Network analysis of comorbid insomnia and depressive symptoms among psychiatric practitioners during the COVID-19 pandemic
2023; American Academy of Sleep Medicine; Volume: 19; Issue: 7 Linguagem: Inglês
10.5664/jcsm.10586
ISSN1550-9397
AutoresNa Zhao, Yan-Jie Zhao, Feng‐Rong An, Qinge Zhang, Sha Sha, Zhaohui Su, Teris Cheung, Todd Jackson, Yu‐Feng Zang, Yu‐Tao Xiang,
Tópico(s)Functional Brain Connectivity Studies
ResumoFree AccessScientific InvestigationsNetwork analysis of comorbid insomnia and depressive symptoms among psychiatric practitioners during the COVID-19 pandemic Na Zhao, PhD, Yan-Jie Zhao, PhD, Fengrong An, MSc, Qinge Zhang, MD, Sha Sha, MD, Zhaohui Su, PhD, Teris Cheung, PhD, Todd Jackson, PhD, Yu-Feng Zang, PhD, Yu-Tao Xiang, MD, PhD Na Zhao, PhD Unit of Psychiatry, Department of Public Health and Medicinal Administration, and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao Special Administrative Region (SAR), China Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China , Yan-Jie Zhao, PhD Unit of Psychiatry, Department of Public Health and Medicinal Administration, and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao Special Administrative Region (SAR), China Center for Cognition and Brain Sciences, University of Macau, Macao SAR, China Institute of Advanced Studies in Humanities and Social Sciences, University of Macau, Macao SAR, China , Fengrong An, MSc Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University & Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China , Qinge Zhang, MD Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University & Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China , Sha Sha, MD Beijing Key Laboratory of Mental Disorders, National Clinical Research Center for Mental Disorders, National Center for Mental Disorders, Beijing Anding Hospital, Capital Medical University & Advanced Innovation Center for Human Brain Protection, Capital Medical University, Beijing, China , Zhaohui Su, PhD School of Public Health, Southeast University, Nanjing, China , Teris Cheung, PhD School of Nursing, Hong Kong Polytechnic University, Hong Kong SAR, China , Todd Jackson, PhD Department of Psychology, University of Macau, Macao SAR, China , Yu-Feng Zang, PhD Address correspondence to: Dr. Yu-Tao Xiang, MD, PhD, 1/F, Building E12, Faculty of Health Sciences, University of Macau, Avenida da Universidade, Taipa, Macau SAR, China; Tel: +853-8822-4223; Fax: +853-2288-2314; Email: E-mail Address: [email protected]; and Dr. Yu-Feng Zang, PhD, Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China; Email: E-mail Address: [email protected] Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China , Yu-Tao Xiang, MD, PhD Address correspondence to: Dr. Yu-Tao Xiang, MD, PhD, 1/F, Building E12, Faculty of Health Sciences, University of Macau, Avenida da Universidade, Taipa, Macau SAR, China; Tel: +853-8822-4223; Fax: +853-2288-2314; Email: E-mail Address: [email protected]; and Dr. Yu-Feng Zang, PhD, Center for Cognition and Brain Disorders, Institutes of Psychological Sciences, Hangzhou Normal University, Hangzhou, China; Email: E-mail Address: [email protected] Unit of Psychiatry, Department of Public Health and Medicinal Administration, and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao Special Administrative Region (SAR), China Center for Cognition and Brain Sciences, University of Macau, Macao SAR, China Institute of Advanced Studies in Humanities and Social Sciences, University of Macau, Macao SAR, China Published Online:July 1, 2023https://doi.org/10.5664/jcsm.10586SectionsAbstractEpubPDFSupplemental Material ShareShare onFacebookTwitterLinkedInRedditEmail ToolsAdd to favoritesDownload CitationsTrack Citations AboutABSTRACTStudy Objectives:Insomnia and depression are common mental health problems reported by mental health professionals during the COVID-19 pandemic. Network analysis is a fine-grained approach used to examine associations between psychiatric syndromes at a symptom level. This study was designed to elucidate central symptoms and bridge symptoms of a depression-insomnia network among psychiatric practitioners in China. The identification of particularly important symptoms via network analysis provides an empirical foundation for targeting specific symptoms when developing treatments for comorbid insomnia and depression within this population.Methods:A total of 10,516 psychiatric practitioners were included in this study. The Insomnia Severity Index (ISI) and 9-item Patient Health Questionnaire (PHQ-9) were used to estimate prevalence rates of insomnia and depressive symptoms, respectively. Analyses also generated a network model of insomnia and depression symptoms in the sample.Results:Prevalence rates of insomnia (ISI total score ≥8), depression (PHQ-9 total score ≥5) and comorbid insomnia and depression were 22.2% (95% confidence interval: 21.4–22.9%), 28.5% (95% confidence interval: 