BACKGROUND:Depressive symptoms and sleep problems are prevalent among older adults with depression. To reduce their adverse impact, it is important to understand the changes in symptom patterns as the Coronavirus disease 2019 (COVID-19) pandemic emerged. This longitudinal study examined the interactive changes between depressive symptoms and sleep problems among older adults with depression before and during the COVID-19 pandemic from a network perspective in the USA. METHODS:This network analysis study was based on data from the three waves (2016, 2018, and 2020) of the Health and Retirement Study (HRS). Depressive symptoms were measured using the eight-item version of the Center for Epidemiologic Studies Depression Scale (CESD-8), and sleep problems were assessed with the four-item Jenkins Sleep Scale (JSS-4). The study examined central symptoms and bridge symptoms within the network model. RESULTS:A total of 2905 older adults with depression were included in the analyses. The prevalence of depressive symptoms did not significantly change in the study wave during the COVID-19 compared to the pre-pandemic waves. "Feeling Depressed" was the most central symptom of the depression-sleep problems network in the 2016 wave, while "Feeling Sad" was the most central symptom in both the 2018 and 2020 waves. Additionally, "Feeling Loneliness" was the key bridge symptom of the depression-sleep problems network in the 2016 wave, while "Not Enjoying Life" was the key bridge symptom in the 2018 wave, and "Feeling Rested in Morning" was the key bridge symptom in the 2020 wave. CONCLUSION:The findings highlighted that central and bridge symptoms were potential targets in treating depressive symptoms and sleep problems among older adults with depression across the study period in the USA.

Metabolic abnormalities are prevalent among individuals with schizophrenia, contributing to increased medical burden and premature mortality. However, representative data from mainland China remain scarce, particularly within inpatient populations. This study aims to investigate the prevalence and risk profiles of metabolic abnormalities among inpatients with schizophrenia in mainland China. We conducted a large-scale, multicenter cross-sectional study, consecutively enrolling adult inpatients with schizophrenia from 218 psychiatric and general hospitals across six major regions of China between January and December 2023. Metabolic abnormalities were defined according to standard clinical criteria for waist circumference, blood pressure, serum lipids, and fasting glucose. Chi-square tests and ordinal logistic regression analyses were used to identify high-risk profiles. A total of 18,499 valid cases were analyzed, with 84.51

Background:Patient safety culture (PSC) is a critical component of healthcare quality, particularly in psychiatric settings where unique risks exist. In China, research on PSC within mental health institutions remains underdeveloped, and the factors influencing it are poorly understood. Methods:This cross-sectional study utilized the Chinese version of the Hospital Survey on Patient Safety Culture (HSOPSC) to assess PSC among 2,524 mental health nurses from multiple Level-2 and Level-3 psychiatric hospitals in China (2021-2023). We specifically examined the impact of hospital level on PSC perceptions. Multivariate regression models were employed to identify predictors of PSC composites. Results:The overall positive response rate (PPR) across the 12 PSC dimensions was 62.4%, indicating a moderate culture. Strengths were "Teamwork Within Units" (PPR = 83.3%) and "Organizational Learning" (PPR = 82.8%), while critical areas for improvement were "Nonpunitive Response to Error" (PPR = 46.6%) and "Staffing" (PPR = 43.3%). Regression analyses revealed that hospital level was a significant predictor of PSC outcomes, alongside years of experience and daily overtime hours (P < 0.05). Conclusions:This pioneering national study reveals that the PSC landscape in Chinese psychiatric hospitals is characterized by specific strengths but also critical weaknesses, significantly influenced by hospital level. The findings compel a move away from one-size-fits-all approaches. We recommend stratified interventions: foundational support for Level-2 hospitals and advanced quality initiatives for Level-3 hospitals, with universal prioritization of addressing staffing shortages and fostering a nonpunitive "Just Culture".

