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Article

Sources of Sleep Disturbances and Psychological Strain for Hospital Staff Working during the COVID-19 Pandemic

by
Nasrin Abdoli
1,
Vahid Farnia
1,
Somayeh Jahangiri
1,
Farnaz Radmehr
2,
Mostafa Alikhani
1,
Pegah Abdoli
1,
Omran Davarinejad
2,
Kenneth M. Dürsteler
3,4,
Annette Beatrix Brühl
5,
Dena Sadeghi-Bahmani
1,5,6,7 and
Serge Brand
1,5,6,8,9,*
1
Substance Abuse Prevention Research Center, Health Institute, Kermanshah University of Medical Sciences, Kermanshah 6719851115, Iran
2
Clinical Research Development Center, Imam Khomeini and Mohammad Kermanshahi and Farabi Hospitals, Kermanshah University of Medical Sciences, Kermanshah 6719851451, Iran
3
Psychiatric Clinics, Division of Substance Use Disorders, University of Basel, 4002 Basel, Switzerland
4
Center for Addictive Disorders, Department of Psychiatry, Psychotherapy and Psychosomatics, Psychiatric Hospital, University of Zurich, 8001 Zurich, Switzerland
5
Center for Affective, Stress and Sleep Disorders (ZASS), Psychiatric University Hospital Basel, 4002 Basel, Switzerland
6
Sleep Disorders Research Center, Kermanshah University of Medical Sciences, Kermanshah 6719851451, Iran
7
Departments of Physical Therapy, University of Alabama at Birmingham, Birmingham, AL 35209, USA
8
Department of Sport, Exercise and Health, Division of Sport Science and Psychosocial Health, University of Basel, 4052 Basel, Switzerland
9
School of Medicine, Tehran University of Medical Sciences, Tehran 1417466191, Iran
*
Author to whom correspondence should be addressed.
Int. J. Environ. Res. Public Health 2021, 18(12), 6289; https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18126289
Submission received: 19 April 2021 / Revised: 1 June 2021 / Accepted: 5 June 2021 / Published: 10 June 2021
(This article belongs to the Collection Outbreak of a Novel Coronavirus: A Global Health Threat)
Review Reports Versions Notes

Abstract

:
Hospital staff members reported increased stress-related workload when caring for inpatients with COVID-19 (“frontline hospital staff members”). Here, we tested if depression, anxiety, and stress were associated with poor sleep and lower general health, and if social support mediated these associations. Furthermore, we compared current insomnia scores and general health scores with normative data. A total of 321 full-time frontline hospital staff members (mean age: 36.86; 58% females) took part in the study during the COVID-19 pandemic. They completed a series of questionnaires covering demographic and work-related information, symptoms of depression, anxiety, stress, social support, self-efficacy, and symptoms of insomnia and general health. Higher symptoms of depression, anxiety, and stress were associated with higher symptoms of insomnia and lower general health. Higher scores of depression, anxiety, and stress directly predicted higher insomnia scores and lower general health scores, while the indirect effect of social support was modest. Compared to normative data, full-time frontline hospital staff members had a 3.14 higher chance to complain about insomnia and a significantly lower general health. Symptoms of insomnia and general health were unrelated to age, job experience, educational level, and gender. Given this background, it appears that the working context had a lower impact on individuals’ well-being compared to individual characteristics.

1. Introduction

Like all countries, Iran is struggling with the COVID-19 pandemic, which is a challenge for societies, individuals, and healthcare systems [1,2]. Relatedly, to decrease the spread of the virus and prevent further deaths and severe cases of infection, the national government, advised by health authorities, imposed confinements [3,4,5]. To illustrate this, health authorities temporarily legislated to close borders, schools, universities, sports events, and religious and cultural centers, and to disallow gatherings in open spaces.
Furthermore, by 14 January 2021, in Iran, about 16,291 hospital beds were needed to treat COVID-19 patients, while about 45,159 hospital beds were available (https://covid19.healthdata.org/iran-(islamic-republic-of)?view=resource-use&tab=trend&resource=all_resources (Retrieved on 14 January 2021)).
Despite such favorable numbers of available hospital beds, data from rapid reviews and meta-analyses showed that hospital staff members in contact with affected patients (“frontline hospital staff members”) appeared to be at an increased risk to report symptoms of exhaustion, depression, and anxiety [6]. In general, health care workers were at an increased risk of developing short- and long-term mental health problems [7], and compared to non-frontline hospital staff members, frontline hospital staff members reported higher levels of acute and post-traumatic stress symptoms and psychological distress [6,8]. In particular, the following risk factors were reported: being younger, being more junior, being the parents of dependent children, having an infected family member, lack of practical support, and stigma [6]. Moreover, a rapid review showed that depression, anxiety, and psychological distress were common among hospital staff members in contact with infected patients [9]. The use of masks and general infection control training were associated with decreased infection risk, while the impact of such devices on psychological health was not reported.
Concerning the psychological distress of hospital staff members in Iran, frontline nurses reported higher scores of job-related stress and burnout when compared to non-frontline nurses [10]; age and job experience were not confounders, and social support of friend and families were equal in both groups. By contrast, higher age, higher educational degrees, and being male predicted lower stress among frontline nurses aged 40.6 years on average [11]. Among 495 nurses (71% females), job-related mental pressure, physical pressure, and time pressure were associated with being female, being a frontline nurse, and reporting a lower educational/vocational level [12]. Importantly, a higher task load was associated with lower general health [12]. Among 869 nurses (71.2% females), higher prevalence rates of depression (41.7%), anxiety (52.2%), and stress (53.9%) were related to female gender and being a frontline hospital staff member [13]. Results from quantitative studies showed that frontline nurses reported anxieties related to the disease, fear of infecting their families, emotional distress of delivering bad news, and conflicts between (dysfunctional) fears and the conscience to accomplish an important job [14]. Other frontline nurses appreciated supportive resources and social support [15]. Among 599 health care practitioners working in eight different Iranian cities, quality of life was associated with higher social support [16]. About 35% of 761 (frontline) nurses reported fear of getting infected, while the subjective feeling of self-efficacy was associated with protective behavior [17].
For sleep complaints in general, and insomnia in particular, it appears that no study has focused on insomnia among frontline hospital staff members in Iran. This is surprising for several reasons: Following the hyperarousal hypothesis of insomnia [18], psychophysiological arousal leads to insomnia at the short and long term. Psychiatric issues and poor sleep were related [19,20], as also poor sleep, poor emotion regulation, and stress were related [21,22,23,24]. Salari et al. [25] summarized in their systematic review and meta-analysis of seven studies that stress was the major cause of sleep disturbances among medical staff members and nurses during the COVID-19 pandemic. Given this background and given the lack of data among frontline hospital staff members in Iran, the key aim of the present study was to investigate the associations between psychological issues (depression, anxiety, stress) and insomnia. We also examined the associations between psychological issues and general health, while social support was further introduced as a mediating factor. To this end, 321 frontline hospital staff nurses completed a series of questionnaires on depression, anxiety, stress, self-efficacy, social support, insomnia, and general health.
The following two hypotheses and one research question were formulated. First, following others [18,21,22,25,26], we expected that higher scores of depression, anxiety, and stress would be related to insomnia and lower general health. Second, following others [11,12,13], we expected that frontline hospital staff members would report higher insomnia and lower general health, compared to normative data of healthy people. In doing so, we would be able to judge if hospital staff members reported similar or higher insomnia scores when compared to the general population. The exploratory research question was: Does social support mediate the association between depression, anxiety, stress, insomnia, and general health? The question was exploratory, because social support was either an important health factor [15,16] or not [10]. We suggest that the pattern of results might help to identify if and which frontline hospital staff members might need particular support to cope with COVID-19-related psychological issues.

2. Methods

2.1. Study Procedure

Frontline and full-time working hospital staff members of the Farabi Hospital (Kermanshah University of Medical Sciences, Kermanshah, Iran) were approached between August 2020 and November 2020 to participate in this cross-sectional study on psychological issues (depression, anxiety, stress), self-efficacy, social support, insomnia, and general health. To this end, the study was posted on the intranet and social network sites of the hospital. Staff members interested in participation could click and follow the link to be contacted by a study team member. All participants were informed about the aims of the study and that their data would be anonymous and held securely. Thereafter, they signed a written informed consent. Participants completed a series of questionnaires covering sociodemographic data, educational/vocational training, job duration, and depression, anxiety, stress, self-efficacy, social support, insomnia, and general health. Participants needed about 20 min to complete all questionnaires. The ethical committee of the Kermanshah University of Medical Sciences (KUMS; Kermanshah, Iran: register nr: 3010523) approved the study, which was performed in accordance with the current revision [27] of the Declaration of Helsinki.