27.6–29.4%), and 16.0% (95% confidence interval: 15.3–16.7%), respectively. Network analysis revealed that "Distress caused by sleep difficulties" (ISI7) and "Sleep maintenance" (ISI2) had the highest strength centrality, followed by "Motor dysfunction" (PHQ8) and "Sad mood" (PHQ2). Furthermore, the nodes "Sleep dissatisfaction" (ISI4), "Fatigue" (PHQ4), and "Motor dysfunction" (PHQ8) had the highest bridge strengths in linking depression and insomnia communities.Conclusions:Both central and bridge symptoms (ie, Distress caused by sleep difficulties, Sleep maintenance, Motor dysfunction, Sad mood, Sleep dissatisfaction, and Fatigue) should be prioritized when testing preventive measures and specific treatments to address comorbid insomnia and depression among psychiatric practitioners during the COVID-19 pandemic.Citation:Zhao N, Zhao Y-J, An F, et al. Network analysis of comorbid insomnia and depressive symptoms among psychiatric practitioners during the COVID-19 pandemic. J Clin Sleep Med. 2023;19(7):1271–1279.BRIEF SUMMARYCurrent Knowledge/Study Rationale: This study explored the prevalence and network structure of insomnia and depression among psychiatric practitioners during the COVID-19 pandemic. Analyses were designed to identify central and bridge symptoms that could be targeted when developing future treatments designed to reduce disorder severity and the comorbidity of depression and insomnia in this population.Study Impact: Network analysis revealed that "Distress caused by sleep difficulties" (ISI7) and "Sleep maintenance" (ISI2) had the highest strength centrality, while "Sleep dissatisfaction" (ISI4), "Fatigue" (PHQ4), and "Motor dysfunction" (PHQ8) had the highest bridge strengths in the network of depression and insomnia symptoms. Central and bridge symptoms should be prioritized within treatments designed to prevent and reduce comorbid insomnia and depression among psychiatric practitioners during the COVID-19 pandemic.INTRODUCTIONSince early 2020, the coronavirus disease 2019 (COVID-19) has been reported in more than 200 countries and territories as a pandemic, causing substantial personal distress, impaired functioning, and economic loss to many subpopulations.1–4 In combatting the COVID-19 pandemic, health care workers were confronted with excessive workloads and limited time to recover, high risk for personal infections, insufficient protective equipment, widespread distress and loss of life among patients.5–8 Together, these stressors increased the vulnerability of health care professionals to mental health problems.9–12 Recent meta-analyses have revealed high rates of common mental health problems, such as insomnia (38.9%)9 and depression (31.0%),11 in health care workers during the COVID-19 pandemic compared with rates in the general population (insomnia: 32.3%; depression: 26.0%).11,13Among health care workers, psychiatric practitioners are susceptible to experiencing mental health problems.14–17 Apart from overwhelming workloads in daily practice, psychiatric practitioners are required to provide timely support for frontline health care workers in other specialties at high personal risk for mental health problems.18,19 In addition, because psychiatric patients have higher infection risk than patients with physical illnesses due to their limited ability to protect themselves, psychiatric practitioners require additional time and energy to take care of their patients.15,17 Consequently, many psychiatric practitioners may experience depression and/or insomnia, either separately or in tandem.15 Compared with experiencing insomnia or depression as stand-alone diagnoses, comorbid insomnia and depression are associated with more severe negative health outcomes, including severe chronic pain,20,21 irritable bowel syndrome,22 and increased suicide risk.23,24Therefore, to reduce the risk and consequences of comorbid insomnia and depression, it is important to elucidate particular factors that may contribute to the risk of comorbidity. Although several previous studies25–28 focused on comorbid insomnia and depression, a potential limitation of their analysis approaches was their reliance on total scores of standard scales as a means of examining comorbid insomnia and depression. A possible drawback of such strategies is their untested, underlying assumption that all individual symptoms of insomnia and depression have an equal weight in their contributions to each of these syndromes or to comorbidity.As a novel approach, network analysis can provide novel insights into the co-occurrence of psychiatric syndromes at the level of individual symptoms rather than average or total scores on measures of symptomatology.29 For example, network analysis can illustrate insomnia and depressive symptoms as an interacting network structure and identify particular symptoms that appear to make the strongest contributions to each syndrome and its comorbidity. Specifically, according