Importance. The global prevalence of mental health problems demands a short but comprehensive screening and monitoring scale of major psychiatric disorders. Objective. To use advanced machine learning techniques to develop a concise and comprehensive screening and monitoring tool for psychiatric disorder risks and evaluate its reliability and validity. Design, setting, and participants. We obtained three datasets of outpatient participants at a psychiatric hospital in China who completed the 567-item Minnesota Multiphasic Personality Inventory (MMPI). We used the first dataset (N=6,704) for model training, testing, and internal validation to obtain a shortened version (100 items) using the stacked generalization ensemble of several machine-learning techniques. We validated the shortened scale against two prospective pristine datasets (N=928, unpreregistered; N=484, preregistered). Main outcomes and measures. The Area Under the Curve of the Receiver Operating Characteristic (AUC of ROC) to measure validity and Cronbach’s Alpha to measure reliability. Results. We reduced the length of the MMPI-2 by over 82%, from 567 to 100 items. The shortened scale can measure ten target conditions with at least 85% AUC and a Cronbach’s Alpha 0.97. We implemented the shortened scaleinto a mobile-friendly web application. Conclusions and relevance. An advanced machine-learning approach can significantly reduce a long scale to a shortened one while retaining high validity and reliability. This shortened scale can not only help alleviate pressure on healthcare systems burdened by the increased number of patients with mental health concerns but also allows for regular health monitoring or screening by individuals.

Objective The literature on large-scale studies of Chinese patients with adolescent-onset bipolar disorder (adolescent-onset BD) was limited. Based on the analysis of the National Bipolar Mania Pathway Survey (BIPAS) Phase II data, we examined the demographic and clinical characteristics of adults with adolescent-onset BD. Methods Among 899 participants diagnosed with BD from 20 mental health services, demographics and clinical data were collected at screening. Comparisons were made using chi-square (or Fisher's exact) tests and ANOVA. Multivariate logistic regression identified independent factors for adolescent-onset BD, and a CHAID decision tree analysis (SPSS) was constructed to detect risk factors. Results In the sample, 360 (40%) had adolescent-onset BD and 539 (60%) adult-onset BD. Significant differences between the two groups were observed in current age, number of episodes, years of education, gender, age of onset, education level, marital status, occupation, comorbid chronic physical illness, and first episode type. Stratified analysis also revealed significant differences between adolescent-onset BD I and BD II. Multivariate logistic regression identified younger onset age, more frequent episodes, lower education level, marital status, occupation, first episode type, and prior hospitalization as independent factors for adolescent-onset BD. The decision tree model selected current age as the first splitting variable, followed by occupation and marital status as the second, and years of education and prior hospitalization as the third. Conclusions Adolescent-onset BD exhibits distinct demographic and clinical features compared to adult-onset BD. Early recognition and tailored treatment strategies may improve prognosis and outcomes in this population.

Background Emotional disorders in adolescents have emerged as a prominent issue in recent years.Current mainstream clinical assessment approaches for such conditions predominantly rely on interviews and rating scales,which are limited by inherent drawbacks such as high subjectivity and recall bias.Accordingly,there exists an urgent clinical need for the development of objective,quantifiable auxiliary diagnostic tools.In previous studies,frontal electroencephalography(EEG)has demonstrated significant value in assessing depressive and anxiety.However,the lack of standardized quantitative metrics and intuitive visual analytical approaches has severely restricted clinical interpretability of EEG data and diminished patient engagement.To address these key limitations,the present study proposes an innovative analytical framework that converts frontal EEG signals into quantifiable visual metrics to enhance clinical comprehension and acceptance.Objective To explore the value of frontal EEG in assessing anxiety,depression,and sleep quality in adolescents with emotional disorders,with the aim of providing objective auxiliary tools for clinical diagnosis and assessment of adolescents with emotional disorders.Methods This cross-sectional study recruited 105 adolescents aged 12-18 years who visited the outpatient department of a specialized mental hospital in Beijing from April 2023 to April 2024.All participants met the diagnostic criteria for mood(affective)disorders or anxiety disorders in the International Classification of Diseases,tenth edition(ICD-10).Frontal EEG signals were collected within a big data analytics-driven framework and further processed by EEG system to generate six quantitative cerebral function indices,namely brain load,tension and excitement,emotional stress,sleepiness index,cerebral vitality,and cerebral fatigue.In addition,validated standardized scales,including the Self-rating Anxiety Scale(SAS),the Self-rating Depression Scale(SDS),and the Pittsburgh Sleep Quality Index(PSQI),were administered for anxiety,depressive symptoms,and sleep quality,respectively.Results In adolescent patients with emotional disorders,the SAS score exhibited significant positive correlations with brain load(rs=0.328,P<0.01),emotional stress(rs=0.341,P<0.01),and cerebral fatigue(rs=0.286,P<0.01).The SDS score was positively correlated with brain load(rs=0.275,P<0.01),emotional stress(rs=0.241,P<0.05),and cerebral fatigue(rs=0.311,P<0.01),while showing a significant negative correlation with cerebral vitality(rs=-0.212,P<0.05).Additionally,the PSQI total score demonstrated positive correlations with brain load(rs=0.340,P<0.01),emotional stress(rs=0.322,P<0.01),and cerebral fatigue(rs=0.229,P<0.05).Conclusion Frontal EEG-derived indices,including brain load,emotional stress,cerebral fatigue and cerebral vitality,may serve as objective markers for reflecting anxiety,depression,and sleep quality in adolescents with emotional disorders.

