BackgroundMental health professionals (MHPs) are susceptible to fatigue, particularly during public health crises like the COVID-19 pandemic. This study examined nonlinear relationships between fatigue, post-traumatic stress disorder (PTSD), and fear of COVID-19 (FOC) among MHPs.MethodsA multi-site survey was conducted from January to February 2023. Fatigue was assessed using the Fatigue Visual Analogue Scale (VAS), PTSD with the Post-Traumatic Stress Disorder Checklist for Civilians (PCL-C), and FOC with the Fear of COVID-19 Scale (FCV-19S). Data were analyzed using logistic regression and restricted cubic splines to explore non-linear associations.ResultsOf the 9,858 COVID-recovered MHPs, the prevalence of significant PTSD symptoms (PCL-17 ≥ 50) was 6.85% (95% CI: 6.35% - 7.35%), while significant fear of COVID-19 (FOC ≥ 16) was observed in 61.28% (95% CI: 60.32% - 62.24%). Higher fatigue levels were significantly associated with increased odds for exacerbated PTSD symptomatology (OR = 1.75, 95% CI: 1.65 - 1.86, p < 0.001) and FOC severity (OR = 1.19, 95% CI: 1.16 - 1.21, p < 0.001). Restricted cubic splines analysis revealed nonlinear relationships. Specifically, as fatigue rose towards an inflection point of 5.00, its association with PTSD symptoms strengthened, while its association with FOC showed a decelerating growth.ConclusionThis study underscored fatigue as a factor significantly associated with COVID-recovered MHPs, particularly regarding the presence of PTSD and FOC. However, due to the cross-sectional study design, the direction of causality between fatigue, PTSD, and FOC could not be determined. Regular monitoring and targeted interventions are crucial for managing fatigue during public health crises. Healthcare organizations should provide appropriate work-rest schedules and supportive policies during such periods.

BackgroundThe COVID-19 pandemic took a toll on everyone’s health and mental health professionals were no exception. This study examined the trajectory of the relationship between levels of physical fatigue and each of depression and anxiety in mental health professionals (MHPs) recovering from COVID-19.MethodsA national survey of 9,858 MHPs who had recovered from COVID-19 was conducted between January and February 2023. The nine-item Patient Health Questionnaire (PHQ-9), the 7-item Generalized Anxiety Disorder (GAD-7) scale, and a numerical rating scale were used to measure depression, anxiety and physical fatigue, respectively. Logistic regression with restricted cubic spline (RCS) models were created to examine the association of physical fatigue with depression and anxiety.ResultsThe prevalence of depression and anxiety in MHPs who recovered from COVID-19 infection were 47.0% (95%CI: 46.0-48.0%) and 28.9% (95%CI: 28.0-29.8%) respectively. The prevalence of moderate to severe physical fatigue was 44.2% (95%CI: 43.2-45.2%). The RCS models revealed a significant nonlinear relationship between physical fatigue and both depression and anxiety, with an inflection point at a fatigue score of 4. Above this threshold, the risk of both conditions increased significantly. Participants with poor perceived health and lower socioeconomic status had a significantly greater increase in depression and anxiety when fatigue levels were higher.ConclusionsModerate to severe physical fatigue was associated with depression and anxiety in MHPs recovering from COVID-19. Interventions aimed at alleviating fatigue may play a critical role in improving mental health outcomes in this vulnerable population.