2.2. Participants

Of the 730 staff members of the hospital, a total of 588 were working frontline. Of those, the research team contacted 429 individuals. Inclusion criteria were: 1. Age between 18 and 65 years; 2. working full time as a hospital staff member; 3. working as frontline staff during the entire time of the COVID-19 pandemic; 4. self-reporting either early or normal daytime shift within the last six months with routine day-offs; 5. signed written informed consent. Exclusion criteria were: 1. Irregular shifts, including night shifts within the last six months; 2. unable or unwilling to comply with the study requirements (e.g., completing self-rating questionnaires in Farsi); 3. self-reporting sleep disturbances as a result of caring for small children (including breast feeding) or obstructive sleep apnea; 4. intake of sleep-altering medications such as antihistaminic medication or prescribed psychopharmaceutic medication. Of the 429 initially approached, 29 were not working full time, or were not frontline staff, or working night shifts. Next, of the 400 persons initially interested in the study, 321 (80.25%) agreed to participate. Table 1 provides participants’ descriptive and inferential characteristics, both for the whole sample and separately for female and male participants (see Results section).

2.3. Measures

2.3.1. Sociodemographic and Job-Related Questions

Participants reported on their age (years), gender (female, male), civil status (single, married, divorced); duration of job experience (<1, 1–2, 3–5, 6–10, >11 years), and educational background (high school and diploma, high diploma, bachelor degree, master’s degree).

2.3.2. Depression, Anxiety, and Stress (DASS-21)

To assess depression, anxiety, and stress, the DASS-21 was employed [28]. The Farsi version showed satisfactory psychometric properties [29]. This questionnaire consists of 21 questions on depression (7 questions), anxiety (7 questions), and stress (7 questions). Answers are given on four-point rating scales ranging from 0 (= does not apply to me at all) to 3 (= absolutely applies to me), with higher sum scores reflecting higher depression, anxiety, and stress (Cronbach’s alpha: 0.89).

2.3.3. Social Support—Multidimensional Scale of Social Support (MSPSS)

To assess social support, participants completed the Multidimensional Scale of Social Support (MSPSS) [30,31]. The Farsi version showed satisfactory psychometric properties [32]. Subscales focus on social support from family, friends, and significant others in life. Answers are given on a seven-point Likert scales ranging from 0 (=strongly disagree) to 6 (=strongly agree), with higher sum scores reflecting a higher subjectively perceived social support (Cronbach’s alpha: 0.87).

2.3.4. Self-Efficacy—General Self-Efficacy Scale-10 (GES-10)

To assess self-efficacy, participants completed the 10-item General Self-Efficacy Scale-10 [33]. The Farsi version showed satisfactory psychometric properties [34]. Answers are given on a four-point Likert scale with the anchor points 1 (=not correct at all) to 4 (=completely correct). Sum scores range between 10 and 40, with higher scores reflecting higher self-efficacy (Cronbach’s alpha: 0.90).

2.3.5. Insomnia—Athens Insomnia Scale (AIS)

The Athens Insomnia Scale (AIS) [35] comprises eight items used to assess the severity and effects of insomnia. The Farsi version had satisfactory psychometric properties [36]. All items are rated using a four-point Likert scale (0 = no problem or equivalent meaning; 3 = very severe problem or equivalent meaning). The total sum score ranges from 0 to 24, with higher scores reflecting higher self-rated insomnia. The cut-off point is 6 [35] (Cronbach’s alpha: 0.92).

2.3.6. General Health

The General Health Questionnaire-12 (GHQ-12) is a measure used to self-assess current mental health [37]. The Farsi version showed satisfactory psychometric properties [38]. Answers are given on four-point Likert scales with the anchor points 0 (=not at all) to 3 (=definitely true), with higher mean scores reflecting a higher general health (Cronbach’s alpha: 0.91).

2.4. Statistical Analysis

Preliminary calculations: With a series of Spearman rank correlations, the associations between age, duration of job experience, and educational level and dimensions of depression, anxiety, stress, self-efficacy, social support, general health, and insomnia were calculated. Correlation coefficients were between rho = −0.03 and 0.04; it followed that age, duration of job experience, and educational level were not introduced as possible confounders. Next, with a series of t-tests, gender differences for depression, stress, anxiety, self-efficacy, social support, general health, and insomnia were tested; t-values were between 0.10 and 0.80; it followed that gender was not introduced as a possible confounder.
An X2-test was performed to compare insomnia scores (yes vs. no) with normative data taken from Morin et al. [39].
A one-sample t-test was performed to compare participants’ general health mean score with the mean of score (3.70) of a normative sample of healthy adults [40].
A series of Pearson’s correlations was performed to calculate the associations between dimensions of depression, anxiety, stress, self-efficacy, social support, insomnia, and general health.
Next, two models were performed to test the direct and indirect effects of depression, anxiety, stress and social support on insomnia and general health scores. To this end, in the first model, depression, anxiety, and stress predicted directly insomnia and general health, and indirectly via social support. In the second model, social support directly predicted insomnia and general health, and indirectly via depression, anxiety, and stress.
Preliminary conditions to perform multiple regression analyses were met [41,42,43]: N = 321 > 100; predictors explained the dependent variables (Rs = 0.506–0.539, R2s = 0.26–0.29; the number of predictors (4) · 10 = 40 < N (321)), and the Durbin–Watson coefficient was between 1.5 and 2.5, indicating that the residuals of the predictors were independent. Furthermore, the variances inflation factors (VIF) were between 1.20 and 1.50; while there are no strict cut-off points to report the risk of multicollinearity, VIF < 1 and VIF > 10 indicate multicollinearity [41,42,43].
All statistical computations were performed with SPSS® 25.0 (IBM Corporation, Armonk, NY, USA) for Apple Mac®.

3. Results

3.1. Preliminary Calculations

With a series of Spearman rank correlations the associations between age, duration of job experience, and educational level and dimensions of depression, anxiety, stress, self-efficacy, social support, general health, and insomnia were calculated. Correlation coefficients were between rho = −0.03 and 0.04; it followed that age, duration of job experience, and educational level were not introduced as possible confounders. Next, with a series of t-tests, gender differences for depression, stress, anxiety, self-efficacy, social support, general health, and insomnia were tested; t-values were between 0.10 and 0.80; it followed that gender was not introduced as possible confounder.

3.2. Participants’ Characteristics

Table 1 provides the descriptive and statistical overview of participants’ sociodemographic and work-related characteristics. Table 1 also reports the descriptive statistics of depression, anxiety, stress, self-efficacy, social support, insomnia, and general health.

3.3. Correlation Coefficients between Depression, Anxiety, Stress, and Self-Efficacy Dimensions of Social Support, Insomnia and General Health

Table 2 provides the correlation coefficients between dimensions of depression, anxiety, stress, self-efficacy, of social support, insomnia, and general health.
Scores of depression, anxiety and stress were highly interrelated (large correlations). Such higher scores were also associated with dimensions of lower social support (medium correlations) and lower general health (medium to large correlations), and higher dimensions of insomnia (medium to large correlations). Depression, anxiety, and stress were unrelated to self-efficacy (trivial correlations).
Higher scores of self-efficacy were related to higher social support (small correlations) and higher general health (small to medium correlations) but not to insomnia (trivial correlation).
Higher social support was related to higher general health (medium correlations) and lower insomnia (medium correlations), whereas only negative symptoms of general health were associated with insomnia (small correlation).

3.4. Insomnia Scores; Dichotomization: Yes vs. No, and Comparison with Normative Data

For the Athens Insomnia Scale (AIS), following Soldatos et al. [35], a score of six and more points indicates insomnia. Of the 321 participants, 44 (13.7%) had an insomnia score of 0 to 5 points; 272 (86.3%) had a score of six points and higher. Following Morin et al. [39], 37.5% of the general population report insomnia. Compared to the general population, participants reported statistically significantly higher insomnia (X2(N = 642, df = 1) = 201.32, p = 0.0001). The odds to report insomnia was 3.14-fold higher (CI: 2.64–3.75) among participants compared to the general population.

