Anxiety symptoms is prevalent among college students and is associated with a range of detrimental consequences. Self-compassion and emotion regulation difficulties are important factors affecting anxiety symptoms, but their functional mechanism and longitudinal correlation are still unclear. This three-year longitudinal study (baseline: n = 5785, 48.2% of female, Mage = 18.63 years, SD = 0.88; T1 to T5: n = range from 4312 to 5497) aimed to validate the emotion regulation model of self-compassion by examining the associations between self-compassion, emotion regulation difficulties, and anxiety symptoms. Random intercept cross-lagged panel models (RI-CLPMs) was used to distinguish within-person variations overtime from stable between-person differences. The results obtained from the RI-CLPMs indicated that there is a bidirectional effect between self-compassion and anxiety symptoms at the within-person level. Emotional regulation difficulties played a longitudinal mediating role in the prediction from self-compassion to anxiety symptoms at the within-person level, validating the emotion regulation model of self-compassion. The current study indicates that cultivating self-compassion in college students is crucial as it can improve their emotion regulation skills and alleviate anxiety symptoms.

This study utilized network analysis to explore the intricate relationships between anxiety, depression, and quality of life in a cohort of hospitalized schizophrenia patients. Through a cross-sectional design, the investigation aimed to identify key symptoms and bridge connections to inform tailored clinical interventions and improve patient well-being. Symptom severity was measured using the Hamilton Depression Rating Scale, Hamilton Anxiety Rating Scale, and Schizophrenia Quality of Life Scale, with network analysis elucidating central nodes and bridging symptoms within the patient sample of 1328 individuals. Findings revealed psychic anxiety, insomnia, and depressed mood as pivotal within the network, significantly impacting overall symptomatology and quality of life. Furthermore, symptoms such as tension and fears were identified as essential connectors among different symptom domains, highlighting potential intervention targets. The study underscores the complex dynamics between anxiety, depression, and quality of life in schizophrenia, advocating for an integrated treatment approach that focuses on critical symptoms to enhance overall well-being. This approach suggests a paradigm shift towards personalized care in schizophrenia management, aiming to optimize outcomes by addressing the root of symptom networks.

Depression and anxiety are prevalent mental health issues among older adult widowed adults. However, the symptom-level relationships between these conditions remain unclear. Due to the high correlations and complex relationships among various symptoms, this study employs network analysis to explore differences in the network structures of depression and anxiety symptoms between widowed and non-widowed older adults.

Propensity score matching was used to identify widowed older adults with similar demographic characteristics. Data from 1,736 widowed and 1,736 matched controls were analyzed using the Chinese Longitudinal Healthy Longevity Survey (2017-2018). Depression and anxiety were measured by the Center for Epidemiologic Studies Depression Scale-10 (CESD-10) and the seven-item Generalized Anxiety Disorder Scale (GAD-7), respectively. Central and bridge symptoms were evaluated using expected influence (EI) and bridge expected influence (BEI), respectively.

Network analysis revealed similarities in central symptoms between widowed and non-widowed older adults, with both groups exhibiting "Feeling depressed or down" (CESD3), "Feeling tense and having difficulty relaxing" (GAD4), and "Being unable to stop or control worrying" (GAD2) as core symptoms. However, differences emerged in bridge symptoms. In the widowed group, "Feeling anxious, worried, or distressed" (GAD1) was most strongly connected to "Felt lonely" (CESD8); "Worrying too much about various things" (GAD3) was strongly linked to "Feeling increasingly exhausted and useless with age" (CESD4); and "Feeling depressed or down" (CESD3) had a strong association with "Becoming easily annoyed or irritable" (GAD6). In the non-widowed group, "Feeling anxious, worried, or distressed" (GAD1) exhibited the strongest association with "Having good sleep quality" (CESD10); "Getting upset over small matters" (CESD1) was closely connected to "Feeling anxious, worried, or distressed" (GAD1); and "Worrying too much about various things" (GAD3) was most strongly connected to "Feeling depressed or down" (CESD3).

Common central and bridge symptoms highlight universal intervention targets. Addressing "Feeling depressed or down" in widowed and "Getting upset over small matters" in non-widowed older adults may help prevent depression-anxiety comorbidity. These findings support targeted interventions to improve mental health outcomes. Future research should evaluate tailored intervention effectiveness.

This study employs a network analysis approach to explore the interconnections between anxiety, depression, and sleep problems and smartphone addiction among college students using network analysis, offering a new perspective on these prevalent mental health issues.

A cross-sectional study was conducted among art students at a public university in the province of Fujian, China. Data were collected using the Generalized Anxiety Disorder Scale-7, Patient Health Questionnaire-9, Pittsburgh Sleep Quality Index, and Mobile Phone Addiction Index. The R package was used in the analysis for statistical analysis, and information was collected using multi-stage sampling as well as stratified sampling. Network analysis was utilized to identify bivariate associations between symptoms, core components, co-occurring patterns, and key nodes within the network. Network stability and accuracy were assessed using the bootstrap method, and network comparisons were conducted across subgroups based on gender, residential condition, and sibling status.

The study included 2,057 participants. The network analysis revealed uncontrollable worry as the most central symptom, with low energy and excessive worry also identified as key symptoms within the network. Bridge symptoms such as daytime dysfunction, self-harm or suicidal ideation, abnormal behavior and speech, and sensory fear were found to be critical in linking anxiety, depression, and sleep problems. The network of comorbid symptoms and smartphone addiction highlighted inefficiency and loss of control as central factors influencing mental health. No significant differences in network characteristics were found across the subgroups, suggesting the universality of the identified network structure.

This study delineates the intricate network of anxiety, depression, sleep problems, and smartphone addiction among college students, identifying key symptomatic intersections and their implications for mental health.

Nursing students experience higher rates of anxiety and depression than students in other disciplines due to the demanding academic requirements and clinical training challenges. Loneliness and symptoms of anxiety and depression occur simultaneously; however, the specific interrelationships between these states remain inadequately investigated. This study aimed to utilize network analysis to examine the item-level reciprocal action between loneliness, anxiety, and depression among nursing students.

A total of 888 nursing students were assessed using the short-form UCLA Loneliness Scale (ULS-6), the Generalized Anxiety Disorder 7-item Questionnaire (GAD-7), and the Patient Health Questionnaire-9 (PHQ-9) on loneliness, anxiety, and depression, respectively. Descriptive analyses were conducted using SPSS 26.0, whereas other statistical analytical procedures were performed using R software. The Gaussian graphical model was used to estimate network, and the Network Comparison Test was applied to compare differences in networks across gender and grades.