Background:Gender differences in COVID-19-related fear among mental health professionals (MHPs) have been inadequately studied. This study compared the gender differences in prevalence, correlates and network structure of COVID-19 fear among MHPs in China in the post-pandemic era. Methods:A nationwide cross-sectional study was conducted between January 22 and February 10, 2023. Binary logistic regression was used to identify factors associated with COVID-19 fear. Expected Influence was used to identify the most central (influential) symptoms in gender-specific networks, while network comparison tests were conducted to assess the differences between male and female models. Results:Among 7,199 MHPs, the overall prevalence of COVID-19 fear was 63.5% (95% CI: 62.3%-64.6%), with 61.7% (95% CI: 58.9%-64.4%) in males and 65.0% (95% CI: 63.7%-66.2%) in females. For male MHPs, factors associated with COVID-19 fear included having married status (OR: 1.39; 95% CI: 1.02-1.90; P = 0.037), poorer economic status (poor vs. good: OR: 1.91; 95% CI: 1.23-2.98; P = 0.004), more severe insomnia (OR: 1.04; 95% CI: 1.01-1.07; P = 0.018), and depression (OR: 1.09; 95% CI: 1.05-1.12; P < 0.001). For female MHPs, the significant factors included having married status (OR: 1.21; 95% CI: 1.06-1.37; P = 0.004), poorer economic status (poor vs. good: OR: 1.39; 95% CI: 1.11-1.73; P = 0.004), more severe insomnia (OR: 1.05; 95% CI: 1.03-1.06; P < 0.001), depression (OR: 1.09; 95% CI: 1.08-1.11; P < 0.001), and quarantine experience (OR: 1.17; 95% CI: 1.04-1.30; P = 0.006). Network analysis revealed that the most central symptom in the male network was FOC6 (sleep difficulties due to COVID-19 concerns), while the corresponding node in the female network model was FOC7 (palpitations when thinking about COVID-19). Conclusion:COVID-19-related fear was more prevalent among female MHPs than males. Specific interventions targeting the central symptoms in each network should be implemented to alleviate COVID-19 fear effectively and improve the mental health of MHPs in the post-pandemic era.

Psychiatric syndromes are common following recovery from Coronavirus Disease 2019 (COVID-19) infection. This study investigated the prevalence and the network structure of depression, insomnia, and suicidality among mental health professionals (MHPs) who recovered from COVID-19. Depression and insomnia were assessed with the Patient Health Questionnaire (PHQ-9) and Insomnia Severity Index questionnaire (ISI7) respectively. Suicidality items comprising suicidal ideation, suicidal plan and suicidal attempt were evaluated with binary response (no/yes) items. Network analyses with Ising model were conducted to identify the central symptoms of the network and their links to suicidality. A total of 9858 COVID-19 survivors were enrolled in a survey of MHPs. The prevalence of depression and insomnia were 47.10% (95% confidence interval (CI) = 46.09–48.06%) and 36.2% (95%CI = 35.35–37.21%), respectively, while the overall prevalence of suicidality was 7.8% (95%CI = 7.31–8.37%). The key central nodes included “Distress caused by the sleep difficulties” (ISI7) (EI = 1.34), “Interference with daytime functioning” (ISI5) (EI = 1.08), and “Sleep dissatisfaction” (ISI4) (EI = 0.74). “Fatigue” (PHQ4) (Bridge EI = 1.98), “Distress caused by sleep difficulties” (ISI7) (Bridge EI = 1.71), and “Motor Disturbances” (PHQ8) (Bridge EI = 1.67) were important bridge symptoms. The flow network indicated that the edge between the nodes of “Suicidality” (SU) and “Guilt” (PHQ6) showed the strongest connection (Edge Weight= 1.17, followed by “Suicidality” (SU) - “Sad mood” (PHQ2) (Edge Weight = 0.68)). The network analysis results suggest that insomnia symptoms play a critical role in the activation of the insomnia-depression-suicidality network model of COVID-19 survivors, while suicidality is more susceptible to the influence of depressive symptoms. These findings may have implications for developing prevention and intervention strategies for mental health conditions following recovery from COVID-19.

Background:Suicidality is a global public health problem which has increased considerably during the coronavirus disease 2019 (COVID-19) pandemic. This study examined the inter-relationships between depressive symptoms and suicidality using network analysis among Macau residents after the "relatively static management" COVID-19 strategy.Methods:An assessment of suicidal ideation (SI), suicide plan (SP), suicide attempt (SA) and depressive symptoms was conducted with the use of individual binary response items (yes/no) and Patient Health Questionnaire (PHQ-9). In the network analysis, central and bridge symptoms were identified in the network through "Expected Influence" and "Bridge Expected Influence", and specific symptoms that were directly associated with suicidality were identified via the flow function. Network Comparison Tests (NCT) were conducted to examine the gender differences in network characteristics.Results:The study sample included a total of 1008 Macau residents. The prevalence of depressive symptoms and suicidality were 62.50% (95% CI = 59.4-65.5%) and 8.9% (95% CI = 7.2-10.9%), respectively. A network analysis of the sample identified SI ("Suicidal ideation") as the most central symptom, followed by SP ("Suicide plan") and PHQ4 ("Fatigue"). SI ("Suicidal ideation") and PHQ6 ("Guilt") were bridge nodes connecting depressive symptoms and suicidality. A flow network revealed that the strongest connection was between S ("Suicidality") and PHQ6 ("Guilt"), followed by S ("Suicidality") and PHQ 7 ("Concentration"), and S ("Suicidality") and PHQ3 ("Sleep").Conclusion:The findings indicated that reduction of specific depressive symptoms and suicidal thoughts may be relevant in decreasing suicidality among adults. Further, suicide assessment and prevention measures should address the central and bridge symptoms identified in this study.