BackgroundTo improve treatment outcomes and enhance the prognosis of patients with PTSD (Post-Traumatic Stress Disorder), the efficacy and patient acceptance of antidepressant treatment for PTSD are comprehensively evaluated, and its clinical application value is explored.MethodsA retrospective study is conducted, with 200 patients divided into a medication group and a combination group, with 100 patients in each group. The medication group receives a single antidepressant (primarily a selective serotonin reuptake inhibitor) for 8 weeks, while the combination group receives the same medication plus a 60-minute weekly trauma-focused cognitive behavioral therapy (TF-CBT). Symptom severity, side effects, and adherence are assessed using standardized clinical interviews and self-rating scales. Correlation analysis and multiple linear regression (MLR) are used to explore the relationships between these indicators.ResultsThe combination group has higher medication satisfaction (p < 0.001), lower frequency of side effects (insomnia: 0.70 ± 0.42 times vs. 1.25 ± 0.47 times, p < 0.001), and higher medication compliance rate (93.0% vs. 85.0%, p < 0.001).ConclusionsCompared with antidepressant monotherapy, combining antidepressant and CBT treatment significantly alleviates patient symptoms, reduces side effects, and improves treatment adherence and satisfaction. This study provides a new perspective for tailoring treatment plans and improving treatment precision and effectiveness.

Background:The interrelationships among the negative symptoms of schizophrenia and factors affecting quality of life remain unclear. To address this gap, the present study explores the complex interrelationships among anhedonia, self-stigma, self-esteem, psychological resilience, and social support in individuals with schizophrenia. Methods:A comprehensive battery of measures was used to assess 447 patients with schizophrenia. Undirected network analysis was employed to examine the centrality of various factors, and Bayesian network analysis was used to explore the indirect influence of upstream variables on anhedonia through psychological factors. Results:Self-stigma was identified as the most central factor. Among the subdimensions of anhedonia, abstract anticipatory pleasure and contextual consummatory pleasure demonstrated the highest centrality. Negative symptoms of schizophrenia and social support were found to indirectly influence different aspects of anhedonia through psychological factors such as self-esteem and resilience. In one of the identified pathways, social support influenced psychological resilience, which in turn affected abstract anticipatory pleasure, contextual consummatory pleasure, and contextual anticipatory pleasure. Conclusions:Our findings suggest that social support is associated with pleasure experiences partly through psychological resilience and underscore the potential mediating role of psychological factors in linking clinical symptoms, social resources, and hedonic capacity in schizophrenia.

OBJECTIVE:Crisis lines face challenges in identifying individuals at high suicide risk. We aimed to compare two routine methods for predicting suicide acts at different time points. METHODS:In this prospective cohort study, we recruited and monitored 8859 callers from the Beijing Psychological Support Hotline. We evaluated their suicide risk through two strategies during their index calls: (a) one screening about suicidal ideation, plan, or behavior in the last two weeks and (b) the Comprehensive Suicidal Risk Assessment Scale, which included more risk factors, such as depression, hopelessness, and psychological distress. We monitored their suicidal behaviors for one year through telephone interviews. RESULTS:The number of callers who attempted suicide or died by suicide within 24 hours (1.2%), 30 days (3.8%), 180 days (6.9%), or 365 days (9.1%) was: 102, 341, 615, and 802, respectively. The sensitivities, specificities, and positive predictive values for screening for recent suicidal history were higher than those of the comprehensive scale for predicting suicidal acts within each time point. However, with the increasing duration of the follow-up, the screening had a poorer predictive ability than the comprehensive scale (predicting suicide acts within 270 days: Youden's index, 34.5% vs. 36.2%.; the Area under the Receiver Operator Characteristic Curve, 67.2% vs. 68.1%), especially among those without a history of suicide attempts. CONCLUSIONS:Screening for recent suicidal history is valid for predicting suicidal acts within six months; however, screening is insufficient for predicting long-term suicidal acts compared to assessing more suicide risk factors.

