Insomnia plays a key role in mental health problems of COVID-19 patients in Square Cabin Hospitals: a network analysis

doi:10.21203/rs.3.rs-2507253/v1

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Insomnia plays a key role in mental health problems of COVID-19 patients in Square Cabin Hospitals: a network analysis

https://doi.org/10.21203/rs.3.rs-2507253/v1

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Background: While the emergence of the Coronavirus Disease 2019 (COVID-19) has greatly affected the mental health and wellbeing of people everywhere, the core symptoms of common syndromes of psychological distress remain unclear, especially among COVID-19 patients who have been quarantined in Square Cabin Hospitals. Using the method of network analysis, the current study explored the relations between stress, depression, anxiety and insomnia symptoms among COVID-19 patients in Shanghai Four-Leaf Clover Cabin Hospital.
Method: The study used an online survey to gauge the mental health issues of COVID-19 patients at Shanghai Four-Leaf Clover Cabin Hospital from April 18 to May 19, 2022. The Perceived Stress Scale, Patient Health Questionnaire, Generalized Anxiety Disorder Scale, and Insomnia Severity Index were used to assess perceived stress symptoms, depressive symptoms, anxiety symptoms, and insomnia symptoms, respectively. The central symptoms and bridge symptoms were determined using a regularized partial correlation network.
Results: A total of 1014 patients was included in this study. Overall, 61.5%, 49.2% , and 56.1% of all patients reported symptoms of depressive, anxiety, and insomnia, respectively. “Distress caused by the sleep difficulties” had the highest expected influence, followed by “Excessive worry”, “Severity of sleep onset”, “Overburden”, “Uncontrollable worry”, “Trouble relaxing”, “Restlessness”. Seven bridge symptoms were identified: “Sad Mood”, “Severity of sleep onset”, “Sleep”, “Motor”, “Feeling afraid”, “Nervousness”, “Appetite”. In addition, significant differences in network global strength were found between women and men.

Conclusions: Psychological distress was prevalent among COVID-19 patients and insomnia symptoms played a key role in the network of psychological distress symptoms. As a result, specific insomnia symptoms should be closely monitored as potential intervention targets in alleviating or preventing common, frequently co-occurring psychiatric syndromes.

COVID-19

insomnia

stress

anxiety

depression

network analysis

Before 2020, mental disorders such as depression and anxiety were already the main causes of health-related burden worldwide. The emergence of the COVID-19 pandemic, caused by the severe acute respiratory syndrome coronavirus (SARS-COV), has greatly affected the mental health and wellbeing of people around the world. The COVID-19 pandemic had increased the mental stress of people worldwide and the global prevalence of anxiety and depression increased substantially by more than 25% in the first year of the pandemic, according to a report released by the World Health Organization in 2022 [1]. Loneliness, fear of infection, pain and death for oneself and loved ones, and financial worries are all stressors that lead to anxiety and depression.

Although vaccines have been deployed on a massive scale, a large number of people are still contracting the virus. According to the latest WHO real-time statistics, as of December 23, 2022 Central European Time (CEST), there were 651,918,402 COVID-19 cases and 6,656,601 deaths worldwide(https://www.who.int/data). China has confirmed 10,167,676 COVID-19 cases and 31,585 deaths(https://www.who.int/countries/chn/). There are several surveys that have found symptoms of anxiety (including post-traumatic stress disorder), depression and insomnia in COVID-19 patients [2–5]. Coronavirus infections could cause delirium, poor memory, anxiety, depression, manic symptoms, and insomnia, according to a meta-analysis that estimated the incidence of psychiatric disorders following outbreaks of the severe acute respiratory syndrome (SARS) and Middle East Respiratory Syndrome [6]. In a retrospective cohort study of 62354 COVID-19 cases in The Lancet Psychiatry found that a diagnosis of COVID-19 was associated with an increased incidence of a first psychiatric diagnosis in the subsequent 14 to 90 days [7]. A study on psychological distress in quarantined patients with COVID-19 indicated that these patients experienced significant psychological distress, so it is recommended that necessary measures should be taken to address depression and other psychiatric symptoms for COVID-19 patients when implementing psychological interventions [8]. Previous studies of similar viral respiratory illnesses have shown that infected patients continue to have varying degrees of mental health problems even after being discharged from hospital, suggesting that the mental states of these people should not be ignored [9, 10].