BackgroundUsing network analysis, the interactions between mental health problems at the symptom level can be explored in depth. This study examined the network structure of depressive and anxiety symptoms and suicidality among mental health professionals after the end of China's Dynamic Zero-COVID Policy.MethodsA total of 10,647 mental health professionals were recruited nationwide from January to February 2023. Depression and anxiety were assessed using the 9-item Patient Health Questionnaire (PHQ-9) and 7-item Generalized Anxiety Disorder Scale (GAD-7), respectively, while suicidality was defined by a ‘yes’ response to any of the standard questions regarding suicidal ideation (SI), suicide plan (SP) and suicide attempt (SA). Expected Influence (EI) and Bridge Expected Influence (bEI) were used as centrality indices in the symptom network to characterize the structure of the symptoms.ResultsThe prevalence of depression, anxiety, and suicidality were 45.99 %, 28.40 %, and 7.71 %, respectively. The network analysis identified GAD5 (“Restlessness”) as the most central symptom, followed by PHQ4 (“Fatigue”) and GAD7 (“Feeling afraid”). Additionally, PHQ6 (“Guilt”), GAD5 (“Restlessness”), and PHQ8 (“Motor disturbance”) were bridge nodes linking depressive and anxiety symptoms with suicidality. The flow network indicated that the strongest connections of S (“Suicidality”) was with PHQ6 ("Guilt"), GAD7 (“Feeling afraid”), and PHQ2 ("Sad mood").ConclusionsDepression, anxiety, and suicidality among mental health professionals were highly prevalent after China's Dynamic Zero-COVID Policy ended. Effective measures should target central and bridge symptoms identified in this network model to address the mental health problems in those at-risk.

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. 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. Prevalence rates of insomnia (ISI total score ≥8), depression (PHQ-9 total score ≥5) and comorbid insomnia and depression were 22.2

Post-infection sequelae of COVID-19 (PISC) have raised public health concerns. However, it is not clear whether infected mental health professionals (MHPs) with PISC have experienced more psychiatric symptoms than MHPs without PISC do. This study examined differences in the prevalence of self-reported depression, anxiety, insomnia and suicidality as well as the network structures of these symptoms between these two groups. Participants completed questionnaire measures of psychiatric symptoms and demographics. Expected influence was used to measure centrality of symptoms and network comparison tests were adopted to compare differences in the two network models. The sample comprised 2,596 participants without PISC and 2,573 matched participants with PISC. MHPs with PISC had comparatively higher symptom levels related to depression (55.2% vs. 23.5 %), anxiety (32.0% vs. 14.9 %), insomnia (43.3% vs. 17.3 %), and suicidality (9.6% vs. 5.3 %). PHQ4 ("Fatigue"), PHQ6 ("Guilt"), and GAD2 ("Uncontrollable Worrying") were the most central symptoms in the "without PISC" network model. Conversely, GAD3 ("Worry too much"), GAD5 ("Restlessness"), and GAD4 ("Trouble relaxing") were more central in the "with PISC" network model. In sum, MHPs with PISC experienced comparatively more psychiatric symptoms and related disturbances. Network results provide foundations for the expectation that MHPs with PISC may benefit from interventions that address anxiety-related symptoms, while those without PISC may benefit from interventions targeting depression-related symptoms.