to network analysis theory, specific symptoms of a psychiatric syndrome can trigger or attenuate symptoms of other psychiatric syndromes.30–33 Instead of describing psychiatric disorders as the result of a common latent factor structure, network modeling illustrates psychiatric syndromes as interchangeable or reciprocal results of various symptoms.34,35 Additionally, network analysis provides an empirical approach for generating hypotheses regarding the most central or influential symptoms contributing to the onset and maintenance of a particular psychiatric syndrome as well as specific bridge symptoms that are critical to the risk for and perpetuation of comorbid psychiatric syndromes.29,36,37 As such, network analysis may help tailor treatments in targeting central symptoms and/or bridge symptoms to attenuate overall disorder severity31 and/or prevent the onset of co-occurring syndromes.To date, no epidemiological studies have been published on the prevalence of comorbid insomnia and depressive symptoms (insomnia and depression hereafter) among psychiatric practitioners working in the COVID-19 pandemic nor has network analysis been used to identify particular symptoms critical to these syndromes or their comorbidity within this population. To address these gaps in the literature, we examined the prevalence and network structure of comorbid insomnia and depression in a large sample of psychiatric practitioners at work during the COVID-19 pandemic.METHODSParticipantsThis cross-sectional online survey was conducted in a large sample of psychiatric practitioners in China between March 15 and 20, 2020. Data were collected using the WeChat-embedded Questionnaire Star program (WeChat: Tencent Holdings Limited, Shenzhen, China; Questionnaire Star Program: Changsha Ranxing Information Technology Co., Ltd., Changsha, China) based on a snowballing sampling method. WeChat is a widely used social communication software program that was used to support weekly health status reports in all China-based public psychiatric hospitals during the COVID-19 pandemic. Therefore, presumably, all psychiatric practitioners in China are WeChat users. Inclusion in the study sample was based on the following criteria: (1) aged 18 years or older, (2) employment as psychiatric practitioners (eg, nurses, nursing assistants, and psychiatrists) working in public psychiatric hospitals or units during the COVID-19 pandemic, (3) the ability to understand Chinese and the contents of the survey, and (4) not infected with COVID-19 at the time of the survey. Electronic written informed consent was signed by all participants, and the study protocol was approved by the Institutional Review Board of Beijing Anding Hospital in China.Data collectionInsomnia was assessed with the validated Chinese version of the Insomnia Severity Index (ISI) questionnaire.38,39 The ISI consists of 7 items investigating different domains of insomnia, including the following: Difficulty falling asleep, Sleep maintenance, Waking up early, Sleep dissatisfaction, Interference of sleep problems with daily life, Noticeability of sleep problems by others, and Distress caused by sleep problems. Each item was scored from 0 (no problem) to 4 (very severe problem). Total ISI scores range from 0 to 28, with higher scores indicating more severe insomnia. Total ISI scores ≥8 are indicative of insomnia.40Depression was measured using the validated Chinese version of the 9-item Patient Health Questionnaire (PHQ-9).41 There are 9 items in the PHQ-9, each of which is scored from 0 (not at all) to 3 (almost every day). Total PHQ-9 scores range from 0 to 27, and scores ≥5 are considered to reflect "having depression".41 PHQ-9 items assess anhedonia, depressed mood, sleep disturbances, fatigue, appetite problems, guilt, concentration problems, motor dysfunction, and suicidal ideation.42 Both the 7-item ISI and PHQ-9 have good psychometric properties in Chinese populations.43–45Network estimationAll analyses were conducted using R program (version: 4.1.1; The R Foundation for Statistical Computing, Vienna, Austria). Initially, descriptive statistics, including means and standard deviations (SDs) of all ISI and PHQ-9 item scores were calculated. Following previous studies,33,46 we estimated the informativeness of each symptom (ie, SDs of symptoms) using the describe function in the R package.47 The goldbricker function in R package networktools48 was also used to calculate possible item redundancy (ie, <25% of the significant different correlations) as recommended previously.46,48 In this research, the "Sleep disturbances" item of the PHQ-9 (PHQ3) was excluded from analyses because of its content overlap with insomnia symptoms assessed by the ISI.The insomnia and depression network structure was estimated using estimatenetwork function in the R package bootnet.49 In the network model, insomnia and depressive symptoms were defined as "nodes," whereas