3.5. Mean Score of the General Health Questionnaire

For the General Health Questionnaire-12, Montazeri et al. [40] reported a mean score of 3.70 among healthy adults. In comparison, participants had a statistically significantly lower general health mean score (M = 1.30; SD = 0.42; t(320) = 102.87, p = 0.000).

3.6. Testing the Direct and Indirect Effects of Depression, Anxiety, Stress, and Social Support on Insomnia and General Health

3.6.1. Insomnia

Two models were tested (Table 3 and Table 4). In the first model, depression, anxiety, and stress predicted insomnia directly, and indirectly via social support. In the second model, social support predicted insomnia directly, and indirectly via depression, anxiety and stress.
To predict insomnia, results were as follows:
Comparing the two models, Model 1 had greater predictive power than Model 2; thus, it turned out that depression, anxiety, and stress directly predicted insomnia, whereas the indirect contribution of social support was modest.

3.6.2. General Health

Two models were tested (Table 5 and Table 6). In the first model, depression, anxiety and stress predicted insomnia directly, and indirectly via social support. In the second model, social support predicted insomnia directly, and indirectly via depression, anxiety and stress.
To predict general health, results were as follows:
Comparing the two models, Model 1 had greater predictive power than Model 2; thus, it turned out that depression, anxiety, and stress directly predicted general health, whereas the indirect contribution of social support was modest.

4. Discussion

The key findings of the present study were that among full-time frontline hospital staff members involved in the management of patients with COVID-19, higher scores of depression, anxiety, and stress were associated with higher insomnia and lower general health. Furthermore, frontline hospital staff in the present study reported insomnia scores that were 3.14-fold higher compared to the general population, and general health was lower compared to normative data. Next, depression, anxiety, and stress predicted higher insomnia scores and lower general health scores, both directly and indirectly via lower social support, though the mediating influence of social support was modest. The present results expand upon previous research in three ways: 1. This was the first study among frontline hospital staff members in Iran to assess insomnia. 2. Scores of depression, anxiety, and stress were the main predictors of insomnia and low general health, while social support had a modest influence of insomnia and general health. 3. In this line, the contribution of demographic (age, gender) and work-related dimensions (job experience, educational level) to explain insomnia and general health was spurious. The present results are of practical importance: frontline hospital staff members appear to be at an increased risk to report psychological issues, insomnia and low general health, irrespective of their work-related and personal background.
Two hypotheses and one research question were formulated, and each of these is considered now in turn.
With the first hypothesis, we assumed that higher scores of depression, anxiety, and stress were related to insomnia and lower general health, and the data did confirm this. Accordingly, the present patterns of results comply with previous results [18,21,22,25,26]. However, the present results expand upon the current research in that this pattern of associations was observed among frontline full-time hospital staff members in Iran. Specifically, no research conducted in Iran focused so far on sleep patterns among full-time hospital staff members working with patients with COVID-19. This is surprising for the following reasons: First, restoring sleep is highly associated with favorable psychological functioning, such as low anxiety, low stress [21,44,45], optimism [46], mental toughness [47,48,49,50,51,52,53], and lower odds of suffering from psychiatric disorders [19,20]. Second, poor sleep is related to work-related issues and higher absenteeism [54,55]. Third, it appeared that hospital staff members caring for patients with COVID-19 reported particularly high scores of anxiety [9,11,14,56,57,58,59,60,61,62]; however, higher anxiety scores and poor sleep are again largely associated [21,44,45,63,64,65]. Fourth, data from studies conducted in Iran showed that frontline hospital staff members reported particularly high scores of anxiety [11,14,66,67,68]. Taken together, the results of the present study appeared to have the potential to add to the current literature in an important fashion.
With the second hypothesis we assumed that frontline hospital staff members would report higher insomnia and lower general health compared to normative data of healthy people, and again the data did confirm this. Accordingly, the results are in line with previous studies [11,12,13]. The novelty of the present findings is that we proved high insomnia and low general health among full-time frontline hospital staff compared to normative data. In doing so, it was not necessary to assess healthy controls or non-frontline full-time staff members. Furthermore, as mentioned above, we were able to show that poor sleep was associated with higher scores of anxiety, depression, and stress.
The question arises as to how such a kind of association could be explained. The following theories are offered: First, following the hyperarousal model of insomnia [18], insomnia understood as qualitatively poor sleep is the result of a bi-directional process of dysfunctional cognitive-emotional processes and neurophysiological processes, such as increased cortisol and orexin secretion and decreased adenosine secretion. Thus, poor sleep is both the consequence and the trigger of dysfunctional cognitive-emotional processes, which in turn are associated with deteriorated neurophysiological processes. Second, the cognitive-emotional hyperarousal model of Harvey and colleagues [65,69,70,71,72,73,74] assumes that dysfunctional and hyper-aroused cognitive-emotional processes unfavorably impacts on sleep onset and sleep duration. Third, the cortical hyperarousal model [75,76,77] claims that poor sleep is both the trigger and the result of an increased cortical activity. Fourth, Palmer and Alfano [78] proposed an umbrella model with poor emotion regulation as an organizing framework unfavorably impacting on sleep. Fifth, an additional theoretical and empirical direction focuses on the importance of prefrontal activity (PFC) and its impairment during sleep. Yoo et al. [79] showed that among individuals with insomnia, the PFC was less activated than limbic areas: poor sleep appeared to be the result of a decrease in top–down prefrontal control. Given this, Yoo et al. [79] speculated that a night of sleep may “reset” the correct brain reactivity to next-day emotional challenges by maintaining the functional integrity of this PFC-amygdala circuit.
To summarize, while the quality of the present results does not allow a deeper understanding of the underlying cognitive-emotional, processes, it appears plausible that participants’ poor sleep was both the trigger and the result of a lower PFC control during the night, which were associated with a hyper-aroused cortical activity, dysregulated neurophysiological processes, and dysfunctional cognitive-emotional processing.
Next, we took as exploratory the research question, if and to what extent social support mediated the association between depression, anxiety, and stress and insomnia and general health. To this end, two different models were tested. In the first model, depression, anxiety, and stress directly impacted upon insomnia and general health, and indirectly via social support. In the second model, social support directly impacted upon insomnia and general health, and indirectly via depression, anxiety, and stress. It turned out that scores of depression, anxiety, and stress had a superior impact on insomnia and general health, both directly and indirectly via social support.
With regard to the importance of social support for general psychological well-being among hospital staff members, the results are mixed: while two studies emphasized the importance of social support as a health and protective factor among frontline hospital staff members in Iran [15,16], this was not confirmed in another study [10]. Thus, on a continuum of the importance of social support ranging from no importance [10] to high importance [15,16], our results are headed towards the pole of no importance [10]. The quality of the studies does not allow a deeper understanding and comparison between the previous and current study. Given this, a reasonable explanation remains difficult.
We also note that sociodemographic (age, gender) and work-related dimensions (working experience, educational/vocational levels) were completely unrelated to psychological functioning (depression, anxiety, stress, insomnia, general health). Again, these results are at odds with previous studies: compared to their male counterparts, female frontline hospital staff members reported higher stress [11,12,13]. Furthermore, higher age and higher vocational levels were associated with lower stress [11,12]. Given these results, our findings are in contrast to previous findings [80]. As such, it appears that work-related conditions had a higher impact on the individual psychological functioning, compared to individual characteristics such as age, gender, educational level, self-efficacy, and social support.
Despite the novelty of the results, the following limitations warrant against overgeneralization. First, strictly taken, the cross-sectional design precludes causality, though, to estimate the direct and indirect effects of predictors on outcome variables, it was necessary to define causal models. Second, by default, it was impossible to review and retrieve the relevant and rapidly growing literature on the topic. To illustrate, in PubMed®, the term “COVID-19” yielded 139,380 hits (retrieved 31 May 2021), “COVID-19 and psychiatry” yielded 5632 hits; “COVID-19 and hospital staff” yielded 5314 hits; “COVID-19 and staff members and Iran” yielded 89 hits, “COVID-19 and staff members and review” yielded 902 hits, and “COVID-19 and staff and meta-analysis” yielded 46 hits. Given this, we are aware that the literature review must be incomplete by nature, which may have affected the formulation of hypotheses. In this regard, Asmundson and Taylor [81] critically commented on the following two points: First, with regard to the COVID-19 pandemic and the use of post-traumatic stress disorder (PTSD), they commented that much of the published work on PTSD related to the pandemic is flawed with respect adequate consideration of PTSD criteria, particularly the assessment of Criterion A, conceptualization of Criterion A events, use of outdated measures, and the timeframe of reported symptoms. As such, quality should still matter over quantity. Second, Asmundson and Taylor [81] also mentioned the issue of the so-called infodemic, which refers to the deluge of information about the pandemic, consisting of a mix of low- to high-quality information, and where also researchers are competing to contribute to the research on the psychology of COVID-19. Third, the survey was performed anonymously, though only participants willing and able and working full time on the COVID-19 front were enrolled. Given this, the sample characteristics might have biased the pattern of results. In particular, it is conceivable that full-time staff working frontline with sleep disturbances and mental health problems were particularly interested and motivated to participate in the study. Relatedly, fourth, it is conceivable that latent and unassessed dimensions, such as leisure time activities, physical activity patterns, substance use, medication use, irregular working schedules, sleep hygiene-related behavior, and pre-existing vulnerabilities, might have biased two or more dimensions in the same or opposite directions. Fifth, a control condition consisting of non-frontline or part-time working hospital staff would have yielded a more fine-grained picture of the associations between sleep and mental health.
Future studies should assess hospital staff members longitudinally; such an approach would allow to draw causal relationships between workplace-related demands and individual psychological dimensions. In this view, it would be important to see if interventions to improve sleep, such as cognitive-behavioral therapy for insomnia [71,82,83,84,85] or Acceptance and Commitment Therapy (ACT) [86], could beneficially impact on depression, anxiety, and stress among frontline hospital staff. Furthermore, future studies should also carefully assess the impact of the implementation of interprofessional learning curricula, as such curricula may enhance understanding of the work of other health professionals, which in turn could result in better patient care and satisfaction with medical services [87].