The results indicated that 58.6% of nursing students exhibited varying degrees of loneliness. Network analysis revealed that loneliness formed a separate cluster with limited connections to anxiety and depression communities. The edges between PHQ3-PHQ4 (insomnia or hypersomnia and fatigue), GAD1-GAD2 (feeling anxious and excessive worrying), ULS1-ULS2 (lonely and no one) showed the strongest positive edges within their communities, respectively. And the strongest inter-community edges were observed between GAD5-PHQ8 (fidgety-retardation), ULS6-PHQ4 (isolation-fatigue), and ULS1-GAD1 (lonely-feeling anxious). The centrality analysis identified GAD2 (excessive worrying), ULS6 (isolation), PHQ4 (fatigue), and PHQ2 (feeling down) as the most central node, indicating their significant influence on the overall network structure. Additionally, PHQ8 (retardation), PHQ2 (feeling down), GAD5 (fidgety), and GAD1(feeling anxious) played a crucial role as bridging symptoms that linked the three communities. In addition, there is no statistically significant difference in the network structure except strength of GAD3 (generalized anxiety) and GAD6 (irritable) between sexes.

This study highlights the high prevalence of loneliness among nursing students and its distinct yet limited connection to anxiety and depression, emphasizing its unique role as a standalone psychological construct. The central symptoms in the network and important bridge symptoms across different psychological communities highlight the complexity of mental health symptom networks. This underscores the importance of targeting central symptoms for domain-specific interventions and addressing bridge symptoms to mitigate comorbidities across psychological conditions among nursing students.

The co-occurrence of depression and anxiety is relatively more common among adolescents. Emerging evidence suggests that adaptability might affect this phenomenon. Network analysis can provide insight into the dynamics between symptoms of mental disorders. Therefore, we used network analysis 1) to explore symptom networks and 2) to investigate the association between adaptability and symptoms based on a longitudinal design.

833 Chinese adolescents (449 males and 384 female) were recruited, with adaptability, depressive and anxiety symptoms measured at T1, 426 of them were followed up one year later at T2. Symptom networks were constructed for all participants and for the two groups based on their adaptability scores at T1. Furthermore, mediation analysis was performed to examine the relationship between adaptability and bridge symptoms at both timepoints.

Irritable and Guilty showed the highest expected value at T1 (p < 0.05). The high adaptability group's symptom network (HGN) was less connected than the low adaptability group's symptom network (LGN) (p < 0.001). Furthermore, Irritable (T1) and adaptability (T2) were sequential mediators (p < 0.001) between adaptability (T1) and Irritable (T2).

These findings suggest that adaptability might affect the network dynamics, underscoring its importance to the occurrence of depression and anxiety among adolescents. Irritable and Guilty being the bridge symptoms may indicate the interventions to target in adolescents with comorbid depression and anxiety.

Insomnia, depression, anxiety, and stigma are prevalent and often coexist in patients with Tuberculosis (TB), potentially exacerbating one another. However, the complex intrinsic associations among these four disorders remain unclear, particularly concerning the role of stigma in relation to the other disorders.

A cross-sectional study was conducted at West China Hospital and the Fourth People's Hospital of Guangxi from November 2023 to June 2024. The levels of insomnia, depression, anxiety, and stigma among TB patients were assessed using the Pittsburgh Sleep Quality Index (PSQI), Patient Health Questionnaire-9 (PHQ-9), Generalized Anxiety Disorder-7 (GAD-7), and the TB-Related Stigma Scale (TRSS). Network analysis was used to identify the central and bridge symptoms and explore the role of stigma within the insomnia-depression-anxiety-stigma network.

PHQ1 (anhedonia), GAD1 (nervousness), GAD5 (restlessness), and PHQ3 (sleep problems) are central to the network. Bridge symptoms, including PHQ3 (sleep problems), PSQI5 (sleep disturbances), and GAD5 (restlessness) link the depression, insomnia, and anxiety communities. TRSS1 (family's negative perception) of the stigma community exhibited the highest betweenness and second highest bridge betweenness in the network, highlighting the mediating role of family support across insomnia and psychological symptoms. Additionally, the global strength invariance test indicates that gender, age and education level do not significantly impact the network structure.

Depression (anhedonia and sleep problems) and anxiety (nervousness and restlessness) are the primary concerns requiring intervention in TB patients. In addition, sleep problems act as a bridge in the overall network. Stigma, particularly negative perceptions from family, may play a crucial mediating role in sustaining the entire symptom network. Consequently, these symptoms could represent potential targets for intervention.

Revisiting the epidemiology of posttraumatic stress symptoms (PTSSs) among university students during the coronavirus disease 2019 (COVID-19) pandemic as well as understanding the mental health help-seeking behavior of individuals with PTSSs has critical implications for public mental health strategies in future medical pandemics.

To investigate the prevalence and correlates of PTSSs among university students during the first wave of the COVID-19 pandemic in China and to examine mental health help-seeking behaviors among these students.

A total of 2507 Chinese university students were recruited via snowball sampling. The students completed the Seven-item Screening Scale for Post-traumatic Stress Disorder during the first wave of the COVID-19 pandemic in China. Sociodemographic characteristics, pandemic-related characteristics, and mental health help-seeking behaviors of students with PTSSs were also collected.

The prevalence of PTSSs among the participants was 28.0%. Seven significant correlates of PTSSs were identified (odds ratio = 1.23-3.65, P ≤ 0.024): Female sex, being 19 years old or older, living with others or alone, a low level of family economic status, fair or poor interpersonal relationships, severe or very severe local pandemic, and having family members diagnosed with COVID-19. However, only 3.28% of the students with PTSSs reported seeking help from mental health specialists. Among the 23 students who sought help from mental health specialists, 13 opted for online or telephone-based psychological consultation.

Our data suggest that there was a high risk of PTSSs among university students and a high level of unmet mental health needs during the COVID-19 pandemic. The delivery of mental health services online or via telephone is a promising approach to address these unmet needs.

Excessive screen time was associated with mental health problems, yet whether the associations differ by screen content types remain unknown. Here, we conducted a cross-sectional study of adolescents from 156 junior and high school to investigate the association of types of on-screen content with mental health problems in China. The exposure variable was screen exposure, measured by the self-reported questionnaire. Symptoms of depression and anxiety were assessed by Patient Health Questionnaire-9 (PHQ-9) and Generalized Anxiety Disorder Scale-7 (GAD-7), respectively. Suicide-related behavior was adapted from the Centers for Disease Control's Youth Risk Behavior Surveillance. Generalized linear models and network analysis were performed. Of the 15,194 adolescents included in the final analysis, 8,019 were males and mean (SD) age was 14.50 (1.68) years. 34.6% of adolescents had an average screen time of ≥ 4 h/day on weekends. The most frequently used screen contents (≥ 4 h/d) were electronic games (9.1%), followed by short video (6.6%). Learning online had strong associations with depression (OR = 2.03, 95%CI: 1.29-3.11, P = 0.002) and anxiety (OR = 2.05, 95%CI: 1.24-3.27, P = 0.004) symptoms. The associations between on-screen content and mental health varied. The strongest edges linked to screen content types were edge between "Electronic games" and "Anhedonia" in the network model. "Control worry" was the most central nodes, followed by "Irritable" and "Worry A Lot". Findings suggested that both screen time and different types of screen content were associated with mental health problems among adolescents. Limiting screen exposure and developing effective interventions to improve mental health should be a priority.

