Existing research proposed that moving from a disorder-level analysis to a symptom-level analysis may provide a more fine-grained understanding of psychopathology. This study aimed to explore the relations between two dimensions (i.e., cognitive reappraisal, CR; expressive suppression, ES) of emotion regulation and individual symptoms of depression and anxiety among medical staff during the late stage of COVID-19 pandemic. We examined depression symptoms, anxiety symptoms and emotion regulation among 420 medical staff during the late stage of COVID-19 pandemic via network analysis. Two networks (i.e. emotion regulation-depression network and emotion regulation-anxiety network) were constructed in the present study. Bridge centrality index was calculated for each variable within the two networks. Among the present sample, the prevalences of depression and anxiety are 39.5% and 26.0%. CR and ES showed distinct connections to symptoms of depression and anxiety. Results of bridge centrality showed that in both networks, CR had a negative bridge expected influence value while ES had a positive bridge expected influence value. The results revealed the specific role of CR and ES in relation to depression and anxiety at a symptom level. Implications for clinical preventions and interventions are discussed.

The rapid growth in the use of social media applications on the internet has significant impacts on mental health, especially among university students. This study aims to explore the network characteristics and core symptoms between social media addiction, mental health issues (anxiety, depression, stress), and academic burnout. Understanding these relationships is crucial for enhancing psychological interventions and improving academic performance in the digital age. A cross-sectional study was conducted with undergraduates and doctoral students from Air Force Medical University. Participants (n = 432) completed self-report scales, including the Depression Anxiety and Stress Scale, Academic Burnout Inventory, and Bergen Social Media Addiction Scale. Network analysis was performed using R to model the relationships between study variables, employing Gaussian Graphical Models and the least absolute shrinkage and selection operator technique for robust network estimation. Bridge Expected Influence results indicate that “Depression” is consistently the most central node, playing a critical role in the network connecting social media addiction, academic burnout, and psychological stress. The study reveals a significant positive correlation between social media addiction and both academic burnout and mental health issues. Particularly, excessive use of social media can distract students, leading to academic burnout, and exacerbating feelings of anxiety and depression. Social media addiction has a significant impact on the psychological health and academic performance of college students. This study highlights the potential risks associated with social media usage behaviors and provides a scientific basis for developing related intervention measures. These interventions aim to help college students reduce their dependence on social media, thereby restoring a healthy state of life and study.

Background: Problematic smartphone use (PSU) has become an issue of great concern in the age of smartphones. PSU is associated with emotional problems, one of which is depression, as shown by empirical studies. However, previous studies have been limited in that they have focused solely on the total score for symptoms of PSU and depression while ignoring the symptomatic heterogeneity of these two concepts.Method: This study filled this gap by exploring links between symptoms of PSU and depression among 1,849 university students (59.17% female participants, 17–23 years old). Network analysis was utilized to reveal symptom connections, central symptoms, and bridge symptoms between PSU and depression.Results: (1) A total of 17 of 81 symptom connections (about 21%) between PSU and depression existed in the symptom network. For example, “self-control failure” for PSU was positively correlated with “concentration problems” for depression; (2) “recklessly continuing” for PSU and “fatigue” for depression were central symptoms within the PSU symptom network and depression symptom network, respectively; (3) “escaping negative moods” for PSU and “concentration problems” for depression were bridge symptoms. The former was maximumly connected with the depression symptoms and the latter was maximumly connected with the PSU symptoms; and (4) gender had very minimal influence on the network characteristics.Conclusion: The results are in keeping with the central idea of the compensatory internet use theory that excessive smartphone use may be a coping strategy for depressed emotions derived from escaping motivation. Moreover, concentration problems may be a mediator explaining how negative emotions (e.g., depression) cause PSU, which is undefined in current internet use theories. Finally, symptom connections, central symptoms, and bridge symptoms could be potential targets for the prevention and intervention of PSU and depression in young adults.