5. Conclusions

Compared to normative data of healthy controls, full-time frontline hospital staff members involved in the treatment of patients with COVID-19 reported higher insomnia and lower general health. Higher scores of depression, anxiety, and stress directly predicted higher insomnia and lower general health. Higher social support had a modest direct and indirect effect on insomnia and general health. Age, gender, educational level, and job experience were unrelated to insomnia and general health. The pattern of results suggests that irrespective of sociodemographic and job-related characteristics, interventions to improve depression, anxiety, stress may favorably impact on sleep and general health.

Author Contributions

Conceptualization: N.A., V.F., S.J., F.R., M.A., P.A., O.D., K.M.D., A.B.B., D.S.-B. and S.B.; methodology: N.A., V.F., S.J., F.R., M.A., P.A., O.D., K.M.D., A.B.B., D.S.-B. and S.B.; software. P.A. and S.J.; validation: F.R., M.A., P.J.; and S.J.; formal analysis: D.S.-B., K.M.D. and S.B.; data curation: N.A., V.F., P.A., F.R., M.A., O.D., S.B. and M.M.; project administration: N.A., V.F., S.J., F.R., M.A. and O.D.; supervision: M.A., D.S.-B. and S.B.; writing original draft: A.B.B., D.S.-B. and S.B.; writing review and editing: N.A., M.A., A.B.B., D.S.-B. and S.B.; final revision and final version: N.A., V.F., S.J., F.R., M.A., P.A., O.D., K.M.D., A.B.B., D.S.-B. and S.B. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by the Infectious Diseases Research Center, Vice-Chancellor for research and Research and Technology, Kermanshah University of Medical Sciences, Kermanshah, Iran, grant number4000141.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of the Kermanshah University of Medical Sciences (Kermanshah, Iran; registration No. IR.KUMS.REC.1400.063 at the date of 20 April 2021; grant number: 4000141).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

Data are available upon request to competent experts.

Acknowledgments

The Infectious Diseases Research Center and the Vice Chancellor for Research and Technology of the Kermanshah University of Medical Sciences (Kermanshah, Iran) approved and supported the present study We also extend our thanks to Substance Abuse Prevention Research Center and clinical research development center of Imam Khomeini and Mohammad Kermanshahi and Farabi Hospitals affiliated to Kermanshah University of Medical Sciences (Kermanshah, Iran).

Conflicts of Interest

All authors declare no conflicts of interest.