Studies on post-traumatic stress symptoms (PTSS) among mental health professionals (MHPs) are limited, particularly since restrictions due to coronavirus disease (COVID-19) have been lifted such as the recent termination of China’s Dynamic Zero-COVID Policy. The current study filled this gap by exploring the prevalence, correlates, and network structure of PTSS as well as its association with suicidality from a network analysis perspective. A cross-sectional, national survey was conducted using a convenience sampling method on MHPs between January 22 and February 10, 2023. PTSS were assessed using the Post-Traumatic Stress Disorder Checklist-Civilian version, while suicidality was assessed using standardized questions related to ideation, plans, and attempts. Univariate and multivariate analyses examined correlates of PTSS. Network analysis explored the structure of PTSS and suicidality. The centrality index of “Expected influence” was used to identify the most central symptoms in the network, reflecting the relative importance of each node in the network. The “flow” function was adopted to identify specific symptoms that were directly associated with suicidality. A total of 10,647 MHPs were included. The overall rates of PTSS and suicidality were 6.7% ( n = 715; 95% CI = 6.2–7.2%) and 7.7% ( n = 821; 95% CI = 7.2–8.2%), respectively. Being married (OR = 1.523; P < 0.001), quarantine experience (OR = 1.288; P < 0.001), suicidality (OR = 3.750; P < 0.001) and more severe depressive symptoms (OR = 1.229; P < 0.001) were correlates of more PTSS. Additionally, higher economic status (e.g., good vs. poor: OR = 0.324; P = 0.001) and health status (e.g., good vs. poor: OR = 0.456; P < 0.001) were correlates of reduced PTSS. PCL6 (“Avoiding thoughts”; EI = 1.189), PCL7 (“Avoiding reminders”; EI = 1.157), and PCL11 (“Feeling emotionally numb”; EI = 1.074) had the highest centrality, while PCL12 (“Negative belief”), PCL 16 (“Hypervigilance”) and PCL 14 (“Irritability”) had the strongest direct, positive associations with suicidality. A high prevalence of lingering PTSS was found among MHPs immediately after China’s “Dynamic Zero-COVID Policy” was terminated. Avoidance and hyper-arousal symptoms should be monitored among at-risk MHPs after the COVID-19 pandemic and serve as potential targets for the prevention and treatment of PTSS in this population.

Background Nurses in Ophthalmology Department (OD) had a high risk of infection during the novel coronavirus disease 2019 (COVID-19) pandemic. This study examined the prevalence, correlates, and network structure of depression, and explored its association with quality of life (QOL) in Chinese OD nurses. Methods Based on a cross-sectional survey, demographic and clinical data were collected. Depression was measured with the 9-item Self-reported Patient Health Questionnaire (PHQ-9), and QOL was measured using the World Health Organization Quality of Life Questionnaire-brief version (WHOQOL-BREF). Univariate analyses, multivariate logistic regression analyses, and network analyses were performed. Results Altogether, 2,155 OD nurses were included. The overall prevalence of depression among OD nurses was 32.71% (95%CI: 30.73–34.70%). Multiple logistic regression analysis revealed that having family or friends or colleagues who were infected (OR = 1.760, p = 0.003) was significantly associated with higher risk of depression. After controlling for covariates, nurses with depression reported lower QOL (F(1, 2,155) = 596.784, p < 0.001) than those without depression. Network analyses revealed that ‘Sad Mood’, ‘Energy Loss’ and ‘Worthlessness’ were the key central symptoms. Conclusion Depression was common among OD nurses during the COVID-19 pandemic. Considering the negative impact of depression on QOL and daily life, regular screening for depression, timely counselling service, and psychiatric treatment should be provided for OD nurses, especially those who had infected family/friends or colleagues. Central symptoms identified in network analysis should be targeted in the treatment of depression.