BACKGROUND:Acute heart failure (AHF) is a complex high-mortality condition, with risks escalating as age increases. Due to the complexity and heterogeneity of AHF, the development of effective prognostic indicators remains challenging. This study aims to assess the prognostic ability of plasma metabolites, gut microbiota, together with clinical indicators, in predicting mortality risk in AHF patients. METHODS:Plasma trimethylamine N-oxide (TMAO) levels were quantified alongside routine clinical parameters. Non-targeted metabolomic profiling was performed, and gut microbiota composition was determined through 16S rRNA gene sequencing. The predictive power of potential biomarkers for 1-year mortality was identified and evaluated using Cox regression analysis and receiver operating characteristic (ROC) curves. RESULTS:The prognostic value of N-terminal pro-B-type natriuretic peptide (NT-proBNP) alone was shown to have an area under the curve (AUC) of approximately 0.7, while the predictive utility of TMAO was limited. The addition of age and BUN to NT-proBNP enhances prognostic accuracy in AHF. Metabolomic analyses disclosed a subset of 9 metabolites emerged as novel prognostic biomarkers independent of age, BUN, and NT-proBNP. Microbiomic analyses revealed that three differential genera were associated with prognosis in AHF patients. Furthermore, a compact prognostic biomarker panel including NT-proBNP, age, BUN, homoarginine, MHPG sulfate, N-acetylmethionine, methionine methyl ester, leucyl-alanine, proline-hydroxyproline, and Faecalibacterium was developed, achieving an AUC of 0.902 and Brier score of 0.120. CONCLUSIONS:Multi-omics analyses unveiled novel prognostic metabolites and gut microbes associated with 1-year mortality in AHF. Integrating these biomarkers with clinical parameters provided a potential model for improving the prognosis of AHF.

BackgroundObesity and dysregulated cytokine levels are prevalent in schizophrenia patients undergoing antipsychotic treatment. While cytokines are implicated in obesity, their relationship with psychopathology in schizophrenia remains underexplored. This study investigated associations between body mass index (BMI), cytokine levels, and clinical symptoms in chronic schizophrenia patients.MethodsIn this cross-sectional study,201chronic schizophrenia patients (Chinese Han population) were stratified into high BMI (BMI≥25kg/m2) and low BMI (BMI<25kg/m2) groups. Psychopathology was assessed using the Positive and negative Syndrome Scale (PANSS). Serum cytokine (IL-2, IL-6, TNF-α) and metabolic parameters were measured in 69 participants.ResultsA significant negative correlation was observed between BMI and IL-2(p=0.013). TNF-α levels inversely correlated with PANSS total (p=0.010) and general psychopathology scores(p=0.042). The high BMI group exhibited lower PANSS negative subscores and elevated glucose, triglycerides (TG) and apolipoprotein B (ApoB) compared to the low BMI group(all p<0.05). Multivariate regression identified IL-2 as an independent factor associated with lower BMI, while TNF-α independently contributed to general psychopathology.ConclusionsHigher BMI in chronic schizophrenia is associated with reduced IL-2 levels, attenuated negative symptoms, and adverse lipid profiles. TNF-α may modulate psychopathology severity. These findings highlight complex interactions between metabolic dysregulation, immune markers, and clinical manifestations in schizophrenia.

There is a high prevalence of suicidal ideation (SI), suicide attempts (SA), and non-suicidal self-injury (NSSI) in bipolar disorder (BD). Understanding the nature of suicidality and NSSI in BD is an important way to inform optimal intervention for reducing suicide risk. We aimed to investigate the prevalence and correlates of SI, SA, and NSSI in patients with BD using data from a national survey. We used network analysis to explore the associations among suicidality, NSSI, addictive features of NSSI, and symptoms of BD. Participants with BD were recruited from 20 research centers in China. Suicidality, NSSI, addictive features, and symptoms of BD were measured via a standardized electronic case report form. We used logistic regression and network analysis for data analysis. Of the 1,055 participants recruited, over 50

