Background: There is conclusive evidence of a multifaceted and bidirectional relationship between loneliness and depression and anxiety. Nonetheless, more extensive research is needed to examine their relationships at a more granular level. This study employed a network analysis approach to identify the pathological mechanisms underpinning those relationships and to identify important bridge nodes as potential targets for intervention. Methods: 941 University students were included in this study. The ULS-6 (the short-form UCLA Loneliness Scale) was used to assess loneliness, the PHQ-9 (Patient Health questionnaire-9) and GAD-7 (Generalized anxiety disorder 7-item) scales were used to assess the symptoms of depression and anxiety. We constructed two network structures of loneliness-anxiety and loneliness-depression and computed bridge expected influence for each symptom. In addition, we showed a flow network of “Suicide” containing symptoms of depression and loneliness. Results: All edges were positive in both networks constructed and the strongest edges were present within disorder communities. The overall connection between loneliness and depression was stronger compared to anxiety. The results demonstrated that the loneliness item “People are around me but not with me” was identified as bridge symptom in both networks. Furthermore, “Suicide” was directly connected to five symptoms of depression and four items of loneliness, with the strongest connections being between it and “Feeling of worthlessness” and “Psychomotor agitation/retardation”. Conclusions: Our findings provide a more nuanced explanation of the link between loneliness and depression and anxiety. The results identified the bridge symptom “People are around me but not with me”, which had the strongest effect on enhancing symptoms of depression and anxiety. Clinical improvements based on the findings of this study and the impact of the intervention are discussed.

Background: Evidence from previous studies has confirmed that functionally impaired elderly individuals are susceptible to comorbid anxiety and depression. Network theory holds that the comorbidity emerges from interactions between anxiety and depression symptoms. This study aimed to investigate the fine-grained relationships among anxiety and depression symptoms in the functionally impaired elderly and identify central and bridge symptoms to provide potential targets for intervention of these two comorbid disorders.Methods: A total of 325 functionally impaired elderly individuals from five communities in Xi'an, China, were recruited for our investigation. The GAD-7 and PHQ-9 were used to measure anxiety and depression, respectively. SPSS 22.0 software was used for descriptive statistics, and R 4.1.1 software was used for network model construction, expected influence (EI) evaluation and bridge expected influence (BEI) evaluation.Results: In the network, there were 35 edges (indicating partial correlations between symptoms) across the communities of anxiety and depression, among which the strongest edge was A1 “Nervousness or anxiety”-D2 “Depressed or sad mood.” A2 “Uncontrollable worry” and D2 “Depressed or sad mood” had the highest EI values in the network, while A6 “Irritable” and D7 “Concentration difficulties” had the highest BEI values of their respective community. In the flow network, the strongest direct edge of D9 “Thoughts of death” was with D6 “Feeling of worthlessness.”Conclusion: Complex fine-grained relationships exist between anxiety and depression in functionally impaired elderly individuals. “Uncontrollable worry,” “depressed or sad mood,” “irritable” and “concentration difficulties” are identified as the potential targets for intervention of anxiety and depression. Our study emphasizes the necessity of suicide prevention for functionally impaired elderly individuals, and the symptom “feeling of worthlessness” can be used as an effective target.

Background: As a common social phenomenon, nurses’ occupational burnout has a high incidence rate, which seriously affects their mental health and nursing level. The current assessment mostly uses the total score model and explores the influence of external factors on burnout, while the correlation between burnout items or dimensions is less explored. Ignoring the correlation between the items or dimensions may result in a limited understanding of nurse occupational burnout. This paper explores the item and dimension network structure of the Maslach Burnout Inventory-General Survey (MBI-GS) in Chinese nurses, so as to gain a deeper understanding of this psychological construct and identify potential targets for clinical intervention. Methods: A total of 493 Chinese nurses were recruited by cluster sampling. All participants were invited to complete the survey on symptoms of burnout. Network analysis was used to investigate the item network of MBI-GS. In addition, community detection was used to explore the communities of MBI-GS, and then network analysis was used to investigate the dimension network of MBI-GS based on the results of community detection. Regularized partial correlation and non-regularized partial correlation were used to describe the association between different nodes of the item network and dimension network, respectively. Expected influence and predictability were used to describe the relative importance and the controllability of nodes in both the item and dimension networks. Results: In the item network, most of the strongly correlated edges were in the same dimension of emotional exhaustion (E), cynicism (C) and reduced professional efficacy (R), respectively. E5 (Item 5 of emotional exhaustion, the same below) “I feel burned out from my work”, C1 “I have become more callous toward work since I took this job”, and R3 “In my opinion, I am good at my job” had the highest expected influence (z-scores = 0.99, 0.81 and 0.94, respectively), indicating theirs highest importance in the network. E1 “I feel emotionally drained from my work” and E5 had the highest predictability (E1 = 0.74, E5 = 0.74). It shows that these two nodes can be interpreted by their internal neighbors to the greatest extent and have the highest controllability in the network. The spinglass algorithm and walktrap algorithm obtained exactly the same three communities, which are consistent with the original dimensions of MBI-GS. In the dimension network, the emotional exhaustion dimension was closely related to the cynicism dimension (weight = 0.65). Conclusions: The network model is a useful tool to study burnout in Chinese nurses. This study explores the item and domain network structure of nurse burnout from the network perspective. By calculating the relevant indicators, we found that E5, C1, and R3 were the most central nodes in the item network and cynicism was the central node in the domain network, suggesting that interventions aimed at E5, C1, R3 and cynicism might decrease the overall burnout level of Chinese nurses to the greatest extent. This study provides potential targets and a new way of thinking for the intervention of nurse burnout, which can be explored and verified in clinical practice.