With the growing prevalence of anxiety symptoms among university students, self-control is an important potential influence. This study aims to understand the network structure of self-control and anxiety, and to identify the core symptoms within this network. It will provide a theoretical basis for the prevention and intervention of anxiety symptoms in university students.

We used network analysis to study anxiety and self-control in 3,792 university students from six schools in Heilongjiang Province, China. We checked for linear connections in the networks using a restricted cubic spline. We conducted the analyses and made graphs using R software.

(i) The total sample network showed that higher levels of self-control in university students were associated with lower anxiety levels and were validated by the restrictive cubic spline. (ii) There was the strongest negative correlation (edge weight = -0.42) between Without thinking (SC7) and Panic (A5), and the edge weight coefficients of this self-control component and anxiety symptoms were greater than those of other self-control components. (iii) Physical exertion (A6) and Scared (A7) were identified as the core symptoms of the overall network, with expected influence of 1.08 and 1.08 (Z-score). (iv) A significant difference was observed between the anxiety positive network structure and the total sample network structure, with the strongest positive correlation between Iron self-control (SC3) and Breathing difficulty (A2) (edge weight = 0.22), with the strongest negative correlation between Certain things (SC2) and Situations (A4) (edge weight = -0.35). (v) The self-control component Iron self-control (SC3) had only one positive edge in the rural network, and only two positive edges in not one child network.

The present study offered a new perspective on the relationship between self-control and anxiety using network analysis for the first time. The control component Without thinking (SC7) was an important concept influencing the negative correlation of anxiety, and Physical exertion (A6) and Scared (A7) were core symptoms in the total network. Heterogeneity analyses showed a tendency for the more self-controlled to be more anxious in the anxiety positive sample. These results may be a potential target for preventing and intervening anxiety in university students.

Burnout, depression, anxiety, and stress negatively impact the well-being and retention of healthcare professionals. The interplay of these symptoms is understudied. Utilizing network analysis, this study examined the interrelationships among these symptom clusters in clinical therapists in China. An anonymous survey was conducted among clinical therapists from 41 tertiary psychiatric hospitals in China. Burnout was assessed using the Maslach Burnout Inventory-Human Service Survey (MBI-HSS), while symptoms of depression, anxiety, and stress were assessed via the Depression, Anxiety, and Stress Scale-21 (DASS-21). Analyses were performed to identify central symptoms and bridge symptoms of this network. A total of 419 participants were included in this survey. The prevalence rate for burnout, depression, anxiety, and stress was 19.8%, 22.2%, 17.9%, and 8.6%, respectively. Network analysis indicated that stress symptoms had the highest expected influence values, closely followed by emotional exhaustion from MBI-HSS. Notably, emotional exhaustion emerged as the strongest bridge of expected influence. The stability of the expected influence and bridge expected influence was robust, with coefficients at 0.75. The study's findings underscore the importance of recognizing the central symptoms and bridge symptoms, which could lead to more effective early detection and intervention for burnout, depression, anxiety, and stress among clinical therapists.

The health-related quality of life (HRQOL) of college students is not promising and needs to be improved through effective interventions. This study aimed to investigate the associations of nutrition literacy (NL) with HRQOL and anxiety among college students, and to identify the role of anxiety in the relationship between NL and HRQOL. The cross-sectional survey was conducted via the "Wenjuanxing" platform from September to November 2023. The Food and Nutrition Literacy Questionnaire (FNLQ), the Generalized Anxiety Disorder (GAD-7) diagnostic tool and the SF-12 scale were used to assess NL, anxiety, and HRQOL, respectively. Logistic regression models, mediation analysis, additive and multiplicative interaction analyses were used. 2066 college students participated in the analysis. After adjusting for potential confounders, we found that college students with higher NL had higher HRQOL (OR = 2.52, 95% CI: 2.09-3.03, p < 0.001) and a lower risk of anxiety (OR = 0.53, 95% CI: 0.39-0.73, p < 0.001). Additionally, anxious college students had lower HRQOL (OR = 0.16, 95% CI: 0.11-0.24, p < 0.001). Furthermore, mediation analysis confirmed that anxiety was a partial mediator of the relationship between NL and HRQOL (β = 0.600, 95% CI: 0.406-0.779/0.430-0.818). Significant additive interactions were found between NL and Anxiety (RERI = 6.96, 95% CI: 2.74-11.17; AP = 0.51, 95% CI: 0.37-0.64; SI = 2.21, 95% CI: 1.58-3.07). Higher levels of NL are associated with better HRQOL and lower anxiety among college students. Additionally, anxiety partially mediated the relationship between NL and HRQOL. Furthermore, there is a synergy between NL and anxiety.

Horizontal violence can cause serious mental health problems for nurses, particularly anxiety, depression, and post-traumatic stress disorder. However, the intrinsic linkage mechanism between mental symptoms of anxiety, depression, and post-traumatic stress disorder in nurses exposed to horizontal violence is unclear. This study aims to elucidate the characteristics of anxiety, depression, and post-traumatic stress disorder networks among nurses with horizontal violence exposure.

Data for this cross-sectional study were obtained from the baseline portion of a short longitudinal survey conducted at four tertiary hospitals in Shandong Province, China. A total of 510 nurses with horizontal violence exposure completed the General Information Scale, the Negative Acts Questionnaire, the Seven-item Generalized Anxiety Disorder Scale, the Nine-item Patient Health Questionnaire, and the Four-item SPAN. The network model was constructed using network analysis. The expected influence and the bridge expected influence of nodes were calculated. The stability and accuracy of the network were estimated.

The results show that A4 (Trouble relaxing) and P1 (Startle) had the highest expected influence in the network. D9 (Suicidality ideation) and A5 (Restlessness) were the key bridge symptoms.

"Trouble relaxing", "Startle", "Suicidality ideation", and "Restlessness" are all mental symptoms that need to be urgently improved the most in nurses exposed to horizontal violence. Nursing administrators and policymakers should implement mental health intervention programs for these symptoms as early as possible to maximize nurses' mental health.

Anxiety and depression often co-occur, exhibiting high comorbidity, with their trends evolving over time. However, the specific pathways through which comorbid symptoms of anxiety and depression evolve and interact remain unclear. To investigate these questions, this study employed Network Analysis (NA) and Longitudinal Network Analysis (LNA) to explore the central symptoms of anxiety and depression, as well as the temporal evolution of these central symptoms.

