SARS control and psychological ef-
fects of Quarantine, Toronto, Canada. Emerg Infect Dis, 2004; 10: 1206–12
6. Wu KK, Chan SK, Ma TM: Posttraumatic stress after SARS. Emerg Infect Dis,
2005; 11: 1297–300
7. Lange T, Dimitrov S, Born J: Effects of sleep and circadian rhythm on the
human immune system. Ann NY Acad Sci, 2010; 1193: 48–59
8. Yao KW, Yu S, Cheng SP, Chen IJ: Relationships between personal, depres-
sion and social network factors and sleep quality in community-dwelling
older adults. J Nurs Res, 2008; 16: 131–39
9. Brugha TS: Social networks and social support. Curr Opin Psychiat, 1990;
3: 264–68
10. Prati B, Pietrantoni L: The relation of perceived and received social sup-
port to mental health among first responders: A meta-analytic review. J
Community Psychol, 2010; 38: 403–17
11. Kent de Grey RG, Uchino BN et al: Social support and sleep: A meta-anal-
ysis. Health Psychol, 2018; 37: 787–98
12. Peng EY, Lee MB, Tsai ST et al: Population-based post-crisis psychologi-
cal distress: An example from the SARS outbreak in Taiwan. J Formos Med
Assoc, 2010; 109: 524–32
13. Bandura A: Self-efficacy: Toward a unifying theory of behavioral change.
Psychol Rev, 1977; 84: 191–215
14. Dahlgren A, Kecklund G, Akerstedt T: Different levels of work-related stress
and the effects on sleep, fatigue and cortisol. Scand J Work Environ Health,
2005; 31: 277–85
15. Chen XF, Zhang Y, Xu XL et al: The mediating effects of anxiety, self-effica-
cy and sleep quality on the relationship between doctor-patient’s empa-
thy and inflammatory marker in patients with ulcerative colitis. Med Sci
Monit, 2019; 25: 7889–97
16. Zhang W, Xu H, Zhao S et al: Prevalence and influencing factors of co-mor-
bid depression in patients with type 2 diabetes mellitus: A general hospi-
tal based study. Diabetol Metab Syndr, 2015; 7: 60
17. Olatunji BO, Deacon BJ, Abramowitz JS, Tolin DF: Dimensionality of somatic
complaints: Factor structure and psychometric properties of the Self-Rating
Anxiety Scale. J Anxiety Disord, 2006; 20: 543–61
18. Zhang X, Zhan Y, Liu J et al: Chinese translation and psychometric testing
of the cardiac self-efficacy scale in patients with coronary heart disease in
mainland China. Health Qual Life Outcomes, 2018; 16: 43
19. Cardeña E, Koopman C, Classen C et al: Psychometric properties of the
Stanford Acute Stress Reaction Questionnaire (SASRQ): A valid and reli-
able measure of acute stress. J Trauma Stress, 2000; 13: 719–34
20. Carpenter JS, Andrykowski MA: Psychometric evaluation of the Pittsburgh
Sleep Quality Index. J Psychosom Res, 1998; 45: 5–13
21. Yang NX, Xiao H, Wang W et al: Effects of doctors’ empathy abilities on the
cellular immunity of patients with advanced prostate cancer treated by or-
chiectomy: The mediating role of patients’ stigma and self-efficacy. Patient
Prefer Adher, 2018; 12: 1305–14
22. Yang NX, Xiao H, Cao YN et al: Influence of oncology nurses’ empathy on
lung cancer patients’ cellular immunity. Psychol Res Behav Manag, 2018;
11: 279–87
23. Adamczyk K, Segrin C: Perceived social support and mental health among
single vs. partnered polish young adults. Curr Psychol, 2015; 34: 82–96
24. Glozah FN: Exploring Ghanaian adolescents’ meaning of health and well-
being: A psychosocial perspective. Int J Qual Stud Health Well-being, 2015;
10: 26370
25. Segrin C, Passalacqua SA: Functions of loneliness, social support, health be-
haviors, and stress in association with poor health. Health Commun, 2010;
25: 312–22
e923549-7
Indexed in: [Current Contents/Clinical Medicine] [SCI Expanded] [ISI Alerting System]
[ISI Journals Master List] [Index Medicus/MEDLINE] [EMBASE/Excerpta Medica]
[Chemical Abstracts/CAS]
Xiao H. et al.:
Medical staff treating COVID-19
© Med Sci Monit, 2020; 26: e923549
CLINICAL RESEARCH
This work is licensed under Creative Common Attribution-
NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
26. Zhu MX, Sa D, Wu YL: The impact of social support on optimistic Tendency
of college students: The mediating effects of sense of coherence and resil-
ience. Psychol Sci, 2016; 39: 371–76
27. Alvaro P, Roberts RM, Harris JK: A systematic review assessing bidirection-
ality between sleep disturbances, anxiety, and depression. Sleep, 2013; 36:
1059–68
28. Johnson EO, Roth T, Breslau N: The association of insomnia with anxiety
disorders and depression: Exploration of the direction of risk. J Psych Res,
2006; 40: 700–8
29. Reeth OV, Weibel L, Spiegel K et al: Interactions between stress and sleep:
From basic research to clinical situations. Sleep Med Rev, 2000; 4: 201–19
30. Jarrin DC, Chen IY, Ivers H, Morin CM: The role of vulnerability in stress-re-
lated insomnia, social support and coping styles on incidence and persis-
tence of insomnia. J Sleep Res, 2014; 23: 681–88
31. Rutledge CM, La Guardia AC, Bluestein D: Predictors of self-efficacy for
sleep in primary care. J Clin Nurs, 2013; 22: 1254–61
32. Bihlmaier I, Schlarb AA: Self-efficacy and sleep problems. Somnologie, 2016;
20: 275–80
33. Przepiórka A, Błachnio A, Siu NY: The relationships between self-effica-
cy, self-control, chronotype, procrastination and sleep problems in young
adults. Chronobiol Int, 2019; 36: 1–11
34. Miller RL, Pallant JF, Negri LM: Anxiety and stress in the postpartum: Is there
more to postnatal distress than depression? BMC Psychiatry, 2006; 6: 12
35. Bandura A, Adams NE: Analysis of self-efficacy theory of behavior change.
Cognitive Ther Res, 1977; 1: 287–310
36. Kolahkaj B, Zargar F: Effect of mindfulness-based stress reduction on anxi-
ety, depression and stress in women with multiple sclerosis. Nurs Midwifery
Stud, 2015; 4: e29655
37. Poznanski B, Cornacchio D, Coxe S et al: The link between anxiety severi-
ty and irritability among anxious youth: Evaluating the mediating role of
sleep problems. Child Psychiatry Hum Dev, 2018; 49: 352–59
38. Luby JL, Heffelfinger A, Mrakotsky C et al: Alterations in stress cortisol re-
activity in depressed preschoolers relative to psychiatric and no-disorder
comparison groups. Arch Gen Psychiatry, 2003; 60: 1248–55
e923549-8
Indexed in: [Current Contents/Clinical Medicine] [SCI Expanded] [ISI Alerting System]
[ISI Journals Master List] [Index Medicus/MEDLINE] [EMBASE/Excerpta Medica]
[Chemical Abstracts/CAS]
Xiao H. et al.:
Medical staff treating COVID-19
© Med Sci Monit, 2020; 26: e923549
CLINICAL RESEARCH
This work is licensed under Creative Common Attribution-
NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0)