OBJECTIVE:We collected maintenance treatment medication information from Chinese multicenter bipolar disorder patients, exploring the characteristics of medication use and related factors. We analyzed medication guideline adherence and compared trends in medication use with a study from 2014. METHODS:323 patients receiving maintenance therapy for bipolar disorder across 20 hospitals have been recruited. Their prescription information was collected to compare with the recommended drug protocols outlined by CANMAT to assess consistency. Additionally, descriptive statistics were conducted to document the quantities and percentages of different classes of medications used. Regression analysis was employed to explore factors influencing medication choices. RESULT:The rate of medication inconsistency in this study was 12.38 %, characterized by inappropriate use of antidepressants and mood stabilizers. The rate of using additional drugs was 67.80 %, with antipsychotics being the most common adjunctive treatment in the Bipolar I group and antidepressants in the Bipolar II group. Factors influencing the occurrence of adjunctive therapy included previous hospitalization, most recent episode type, and gender. The rate of polypharmacy was 47.10 %, with previous hospitalization and most recent episode type being the main influencing factors. CONCLUSION:There has been a noticeable improvement in guideline concordance in medication use compared to 2014; however, improper use of antidepressants remains a significant clinical issue. The increasing prevalence of adjunctive medication use underscores the need for more personalized medication recommendations in clinical guidelines.

BACKGROUND:LGBTQ+ populations have been reported to have higher rates of depression compared with their heterosexual peers. Such data provided us the impetus to conduct a meta-analysis on the worldwide prevalence of major depressive disorder (MDD) in LGBTQ+ populations and moderating factors that contributed to differences in prevalence estimates between studies. METHODS:A systematic literature search was performed in major international (PubMed, PsycINFO, Web of Science, EMBASE) and Chinese (Chinese Nation Knowledge Infrastructure (CNKI) and WANFANG) databases from dates of inception to 10 December 2021. RESULTS:48 articles comprising 4,616,903 individuals were included in the meta-analysis. The overall prevalence of MDD was 32.2 % (95%CI: 30.8-33.6 %, I2 = 99.6 %, τ2 = 0.284). MDD prevalence was higher in the LGBTQ+ samples from the United States than other countries, though the difference was not significant in moderator analyses. Moderator analyses indicated point and lifetime prevalence of MDD were significantly higher than estimates based on the past year (Q = 6.270, p = 0.043). Furthermore, studies that relied on convenience sampling had a higher prevalence of MDD than those based on other sampling methods (Q = 8.159, p = 0.017). In meta-regression analyses, mean age (B = 0.03, z = 9.54, p < 0.001) and study quality assessment score (B = 0.24, z = 67.64, p < 0.001) were positively associated with pooled prevalence of MDD while mediation data of year of study (B = -0.08, z = -72.55, p < 0.001) and sample size (B = -1.46, z = -37.83, p < 0.001) were negatively associated with pooled prevalence of MDD in LGBTQ+ samples. CONCLUSIONS:MDD is common among in LGBTQ+ individuals. Considering the negative consequences MDD has on daily life and well-being, appropriate prevention and treatment measures should be provided to vulnerable members of these populations. The findings of this meta-analysis could facilitate identifying at-risk subgroups, developing relevant health policy for LGBTQ+ individuals and allocating health resources from an intersectionality perspective.

Background: Obesity is a predisposing risk factor for type 2 diabetes mellitus (T2DM). Actually, not only obese/overweight but also nonobese/lean individuals may be prone to T2DM. This study is aimed at identifying the contribution of adipose tissue to the development of nonobese diabetes (NOD) and obese diabetes (OD). Methods: Serum samples from the nonobese nondiabetes (NOND, n = 47, age = 46.8 ± 8.4, BMI ≤ 23.9 kg/m2) controls, NOD (n = 48, age = 50.7 ± 6.5, BMI ≤ 23.9 kg/m2) and OD (n = 65, age = 49.8 ± 10.2, BMI ≥ 28 kg/m2) patients were utilized to measure the expression of metabolic indicators, adipocytokines, inflammatory factors. Different adipose depots from offspring with corresponding blood glucose and obesity levels of a spontaneously diabetic gerbil line with various degrees of diabetic penetrance and body weights were examined for adipocytokines and inflammation factors detected by ELISA and western blot. Adipose tissue volume and fat cell size of the gerbils were evaluated by magnetic resonance imaging and immunohistochemistry, respectively. Results: The study yielded four key findings. Firstly, in comparison to the NOD group, the OD group exhibited more severe insulin resistance (IR) and metabolic dysfunction in both patients and gerbils, attributed to higher visceral adipose tissue mass and larger fat cell sizes. Secondly, in gerbils, gonadal fat deposition was linked to obesity development, whereas kidney fat deposition correlated with obesity and diabetes occurrence. Thirdly, in both patients and gerbils, the interplay between adiponectin and leptin levels in serum may significantly influence the development of obesity and diabetes. Lastly, heightened expression of MCP3 in gerbils' kidney adipose tissue may serve as a pivotal factor in initiating obesity-associated diabetes. Conclusions: Our study, which may be considered a pilot investigation, suggests that the interaction of adipocytokines and inflammation factors in different adipose depots could play diverse roles in the development of diabetes or obesity.