The study focused on 606 high school students who were not in their final year in Shandong of China, with assessments conducted from March to September 2022. The bootnet package in R was used for establishing NA and LNA models, as well as for conducting accuracy analysis and node stability analysis.

The results of the NA indicated that adolescent highly susceptible to anxiety and depression. And uncontrollable worry was a common central symptom, while irritability emerged as a central bridging symptom across all three NAs. The LNA results revealed that suicidal ideation and worthlessness were key central symptoms in the LNA. Furthermore, worthlessness played a pivotal role in the developmental pathway of "suicidal ideation → worthlessness → anxiety and uncontrollable worry." A reduction in suicidal ideation was associated with decreased severity in other symptoms.

The findings suggest that adolescent anxiety and depression are in a state of vulnerability, and that irritability, worthlessness, and suicidal ideation are potential targets for interventions to address adolescent anxiety and depression.

Chronic diseases and pain exacerbate depressive symptom in Syria. Limited research on hospital-induced depressive symptom among Syrian patients with chronic diseases warrants further study. A cross-sectional study in four Damascus hospitals revealed high rates of pain and depressive symptom. This study aims to investigate the prevalence and severity of chronic pain and hospital-induced depressive symptom in Syrian patients, as well as the relationship between pain, depressive symptom, and medication behavior. This study analyzes the impact of pain, hospitalization, and medication on patients with chronic diseases. The four Damascus hospitals included 453 patients from various departments. Data were collected through structured interviews and internationally recognized scales such as the PSEQ, HADS, and MMAS. These findings offer insights into pain management and psychological well-being, with implications for patient care and support strategies. The study involved 453 patients with chronic diseases, with gender distribution showing 46.6% females and 53.4% males. The age range was from 7 to 87 years, with an average of 46.87 years. Chi-square tests revealed a significant connection between gender and HADS-A scores, where 48.3% of females had abnormalities (χ2 (1, N = 453) = 7.125, p = 0.028). Marital status was significantly associated with anxiety and depressive symptom levels, particularly among widowed and divorced patients. Employment status, education, and comorbidity were linked to abnormal HADS-A scores, while education level showed a positive correlation with HADS-D scores. ANOVA tests showed significant differences in MMAS scores across income groups (F (3, 449) = 3.167, p = 0.024), with a notable difference between low-income and lower-middle-income groups (mean difference = 0.389, p = 0.031. Chronic pain and HID are prevalent among Syrian patients with chronic diseases and influenced by socio-demographic factors. Personalized interventions are needed to address psychological symptoms and medication behavior.

The outbreak of the COVID-19 pandemic has disrupted people's routine, bringing uncertainty and stress, leading to mental health issues. This situation is particularly acute among Chinese prospective graduates in higher education stage as they cannot predict the outcomes of their studies, graduation, and career development, and therefore experience career-related stress.

This study included 4041 prospective graduates in higher education stage (using handy sampling) recruited in March 2020 as participants (M Age = 22.56, SD = 1.865), utilizing a Sparse Gaussian Graphical Model for regularized partial correlation network analysis of depression symptoms (by PHQ-9), anxiety symptoms (by GAD-7), and career-related stress, which aims to explore the role of career-related stress in the symptom networks of depression and anxiety among prospective graduates in their final semester.

The results revealed that fatigue, sad mood, and psychomotor symptoms in depression, as well as uncontrollable worry and trouble relaxing in anxiety, were central symptoms in the network. Additionally, sad mood and guilt belonging to depressive symptoms, and feeling afraid, restlessness, and irritability belonging to anxiety symptoms, served as bridge symptoms connecting symptom communities. Specifically, guilt as a depressive symptom showed a strong association with employment stress in career-related stress. There were no significant differences in network structure and global strength based on participants' gender. However, despite no significant differences in network structure, the global strength of prospective graduates from Wuhan was significantly lower than samples from non-Hubei provinces, possibly indicative of a Typhoon Eye Effect.

The findings of this study can inspire psychological professionals in higher education institutions to provide support for mental interventions and therapies for prospective graduates, and addressing career development issues.

As the stages of the COVID-19 pandemic evolved, the symptoms of depression, anxiety, and insomnia have increasingly manifested among Chinese college students. The aim of this study is to investigate the relationships between these symptoms through network analysis among Chinese college students during COVID-19.

A three-wave cross-sectional survey was conducted at 22 colleges in Guangdong Province, involving 381,152 students during three specific time intervals: T1 (baseline, February 3 to 10, 2020), T2 (19 months after baseline, June 10 to 18, 2021), and T3 (37 months after baseline, March 15 to April 22, 2023). Depression (PHQ-9), anxiety (GAD-7), and insomnia (YSIS) were used separately. We analyzed two key network indices: "Expected influence" and "Bridge expected influence". Network stability was assessed through a case-dropping bootstrap program.

The effective sample sizes for the three periods were as follows: T1 - 164,101 (103,645 females, 63.2 %), T2 - 86,767 (52,146 females, 60.1 %), and T3 - 130,284 (76,720 females, 58.9 %). Across these three periods, the key central symptoms were "Fatigue" (PHQ4), "Restlessness" (GAD5), "Uncontrollable worrying" (GAD2), "Worry too much" (GAD3) and "Sleep insufficiency" (YSIS6). Notably, "Fatigue" (PHQ4), "Restlessness" (GAD5) and "Irritability" (GAD6) consistently served as bridge symptoms. In the T1 and T2 period, "Motor" (PHQ8) acted as a bridge symptom but weakened in T3.

Throughout the three periods, the mental health issues among Chinese college students displayed characteristics of somatization within the depression-anxiety-insomnia comorbidity network. Over time, anxiety symptoms appeared to become more prominent. Consequently, this study highlights the importance of accurately identifying and promptly intervening in these core symptoms of mental health among college students, as these symptoms may evolve across different stages of a pandemic.

The COVID-19 pandemic has exacerbated mental health challenges, particularly depression among college students. Detecting at-risk students early is crucial but remains challenging, particularly in developing countries. Utilizing data-driven predictive models presents a viable solution to address this pressing need.

1) To develop and compare machine learning (ML) models for predicting depression in Argentinean students during the pandemic. 2) To assess the performance of classification and regression models using appropriate metrics. 3) To identify key features driving depression prediction.