References

  1. Liu, J.; Bao, Y.; Huang, X.; Shi, J.; Lu, L. Mental health considerations for children quarantined because of COVID-19. Lancet Child Adolesc. Health 2020, 4. [Google Scholar] [CrossRef] [Green Version]
  2. Chakraborty, I.; Maity, P. COVID-19 outbreak: Migration, effects on society, global environment and prevention. Sci. Total Environ. 2020, 728, 138882. [Google Scholar] [CrossRef] [PubMed]
  3. Wang, G.H.; Zhang, Y.T.; Zhao, J.; Zhang, J.; Jiang, F. Mitigate the effects of home confinement on children during the COVID-19 outbreak. J. Shanghai Jiaotong Univ. (Med. Sci.) 2020, 40, 279–281. [Google Scholar] [CrossRef]
  4. Ghosh, R.; Dubey, M.; Chatterjee, S.; Dubey, S. Impact of COVID-19 on children: Special focus on psychosocial aspect. Minerva Pediatrica 2020, 72, 226–235. [Google Scholar] [CrossRef] [PubMed]
  5. Dubey, S.; Biswas, P.; Ghosh, R.; Chatterjee, S.; Dubey, M.J.; Chatterjee, S.; Lahiri, D.; Lavie, C.J. Psychosocial impact of COVID-19. Diabetes Metab. Syndr. 2020, 14, 779–788. [Google Scholar] [CrossRef]
  6. Kisely, S.; Warren, N.; McMahon, L.; Dalais, C.; Henry, I.; Siskind, D. Occurrence, prevention, and management of the psychological effects of emerging virus outbreaks on healthcare workers: Rapid review and meta-analysis. BMJ (Clin. Res. Ed.) 2020, 369, m1642. [Google Scholar] [CrossRef]
  7. Pollock, A.; Campbell, P.; Cheyne, J.; Cowie, J.; Davis, B.; McCallum, J.; McGill, K.; Elders, A.; Hagen, S.; McClurg, D.; et al. Interventions to support the resilience and mental health of frontline health and social care professionals during and after a disease outbreak, epidemic or pandemic: A mixed methods systematic review. Cochrane Database Syst. Rev. 2020, 11, Cd013779. [Google Scholar] [CrossRef]
  8. Salehi, M.; Amanat, M.; Mohammadi, M.; Salmanian, M.; Rezaei, N.; Saghazadeh, A.; Garakani, A. The prevalence of post-traumatic stress disorder related symptoms in Coronavirus outbreaks: A systematic-review and meta-analysis. J. Affect. Disord. 2021, 282, 527–538. [Google Scholar] [CrossRef]
  9. Chou, R.; Dana, T.; Buckley, D.I.; Selph, S.; Fu, R.; Totten, A.M. Epidemiology of and Risk Factors for Coronavirus Infection in Health Care Workers: A Living Rapid Review. Ann. Intern. Med. 2020, 173, 120–136. [Google Scholar] [CrossRef]
  10. Sarboozi Hoseinabadi, T.; Kakhki, S.; Teimori, G.; Nayyeri, S. Burnout and its influencing factors between frontline nurses and nurses from other wards during the outbreak of Coronavirus Disease -COVID-19- in Iran. Investig. Educ. Enferm. 2020, 38. [Google Scholar] [CrossRef]
  11. Hosseinzadeh-Shanjani, Z.; Hajimiri, K.; Rostami, B.; Ramazani, S.; Dadashi, M. Stress, Anxiety, and Depression Levels Among Healthcare Staff During the COVID-19 Epidemic. Basic Clin. Neurosci. 2020, 11, 163–170. [Google Scholar] [CrossRef]
  12. Shoja, E.; Aghamohammadi, V.; Bazyar, H.; Moghaddam, H.R.; Nasiri, K.; Dashti, M.; Choupani, A.; Garaee, M.; Aliasgharzadeh, S.; Asgari, A. Covid-19 effects on the workload of Iranian healthcare workers. BMC Public Health 2020, 20, 1636. [Google Scholar] [CrossRef]
  13. Zandifar, A.; Badrfam, R.; Mohammadian Khonsari, N.; Assareh, M.; Karim, H.; Azimzadeh, M.; Noori Sepehr, M.; Tajbakhsh, R.; Rahimi, F.; Ghanipour, N.; et al. COVID-19 and medical staff’s mental health in educational hospitals in Alborz Province, Iran. Psychiatry Clin. Neurosci. 2020, 74, 499–501. [Google Scholar] [CrossRef] [PubMed]
  14. Galehdar, N.; Kamran, A.; Toulabi, T.; Heydari, H. Exploring nurses’ experiences of psychological distress during care of patients with COVID-19: A qualitative study. BMC Psychiatry 2020, 20, 489. [Google Scholar] [CrossRef]
  15. Alizadeh, A.; Khankeh, H.R.; Barati, M.; Ahmadi, Y.; Hadian, A.; Azizi, M. Psychological distress among Iranian health-care providers exposed to coronavirus disease 2019 (COVID-19): A qualitative study. BMC Psychiatry 2020, 20, 494. [Google Scholar] [CrossRef] [PubMed]
  16. Vafaei, H.; Roozmeh, S.; Hessami, K.; Kasraeian, M.; Asadi, N.; Faraji, A.; Bazrafshan, K.; Saadati, N.; Kazemi Aski, S.; Zarean, E.; et al. Obstetrics Healthcare Providers’ Mental Health and Quality of Life During COVID-19 Pandemic: Multicenter Study from Eight Cities in Iran. Psychol. Res. Behav. Manag. 2020, 13, 563–571. [Google Scholar] [CrossRef] [PubMed]
  17. Bashirian, S.; Jenabi, E.; Khazaei, S.; Barati, M.; Karimi-Shahanjarini, A.; Zareian, S.; Rezapur-Shahkolai, F.; Moeini, B. Factors associated with preventive behaviours of COVID-19 among hospital staff in Iran in 2020: An application of the Protection Motivation Theory. J. Hosp. Infect. 2020, 105, 430–433. [Google Scholar] [CrossRef]
  18. Riemann, D.; Spiegelhalder, K.; Feige, B.; Voderholzer, U.; Berger, M.; Perlis, M.; Nissen, C. The hyperarousal model of insomnia: A review of the concept and its evidence. Sleep Med. Rev. 2010, 14, 19–31. [Google Scholar] [CrossRef]
  19. Baglioni, C.; Nanovska, S.; Regen, W.; Spiegelhalder, K.; Feige, B.; Nissen, C.; Reynolds, C.F.; Riemann, D. Sleep and mental disorders: A meta-analysis of polysomnographic research. Psychol. Bull. 2016, 142, 969–990. [Google Scholar] [CrossRef] [PubMed]
  20. Hertenstein, E.; Feige, B.; Gmeiner, T.; Kienzler, C.; Spiegelhalder, K.; Johann, A.; Jansson-Frojmark, M.; Palagini, L.; Rucker, G.; Riemann, D.; et al. Insomnia as a predictor of mental disorders: A systematic review and meta-analysis. Sleep Med. Rev. 2018, 43, 96–105. [Google Scholar] [CrossRef]
  21. Baglioni, C.; Spiegelhalder, K.; Lombardo, C.; Riemann, D. Sleep and emotions: A focus on insomnia. Sleep Med. Rev. 2010, 14, 227–238. [Google Scholar] [CrossRef] [PubMed]
  22. Brand, S.; Kirov, R.; Kalak, N.; Gerber, M.; Puhse, U.; Lemola, S.; Correll, C.U.; Cortese, S.; Meyer, T.; Holsboer-Trachsler, E. Perfectionism related to self-reported insomnia severity, but not when controlled for stress and emotion regulation. Neuropsychiatr. Dis. Treat. 2015, 11, 263–271. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  23. Khazaie, H.; Zakiei, A.; McCall, W.V.; Noori, K.; Rostampour, M.; Sadeghi Bahmani, D.; Brand, S. Relationship between Sleep Problems and Self-Injury: A Systematic Review. Behav. Sleep Med. 2020, 1–16. [Google Scholar] [CrossRef]
  24. Kirov, R.; Brand, S.; Kolev, V.; Yordanova, J. The sleeping brain and the neural basis of emotions. Behav. Brain Sci. 2012, 35, 155–156. [Google Scholar] [CrossRef] [Green Version]
  25. Salari, N.; Khazaie, H.; Hosseinian-Far, A.; Ghasemi, H.; Mohammadi, M.; Shohaimi, S.; Daneshkhah, A.; Khaledi-Paveh, B.; Hosseinian-Far, M. The prevalence of sleep disturbances among physicians and nurses facing the COVID-19 patients: A systematic review and meta-analysis. Glob. Health 2020, 16, 92. [Google Scholar] [CrossRef] [PubMed]
  26. Brand, S.; Kirov, R.; Kalak, N.; Gerber, M.; Schmidt, N.B.; Lemola, S.; Correll, C.U.; Holsboer-Trachsler, E. Poor Sleep Is Related to Lower Emotional Competence Among Adolescents. Behav. Sleep Med. 2016, 14, 602–614. [Google Scholar] [CrossRef]
  27. World Medical Association. World Medical Association Declaration of Helsinki: Ethical principles for medical research involving human subjects. JAMA 2013, 310, 2191–2194. [Google Scholar] [CrossRef] [Green Version]