Background: DSM-5 proposes the concept of bipolar disorder with “mixed features ”, which is of great benefit to clinical practice. However, the clinical management of BD with mixed features is more challenging.This investigation examined the prescribing patterns and factors influencing guidelines disconcordance for the acute treatment of bipolar disorder with mixed features in mainland China. Methods:This real-world study enrolled 688 patients with acute bipolar disorder through the National Bipolar Pathway Survey Replication (BIPAS-R). We used CUDOS-M and MINI-M scales based on DSM-5 criteria to improve the sensitivity of screening for bipolar disorder with mixed features. Guideline inconsistency judgments were determined by comparison with the Canadian Network for Mood and Anxiety Treatments(CANMAT) guidelines for treatment recommendations for bipolar disorder with mixed features. Logstic regression was used to analyze the influencing factors of guideline disconcordance. Results: Among 688 cases of acute bipolar disorder, 235 cases (34.2%) were (hypo) mania with mixed features and 213 cases (30.9%) were depression with mixed feature. Without considering the order of treatment, the inconsistency rates of (hypo) mania and depression with mixed features with the guidelines were 29.4% and 55.4%, respectively. (Hypo) mania with mixed features BD-II (OR=0.52; 95% CI 0.29-0.93), age at study entry > 24 years (OR=2.4; 95% CI 1.3-4.3), and the number of episodes > 4 in the past year in depression with mixed features (OR=1.9; 95% CI 1.08-3.6), which increased the risk of treatment disconcordance of guidelines. Conclusions:Our findings suggest that BD with mixed features is more common.

Bipolar disorder (BD) is a major mental disorder that significantly impairs behavior and social functioning. This study assessed the network structure of prodromal symptoms in patients with BD prior to their index mood episode. Semi-structured interviews were conducted with the Bipolar Prodrome Symptom Scale-Retrospective (BPSS-R) to examine patients' prodromal symptoms. Network analysis was conducted to elucidate inter-relations between prodromal symptoms. A total of 120 eligible patients participated in this study. Network analysis indicated that the observed model was stable. The edge Mania3-Depression9 ('Racing thoughts' - 'Thinking about suicide', edge weight = 14.919) showed the strongest positive connection in the model, followed by the edge Mania1-depression1 ('Extremely energetic/active' - 'Depressed mood', edge weight = 14.643). The only negative correlation in the model was for Mania7-depression2 ('Overly self-confident' - 'Tiredness or lack of energy', edge weight = -1.068). Nodes Mania3 ('Racing thoughts'), Depression9 ('Thinking about suicide'), Mania1 ('Extremely energetic/active'), and Depression1 ('Depressed mood') were the most central symptoms. Both depressive and manic or hypomanic symptoms appeared in the prodromal phase. Symptoms reflecting 'Racing thoughts', 'Thinking about suicide', 'Extremely energetic/active', and 'Depressed mood' should be thoroughly assessed and targeted as crucial prodromal symptoms in interventions to reduce the risk of BD episodes.