BackgroundChina recorded a massive COVID-19 pandemic wave after ending its Dynamic Zero-COVID Policy on January 8, 2023. As a result, mental health professionals (MHPs) experienced negative mental health consequences, including an increased level of fear related to COVID-19. This study aimed to explore the prevalence and correlates of COVID-19 fear among MHPs following the end of the Policy, and its association with quality of life (QoL) from a network analysis perspective.MethodsA cross-sectional national study was conducted across China. The correlates of COVID-19 fear were examined using both univariate and multivariate analyses. An analysis of covariance (ANCOVA) was conducted to determine the relationship between fear of COVID-19 and QoL. Central symptoms were identified using network analysis through the “Expected Influence” of the network model while specific symptoms directly correlated with QoL were identified through the “flow function.”ResultsA total of 10,647 Chinese MHPs were included. The overall prevalence of COVID-19 fear (FCV-19S total score ≥ 16) was 60.8% (95% CI = 59.9–61.8%). The binary logistic regression analysis found that MHPs with fear of COVID-19 were more likely to be married (OR = 1.198; p < 0.001) and having COVID-19 infection (OR = 1.235; p = 0.005) and quarantine experience (OR = 1.189; p < 0.001). Having better economic status (good vs. poor: OR = 0.479; p < 0.001; fair vs. poor: OR = 0.646; p < 0.001) and health status (good vs. poor: OR = 0.410; p < 0.001; fair vs. poor: OR = 0.617; p < 0.001) were significantly associated with a lower risk of COVID-19 fear. The ANCOVA showed that MHPs with fear of COVID-19 had lower QoL [F = 228.0, p < 0.001]. “Palpitation when thinking about COVID-19” was the most central symptom in the COVID-19 fear network model, while “Uncomfortable thinking about COVID-19” had the strongest negative association with QoL (average edge weight = −0.048).ConclusionThis study found a high prevalence of COVID-19 fear among Chinese MHPs following the end of China’s Dynamic Zero-COVID Policy. Developing effective prevention and intervention measures that target the central symptoms as well as symptoms correlated with QoL in our network structure would be important to address COVID-19 fear and improve QoL.

BackgroundThe COVID-19 pandemic has greatly affected treatment-seeking behaviors of psychiatric patients and their guardians. Barriers to access of mental health services may contribute to adverse mental health consequences, not only for psychiatric patients, but also for their guardians. This study explored the prevalence of depression and its association with quality of life among guardians of hospitalized psychiatric patients during the COVID-19 pandemic.MethodsThis multi-center, cross-sectional study was conducted in China. Symptoms of depression and anxiety, fatigue level and quality of life (QOL) of guardians were measured with validated Chinese versions of the Patient Health Questionnaire – 9 (PHQ-9), Generalized Anxiety Disorder Scale – 7 (GAD-7), fatigue numeric rating scale (FNRS), and the first two items of the World Health Organization Quality of Life Questionnaire - brief version (WHOQOL-BREF), respectively. Independent correlates of depression were evaluated using multiple logistic regression analysis. Analysis of covariance (ANCOVA) was used to compare global QOL of depressed versus non-depressed guardians. The network structure of depressive symptoms among guardians was constructed using an extended Bayesian Information Criterion (EBIC) model.ResultsThe prevalence of depression among guardians of hospitalized psychiatric patients was 32.4% (95% CI: 29.7–35.2%). GAD-7 total scores (OR = 1.9, 95% CI: 1.8–2.1) and fatigue (OR = 1.2, 95% CI: 1.1–1.4) were positively correlated with depression among guardians. After controlling for significant correlates of depression, depressed guardians had lower QOL than non-depressed peers did [F(1, 1,101) = 29.24, p < 0.001]. “Loss of energy” (item 4 of the PHQ-9), “concentration difficulties” (item 7 of the PHQ-9) and “sad mood” (item 2 of the PHQ-9) were the most central symptoms in the network model of depression for guardians.ConclusionAbout one third of guardians of hospitalized psychiatric patients reported depression during the COVID-19 pandemic. Poorer QOL was related to having depression in this sample. In light of their emergence as key central symptoms, “loss of energy,” “concentration problems,” and “sad mood” are potentially useful targets for mental health services designed to support caregivers of psychiatric patients.