  28. Lovibond, P.F.; Lovibond, S.H. The structure of negative emotional states: Comparison of the Depression Anxiety Stress Scales (DASS) with the Beck Depression and Anxiety Inventories. Behav. Res. Ther. 1995, 33, 335–343. [Google Scholar] [CrossRef]
  29. Jafari, P.; Nozari, F.; Ahrari, F.; Bagheri, Z. Measurement invariance of the Depression Anxiety Stress Scales-21 across medical student genders. Int. J. Med. Educ. 2017, 8, 116–122. [Google Scholar] [CrossRef] [Green Version]
  30. Dahlem, N.W.; Zimet, G.D.; Walker, R.R. The Multidimensional Scale of Perceived Social Support: A confirmation study. J. Clin. Psychol. 1991, 47, 756–761. [Google Scholar] [CrossRef]
  31. Zimet, G.D.; Powell, S.S.; Farley, G.K.; Werkman, S.; Berkoff, K.A. Psychometric characteristics of the Multidimensional Scale of Perceived Social Support. J. Personal. Assess. 1990, 55, 610–617. [Google Scholar] [CrossRef]
  32. Bagherian-Sararoudi, R.; Hajian, A.; Ehsan, H.B.; Sarafraz, M.R.; Zimet, G.D. Psychometric properties of the persian version of the multidimensional scale of perceived social support in iran. Int. J. Prev. Med. 2013, 4, 1277–1281. [Google Scholar] [PubMed]
  33. Schwarzer, R.J.M. Generalized Self-Efficacy Scale. In Measures in Health Psychology: A User’s Portfolio. Causal and Control Beliefs; Weinman, S., Wright, S., Johnston, M., Eds.; NFER-NELSON: Windsor, UK, 1995; p. 37. [Google Scholar]
  34. Farnia, V.; Asadi, R.; Abdoli, N.; Radmehr, F.; Alikhani, M.; Khodamoradi, M.; Behrouz, B.; Salemi, S. Psychometric properties of the Persian version of General Self-Efficacy Scale (GSES) among substance abusers the year 2019–2020 in Kermanshah city. Clin. Epidemiol. Glob. Health 2020, 8, 949–953. [Google Scholar] [CrossRef] [Green Version]
  35. Soldatos, C.R.; Dikeos, D.G.; Paparrigopoulos, T.J. Athens Insomnia Scale: Validation of an instrument based on ICD-10 criteria. J. Psychosom. Res. 2000, 48, 555–560. [Google Scholar] [CrossRef]
  36. Giahi, O.; Khoubi, J.; Amiri, M. The association between insomnia and cardiovascular risk factors in bus drivers in Iran. Work 2016, 55, 207–214. [Google Scholar] [CrossRef]
  37. Goldberg, D.P.; Hillier, V.F. A scaled version of the General Health Questionnaire. Psychol. Med. 1979, 9, 139–145. [Google Scholar] [CrossRef] [PubMed]
  38. Dadfar, M.; Kalibatseva, Z.; Lester, D. Reliability and validity of the Farsi version of the Patient Health Questionnaire-9 (PHQ-9) with Iranian psychiatric outpatients. Trends Psychiatry Psychother. 2018, 40, 144–151. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Morin, C.M.; Jarrin, D.C.; Ivers, H.; Mérette, C.; LeBlanc, M.; Savard, J. Incidence, Persistence, and Remission Rates of Insomnia Over 5 Years. JAMA Netw. Open 2020, 3, e2018782. [Google Scholar] [CrossRef]
  40. Montazeri, A.; Harirchi, A.M.; Shariati, M.; Garmaroudi, G.; Ebadi, M.; Fateh, A. The 12-item General Health Questionnaire (GHQ-12): Translation and validation study of the Iranian version. Health Qual. Life Outcomes 2003, 1, 66. [Google Scholar] [CrossRef] [Green Version]
  41. Brosius, F. SPSS: Umfassendes Handbuch zu Statistik und Datenanalyse—Comprehensive Textbook for Statistics and Data Analysis; mitp Verlags GmBH & Co: Frechen, Germany, 2018. [Google Scholar]
  42. Hair, J.F.; Black, C.W.; Babin, B.J.; Anderson, R.E. Multivariate Data Analysis, 7th ed.; Pearson Education Limited: Essex, UK, 2014. [Google Scholar]
  43. Rudolf, M.; Müller, J. Multivariate Analyses; Hogrefe: Goettingen, Germany, 2004. [Google Scholar]
  44. Alvaro, P.K.; Roberts, R.M.; Harris, J.K. A Systematic Review Assessing Bidirectionality between Sleep Disturbances, Anxiety, and Depression. Sleep 2013, 36, 1059–1068. [Google Scholar] [CrossRef]
  45. Baum, K.T.; Desai, A.; Field, J.; Miller, L.E.; Rausch, J.; Beebe, D.W. Sleep restriction worsens mood and emotion regulation in adolescents. J. Child Psychol. Psychiatry Allied Discip. 2014, 55, 180–190. [Google Scholar] [CrossRef] [Green Version]
  46. Lemola, S.; Raikkonen, K.; Scheier, M.F.; Matthews, K.A.; Pesonen, A.K.; Heinonen, K.; Lahti, J.; Komsi, N.; Paavonen, J.E.; Kajantie, E. Sleep quantity, quality and optimism in children. J. Sleep Res. 2011, 20, 12–20. [Google Scholar] [CrossRef] [PubMed]
  47. Brand, S.; Gerber, M.; Kalak, N.; Kirov, R.; Lemola, S.; Clough, P.J.; Pühse, U.; Holsboer-Trachsler, E. “Sleep Well, Our Tough Heroes!”—In Adolescence, Greater Mental Toughness is Related to Better Sleep Schedules. Behav. Sleep Med. 2014, 12, 444–454. [Google Scholar] [CrossRef] [PubMed]
  48. Brand, S.; Gerber, M.; Kalak, N.; Kirov, R.; Lemola, S.; Clough, P.J.; Pühse, U.; Holsboer-Trachsler, E. Adolescents with greater mental toughness show higher sleep efficiency, more deep sleep and fewer awakenings after sleep onset. J. Adolesc. Health 2014, 54, 109–113. [Google Scholar] [CrossRef] [Green Version]
  49. Brand, S.; Kalak, N.; Gerber, M.; Clough, P.J.; Lemola, S.; Puhse, U.; Holsboer-Trachsler, E. During early and mid-adolescence, greater mental toughness is related to increased sleep quality and quality of life. J. Health Psychol. 2014. [Google Scholar] [CrossRef] [Green Version]
  50. Brand, S.; Kalak, N.; Gerber, M.; Clough, P.J.; Lemola, S.; Sadeghi Bahmani, D.; Puhse, U.; Holsboer-Trachsler, E. During early to mid adolescence, moderate to vigorous physical activity is associated with restoring sleep, psychological functioning, mental toughness and male gender. J. Sports Sci. 2017, 35, 426–434. [Google Scholar] [CrossRef]
  51. Sadeghi Bahmani, D.; Esmaeili, L.; Shaygannejad, V.; Gerber, M.; Kesselring, J.; Lang, U.E.; Holsboer-Trachsler, E.; Brand, S. Stability of Mental Toughness, Sleep Disturbances, and Physical Activity in Patients with Multiple Sclerosis (MS)-A Longitudinal and Pilot Study. Front. Psychiatry 2018, 9, 182. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  52. Sadeghi Bahmani, D.; Gerber, M.; Kalak, N.; Lemola, S.; Clough, P.J.; Calabrese, P.; Shaygannejad, V.; Puhse, U.; Holsboer-Trachsler, E.; Brand, S. Mental toughness, sleep disturbances, and physical activity in patients with multiple sclerosis compared to healthy adolescents and young adults. Neuropsychiatr. Dis. Treat. 2016, 12, 1571–1579. [Google Scholar] [CrossRef] [PubMed]
  53. Sadeghi Bahmani, D.; Hatzinger, M.; Gerber, M.; Lemola, S.; Clough, P.J.; Perren, S.; von Klitzing, K.; Von Wyl, A.; Holsboer-Trachsler, E.; Brand, S. The origins of mental toughness—Prosocial behavior and low internalizing and externalizing problems at age 5 predict higher mental toughness scores at age 14. Front. Psychol. 2016, 7. [Google Scholar] [CrossRef] [Green Version]
  54. Leger, D. Working with poor sleep. Sleep 2014, 37, 1401–1403. [Google Scholar] [CrossRef] [Green Version]
  55. Brand, S.; Beck, J.; Hatzinger, M.; Harbaugh, A.; Ruch, W.; Holsboer-Trachsler, E. Associations between satisfaction with life, burnout-related emotional and physical exhaustion, and sleep complaints. World J. Biol. Psychiatry Off. J. World Fed. Soc. Biol. Psychiatry 2010, 11, 744–754. [Google Scholar] [CrossRef]