A longitudinal dataset (N = 1492 college students) captured T1 and T2 measurements during the Argentinean COVID-19 quarantine. ML models, including linear logistic regression classifiers/ridge regression (LogReg/RR), random forest classifiers/regressors, and support vector machines/regressors (SVM/SVR), are employed. Assessed features encompass depression and anxiety scores (at T1), mental disorder/suicidal behavior history, quarantine sub-period information, sex, and age. For classification, models' performance on test data is evaluated using Area Under the Precision-Recall Curve (AUPRC), Area Under the Receiver Operating Characteristic curve, Balanced Accuracy, F1 score, and Brier loss. For regression, R-squared (R2), Mean Absolute Error, and Mean Squared Error are assessed. Univariate analyses are conducted to assess the predictive strength of each individual feature with respect to the target variable. The performance of multi- vs univariate models is compared using the mean AUPRC score for classifiers and the R2 score for regressors.

The highest performance is achieved by SVM and LogReg (e.g., AUPRC: 0.76, 95% CI: 0.69, 0.81) and SVR and RR models (e.g., R2 for SVR and RR: 0.56, 95% CI: 0.45, 0.64 and 0.45, 0.63, respectively). Univariate models, particularly LogReg and SVM using depression (AUPRC: 0.72, 95% CI: 0.64, 0.79) or anxiety scores (AUPRC: 0.71, 95% CI: 0.64, 0.78) and RR using depression scores (R2: 0.48, 95% CI: 0.39, 0.57) exhibit performance levels close to those of the multivariate models, which include all features.

These findings highlight the relevance of pre-existing depression and anxiety conditions in predicting depression during quarantine, underscoring their comorbidity. ML models, particularly SVM/SVR and LogReg/RR, demonstrate potential in the timely detection of at-risk students. However, further studies are needed before clinical implementation.

A temporal network of generalized anxiety disorder (GAD) symptoms could provide valuable understanding of the occurrence and maintenance of GAD. We aim to obtain an exploratory conceptualization of temporal GAD network and identify the central symptom.

A sample of participants (n = 115) with elevated GAD-7 scores (Generalized Anxiety Disorder 7-Item Questionnaire [GAD-7] ≥ 10) participated in an online daily diary study in which they reported their GAD symptoms based on DSM-5 diagnostic criteria (eight symptoms in total) for 50 consecutive days. We used a multilevel VAR model to obtain the temporal network.

In temporal network, a lot of lagged relationships exist among GAD symptoms and these lagged relationships are all positive. All symptoms have autocorrelations and there are also some interesting feedback loops in temporal network. Sleep disturbance has the highest Out-strength centrality.

This study indicates how GAD symptoms interact with each other and strengthen themselves over time, and particularly highlights the relationships between sleep disturbance and other GAD symptoms. Sleep disturbance may play an important role in the dynamic development and maintenance process of GAD. The present study may develop the knowledge of the theoretical model, diagnosis, prevention and intervention of GAD from a temporal symptoms network perspective.

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.

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.

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.

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.

Network analysis provides a novel approach to discovering associations between mental disorders at the symptom level. This study aimed to examine the characteristics of the network of depression and anxiety symptoms and their associations with mobile phone addiction (MPA) among Chinese medical students during the late stage of the COVID-19 pandemic. A total of 553 medical students were included. Depression and anxiety symptoms and MPA were measured by the nine-item Patient Health Questionnaire (PHQ-9), the seven-item Generalized Anxiety Disorder Scale (GAD-7), and the Mobile Phone Addiction Index (MPAI), respectively. Central and bridge symptoms were identified with centrality indices and bridge centrality indices. Network stability was examined using the case-dropping procedure. "Uncontrollable worry", "restlessness" and "nervousness" were the central symptoms in the depression and anxiety network. "Restlessness" and "motor" were the most central bridge symptoms linking depression and anxiety. "Concentration", "anhedonia" and "sleep" were most strongly associated with MPA. "Uncontrollable worry", "restlessness", "nervousness," and "motor" may be the symptoms for interventions to target in medical students with comorbid depression and anxiety. From a network perspective, depressive symptoms may be more important than anxiety symptoms in medical students with MPA.

Previous coronavirus, 2019 (COVID-19) research has applied network analysis to examine relationships between psychopathological symptoms but rarely extended to potential risk and protective factors or the influence of COVID-19 infection history. This study examined complex inter-relationships between psychopathological symptoms, COVID-19-related stressors, perceived social support, and COVID-19 infection history among Chinese university/college students during the peak of fifth pandemic wave using a network analysis approach.

A Least Absolute Shrinkage and Selection Operator-regularized partial correlation network using Gaussian graphical model was constructed in 1,395 Chinese university/college students in Hong Kong who completed a survey between 15 March and 3 April, 2022. Depressive, anxiety, and acute/traumatic stress symptoms were measured by Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and Impact of Event Scale-6, respectively. COVID-19-related stressors and perceived social support were measured. Network differences by COVID-19 infection history (COVID-network vs. no_COVID-network) and network communities were examined.

Our results showed that the most influential nodes were depressed mood, uncontrollable worries, and uncontrollable thoughts about COVID-19. The main bridging symptoms were concentration problems and psychomotor problems. The COVID-network, comprising participants with a history of COVID-19 infection only, was significantly stronger than the no_COVID-network. Perceived social support and stress from conflicts with family/friends formed a unique community with negative cognition and suicidal idea in the COVID-network only.

Our findings indicate that specific interventions targeting interpersonal conflicts and concentration problems as well as facilitating stress buffering effects of social support may represent effective strategies to reduce psychological distress in university/college students during COVID-19 and should be considered for future pandemic preparedness.

The move away from investigating mental disorders as whole using sum scores to the analysis of symptom-level interactions using network analysis has provided new insights into comorbidities. The current study explored the dynamic interactions between depressive and anxiety symptoms in older Chinese adults with diabetes mellitus (DM) and identified central and bridge symptoms in the depression-anxiety network to provide potential targets for prevention and intervention for depression and anxiety.

This study used a cross-sectional design with data from the 2017-2018 wave of the Chinese Longitudinal Healthy Longevity Survey (CLHLS). A regularized partial correlation network for depressive and anxiety symptoms was estimated based on self-reported scales completed by 1685 older adults with DM aged 65 years or older. Depressive and anxiety symptoms were assessed using the 10-item Center for Epidemiologic Studies Depression Scale (CESD-10) and the Seven-Item Generalized Anxiety Disorder Scale (GAD-7), respectively. Expected influence (EI) and bridge expected influence (BEI) indices were calculated for each symptom.

According to cutoff scores indicating the presence of depression and anxiety, the prevalences of depression and anxiety in our sample were 52.9% and 12.8%, respectively. The comorbidity rate of depression and anxiety was 11.5%. The six edges with the strongest regularized partial correlations were between symptoms from the same disorder. "Feeling blue/depressed", "Nervousness or anxiety", "Uncontrollable worry", "Trouble relaxing", and "Worry too much" had the highest EI values. "Nervousness or anxiety" and "Everything was an effort" exhibited the highest BEI values.