Background: Existing research has demonstrated that problematic smartphone use (PSU) may reflect a composition of heterogeneous symptoms, with individual PSU symptoms uniquely related to predisposing variables. The Big Five personality traits represent one of the most frequently examined predisposing variables in relation to PSU. However, no studies to date have examined the trait-to-symptom association between the Big Five personality traits and PSU. Using a network analysis approach, we aimed to understand: 1) specific pathways linking each of the Big Five personality traits to PSU symptoms and 2) the bridging effects of each Big Five personality trait on the PSU symptom cluster.Methods: A regularised graphical Gaussian model was estimated among 1,849 Chinese university students. PSU symptoms were assessed with items from the Problematic Smartphone Use Scale. Facets of the Big Five personality traits were assessed with the subscales of the Chinese Big Five Personality Inventory-15. An empirical index (i.e., bridge expected influence) was used to quantify bridge nodes.Results: Results revealed specific and distinct pathways between the Big Five personality traits and PSU symptoms (e.g., Neuroticism-Escapism/Avoidance, Conscientiousness-Preoccupation and Extraversion-Escapism/Avoidance). Further, Neuroticism showed the highest positive bridge centrality among the Big Five personality traits, while Conscientiousness had the highest negative bridge centrality.Discussion and conclusions: The current study provided direct empirical evidence concerning specific pathways between the Big Five personality traits and PSU symptoms and highlighted the influential role of Neuroticism and Conscientiousness as potential targets for early detection and treatment of PSU.

Objective: Sufferers of tinnitus, especially of the prolonged type, frequently suffer from comorbid depression and anxiety. From the perspective of the network model, this comorbidity is thought to be an interacting system of these two symptoms. In our study, we conducted a network analysis of depression and anxiety comorbidity in tinnitus sufferers, aiming to identify the central and bridge symptoms and make informed suggestions for clinical interventions and psychotherapy. Method: A total of 566 tinnitus sufferers were enrolled in our study. The Patient Health Questionnaire-9 (PHQ-9) and the Generalized Anxiety Disorder 7-Item Questionnaire (GAD-7) were selected to evaluate depression and anxiety symptoms, respectively, followed by network analysis to construct the interacting networks. Results: The findings identified six edges of strongest regularized partial correlations in this network. Of these, three were depression symptoms and three were anxiety symptoms. The anxiety symptoms “Unable to control worry” and “Relaxation difficulty” and the depression symptom “Feeling depressed or hopeless” had the highest expected influence centrality. The analysis results also revealed three bridge symptoms: “Afraid something awful might happen”, “Feeling of worthlessness”, and “Trouble concentrating”. As for “Suicidal ideation”, the direct relations between this symptom and “Afraid something awful might happen” and “Feeling depressed or hopeless” were the strongest. Conclusions: The central and bridge symptoms of the interacting network of depression and anxiety symptoms in tinnitus sufferers can be considered a significant transdiagnostic intervention target for the management of this comorbidity. In particular, clinical prevention and psychotherapy should be implemented, targeting the symptoms that have the strongest associations with suicidal ideation.

Background: There are 68.77 million left-behind children in China, who are at a great risk of depression associated with negative life events. Our study aims to investigate the most central symptoms of depression in left-behind children and the relationship between depressive symptoms and negative life events using network analysis. Method: A cross-sectional data set (N = 7255) was used, which included children and adolescents aged 7 to 17. Network analysis was used to evaluate: 1) the most central symptoms among the items included in Child Depression Inventory (CDI) of the left-behind children; 2) bridge symptoms between depressive disorder and Adolescent Self-Rating Life Events Check List (ASLEC) of the left-behind children; 3) differences in networks of depressive disorders between left-behind and non-left-behind children, and 4) differences in the network of depression and negative life events between left-behind and non-left-behind children. The stability and centrality indices of the network were also evaluated in the study. Results: The most central symptoms in the CDI among the left-behind children included self-hatred, crying, fatigue, and sadness. The items with the highest bridge strength centrality in the CDI-ASLEC network included academic stress, discrimination, and school performance decrement. Higher bridge strength values indicate a greater risk of contagion to other communities. The connections in the CDI-ASLEC network are denser in the left-behind children than in non-left-behind children. Limitations: The study which was conducted based on cross-sectional data shows that network analysis can only make undirected estimation, but not causal inferences. Conclusions: We identified the core symptoms of depression and the bridge symptoms between negative life events and depression in the left-behind children. These findings suggest that more attention should be paid to self-hatred, sadness, and fatigue in the treatment of depression in left-behind children. Intervention for academic stress and discrimination of the left-behind children may help to reduce the contagion of negative life events to depression symptoms.