connections between each pair of symptoms were defined as "edges." Network visualization was performed using the R package qgraph.50 Thicker edges reflected stronger associations between each pair of symptoms. Particular colors indicated different directions in the nature of associations between symptoms: green and red colors were used to denote positive and negative correlations, respectively.50 Following a previous study,49 the Extended Bayesian Information Criterion (EBIC) graphical least absolute shrinkage and selection operator (LASSO; EBICglasso) model was adopted to minimize spurious associations due to sampling error. Consequently, the model could shrink weak connections to zero51 and reduce spurious connections, simplifying the final model and making it easier to interpret.52Centrality and predictabilityTo identify the most central (influential) symptoms in the insomnia and depression network model, strength centrality was estimated. Strength centrality is an index (the sum of absolute edge weights of a specific node) that reflects how strongly a node is connected with other nodes.31 This analysis was performed using R package bootnet49 and qgraph.50 Furthermore, the predictability index of each node (ie, the possibility that a given node can be accounted for by its neighboring nodes) was estimated53–55 based on the mgm network model in the R package.53,54Bridge strengths in network models are used to identify symptoms that connect 2 or more different disorders (eg, insomnia and depression communities). Hence, bridge strength for each node was also estimated29,36 using networktools in the R package.Network accuracy and stabilityTo assess the robustness of the estimated network, the accuracy of edge weights and node strength stability were estimated using the R package bootnet.49 Based on nonparametric bootstrapping, a new dataset with 95% probability confidential intervals (CIs) was generated to assess the accuracy of edge weights.31 Results with low overlaps of CIs indicate more accurate edge weights. Stability strength was also evaluated using the correlation stability coefficient (CS-C).49 The CS-C is defined as the maximum case proportion that can be removed from an overall sample while maintaining a correlation above 0.7 with a 95% probability between the subset network and the original network.49 The CS-C should not be lower than 0.25 and is preferably higher than 0.5.49 Finally, differences of each pair of edges or nodes were estimated using a nonparametric bootstrapped method based on CIs with 95% probabilities. Statistically significant differences between each pair of edges or nodes were suggested by the inclusion of zero in the CIs.Network after controlling for covariatesBecause age, sex, marital status, education, and having friends, family members, or colleagues infected with COVID-19 have all been linked to experiences of insomnia and depressive symptoms during the pandemic,15,18,56,57 a network model, controlling for these covariates, was also estimated using the mgm model in R package.53,54RESULTSSample characteristics and prevalence of comorbid insomnia and depressionTable S1 in the supplemental material summarizes basic demographics of participants. A total of 10,516 participants (33.2 ± 8.4 years) were included in this study. Among them, 8,881 (84%) were women, 9,635 (91.7%) had earned a college degree or higher education, and 7,273 (69.2%) were married. The prevalence of insomnia (ISI total score ≥8) was 22.2% (95% CI: 21.4–22.9%). The prevalence of depression (PHQ-9 total score ≥5) was 28.5% (95% CI: 27.6–29.4%). Finally, the prevalence of comorbid insomnia and depression was 16.0% (95% CI: 15.3–16.7%).Network estimation and strength centralityNo item was lower than 0.25 SD compared to its mean informativeness (ie, SD) regarding either the ISI (MSD = 0.80 ± 0.10) or PHQ-9 (MSD = 0.62 ± 0.14). Furthermore, the item redundancy analysis did not identify any ISI or PHQ-9 items that were redundant with other items in measures. Hence, all insomnia and depression symptoms except for the "Sleep disturbances" (PHQ3) in PHQ-9 were retained for analyses.Descriptive information on insomnia and depressive symptoms is shown in Table 1, while the estimated network structure is displayed in Figure 1. A total of 78 edges above zero were identified from the matrix of 120 connections; all edges reflected positive correlations except for the connections of "Sleep dissatisfaction" (ISI4) with "Motor dysfunction" (PHQ8) and "Suicide" (PHQ9). The strongest edges in the insomnia and depression network model were the connection between "Anhedonia" (PHQ1) and "Sad mood" (PHQ2) in the depression community, and connections between "Difficulty in falling asleep" (ISI1) and "Sleep maintenance" (ISI2), "Sleep maintenance" (ISI2) and "Waking up early" (ISI3), and "Noticeability of sleep problems by others" (ISI6) and "Distress caused by sleep difficulties" (ISI7) in the