There are also gender differences in mental diseases, which may be due to sex steroid hormones and genetics [11]. A study on the mental health related factors of inpatients with COVID-19 in Wuhan found that the majority of patients experiencing mental health disturbances. It also found that gender has a direct and moderate impact on mental health, and female sex is a factor that could be used to predict the severity of patients’ mental symptoms [12]. Additionally, a report released by the World Health Organization shows that women's mental health has been more severely affected by the COVID-19 pandemic than men's [1]. Moreover, previous studies demonstrated that females were more likely to suffer from mood disorders, such as depression, anxiety and post-traumatic stress disorder [13, 14]. These studies indicate that the gender differences in mental health of patients with COVID-19 should be considered.

In recent years, the network model of psychopathology has been proposed as another way of conceptualizing mental disorders. Network analysis(NA) could provide insights related to the interaction between psychopathological symptoms and mental health and therefore has a particular advantage in psychiatric research [15]. Two important elements of NA are "nodes" and "edges". Each node represents a symptom and each edge represents the partial correlation coefficient between two nodes [16]. The goal of this approach is to identify the central node which is highly connected to other nodes and emphasize its potential clinical implications. In addition, the new approach provide a new perspective in understanding psychiatric comorbidities [17]. Symptoms that increase the risk of transmission from one disease to other disorders are considered as bridge symptoms and can be identified in NA [17]. Therefore, clinicians can target bridge symptoms to treat or prevent comorbidities. Currently, there have been network studies that have conducted in healthy people during the pandemic. For example, NA of depression, anxiety, insomnia and quality of life among Macau residents during the COVID-19 pandemic [18]. However, no network studies have been conducted to explore psychiatric symptoms in patients with COVID-19.

To bridge these gaps, the current study is the first to apply NA to investigate how symptoms of stress, anxiety, depression, and insomnia relate to each other in COVID-19 patients, more importantly, to identify the central and bridge symptoms that may regulate other symptoms. Further, based on previous studies, we compare the network model of depression, anxiety and insomnia between healthy people and COVID-19 patients.

Participants

This study used an online survey hosted on the Wenjuanwang platform (www.wenjuan.con). The participants were from COVID-19 patients in Shanghai Square Cabin Hospitals from April 18 to May 19, 2022. All the participants gave their electronic informed consent after understanding the research content and the rights and obligations of the researcher and the participants. A total of 1116 questionnaires were collected, 102 invalid questionnaires were excluded. So the effective utilization rate was 90.86%. Exclusion criteria: (1) the time used to complete the survey was < 100s [19]; (2) select the same option for all items; (3) a total score of more than three standard deviations[20].

Measures

Perceived Stress Scale questionnaire (PSS-10)

The perceived stress symptoms were assessed with the Perceived Stress Scale (PSS-10) questionnaire [21]. The PSS-10 includes ten items, each rated on a 5-point scale from 0 (never) to 4 (very common). The scale was divided into two dimensions: "negative items" (6 items, namely the 1st, 2nd, 3rd, 6th, 9th and 10th items) and "positive items" (4 items, namely the 4th, 5th, 7th and 8th items). Higher scores indicate higher levels of perceived stress.

Patient Health Questionnaire (PHQ-9)

The nine-item Patient Health Questionnaire (PHQ-9), based on the nine Diagnostic and Statistical Manual of Mental Disorders-IV (DSM-IV) criteria, was used to assess the frequency of depressive symptoms over the past 2 weeks [22, 23]. The score of each item varies from 0 (not at all) to 3 (nearly every day). The sum of scores ranges from 0 to 27, and the higher total PHQ-9 scores indicate more severe depressive symptoms.