Central and bridge symptoms were highlighted in this study. Targeting these symptoms may be effective in preventing the comorbidity of depressive and anxiety symptoms and facilitate interventions in older Chinese adults with DM who are at risk for or currently have depressive and anxiety symptoms.

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.

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.

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.

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.

The effects of short-term mindfulness are associated with the different patterns (autonomic, audio guided, or experienced and certified mindfulness instructor guided mindfulness). However, robust evidence for reported the impacts of different patterns of mindfulness on mental health and EEG biomarkers of undergraduates is currently lacking. Therefore, we aimed to test the hypotheses that mindfulness training for undergraduates would improve mental health, and increase alpha power over frontal region and theta power over midline region at the single electrode level. We also describe the distinction among frequency bands patterns in different sites of frontal and midline regions. 70 participants were enrolled and assigned to either 5-day mindfulness or a waiting list group. Subjective questionnaires measured mental health and other psychological indicators, and brain activity was recorded during various EEG tasks before and after the intervention. The 5-day mindfulness training improved trait mindfulness, especially observing (p = 0.001, d = 0.96) and nonreactivity (p = 0.03, d = 0.56), sleep quality (p = 0.001, d = 0.91), and social support (p = 0.001, d = 0.95) while not in affect. Meanwhile, the expected increase in the alpha power of frontal sites (p < 0.017, d > 0.84) at the single electrode level was confirmed by the current data rather than the theta. Interestingly, the alteration of low-beta power over the single electrode of the midline (p < 0.05, d > 0.71) was difference between groups. Short-term mindfulness improves practitioners' mental health, and the potentially electrophysiological biomarkers of mindfulness on neuron oscillations were alpha activity over frontal sites and low-beta activity over midline sites.

During the COVID-19 pandemic, junior students who had recently entered university may have experienced particular difficulties. This study aimed to investigate the incidence of anxiety, depression, and sleep status among junior college students during school closure. Junior college students from 3colleges in Anhui Province participated in this study from 6th to 20th April, 2022. The students' data were collected using a designed online questionnaire developed on the "Wen juan xing" website and submitted via cell phone. Ordinal logistic regression analysis indicated that female sex was an independent risk factor for increased anxiety, depression, and insomnia (anxiety: OR 1.503, 95% CI 1.191-1.897; depression: OR 1.14, 95% CI 1.023-1.270; ISI OR 2.052, 95% CI 1.646-2.559). Notably, medical specialty was an independent risk factor for depression and anxiety (anxiety: OR 1.367, 95% CI 1.078-1.734; depression: OR 1.289, 95% CI 1.148-1.448). Moreover, being a freshman was a risk factor for increased depression and insomnia (depression: OR 1.036,95% CI 0.931-1.153; insomnia: (OR 1.157,95% CI 0.961-1.394). The findings indicate that a considerable portion of junior college students experienced psychological problems due to lockdowns during the COVID-19 pandemic.

The psychological impact of the COVID-19 pandemic has been understudied among vulnerable populations. This study aimed to examine the immediate and longer-term changes in the mental health of children with parent-child separation experiences during the COVID-19 pandemic, and identify potential buffering opportunities for mental health.

This longitudinal cohort study used data from 723 rural Chinese children who provided data before (Oct. 2019) the COVID-19 pandemic and during the following 2 years. Changes in the probability of depressive symptoms, anxiety symptoms, non-suicide self-injurious (NSSI), suicidal ideation, suicide plan, and suicide attempt were tested across four waves using generalized estimating models (GEE).

Compared with children who never experienced parent-child separation, children persistently separated from parents since birth experienced greater deterioration in all mental health in the 2-year follow-up (average change: depressive symptoms: β = 0.59, 95% CI [0.26, 0.93]; anxiety symptoms: β = 0.45, 95% CI [0.10, 0.81]; NSSI: β = 0.66, 95% CI [0.31, 1.01]; suicide ideation: β = 0.67, 95% CI [0.38, 0.96]; suicide plan: β = 0.77, 95% CI [0.38, 1.15]; suicide attempt: β = 1.12, 95% CI [0.63, 1.62]). However, children with childhood separation from their parents but reunited with them during the transition to adolescence showed similar even lower changes to counterparts who never experienced parent-child separation (all ps > 0.05).

These results indicating improvements in supportiveness of the caregiving environment during the transition to adolescence may provide the opportunity to buffer the adverse impact of COVID-19 on mental health. Translating such knowledge to inform intervention and prevention strategies for youths exposed to adversity is a critical goal for the field.

Previous studies have frequently reported a high prevalence of co-occurring anxiety and depression among people who experienced stressful events in childhood. However, few have noted the symptomatic relationship of this comorbidity among childhood sexual abuse (CSA) survivors. Therefore, this study's objectives were as follows: (1) to examine the relationship across symptoms between anxiety and depression among CSA survivors; (2) to compare differences between male and female network structures among CSA survivors.

A total of 63 Universities and Colleges in Jilin Province, China, covered 96,218 participants in this study, a sub-set data of which met the criteria of CSA was analyzed with the network analysis. The Childhood Trauma Questionnaire-Short Form (CTQ-SF), measured CSA. Anxiety was measured by the seven-item Generalized Anxiety Disorder Scale (GAD-7), and depression was measured by the Patient Health Questionnaire (PHQ-9). The sex difference between anxiety and depression among CSA survivors was compared.

3,479 college students reported the experience of CSA (CTQ-SF total scores ≥ 8), with a prevalence of 3.62% (95% CI: 3.50-3.73%). Among CSA survivors, control worry, sad mood, and energy were central and bridge symptoms of the anxiety and depression network. Meanwhile, male CSA survivors appeared to have a stronger correlation between guilt and suicide, but female CSA survivors seemed to have a stronger correlation between control worry and suicide. Moreover, the edge of control worry-relax-afraid was stronger in the male network, while the edge of restless-relax was stronger in the female network.

Control worry, sad mood, and energy are crucial to offer targeted treatment and to relieve anxiety and depression symptoms for CSA survivors. Guilt needs more attention for male CSA survivors, while control worry remains more important for female CSA survivors to reduce suicidal ideation and suicide attempts.

Social distancing measures implemented during the COVID-19 pandemic have contributed to increased levels of loneliness, but the specific interactions between loneliness symptoms in the context of the pandemic remain unknown. This study characterized the psychological network of loneliness symptoms in residents during the initial wave of COVID-19 outbreak in China.

The study recruited 8472 Chinese residents (61.5% women, aged 33.0±10.6 years) through online snowball sampling. The Six-item De Jong Gierveld Loneliness Scale (DJGLS) was used to measure loneliness symptoms. Central symptoms of and bridge symptoms between emotional and social loneliness subscales were identified based on centrality and bridge centrality indices, respectively. Network stability was examined using the case-dropping procedures.