insomnia community. For connections linking depression and insomnia symptom communities, the edge between "Sleep dissatisfaction" (ISI4) and "Fatigue" (PHQ4) showed the strongest connection. The detailed connection matrix is shown in Table S2.Table 1 Edge weights and predictabilities of ISI and PHQ-9 items (n = 10,516).Item IDItem ContentItem MeanSDStrengthPredictabilityISI1Difficulty in falling asleep0.680.830.950.67ISI2Sleep maintenance0.520.771.100.70ISI3Waking up early0.510.760.710.52ISI4Sleep dissatisfaction1.281.021.010.63ISI5Interference with daily life0.550.780.990.68ISI6Noticeability of sleep problems by others0.420.700.950.67ISI7Distress caused by sleep difficulties0.460.741.180.74PHQ1Anhedonia0.410.680.900.58PHQ2Sad mood0.360.601.040.62PHQ4Fatigue0.620.761.000.58PHQ5Appetite0.390.680.800.48PHQ6Guilt0.280.590.970.55PHQ7Concentration0.300.610.870.51PHQ8Motor dysfunction0.180.491.060.54PHQ9Suicide0.090.360.710.38ISI = Insomnia Severity Index, PHQ-9 = Patient Health Questionnaire, SD = standard deviation.Figure 1: Estimated network structure of comorbid depressive and insomnia symptoms and the corresponding centrality of each node.The different-size circles represent different strength of the nodes, while the width and saturation of edges indicate the connections and directions (ie, green: positive correlation; red: negative correlation). The ring around each node indicates the predictability, with a filled ring representing that 100% of the variance is accounted for by the other nodes and an empty ring corresponding to 0% predictability. ISI = Insomnia Severity Index, PHQ-9 = Patient Health Questionnaire.Download FigureIn visualizing the insomnia and depression network (Figure 1A), "Distress caused by sleep difficulties" (ISI7) and "Sleep maintenance" (ISI2) had the highest strength centrality, followed by "Motor dysfunction" (PHQ8) and "Sad mood" (PHQ2). In contrast, "Waking up early" (ISI3) and "Suicide" (PHQ9) had the lowest strengths of all symptoms (Figure 1B). The nodes "Sleep dissatisfaction" (ISI4), "Fatigue" (PHQ4), and "Motor dysfunction" (PHQ8) showed the highest bridge strengths, linking depression and insomnia communities (see Figure 2).Figure 2: Estimated network structure of comorbid depressive and insomnia symptoms and the corresponding bridge symptoms.The different-size circles represent different strength of the nodes, while the width and saturation of edges indicate the connections and directions (ie, green: positive correlation; red: negative correlation); the ring around each node indicates the predictability, with a filled ring representing that 100% of the variance accounted for by the other nodes and an empty ring corresponding to 0% predictability. ISI = Insomnia Severity Index, PHQ-9 = Patient Health Questionnaire.Download FigurePredictability test data showed that, on average, 59% of the variance in a node could be accounted for by its neighboring nodes in the model (MSD = 0.59 ± 0.10) (Table 1). Of all of the nodes, "Distress caused by sleep difficulties" (ISI7) had the highest predictability (74%).Network stability and accuracyFigure 3 shows the stability of strength centrality (ie, strength and bridge strength). The CS-C of 0.75 indicated that there was a high correlation between the strength of the subset and the original sample after dropping 70% of the cases from the overall sample. Edge accuracy analyses indicated that the 95% CIs of most edges were narrow, suggesting that the network was highly accurate (Figure S1). Furthermore, the edge and strength difference test (Figure S2) based on the bootstrapped method indicated that the strongest edges and nodes were significantly different from the other edges and nodes, also reflecting high accuracy of the estimated network.Figure 3: Stability of the estimated network structure of comorbid depressive and insomnia symptoms estimated by a case-dropping bootstrapped method.Download FigureNetwork after controlling for covariatesTo examine potential confounding effects caused by age, sex, marital status, education, and having friends, family members, or colleagues infected with COVID-19, the insomnia and depression network model was re-estimated using the mgm model (Figure S2). Analyses underscored high correlations with the original model in terms of strength (r = .972; 95% CI: 0.917–0.991) and edge consistencies (r = .997; 95% CI: 0.996–0.997), suggesting that covariates did not have a significant influence on primary results.DISCUSSIONTo the best of our knowledge, this study is the first to document the prevalence and network structure of comorbid insomnia and depression within a large sample of psychiatric practitioners during the COVID-19 pandemic.Regarding network analyses, "Distress caused by sleep difficulties" (ISI7) and "Sleep maintenance" (ISI2) were the most influential individual symptoms within the network model, followed by "Motor dysfunction" (PHQ8) and "Sad mood" (PHQ2). Furthermore, insomnia