Generalized Anxiety Disorder 7-Item Questionnaire (GAD-7)

The Generalized Anxiety Disorder 7-Item Questionnaire (GAD-7) was used to measure the severity of symptoms of generalized anxiety disorder (GAD) over the last 2 weeks [24, 25]. GAD-7 items are scored on a scale of 0 to 3, representing “none at all”, “several days”, “more than half the days”, and "almost every day", respectively. The sum of scores ranges from 0 to 21, and the higher r GAD-7 scores indicate more severe anxiety symptoms.

Insomnia Severity Index questionnaire (ISI)

Insomnia symptoms were assessed with the Insomnia Severity Index (ISI) questionnaire [26]. The ISI items were assessed on a 5-point scale from 0 to 4, with higher scores indicating more severe insomnia symptoms.

Network analysis

Network estimation

The network was estimated via Gaussian graphical model, which is an undirected network [27]. In the network model, each symptom is regarded as a node, and the partial correlation between two nodes is regarded as an edge. The regularization of the Gaussian graphical model was conducted via the Least absolute shrinkage and selection operator (LASSO) and extended Bayesian information criteria (EBIC) [28]. In this process, the edge with small partial correlation was set to zero, thus edges were shrunk and the symptom network was sparser and easier to interpret. Meanwhile, setting the tuning parameter to 0.5 well balances the sensitivity and specificity of extracting true edges [29, 30]. The Fruchterman-Reingold algorithm was used to visualize the network. Nodes with more and stronger connections appear near the center of the network, while nodes with fewer and weaker connections wander around the periphery of the network [31]. In the visualization network, blue edges represent positive correlations and red edges represent negative correlations. A thicker edge means a stronger correlation between two adjacent nodes. Construct and visualize the network using R-Package qgraph [32].

Centrality and stability

The expected influence of each node was calculated using R-package qgraph [32]. This indicator is more suitable for networks with both positive and negative edges than traditional centrality index (e.g., node centrality) [33]. Having a higher expected influence means the node is more important. In addition, bridge expected influence was calculated by R-package networktools [34]. The higher bridge expected influence, the greater the increased risk of contagion to other communities [35]. In the current network, nodes were previously divided into four communities: stress symptoms, depression symptoms, anxiety symptoms and insomnia symptoms. To identify bridge symptoms, we applied a 80 percentile truncation method of bridge expected influence values [18, 36]. Moreover, the R package mgm was used to calculate the predictability of each node, which is an indicator reflecting the controllability of the network model [37]. When the predictability of a node is high, we can control it by its neighboring nodes; When a node is less predictable, we can intervene directly on its own or look for other variables outside the network to control it.

The accuracy and robustness of network was assessed through conducting the R-package bootnet based on 1000 bootstraps [38]. The correlation stability (CS) coefficient was used to evaluate the centrality indexes of expected influence and bridge expected influence. A value greater than 0.25 indicates moderate stability, and a value greater than 0.5 indicates strong stability.

Network comparison

To test for differences in global connectivity and local connectivity between participating men and women, we used the Network Comparison Test (NCT) [39]. The NCT was repeatedly counted (1000 times) using R package NetworkComparisonTest, and gave us the distribution of values under our null hypothesis (for example, assuming equality between the two groups), enabling one to test whether differ significantly between the two groups [40].

A total of 1014 patients in the shelter hospital in Shanghai (420 women, 594 men) was included in this study. They were all tested positive for COVID-19 and received corresponding treatment. Regarding other demographics, 197 (19.4%) received higher education (i.e., undergraduate/college or above), 628 were currently employed (61.9%). The full range of symptom severity was reflected by patients’ PSS-10, PHQ-9, GAD-7 and ISI-7 scores reflects. Depressive (PHQ-9 ≥ 5), anxiety (GAD-7 ≥ 5) and insomnia symptoms (ISI ≥ 8) were 61.5%, 49.2%, and 56.1% respectively among all the patients. The descriptive statistics of each variable are shown in Table 1.