BACKGROUND:Depressive and anxiety symptoms (depression and anxiety hereafter) are common among psychiatric patients and their caregivers during the COVID-19 pandemic. Network analysis is a novel method to assess the associations between psychiatric syndromes/disorders at the symptom level. This study examined depression and anxiety among caregivers of psychiatric inpatients during the late stage of the COVID-19 pandemic from the perspective of network analysis. METHODS:A total of 1101 caregivers of psychiatric inpatients were included in this study. The severity of depression was assessed using the nine-item Patient Health Questionnaire (PHQ-9), while anxiety was assessed with the seven-item Generalized Anxiety Disorder Scale (GAD-7). The expected index (EI) and bridge EI index were used to identify the central and bridge symptoms, respectively. The stability of the network was evaluated via a case-dropping bootstrap procedure. RESULTS:The prevalence of depression and anxiety were 32.4 % (95%CI: 29.7 %-35.3 %) and 28.0 % (95%CI: 25.4 %-30.7 %), respectively while the prevalence of comorbid depression and anxiety was 24.9 % (95%CI: 22.4 %-27.6 %). The most central symptom was "Fatigue", followed by "Trouble Relaxing" and "Restlessness". The highest bridge symptom was "Restlessness", followed by "Uncontrollable worry" and "Suicide ideation". The bootstrap test indicated that the whole network model was stable, and no network difference was detected between genders and between different education levels. CONCLUSIONS:Depression, anxiety, and comorbid depression and anxiety were common among caregivers of psychiatric inpatients during the late stage of the COVID-19 pandemic. Central and bridge symptoms identified in this network analysis should be considered key target symptoms to address in caregivers of patients.

BackgroundPost-traumatic stress symptoms (PTSS) are commonly reported by psychiatric healthcare personnel during the coronavirus disease 2019 (COVID-19) pandemic and negatively affect quality of life (QOL). However, associations between PTSS and QOL at symptom level are not clear. This study examined the network structure of PTSS and its connection with QOL in psychiatric healthcare personnel during the COVID-19 pandemic.MethodsThis cross-sectional study was carried out between March 15 and March 20, 2020 based on convenience sampling. Self-report measures including the 17-item Post-Traumatic Stress Disorder Checklist – Civilian version (PCL-C) and World Health Organization Quality of Life Questionnaire - Brief Version (WHOQOL-BREF) were used to measure PTSS and global QOL, respectively. Network analysis was used to investigate the central symptoms of PTSS and pattern of connections between PTSS and QOL. An undirected network was constructed using an extended Bayesian Information Criterion (EBIC) model, while a directed network was established based on the Triangulated Maximally Filtered Graph (TMFG) method.ResultsAltogether, 10,516 psychiatric healthcare personnel completed the assessment. “Avoidance of thoughts” (PTSS-6), “Avoidance of reminders” (PTSS-7), and “emotionally numb” (PTSS-11) were the most central symptoms in the PTSS community, all of which were in the Avoidance and Numbing domain. Key bridge symptoms connecting PTSS and QOL were “Sleep disturbances” (PTSS-13), “Irritability” (PTSS-14) and “Difficulty concentrating” (PTSS-15), all of which were within the Hyperarousal domain.ConclusionIn this sample, the most prominent PTSS symptoms reflected avoidance while symptoms of hyper-arousal had the strongest links with QOL. As such, these symptom clusters are potentially useful targets for interventions to improve PTSS and QOL among healthcare personnel at work under pandemic conditions.

Background Studies on sleep problems among caregivers of psychiatric patients, especially during the COVID-19 pandemic, are limited. This study examined the prevalence and correlates of insomnia symptoms (insomnia hereafter) among caregivers of psychiatric inpatients during the COVID-19 pandemic as well as the association with quality of life (QoL) from a network analysis perspective. Methods A multi-center cross-sectional study was conducted on caregivers of inpatients across seven tertiary psychiatric hospitals and psychiatric units of general hospitals. Network analysis explored the structure of insomnia using the R program. The centrality index of “Expected influence” was used to identify central symptoms in the network, and the “flow” function was adopted to identify specific symptoms that were directly associated with QoL. Results A total of 1,101 caregivers were included. The overall prevalence of insomnia was 18.9% (n = 208; 95% CI = 16.7–21.3%). Severe depressive (OR = 1.185; P < 0.001) and anxiety symptoms (OR = 1.099; P = 0.003), and severe fatigue (OR = 1.320; P < 0.001) were associated with more severe insomnia. The most central nodes included ISI2 (“Sleep maintenance”), ISI7 (“Distress caused by the sleep difficulties”) and ISI1 (“Severity of sleep onset”), while “Sleep dissatisfaction” (ISI4), “Distress caused by the sleep difficulties” (ISI7) and “Interference with daytime functioning” (ISI5) had the strongest negative associations with QoL. Conclusion The insomnia prevalence was high among caregivers of psychiatric inpatients during the COVID-19 pandemic, particularly in those with depression, anxiety and fatigue. Considering the negative impact of insomnia on QoL, effective interventions that address insomnia and alteration of sleep dissatisfaction should be developed.