  56. Batra, K.; Singh, T.P.; Sharma, M.; Batra, R.; Schvaneveldt, N. Investigating the Psychological Impact of COVID-19 among Healthcare Workers: A Meta-Analysis. Int. J. Environ. Res. Public Health 2020, 17, 9096. [Google Scholar] [CrossRef]
  57. Berthelot, N.; Lemieux, R.; Garon-Bissonnette, J.; Drouin-Maziade, C.; Martel, É.; Maziade, M. Uptrend in distress and psychiatric symptomatology in pregnant women during the coronavirus disease 2019 pandemic. Acta Obstet. Gynecol. Scand. 2020, 99, 848–855. [Google Scholar] [CrossRef] [PubMed]
  58. Giorgi, G.; Lecca, L.I.; Alessio, F.; Finstad, G.L.; Bondanini, G.; Lulli, L.G.; Arcangeli, G.; Mucci, N. COVID-19-Related Mental Health Effects in the Workplace: A Narrative Review. Int. J. Environ. Res. Public Health 2020, 17, 7857. [Google Scholar] [CrossRef] [PubMed]
  59. Lakhan, R.; Agrawal, A.; Sharma, M. Prevalence of Depression, Anxiety, and Stress during COVID-19 Pandemic. J. Neurosci. Rural Pract. 2020, 11, 519–525. [Google Scholar] [CrossRef]
  60. Ramezani, M.; Simani, L.; Karimialavijeh, E.; Rezaei, O.; Hajiesmaeili, M.; Pakdaman, H. The Role of Anxiety and Cortisol in Outcomes of Patients with Covid-19. Basic Clin. Neurosci. 2020, 11, 179–184. [Google Scholar] [CrossRef]
  61. Salari, N.; Hosseinian-Far, A.; Jalali, R.; Vaisi-Raygani, A.; Rasoulpoor, S.; Mohammadi, M.; Rasoulpoor, S.; Khaledi-Paveh, B. Prevalence of stress, anxiety, depression among the general population during the COVID-19 pandemic: A systematic review and meta-analysis. Glob. Health 2020, 16, 57. [Google Scholar] [CrossRef] [PubMed]
  62. Trumello, C.; Bramanti, S.M.; Ballarotto, G.; Candelori, C.; Cerniglia, L.; Cimino, S.; Crudele, M.; Lombardi, L.; Pignataro, S.; Viceconti, M.L.; et al. Psychological Adjustment of Healthcare Workers in Italy during the COVID-19 Pandemic: Differences in Stress, Anxiety, Depression, Burnout, Secondary Trauma, and Compassion Satisfaction between Frontline and Non-Frontline Professionals. Int. J. Environ. Res. Public Health 2020, 17, 8358. [Google Scholar] [CrossRef]
  63. Cox, R.C.; Olatunji, B.O. A systematic review of sleep disturbance in anxiety and related disorders. J. Anxiety Disord. 2016, 37, 104–129. [Google Scholar] [CrossRef]
  64. Huang, Y.; Zhao, N. Generalized anxiety disorder, depressive symptoms and sleep quality during COVID-19 outbreak in China: A web-based cross-sectional survey. Psychiatry Res. 2020, 288, 112954. [Google Scholar] [CrossRef]
  65. Kyle, S.D.; Beattie, L.; Spiegelhalder, K.; Rogers, Z.; Espie, C.A. Altered emotion perception in insomnia disorder. Sleep 2014, 37, 775–783. [Google Scholar] [CrossRef]
  66. Sahebi, A.; Nejati, B.; Moayedi, S.; Yousefi, K.; Torres, M.; Golitaleb, M. The prevalence of anxiety and depression among healthcare workers during the COVID-19 pandemic: An umbrella review of meta-analyses. Prog. Neuropsychopharmacol. Biol. Psychiatry 2021, 110247. [Google Scholar] [CrossRef] [PubMed]
  67. Wong, L.P.; Alias, H.; Danaee, M.; Ziaee, M.; Adebi, F.; Ziaee, A.; Mohajer, S.; HajiAliBeigloo, R.; Nia, M.N.; Jamei, F.; et al. Uncovering psychobehavioral implications of SARS-CoV-2 infection in Iran. Transbound. Emerg. Dis. 2020. [Google Scholar] [CrossRef]
  68. Zandifar, A.; Badrfam, R. Iranian mental health during the COVID-19 epidemic. Asian J. Psychiatry 2020, 51, 101990. [Google Scholar] [CrossRef]
  69. Espie, C.A.; Inglis, S.J.; Harvey, L.; Tessier, S. Insomniacs’ attributions. psychometric properties of the Dysfunctional Beliefs and Attitudes about Sleep Scale and the Sleep Disturbance Questionnaire. J. Psychosom. Res. 2000, 48, 141–148. [Google Scholar] [CrossRef]
  70. Kyle, S.D.; Aquino, M.R.; Miller, C.B.; Henry, A.L.; Crawford, M.R.; Espie, C.A.; Spielman, A.J. Towards standardisation and improved understanding of sleep restriction therapy for insomnia disorder: A systematic examination of CBT-I trial content. Sleep Med. Rev. 2015, 23, 83–88. [Google Scholar] [CrossRef] [PubMed]
  71. Harvey, A.G. A cognitive model of insomnia. Behav. Res. Ther. 2002, 40, 869–893. [Google Scholar] [CrossRef]
  72. Harvey, A.G. A transdiagnostic approach to treating sleep disturbance in psychiatric disorders. Cogn. Behav. Ther. 2009, 38 (Suppl. S1), 35–42. [Google Scholar] [CrossRef] [PubMed]
  73. Harvey, A.G.; Payne, S. The management of unwanted pre-sleep thoughts in insomnia: Distraction with imagery versus general distraction. Behav. Res. Ther. 2002, 40, 267–277. [Google Scholar] [CrossRef]
  74. Sadler, P.; McLaren, S.; Klein, B.; Jenkins, M.; Harvey, J. Cognitive behaviour therapy for older adults experiencing insomnia and depression in a community mental health setting: Study protocol for a randomised controlled trial. Trials 2015, 16, 538. [Google Scholar] [CrossRef] [Green Version]
  75. Fernandez-Mendoza, J.; Li, Y.; Vgontzas, A.N.; Fang, J.; Gaines, J.; Calhoun, S.L.; Liao, D.; Bixler, E.O. Insomnia is Associated with Cortical Hyperarousal as Early as Adolescence. Sleep 2016, 39, 1029–1036. [Google Scholar] [CrossRef] [Green Version]
  76. Fernandez-Mendoza, J.; Vela-Bueno, A.; Vgontzas, A.N.; Ramos-Platon, M.J.; Olavarrieta-Bernardino, S.; Bixler, E.O.; De la Cruz-Troca, J.J. Cognitive-emotional hyperarousal as a premorbid characteristic of individuals vulnerable to insomnia. Psychosom. Med. 2010, 72, 397–403. [Google Scholar] [CrossRef] [Green Version]
  77. Zhao, L.; Wang, E.; Zhang, X.; Karama, S.; Khundrakpam, B.; Zhang, H.; Guan, M.; Wang, M.; Cheng, J.; Shi, D.; et al. Cortical Structural Connectivity Alterations in Primary Insomnia: Insights from MRI-Based Morphometric Correlation Analysis. BioMed Res. Int. 2015, 2015, 817595. [Google Scholar] [CrossRef] [Green Version]
  78. Palmer, C.A.; Alfano, C.A. Sleep and emotion regulation: An organizing, integrative review. Sleep Med. Rev. 2017, 31, 6–16. [Google Scholar] [CrossRef]
  79. Yoo, S.S.; Gujar, N.; Hu, P.; Jolesz, F.A.; Walker, M.P. The human emotional brain without sleep—A prefrontal amygdala disconnect. Curr. Biol. CB 2007, 17, R877–R878. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  80. Zandifar, A.; Karim, H.; Qorbani, M.; Badrfam, R. Mental Health Care for Medical Staff in Iran during the COVID-19 Pandemic; Different Performance in Alborz Province. Iran. J. Psychiatry 2020, 15, 243–247. [Google Scholar] [CrossRef]
  81. Asmundson, G.J.G.; Taylor, S. Garbage in, garbage out: The tenuous state of research on PTSD in the context of the COVID-19 pandemic and infodemic. J. Anxiety Disord. 2021, 78, 102368. [Google Scholar] [CrossRef] [PubMed]
  82. de Bruin, E.J.; Bogels, S.M.; Oort, F.J.; Meijer, A.M. Efficacy of Cognitive Behavioral Therapy for Insomnia in Adolescents: A Randomized Controlled Trial with Internet Therapy, Group Therapy and A Waiting List Condition. Sleep 2015, 38, 1913–1926. [Google Scholar] [CrossRef] [PubMed]
  83. Harvey, A.G.; Tang, N.K. (Mis)perception of sleep in insomnia: A puzzle and a resolution. Psychol. Bull. 2012, 138, 77–101. [Google Scholar] [CrossRef]
  84. Harvey, A.G.; Tang, N.K.; Browning, L. Cognitive approaches to insomnia. Clin. Psychol. Rev. 2005, 25, 593–611. [Google Scholar] [CrossRef]