Table 1

Descriptive statistics of measurement items.
Item abbreviation	Item mean ± SD	Expected influence^a	Bridge expected influence^a	Predictability
PSS1	2.352 ± 1.188	0.961	0.195	0.491
PSS2	2.329 ± 1.229	1.025	0.172	0.473
PSS3	2.210 ± 1.206	1.029	0.239	0.549
PSS4	2.829 ± 1.316	0.908	0.079	0.466
PSS5	2.580 ± 1.208	0.902	0.047	0.463
PSS6	2.331 ± 1.170	0.912	0.175	0.773
PSS7	2.922 ± 1.351	0.890	0.164	0.886
PSS8	2.647 ± 1.249	0.874	0.006	0.575
PSS9	2.333 ± 1.186	0.999	0.170	0.462
PSS10	2.243 ± 1.220	1.059	0.283	0.475
PHQ1	1.873 ± 0.853	0.969	0.321	0.684
PHQ2	1.770 ± 0.832	0.946	0.420	0.606
PHQ3	2.055 ± 0.985	0.802	0.380	0.660
PHQ4	1.914 ± 0.891	1.041	0.290	0.616
PHQ5	1.767 ± 0.831	0.841	0.325	0.747
PHQ6	1.728 ± 0.866	0.945	0.314	0.499
PHQ7	1.714 ± 0.886	0.904	0.253	0.474
PHQ8	1.571 ± 0.779	0.904	0.347	0.471
PHQ9	1.314 ± 0.658	0.511	0.264	0.462
GAD1	1.709 ± 0.850	1.046	0.327	0.586
GAD2	1.657 ± 0.849	1.059	0.275	0.564
GAD3	1.774 ± 0.878	1.065	0.268	0.554
GAD4	1.735 ± 0.892	1.059	0.198	0.692
GAD5	1.599 ± 0.805	1.057	0.314	0.711
GAD6	1.730 ± 0.873	1.027	0.237	0.661
GAD7	1.608 ± 0.823	0.917	0.331	0.716
ISI1	2.075 ± 1.254	1.062	0.386	0.677
ISI2	2.044 ± 1.239	0.990	0.166	0.590
ISI3	2.402 ± 1.316	0.631	0.200	0.560
ISI4	2.879 ± 1.370	0.209	0.294	0.601
ISI5	2.244 ± 1.224	0.855	0.194	0.569
ISI6	2.115 ± 1.171	1.155	0.122	0.606
ISI7	1.987 ± 1.155	1.027	0.320	0.604
PSS: Perceived Stress Scale-10; ISI: Insomnia Severity Index; PHQ-9: the 9-item Patient Health Questionnaire; GAD-7: 7-item Generalized Anxiety Disorder Scale; SD, standard deviation.
^a The values of expected influence and bridge expected influence were raw data generated from the network.

Network structure

The final network is shown in Fig. 1. The network uses each questionnaire item as a node to represent a symptom, and the connection between two nodes represents the relationship between two symptoms. Several characteristics were immediately obvious. First, 254 of the 528 possible edges (48.1%) were not zero. These all reflect the interrelation between variables. Second, the 10 strongest edges were all distributed in the communities of each mental state, including 5 edges of PSS (The regularized partial correlations between PSS9 and PSS10, between PPS7 and PSS8, between PSS5 and PSS8, between PSS1 and PSS3, between PSS4 and PSS5 were 0.314, 0.313, 0.300, 0.286, 0.269, respectively), 3 edges of ISI (The regularized partial correlations between ISI1 and ISI2, between ISI5 and ISI6, between ISI6 and ISI7 were 0.532, 0.453, 0.435, respectively), 1 edge of PHQ (The regularized partial correlations between PHQ1 and PHQ2 were 0.306), and 1 edge of GAD (The regularized partial correlations between GAD2 and GAD3 were 0.288). Third, the predictability of each node represented the percentage of the variance in a node explained by all its neighboring nodes. These node predictability values ranged from 46.2% (PHQ9) to 88.6% (PSS7) with an average of 59.2% (Fig. 1 and Table 1).