and depression symptom communities were bridged by "Fatigue" (PHQ4), "Sleep dissatisfaction" (ISI4), and "Motor dysfunction" (PHQ8), which suggests that these symptoms should be prioritized in treatment to reduce and prevent comorbid insomnia and depression.As the most central symptoms in the insomnia and depression network model, the node "Distress caused by sleep difficulties" (ISI7) reflected worry or distress caused by sleep disturbances, while the node "Sleep maintenance" (ISI2) reflected difficulties remaining asleep. Both insomnia and depression have had robust links with worry and discomfort associated with sleep disturbances in previous studies.31,58 For example, a community-based study found that anxiety preceded insomnia (73%) in a sample with comorbid anxiety and insomnia and increased risk for the onset of insomnia (hazard ratio = 3.5), while prior insomnia was not associated with subsequent anxiety.59 Other studies found that physical discomfort also has significant associations with sleep disturbances.60–62 Individuals with severe physical discomfort and fatigue often report more severe insomnia symptoms and depression than do those without fatigue.60Our study was conducted in the middle of March 2020 when the COVID-19 outbreak had reached its peak in China. At that time, psychiatric practitioners were confronted with extremely demanding workloads, insufficient time for recovery and recuperation due to the enormous number of infected persons and individuals suspected of having the virus, shortages of protective equipment in the course of providing patient care, and feelings of uncertainty about the virus and their personal risk for morbidity and mortality. Together, this constellation of stressors may have increased the risk for comorbid insomnia and depression within these professions.15,18Similar to previous findings of depression network models,63 we found that "Motor dysfunction" (PHQ8) and "Sad mood" (PHQ2) were other central symptoms. The prominence of these symptoms may have resulted, in part, from strict public health measures during the pandemic, such as mass quarantines and social distancing of health care workers from their families and friends, restricted outdoor physical activities, and reduced opportunities for recreational activities,64,65 all of which may have increased the likelihood of depressed mood and associated motor impairments.65In the insomnia and depression network model, "Fatigue" (PHQ4), "Sleep dissatisfaction" (ISI4), and "Motor dysfunction" (PHQ8) constituted the most important core of symptoms connecting insomnia and depression communities. The originally derived network model was also highly stable, even after controlling for the impact of several demographic factors having links with depression and insomnia. For persons who were affected, depression and insomnia are believed to have a reciprocal relationship.4,56,66,67 A study conducted during the COVID-19 pandemic found that depression could account for 60% of the variance in sleep disturbances in the general population.66 Conversely, sleep disturbances can trigger new and recurrent episodes of depression.67 Although comorbid insomnia and depression have been widely reported,4,25–28 this network analysis provides potentially new insights regarding interactions between these disorders at the symptom level. Within the complex architecture of insomnia and depressive symptoms, we found that "Fatigue" (PHQ4) was directly connected with "Sleep dissatisfaction" (ISI4), which constituted the bridge of the 2 syndromes and is consistent with previous evidence for direct associations between fatigue and sleep quality (ie, sleep dissatisfaction) but not quantifiable aspects of sleep (ie, amount of sleep itself).68 "Fatigue" (PHQ4), characterized by prolonged loss of energy, exhaustion, or feeling weakness,69–71 is a common characteristic of both depression and insomnia.60,72,73 With its progression, fatigue could aggravate motor dysfunction.74 Considering that bridge symptoms can trigger and maintain the comorbid psychiatric syndromes,29,36 targeting bridge symptoms (ie, Fatigue [PHQ4], Sleep dissatisfaction [ISI4], and Motor dysfunction [PHQ8]) in interventions may have utility in preventing and treating comorbid insomnia and depression.Cognitive behavioral therapy (CBT) designed for specific core symptoms can accelerate the remission for those with depression.27,75,76 A recent study using dynamic network intervention analysis on the sequence of CBT-induced improvements in specific symptoms over 5 treatment sessions77 found that CBT interventions typically improved sleep behavior problems (eg, difficulty of falling asleep and sleep maintenance) in initial sessions. Subsequently, after 4 weeks of treatment, subjective sleep complaints (eg, sleep dissatisfaction) also improved. The study authors concluded that effec
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