Background Although network analysis studies of psychiatric syndromes have increased in recent years, most have emphasized centrality symptoms and robust edges. Broadening the focus to include bridge symptoms within a systematic review could help to elucidate symptoms having the strongest links in network models of psychiatric syndromes. We conducted this systematic review and statistical evaluation of network analyses on depressive and anxiety symptoms to identify the most central symptoms and bridge symptoms, as well as the most robust edge indices of networks. Methods A systematic literature search was performed in PubMed, PsycINFO, Web of Science, and EMBASE databases from their inception to May 25, 2022. To determine the most influential symptoms and connections, we analyzed centrality and bridge centrality rankings and aggregated the most robust symptom connections into a summary network. After determining the most central symptoms and bridge symptoms across network models, heterogeneity across studies was examined using linear logistic regression. Results Thirty-three studies with 78,721 participants were included in this systematic review. Seventeen studies with 23 cross-sectional networks based on the Patient Health Questionnaire (PHQ) and Generalized Anxiety Disorder (GAD-7) assessments of clinical and community samples were examined using centrality scores. Twelve cross-sectional networks based on the PHQ and GAD-7 assessments were examined using bridge centrality scores. We found substantial variability between study samples and network features. ‘Sad mood’, ‘Uncontrollable worry’, and ‘Worrying too much’ were the most central symptoms, while ‘Sad mood’, ‘Restlessness’, and ‘Motor disturbance’ were the most frequent bridge centrality symptoms. In addition, the connection between ‘Sleep’ and ‘Fatigue’ was the most frequent edge for the depressive and anxiety symptoms network model. Conclusion Central symptoms, bridge symptoms and robust edges identified in this systematic review can be viewed as potential intervention targets. We also identified gaps in the literature and future directions for network analysis of comorbid depression and anxiety.

ObjectiveThe coronavirus disease (COVID-19) and the public health responses were associated with a huge health burden, which could influence sleep quality. This meta-analysis and systematic review examined the prevalence of poor sleep quality in COVID-19 patients.MethodsPubMed, Web of Science, Embase, and PsycINFO were systematically searched from their respective inception to October 27, 2022. Prevalence rates of poor sleep were analyzed using a random effects model.ResultsTotally, 24 epidemiological and 12 comparative studies with 8,146 COVID-19 patients and 5,787 healthy controls were included. The pooled prevalence of poor sleep quality based on the included studies was 65.0% (95%CI: 59.56–70.44%, I2 = 97.6%). COVID-19 patients had a higher risk of poor sleep quality compared to healthy controls (OR = 1.73, 95% CI: 1.30–2.30, p < 0.01, I2 = 78.1%) based on the 12 comparative studies. Subgroup analysis revealed that COVID-19 patients in low-income countries (p = 0.011) and in studies using a lower Pittsburgh Sleep Quality Index score cut-off (p < 0.001) were more likely to have poor sleep quality. Meta-regression analyses revealed that being female (p = 0.044), older (p < 0.001) and married (p = 0.009) were significantly correlated with a higher risk of poor sleep quality while quality score (p = 0.014) were negatively correlated with the prevalence of poor sleep quality in COVID-19 patients.ConclusionPoor sleep quality was found to be very common in COVID-19 patients. Considering the negative effects of poor sleep quality on daily life, sleep quality should be routinely assessed and appropriately addressed in COVID-19 patients.

Background Non-suicidal self-injury (NSSI) is increasingly prevalent among patients with bipolar disorder (BD), raising concerns in psychology and mental health. Investigating the incidence and factors associated with NSSI is crucial for developing prevention and intervention strategies. Methods NSSI behaviors were identified using the Ottawa Self-injury Inventory. The Clinically Useful Depression Outcome Scale supplemented with questions for the DSM-5 specifier of mixed features (CUDOS-M) and the Mini International Neuropsychiatric Interview (Hypo-) Manic Episode with Mixed Features-DSM-5 Module (MINI-M) were used to evaluate clinical symptoms. Non-parametric tests, chi-square tests, point-biserial correlation and logistic regression analyses were employed for the purposes of data analysis. Results The enrolled sample comprised 1044 patients with BD from 20 research centers across China. Out of 1044 individuals, 446 exhibited NSSI behaviors, with 101 of them being adolescents, leading to a prevalence of 78.3 % among adolescent patients. The most common methods for females and males were “cutting” (41.2 %) and “hitting” (34.7 %), respectively. By binary logistic regression analysis, young age, female, bipolar type II disorder, with suicidal ideation and mixed states, depressive symptoms and without family history of mental disorder were correlates of NSSI in patients with BD (P < 0.05). Limitations As a cross-sectional study, causality between NSSI behaviors and associated factors cannot be established. Reporting and recall biases may occur due to self-rating scales and retrospective reports. Conclusion Our study indicates a concerning prevalence of NSSI, particularly among young patients with BD in China. Future research should focus on understanding NSSI behaviors in this population and developing effective interventions.