  85. van Straten, A.; van der Zweerde, T.; Kleiboer, A.; Cuijpers, P.; Morin, C.M.; Lancee, J. Cognitive and behavioral therapies in the treatment of insomnia: A meta-analysis. Sleep Med. Rev. 2018, 38, 3–16. [Google Scholar] [CrossRef] [PubMed]
  86. Zakiei, A.; Khazaie, H.; Rostampour, M.; Lemola, S.; Esmaeili, M.; Dürsteler, K.; Brühl, A.B.; Sadeghi-Bahmani, D.; Brand, S. Acceptance and Commitment Therapy (ACT) Improves Sleep Quality, Experiential Avoidance, and Emotion Regulation in Individuals with Insomnia-Results from a Randomized Interventional Study. Life (Basel) 2021, 11, 133. [Google Scholar] [CrossRef] [PubMed]
  87. Singh, J.; Salisbury, H. Attitudes and Perceptions of Non- Clinical Health Care Students Towards Interprofessional Learning. Health Interprof. Pract. Educ. 2019, 3, eP1179s. [Google Scholar]
Table
Table 1. Overview of sociodemographic and questionnaire-related descriptive data.
Table 1. Overview of sociodemographic and questionnaire-related descriptive data.
VariableTotal
N (%)		Male
N (%)		Female
N (%)
Civil statusSingle122 (38)35 (25.9)87 (46.8)
Married187 (58.3)95 (70.4)92 (49.5)
Divorced12 (3.7)5 (3.7)7 (3.8)
Total135 (100)186 (100)
EducationHigh school and diploma18 (5.6)10 (7.4)8 (4.3)
High diploma57 (17.8)24 (17.8)33 (17.7)
Bachelor160 (49.8)65 (48.1)95 (51.1)
Master and higher86 (26.8)36 (26.7)50 (36.9)
Total135 (100)186 (100)
Job experience (years)< 115 (4.7)4 (3)11 (5.9)
1–240 (12.5)8 (5.9)32 (17.2)
3–564 (19.9)16 (11.9)48 (25.8)
6–1053 (16.5)22 (16.3)31 (16.7)
≥11149 (46.4)85 (63.0)64 (34.4)
Total135 (100)186 (100)
M (SD)M (SD)M (SD)
Age (years)36.37 (7.72)40.12 (7.32)33.66 (6.82)
Stress-anxiety-depression inventory DASS-21Depression8.90 (5.09)8.57 (5.31)9.13 (4.93)
Anxiety9.09 (5.26)8.92 (5.53)9.22 (5.07)
Stress10.45 (4.91)10.28 (5.08)10.57 (4.80)
Total score DASS28.44 (5.08)27.77 (5.31)28.92 (4.93)
General Self-Efficacy Scale-10 (GSES-10)29.11 (5.72)29.72 (6.09)28.67 (5.41)
Multidimensional Scale of Social Support Questions (MSPSS)Significant other subscale19.66 (5.98)19.58 (5.94)19.73 (6.03)
Family subscale19.63 (5.87)19.84 (5.66)20.00 (6.03)
Friends subscale18.14 (5.77)17.47 (6.00)18.62 (5.57)
Total score MSPSS57.74 (14.82)56.90 (14.34)58.34 (15.16)
General Health Questionnaire-12 (GHQ-12)Negative symptoms of mental health7.42 (4.43)7.25 (4.64)7.55 (4.28)
Positive symptoms of mental health8.22 (3.30)8.23 (3.62)8.22 (3.05)
Total score GHQ-1218.80 (6.02)18.98 (6.37)18.67 (5.73)
Athens Insomnia Scale (AIS)Sleep quantity and quality7.43 (3.30)7.57 (3.21)7.33 (3.37)
Daytime symptoms4.58 (2.00)4.64 (2.05)4.53 (1.97)
Total score AIS12.01 (4.65)12.21 (4.55)11.86 (4.73)
Table
Table 2. Overview of Pearson’s correlation coefficients between dimensions of stress, anxiety, depression, social support, self-efficacy, insomnia, and general health.
Table 2. Overview of Pearson’s correlation coefficients between dimensions of stress, anxiety, depression, social support, self-efficacy, insomnia, and general health.
Dimensions
DASSGeneral Self-EfficacySocial Support
DASS 23456789
1Total score0.91 ***0.90 ***0.90 ***0.03−0.45 ***−0.39 ***−0.40 ***−0.33 ***
2Depression−0.73 ***0.75 ***−0.04−0.45 ***−0.42 ***−0.40 ***−0.32 ***
3Anxiety −0.72 ***0.06−0.40 ***−0.34 ***−0.38 ***−0.29 ***
4 Stress −−0.05−0.36 ***−0.30 ***−0.31 ***−0.29 ***
5 General self-efficacy −0.27**0.25 ***0.25 ***0.19 **
Social support
6 Total score −0.90 ***0.85 ***0.78 ***
7 Significant others −0.71 ***0.54 ***
8 Family −0.42 ***
9 Friends −
Dimensions
General HealthInsomnia
DASS 101112131415
1Total score−0.43 ***0.54 ***−0.060.50 ***0.45 ***0.40 ***
2Depression−0.49 ***0.53 ***−0.19 *0.43 ***0.40 ***0.34 ***
3Anxiety−0.30 ***0.43 ***−0.020.53 ***0.48 ***0.43 ***
4 Stress−0.39 ***0.51 ***−0.020.38 ***0.34 ***0.32 ***
5 General self-efficacy0.27 ***−0.17 ***0.34 ***0.060.020.10 *
Social support
6 Total score0.39 ***−0.34 ***0.26 ***−0.29 ***−0.27 ***−0.24 ***
7 Significant others0.37 ***−0.31 ***0.26 ***−0.24 ***−0.21 **−0.20 ***
8 Family0.27 ***−0.22 ***0.19 ***−0.33 ***−0.31 ***−0.25 ***
9 Friends0.36 ***−0.33 ***0.20 **−0.16 **−0.15 **−0.14**
General health
10 Total score−−0.84 ***0.67 ***−0.14 **−0.16 **0.−06
11 Negative symptoms −−0.19 **0.22 ***0.21 ***0.15 ***
12 Positive symptoms −0.040.000.09
Insomnia
13 Total score −0.93 ***0.79 ***
14 Sleep quality and quantity −0.51 ***
15 Daytime symptoms −
Notes: DASS = Depression, Anxiety, and Stress Scale; * = p < 0.05; ** = p < 0.01; *** = p < 0.001.
Table
Table 3. Equation models to predict insomnia. Model 1.
Table 3. Equation models to predict insomnia. Model 1.
rDASS-insomnia=Direct Effectdass
β 		+Indirect Effect via Social Support
β DAS-social support × rsocial support-insomnia
r = 0.50=0.456+−0.45 × −0.087
Notes: DASS = Depression, Anxiety, and Stress Scale; overall score.
Table
Table 4. Equation models to predict insomnia. Model 2.
Table 4. Equation models to predict insomnia. Model 2.
rsocial support-insomnia=Direct Effectsocial support
β 		+Indirect Effect via DASS
β DAS-insomnia × rDAS,-insomnia
r = −0.29=−0.087+−0.456 × −0.45
Notes: DASS = Depression, Anxiety, and Stress Scale; overall score.
Table
Table 5. Equation model to predict general health. Model 1.
Table 5. Equation model to predict general health. Model 1.
rDAS-general health=Direct Effectdass
β 		+Indirect Effect via Social Support
β social support-general health × rDASS-social support
r = −0.43=−0.32+0.25 × −0.45
Notes: DASS = Depression, Anxiety, and Stress Scale; overall score.
Table
Table 6. Equation model to predict general health. Model 2.
Table 6. Equation model to predict general health. Model 2.
rsocial support-general health=Direct Effectsocial support
β 		+Indirect Effect via DAS
β DAS-general health × rDASS,-genral health
r = 0.39=0.25+−0.32 × −0.45
Notes: DASS = Depression, Anxiety, and Stress Scale; overall score.
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Abdoli, N.; Farnia, V.; Jahangiri, S.; Radmehr, F.; Alikhani, M.; Abdoli, P.; Davarinejad, O.; Dürsteler, K.M.; Brühl, A.B.; Sadeghi-Bahmani, D.; et al. Sources of Sleep Disturbances and Psychological Strain for Hospital Staff Working during the COVID-19 Pandemic. Int. J. Environ. Res. Public Health 2021, 18, 6289. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18126289

AMA Style

Abdoli N, Farnia V, Jahangiri S, Radmehr F, Alikhani M, Abdoli P, Davarinejad O, Dürsteler KM, Brühl AB, Sadeghi-Bahmani D, et al. Sources of Sleep Disturbances and Psychological Strain for Hospital Staff Working during the COVID-19 Pandemic. International Journal of Environmental Research and Public Health. 2021; 18(12):6289. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18126289

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Abdoli, Nasrin, Vahid Farnia, Somayeh Jahangiri, Farnaz Radmehr, Mostafa Alikhani, Pegah Abdoli, Omran Davarinejad, Kenneth M. Dürsteler, Annette Beatrix Brühl, Dena Sadeghi-Bahmani, and et al. 2021. "Sources of Sleep Disturbances and Psychological Strain for Hospital Staff Working during the COVID-19 Pandemic" International Journal of Environmental Research and Public Health 18, no. 12: 6289. https://0-doi-org.brum.beds.ac.uk/10.3390/ijerph18126289

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