Background: Internet addiction (IA) is associated with mental health problems but its impact on quality of life (QOL) is understudied. We examined the prevalence of IA and its association with QOL in clinically stable patients with major depressive disorder (MDD). Methods: In a cross-sectional survey between September 2020 and July 2021, the Young's Internet Addiction Test (IAT), the Patient Health Questionnaire-2 (PHQ-2) and the World Health Organization Quality of Life Brief version scale (WHOQOL-BREF) were administered to 1267 patients with MDD. Logistic regression was used to examine the correlates of IA, while analysis of covariance (ANCOVA) was used to examine the association between IA and QOL." Results: The prevalence of IA (IAT total scores >= 50) was 27.2 % (95 % CI: 24.7 %-29.6 %) in MDD patients. Compared to patients without IA, those with IA had lower QOL (F( 1, 1267) = 19.1, P < 0.001). Logistic regression revealed that higher education (senior high school and above; OR = 1.85, 95 % CI: 1.13-3.03), family history of psychiatric disorders (OR = 1.72, 95 % CI: 1.08-2.73), and higher PHQ-2 total score (OR = 1.23, 95 % CI: 1.14-1.32) were positively associated with IA while older age (OR = 0.93, 95 % CI: 0.91-0.96) was inversely related to IA. Conclusion: IA is much more common in clinically stable patients with MDD compared to the reported figures in the general population. It would be prudent to screen and monitor internet use in MDD patients and treat those with IA.

Background and aimsDepression often triggers addictive behaviors such as Internet addiction. In this network analysis study, we assessed the association between Internet addiction and residual depressive symptoms in patients suffering from clinically stable recurrent depressive disorder (depression hereafter).Materials and methodsIn total, 1,267 depressed patients were included. Internet addiction and residual depressive symptoms were measured using the Internet Addiction Test (IAT) and the two-item Patient Health Questionnaire (PHQ-2), respectively. Central symptoms and bridge symptoms were identified via centrality indices. Network stability was examined using the case-dropping procedure.ResultsThe prevalence of IA within this sample was 27.2% (95% CI: 24.7–29.6%) based on the IAT cutoff of 50. IAT15 (“Preoccupation with the Internet”), IAT13 (“Snap or act annoyed if bothered without being online”) and IAT2 (“Neglect chores to spend more time online”) were the most central nodes in the network model. Additionally, bridge symptoms included the node PHQ1 (“Anhedonia”), followed by PHQ2 (“Sad mood”) and IAT3 (“Prefer the excitement online to the time with others”). There was no gender difference in the network structure.ConclusionBoth key central and bridge symptoms found in the network analysis could be potentially targeted in prevention and treatment for depressed patients with comorbid Internet addiction and residual depressive symptoms.

Depressive disorders and internet addiction (IA) are often comorbid. The aims of this study were to examine the network structure of IA in patients with major depressive disorders (MDD) and explore the association between IA and quality of life (QoL) in this population. This was a multicenter, cross-sectional survey. IA and QoL were assessed with the Internet Addiction Test (IAT) and the World Health Organization Quality of Life-brief version, respectively. Node expected influence (EI) was used to identify central symptoms in the network model, while the flow network of QoL was generated to examine its association with IA. A total of 1,657 patients with MDD was included. “Preoccupation with the Internet,” “Job performance or productivity suffer because of the Internet,” and “Neglect chores to spend more time online” were central symptoms. The symptom “Form new relationships with online users” had the strongest direct positive relation with QoL, while “Spend more time online over going out with others” and “Job performance or productivity suffer because of the Internet” had the strongest direct negative relations with QoL. Neglecting work caused by IA correlated with QoL, while making friends online appropriately was related to better QoL among MDD patients. Appropriate interventions targeting the central symptoms may potentially prevent or reduce the risk of IA in MDD patients.