Transitioning from holistic analysis to a fine-grained level analysis may provide further understanding of psychopathology. This study aimed to explore dimension-level relationships between suicidal ideation, meaning in life, and affect in a joint framework using network analysis and to identify potential prevention and intervention targets to address suicidal ideation. A total of 852 healthy adults aged 18–35 years completed self-report scales to assess suicidal ideation, meaning in life, and affect. A regularized partial correlation network was then built to examine the links between these dimensions. Expected influence and bridge expected influence values were calculated for each node. The prevalence of suicidal ideation was 4.2%. The search for and presence of meaning in life and positive and negative affect exhibited distinct and complex links to the three dimensions of suicidal ideation (pessimism, sleep, and despair). The important central nodes were search for meaning in life, sleep, despair, and positive affect, while the critical bridge nodes were positive affect, negative affect, and presence of meaning in life. These findings provide further understanding of the specific roles of meaning in life and affect in suicidal ideation. The identified nodes may be promising targets for prevention and intervention for suicidal ideation.

The mental health burden among adolescents has been increasing, impacting individuals even before formal diagnosis of common mental disorders. Although personality traits, as key indicators of mental health conditions, play a crucial role in the development of mental disorders, there is a gap regarding the trait-to-symptom pathways and similarities and differences between adolescents and young adults. A total of 860 adolescents and 1751 young adults participated in this study. The Chinese Big Five Personality Inventory assessed the Big Five traits, while depression and anxiety symptoms were measured using the Patient Health Questionnaire-9 and the Generalized Anxiety Disorder-7, respectively. Network analysis computed the bridging centrality of Big Five traits and elucidated trait-to-symptom pathways. Furthermore, network comparison was applied to compare network structure between adolescents and young adults. In both age groups, neuroticism exhibited a transdiagnostic activating effect on depression and anxiety. Conscientiousness demonstrated the strongest protective effect against depression, whereas agreeableness was most protective against anxiety. In both samples, neuroticism primarily influenced symptoms associated with negative emotions and thoughts. Comparatively, extraversion exhibited a significant increasing protective effect against depression throughout adolescence, while neuroticism increasingly activated anxiety symptoms. This study highlights the potential to utilize personality traits for early detection and precise intervention in adolescent populations, providing actionable insights. By identifying the level of neuroticism, we can effectively detect high-risk adolescent individuals prior to formal diagnosis. By delineating the neuroticism-to-symptom pathways, we can implement targeted intervention on their pathological interactions.

Background: Behavioral inhibition/activation systems (BIS/BAS) and impulsivity are associated with problematic smartphone use (PSU). However, no studies to date have explored how the subdomains of BIS/BAS and the dimensions of impulsivity relate to the components of PSU in a joint framework. This study aimed to examine the relationships between the three constructs at a fine-grained level and identify the central nodes and bridge nodes of their relationships using network analysis.Methods: A regularized partial correlation network of PSU, BIS/BAS, and impulsivity communities was estimated to investigate the connections between variables and determine the expected influence and bridge expected influence for each variable based on data from 325 Chinese adults. PSU, BIS/BAS, and impulsivity were assessed using the Smartphone Application-Based Addiction Scale (SABAS), BIS/BAS scales, and Barratt Impulsiveness Scale-Version 11 (BIS-11), respectively.Results: In addition to connections within each community, network analysis revealed that there were connections between different communities, especially connections to PSU. I2 “motor impulsivity” was strongly associated with PSU2 “conflict” and PSU6 “relapse”; BASR “BAS-reward responsiveness” was strongly associated with PSU5 “withdrawal.” Nodes BASR “BAS-reward responsiveness” and PSU6 “relapse” were the most central variables, while nodes BASR “BAS-reward responsiveness” and I2 “motor impulsivity” were the strongest bridge variables.Conclusion: The connections between the subdomains of BIS/BAS and the components of PSU and between the dimensions of impulsivity and the components of PSU may be particularly important in the development and maintenance of PSU. The central variables identified here, along with the bridge variables, could be promising and effective targets for the prevention and intervention of PSU.