The nodes in the loneliness network were separated into two groups, corresponding to the social and emotional loneliness domains of the DJGLS. The strongest direction association was between "no reliable people" and "no trustworthy people" (edge weight=0.546). "No trustworthy people" has the highest node strength (1.047) in the loneliness network, followed by "emptiness sense" (0.767) and "no reliable people" (0.749). "Feeling of rejection" (1.672) and "no close people" (0.403) showed the first and second highest bridge strengths, respectively. Both the stability and accuracy tests supported robustness of the whole network.

Interventions targeting central symptoms "no trustworthy people" and "emptiness sense" and central bridge symptom "feeling of rejection" may be effective for alleviating the overall level of loneliness in pandemic-affected Chinese residents.

Burnout, depression, and anxiety are highly prevalent among medical students, which often leads to their attrition. We aim to assess the inter-relationships of depression, burnout, and anxiety symptoms with dropout intention among Chinese medical undergraduates using the network analysis.

A total of 3,648 Chinese medical undergraduates were recruited through snowball sampling. Learning burnout scale, 9-item Patient Health Questionnaire (PHQ-9), and General Anxiety Disorder Scale (GAD-7) was used to assess burnout, depression, and anxiety symptoms, respectively. We used the EBICglasso model to estimate the network. We compared the network based on gender, study phase, and clinical experience.

After removing repeated submissions and incorrect responses to the trap question, 3,536 participants were included in the final analysis. The prevalence of burnout, depression, anxiety, and dropout intention was 38, 62.7, 38.4, and 39% respectively, which is consistent with previous findings. Network analysis suggested that anxiety and depression items clustered together and displayed several strong bridge connections, while burnout items formed another cluster. All the strongest edges were within the respective distress. Cynicism symptoms 'I am fed up with study' and 'I want to study but I feel that studying is boring' were the most central symptoms, while 'fatigue' and 'worthless' were the bridge symptoms within the burnout-depression-anxiety network. Other central symptoms included 'worthless', 'I can handle my courses', 'nervous', and 'uncontrollable worry'. Cynicism symptoms 'I am interested in my major' and 'I feel that the knowledge I have learned is useless' were mostly related to dropout intention. Gender, study phase, and clinical experience didn't affect the global strength of the burnout-depression-anxiety network.

Our results indicated the predominance of cynicism symptoms within the burnout-depression-anxiety network and its substantial impact on dropout intention, suggesting that early detection and intervention for cynicism symptoms in Chinese medical students are in urgent need. Other central and bridge symptoms might also serve as potential targets for the prevention and treatment of burnout, depression, and anxiety among medical students. For example, studies suggest cognitive-behavioral therapy could quickly improve 'worthless', which might be beneficial in treating burnout, depression, and anxiety in medical students.

This research analyses the relations between anxiety symptoms from the network perspective to deepen the understanding of anxiety in front-line medical staff during the COVID-19 pandemic and can also provide a reference for determining potential goals of clinical interventions.

A convenience sampling was adopted, and the Generalized Anxiety Disorder 7-item scale (GAD-7) was administered to front-line medical staff through online platforms. A regularized partial correlation network of anxiety was constructed and then we evaluated its accuracy and stability. The expected influence and predictability were used to describe the relative importance and the controllability, using community detection to explore community structure. The gender-based differences and the directed acyclic graph were implemented.

The connections between A1 "Feeling nervous, anxious or on edge" and A2 "Not being able to stop or control worrying", A6 "Becoming easily annoyed or irritable" and A7 "Feeling afraid as if something awful might happen", etc., were relatively strong; A2 "Not being able to stop or control worrying" and A3 "Worrying too much about different things" had the highest expected influence, and A2 "Not being able to stop or control worrying" had the highest predictability. The community detection identified two communities. The results of the gender network comparison showed the overall intensity of the anxiety network in women was higher than that in men; DAG indicated that A2 "Not being able to stop or control worrying" had the highest probabilistic priority; the lines from A2 "Not being able to stop or control worrying" to A1 "Feeling nervous, anxious or on edge" and A2 "Not being able to stop or control worrying" to A7 "Feeling afraid as if something awful might happen" represented the most important arrows.

There exist broad interconnections among anxiety symptoms of front-line medical staff on the GAD-7. A2 "Not being able to stop or control worrying" might be the core symptom and a potential effective intervention target. It was possible to bring an optimal result for the entire GAD symptom network by interfering with A2 "Not being able to stop or control worrying". GAD may have two "subsystems". The modes of interconnection among anxiety may be consistent between genders.

COVID-19 survivors suffer from persistent mental distress and impaired quality of life (QOL) after recovery from the infection. However, the symptom-symptom interaction between these psychological variables remained unexplored. The present study aimed to determine the symptom network of mental distress (depression, anxiety, sleep disturbance, fatigue, and post-traumatic stress disorder) and their association with QOL among 535 COVID-19 survivors 1 year after hospital discharge.

9-item Patient Health Questionnaire, 7-item Generalized Anxiety Disorder Scale, Chalder fatigue scale, Impact of Event Scale-Revised, Pittsburgh Sleep Quality Index, and 36-Item Short-Form Health Survey were applied to measure depression, anxiety, fatigue, PTSD, sleep disturbances, and QOL, respectively. Two networks were estimated using Gaussian graphical model. Network 1 consisted of mental symptoms to determine the central and bridge symptoms. Network 2 additionally included QOL to determine which mental symptoms were mostly related to QOL.

60% of the COVID-19 survivors experienced mental distress 1 year after hospital discharge. Uncontrollable and excessive worry, psychomotor symptoms, intrusion, and daytime dysfunction were the most central symptoms. Daytime dysfunction and fatigue (especially mental fatigue and loss of energy) served as the bridge symptoms across the mental distress network and exhibited the most substantial association with QOL.

Our study demonstrated several key symptoms that played a vital role in mental distress and QOL among COVID-19 survivors. Prompt screening and targeted interventions for these symptoms might hold great promise in preventing mental distress and improving QOL in COVID-19 survivors.

Lifestyle habits are vital components of the culture of mental health treatment settings. We examined the bridge connection between depressive and anxiety symptoms and lifestyles from a network perspective using a population-based study.

Face-to-face interviews were conducted with a provincially representative sample of 13,768 inhabitants from the Guangdong Sleep and Psychosomatic Health Survey based on standardized evaluation techniques. We identified the central symptoms by expected influence. The interconnection between depression and anxiety symptoms, as well as the bridge connectivity linking depression-anxiety symptoms and lifestyle factors, were assessed using the bridge centrality index. Network stability and sensibility analyses were performed using a case-dropping bootstrap procedure.