Expected influences values were calculated to assess their importance in the network and they were shown on the right panel of Fig. 1 and Table 1. Nodes ISI6 “Distress caused by sleep difficulties”, ISI1 “Severity of sleep onset”, GAD3 “Excessive worry”, PSS10 “Overburden”, GAD2 “Uncontrollable worry”, GAD4 “Trouble relaxing”, GAD5 “Restlessness” had the highest expected influences value. This result indicated that they were the most important nodes in the present network. Bridge expected influences values represents the risk of contagion from the current community to other communities. Regarding bridge symptoms, PHQ2 “Sad Mood”, ISI1 “Severity of sleep onset”, PHQ3 “Sleep”, PHQ8 “Motor”, GAD7 “Feeling afraid”, GAD1 “Nervousness”, and PHQ5 “Appetite” had the highest bridge expected influence values (Fig. 2).

Network stability

The result of bootstrapped stability test for node expected influences and bridge expected influences were shown in Fig. 3. Both CS-coefficients equaled 0.75, indicating that the estimation of expected influences and bridge expected influences were adequately stable.

Network comparisons

The network structures generated from the two genders are shown in Fig. 4. Significant differences in network global strength were found between women and men (Women: 10.903 vs Men: 16.651; S = 0.845, p = 0.012) (Fig. 5), or the network structure distribution of edge weights (M = 0.185, p = 0.489) (Fig. S1). In the male network, ISI6, GAD6 and ISI2 were the most central symptoms. In the female network, GAD5, GAD1 and ISI6 were the most central symptoms.

In order to gain insight into the differences in anxiety-depression-insomnia network between ordinary residents and confirmed patients in the context of COVID-19, we specifically compared the network structure of this study with that of Bai et al [18]. As seen Fig. 6, the highest expected influences value of the nodes in the anxiety-depression- insomnia network of the patients in the Square Cabin Hospitals in Shanghai were ISI6, GAD4 and GAD5. The highest bridge expected influences value of nodes were ISI1, ISI7, GAD1, GAD7 and PHQ8. These results are not consistent with the network results of Macao residents. We also compared the network structure of male and female, and found that there was no difference between genders, which is consistent with the study of Bai et al. (Fig. S2)

To the best of our knowledge, this is the first study to use network analysis to investigate the relationships between PHQ, GAD, ISI and PSS in the COVID-19 patient population. Based on this approach, this study developed a complex network consisting of four communities: the symptoms of depressive, anxiety, insomnia and perceived stress.

The participants of this study were confirmed COVID-19 patients from Shanghai Square Cabin Hospitals and their mental health status was investigated. Our analysis showed that the stronger edges were distributed within their respective symptom communities. It was consistent with previous studies [18, 36, 41, 42]. We found the strongest edge in the present network between ISI1 “Severity of sleep onset” and ISI2 “Sleep maintenance”. The results are controversial. Some scholars studied mental health in Chinese college students and residents of Macau under the COVID-19 pandemic, which supports our results [18, 43]. Other studies showed that the strongest edge appeared between ISI2 “Sleep maintenance” and ISI3 “Early morning wakening problems” in Hollander [44]. The inconsistent results may be due to the differences in research background, focus, and culture [18]. In the present network, the three strongest edges were all found between the ISI items, which indicated that sleep was the most prominent psychological problem among patients. After investigation, we found that patients' sleep problems are related to a variety of reasons. First of all, both the bad emotional experience and the unhealthy mental state could cause sleep problems. Secondly, the physical unhealthy symptoms also affected the patients' sleep. For example, poor sleep quality in COVID-19 patients is associated with slower recovery from lymphocytopenia and a longer hospital stay [45]. Finally, the environment of Square Cabin Hospitals was unconducive to sleep, such as prolonged lighting, noise, and irregular rounds. Five of the top ten edges were distributed in PSS items, which indicated that the patient was still under great pressure during the treatment process. And the node with highest predictability value was PSS7 “Control emotions”, this means that 88.6% of the explanation of PSS7 can be explained from within the network. The node with lowest predictability value was PHQ9 “Suicide”, this means that 46.2% of the explanation of PHQ9 can be explained from within the network.

Expected influences of nodes performed the importance of this node in the network. In the present network, ISI6 “Distress caused by the sleep difficulties”, ISI1 “Severity of sleep onset” and GAD3 “Excessive worry” were the top three nodes with the highest expected influences. So they are the three most important nodes in the present network, and we can alleviate other symptoms in the present network by improving sleep quality and reducing excessive worry [18, 41]. While patients were under medical treatment, they were separated from their families. Middle-aged patients often concerned about the elderly and young children. Other patients worried about the outcome of their treatment. The COVID-19 pandemic has indeed made a negative impact on the psychological state of people all over the world [46], and almost all mentioned changes in depression and anxiety state [47, 48]. Therefore, alleviating the patient's anxiety level also helps to alleviate other symptoms in the network. We also found central symptoms of psychological problems in the present network. The central symptom of stress problems was PSS10 “Overburden”. Reducing the amount of affairs piling up can relieve the perception of stress. Researches have shown that the perception of stress is directly related to the individual's sleep quality, anxiety, depression and other symptoms [49–51]. The central symptom of depressive problems is PHQ4 “Fatigue”, which means that providing some energetic activity or alleviating fatigue may help patients reducing depressive symptoms.

Bridge expected influences of nodes performed transmissibility between each nodes. The bridge nodes can be determined based on the value of the bridge expected influences of each node. So we identified seven bridge nodes, which were classified as depression, anxiety, and insomnia. PHQ2 “Sad Mood” was the top bridge node. This may due to more distress in patients, and activation of depression is more likely to cause other symptoms to co-occur. ISI1 “Severity of sleep onset” was the second bridge node, which indicated that difficulty falling asleep could indeed affect other symptoms in the present network. Logistic regression studies have shown that sleep problems are highly associated with poor mental health, particularly high anxiety [52]. PHQ3 “Sleep” was the third bridge symptom, which was highly correlated with insomnia symptoms. Expected influences and bridge expected influences provides a target for reducing the complex symptoms of stress, depression, anxiety and insomnia in COVID-19 patients. Based on the core symptoms and bridge symptoms, we believe that improving sleep and reducing worry and depression can effectively regulate other symptoms in the network.

We found that women were more susceptible to insomnia than men, which was consistent with the results of previous studies [52, 53]. Women had significantly higher levels of perceived stress, depression, anxiety and insomnia than men. Therefore, women and men had different network structures. In the male network, ISI6 “Distress caused by the sleep difficulties”, GAD6 “Irritability” and ISI2 “Sleep maintenance” were the most central symptoms influence. It may be that men suffer more from insomnia and are prone to mood of anger, which may suggest that improving men's sleep quality and controlling their emotions can reduce other symptoms in the network. In the female network, GAD5 “Restlessness”, GAD1 “Nervousness” and ISI6 “Distress caused by the sleep difficulties” were the most central symptoms influence. Women are emotionally sensitive and easily interference with all sorts of events. Reducing the amount of interference women experienced can alleviate other symptoms in the network. A common symptom in both sexes was distress caused by insomnia, with men more likely to be angry and women more likely to be jittery.