The core symptom that exhibited the highest expected influence was fatigue or little energy, followed by uncontrollable worry, trouble relaxing, and sad mood in the depression-anxiety symptoms network, while guilt was the most interconnected symptom and had the highest bridge strength. Surrounding nodes of each node explained an average variance of 57.63%. Additionally, suicidal thoughts were recognized as collective bridging symptoms connecting lifestyle variables in the network integrating depression-anxiety symptoms with lifestyle factors. Current tobacco and alcohol consumption were positively associated with suicidal thoughts and irritability. Habitual diet rhythm and physical exercise frequency were linked to suicidal thoughts, guilt, and poor appetite or overeating. Suicidal thoughts, irritability, and guilt indicated the greatest connectivity with lifestyle factors. All networks had high stability and accuracy.

These highlighted core and bridge symptoms could serve as latent targets for the prevention and intervention of comorbid depression and anxiety. It might be crucial for clinical practitioners to design effective and targeted treatment and prevention strategies aiming at specific lifestyles and behaviors.

The number of COVID-19 infections has increased sharply and quickly after optimizing the COVID-19 response in China. In the context of this population-size infection, college students' psychological response is yet to be understood.

A cross-sectional study was designed to investigate anxiety, depression, insomnia, and post-traumatic stress disorder (PTSD) symptoms among college students from December 31, 2022, to January 7, 2023. The questionnaire included the Generalized Anxiety Disorder 7 (GAD-7), Patient Health Questionnaire 9 (PHQ-9), Insomnia Severity Index (ISI), Impact of Event Scale (IES-R), and self-designed questionnaire.

Of the 22,624 respondents, the self-reported prevalence of anxiety, depression, insomnia, PTSD, and any of the four psychological symptoms appeared as 12.7 %, 25.8 %, 11.6 %, 7.9 %, and 29.7 %, respectively. The self-reported COVID-19 infection rate was 80.2 %. Changes in the place for learning, longer time online, not recovering after infection, a higher proportion of family member infection, insufficient drug reserve, worry about sequela after infection, future studies, or employment contributed to a higher risk of anxiety/depression/insomnia symptoms or PTSD symptoms. Multinomial logistic regression showed that those who spent more extended time on the Internet, recovered after infection, and had insufficient drug reserves were less likely to have PTSD than anxiety/depression/insomnia symptoms.

The study was a non-probability sampling survey.

Anxiety, depression, insomnia, and PTSD were common psychological symptoms among college students when infection went through a large-scale population. This study highlights the importance of continuing to care for the psychological symptoms of college students, especially timely responses to their concerns related to the epidemic situation and COVID-19 infection.

In the context of the outbreak of COVID-19 within mainland China, to understand the mental health status of university students during campus closure, this study analyzes the relationship between anxiety, depressive symptoms, and psychological capital and to reveals their central symptoms.

A total of 12,945 university students were included in this study from April 10 to 19, 2022. Anxiety and depressive symptoms were measured by the seven-item Generalized Anxiety Disorder Scale (GAD-7) and two-item Patient Health Questionnaires (PHQ-2). Psychological capital was measured using the Psychological Capital Questionnaire (PCQ-24). The centrality and bridge centrality indexes were used to identify central and bridge symptoms, respectively. Network Comparison Test (NCT) was also administered to check whether network traits differed by gender and place of residence.

The most influential node in this study was Trouble relaxing (GAD4), followed by Uncontrollable worry (GAD2) and Excessive worry (GAD3). The main bridging symptoms were Depressed mood (PHQ2), Psychological capital. There are no differences in the network structure of students by place of residence, while there are more significant differences in the network structure of students by gender.

Central and bridging symptoms may be the core symptoms that trigger or maintain the development of anxiety and depression among university students during the COVID-19 campus closure. Timely and reasonable interventions targeting these symptoms may help reduce depression and anxiety in this population. In addition, improving university students' psychological capital may likewise contribute to the development of their good mental health.

The 2019 novel coronavirus disease (COVID-19) outbreak affected people's lifestyles and increased their risk for depressive and anxiety symptoms (depression and anxiety, respectively hereafter). We assessed depression and anxiety in residents of Macau during "the 6.18 COVID-19 outbreak" period and explored inter-connections of different symptoms from the perspective of network analysis.

In this cross-sectional study, 1,008 Macau residents completed an online survey comprising the nine-item Patient Health Questionnaire (PHQ-9) and seven-item Generalized Anxiety Disorder Scale (GAD-7) to measure depression and anxiety, respectively. Central and bridge symptoms of the depression-anxiety network model were evaluated based on Expected Influence (EI) statistics, while a bootstrap procedure was used to test the stability and accuracy of the network model.

Descriptive analyses indicated the prevalence of depression was 62.5% [95% confidence interval (CI) = 59.47-65.44%], the prevalence of anxiety was 50.2% [95%CI = 47.12-53.28%], and 45.1% [95%CI = 42.09-48.22%] of participants experienced comorbid depression and anxiety. "Nervousness-Uncontrollable worry" (GADC) (EI = 1.15), "Irritability" (GAD6) (EI = 1.03), and "Excessive worry" (GAD3) (EI = 1.02) were the most central symptoms, while "Irritability" (GAD6) (bridge EI = 0.43), "restlessness" (GAD5) (bridge EI = 0.35), and "Sad Mood" (PHQ2) (bridge EI = 0.30) were key bridge symptoms that emerged in the network model.

Nearly half of residents in Macau experienced comorbid depression and anxiety during the 6.18 COVID-19 outbreak. Central and bridge symptoms identified in this network analysis are plausible, specific targets for treatment and prevention of comorbid depression and anxiety related to this outbreak.

Facing multiple changes, adolescents are prone to have anxiety and depression concurrently, which would accompany a particularly high risk for suicide. However, most previous studies have ignored the heterogeneity of the components and used latent variable methods to explore the associations between these core variables, resulting in a lack of component-level discussions.

Using a large sample of 9300 adolescents (Mean_age = 13.51; SD_age = 1.33; 49.82 % females), two network pathways of anxiety and depression and the associations between these variables and suicidal behavior were constructed. The central components and the stability of both networks were also identified.

Considering the network of anxiety and depression, there were two strong bridging symptoms of sleep problems and palpitation or tachycardia. The symptoms of depression showed a more vital centrality than anxiety, and the central symptoms were tachycardia, worthlessness, fatigue, and feeling of choking. For the network of suicidal behavior and symptoms of anxiety and depression, besides sleep problems, the edge linking lifetime suicide ideation and attempt and the frequency of suicide ideation in the past year was also a strong edge. Worthlessness connected symptoms of anxiety and depression with suicidal behavior. The central components were tachycardia, worthlessness, the frequency of suicidal ideation over the past year, and fatigue. Additionally, both networks had higher stability in terms of edge and centrality.

Based on the identified relevant strong bridging and central components, effective therapies would target these components first, which would lead to the alleviating effects on other components.