We replicated the work of Bai et al., and found that the same network (insomnia- anxiety-depressive network) was structured differently in different populations [18]. In their study, the core symptoms of the network were ISI2 “Sleep maintenance”, GAD4 “Trouble relaxing”, ISI7 “Interference with daytime functioning”. And what we found was that ISI6 “Distress caused by the sleep difficulties”, GAD4 “Trouble relaxing”, GAD5 “Restlessness” were the core symptoms of the network. Although Macao residents in Bai's study were under the shadow of COVID-19, their lives and work were not completely restricted. They had anxious, trouble relaxing, insomnia and had their lives and work affected because of the fear of being infected with COVID-19. COVID-19 patients in Shanghai Square Cabin Hospitals had their movement restricted in the hospital and were medical treated in a completely different environment from their lives before. They worried about the outcome of their treatment, their separated relatives, and insomnia, etc.. So they became restlessness, trouble relaxing and had the pain caused by insomnia. By comparing the bridge symptoms of the two networks, the bridge symptoms of previous studies were PHQ3 “Sleep”, GAD5 “Restlessness”, GAD6 “Irritability”, ISI1 “Severity of sleep onset”, PHQ8 “Motor” [18]. Our research shows that ISI1“Severity of sleep onset”, ISI7 “Interference with daytime functioning”, GAD1 “Nervousness”, GAD7 “Felling afraid” and PHQ8 “Motor” were the bridge symptoms. The bridge symptoms of COVID-19 patients and Macao residents are not exactly the same. The bridging symptoms of the former are mainly anxiety and insomnia, while the latter are mainly depression and insomnia, which means that the particular symptoms most critical to reduce the risk of contagion between psychiatric syndromes within the network models are not exactly the same.

Psychological distress was prevalent among COVID-19 patients. Insomnia is the most important and central symptom of hospitalized COVID-19 patients, which indicates that regulating insomnia may relieve other symptoms. The structure of the stress- depression-anxiety-insomnia network is different between men and women. Other symptoms in the stress-depression-anxiety-insomnia network [53] of patients can be alleviated by intervention of sleep quality, especially by controlling anger in men and by changing susceptibility in women. The structure of the insomnia-anxiety-depressive network is different between Macau residents during the COVID-19 pandemic and confirmed COVID-19 patients in Shanghai Square Cabin Hospitals. Other symptoms in the insomnia-anxiety-depressive network can be alleviated by intervention of sleep quality and remission of anxiety symptoms, especially by relieving nervousness in Macau residents during the COVID-19 pandemic and eliminating fear in confirmed COVID-19 patients in Shanghai Square Cabin Hospitals.

Acknowledgements

Not applicable.

Authors’ contributions

Tian Zhang: Formal analysis, Visualization, Writing-original draft. Yijun Li: Visualization, Writing-original draft. Jingjing Sun: Data curation. Jing Yu: Data curation. Wei He: Writing-review & editing. Lin Wu: Writing-review & editing. Xinxin Lin: Writing-review & editing. Lingling Wang: Formal analysis. Xiyuan Peng: Data curation. Li Mao: Data curation. Jiaxi Peng: Data curation, Writing-review & editing. Peng Fang: Rources, Supervision, Writing-review & editing.

Approval of the final version for publication: All the authors.

Funding

The study was supported by the Scientific and Technological Research Projects in Aviation Medicine (2020ZTD01, 2020ZTE03), Air Force Medical University “Everest project” (2020cyjhfp), National Science and Technology Foundation Strengthening Plan 173 Key Fund Project (2022-JCJQ-JJ-0856).

Availability of data and materials

The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.

Ethics approval and consent to participate

Ethical approval to conduct the study was obtained from the clinical trial ethics committee of Xijing Hospital of the Air Force Medical University (protocol number KY20212203-F-1). This research was carried out in accordance with the Declaration of Helsinki. All the participants gave their electronic informed consent after understanding the research content and the rights and obligations of the researcher and the participants.

Consent for publication

Not applicable.

Competing interests

None declared.

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Insomnia plays a key role in mental health problems of COVID-19 patients in Square Cabin Hospitals: a network analysis

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Abstract

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Background

Methods

Participants

Measures

Perceived Stress Scale questionnaire (PSS-10)

Patient Health Questionnaire (PHQ-9)

Generalized Anxiety Disorder 7-Item Questionnaire (GAD-7)

Insomnia Severity Index questionnaire (ISI)

Network analysis

Network estimation

Centrality and stability

Network comparison

Results

Network structure

Network stability

Network comparisons

Discussion

Conclusions

Declarations

References

Additional Declarations

Supplementary Files

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