Hopelessness during and after the COVID-19 pandemic lockdown among Chinese college students: A longitudinal network analysis.

INTRODUCTION: In China, recurrent pandemics require frequent city-wide lockdowns and quarantine actions to contain the impact of COVID-19, exposing college students to psychological problems, including hopelessness. Hence, the purpose of helping problematic college students alleviate hopelessness symptoms motivates us to carry out the present study to explore their interrelationship. METHODS: Hopelessness (i.e., a complex phenomenon with important clinical consequences, such as depression and suicidality) was investigated in a large longitudinal sample of college students (N = 2787; 58.59% female; age mean ± SD = 18.34 ± 0.92) who were recruited during and after the COVID-19 lockdown using the Beck Hopelessness Scale (BHS). RESULTS: Applying the novel approach (i.e., symptom network analysis), the results indicated that the edge of #BHS1 (i.e., [NOT] hope-enthusiasm)-#BHS15 (i.e., [NOT] faith-in-the-future) showed the strongest association both in Wave 1 and Wave 2. Similarly, #BHS20 (i.e., not-trying) had the highest node expectedinfluence (centrality) in the hopelessness symptoms network both among Wave 1 and Wave 2. The Network Comparison Test indicated that the global network strength significantly differed between the two time points. As expected, college students' hopelessness will gradually dissipate with the end of segregation control. The stability and accuracy indicated that the network analysis results were trustworthy. CONCLUSIONS: The study findings provide evidence that central nodes and edges connecting symptoms should be addressed. Further interventions and treatments that may target these symptoms are essential to effectively alleviate the overall hopelessness level among college students. Theoretical and clinical potential consequences were discussed in detail.

Effects of personal relative deprivation on the relationship between anger rumination and aggression during and after the COVID-19 pandemic lockdown: A longitudinal moderated network approach.

INTRODUCTION: Several studies have demonstrated a directional link between rage rumination and aggression. However, recent research suggests that this relationship is bidirectional. The current study examined the complex relationships between anger rumination and aggression using a moderated network approach in a longitudinal design while considering personal relative deprivation. METHOD: A total of 665 participants (59.25% female, agemean±SD = 19.01 ± 1.25) were enrolled at two-time points. Assessments included self-report measures of the Anger Rumination Scale, Buss-Perry Aggression Questionnaire, and Relative Deprivation Scale. A Moderated Network Model (MMN) was used to test the complex links among anger rumination, aggression, and personal relative deprivation. RESULTS: The analysis revealed that the link between anger rumination and aggression was complex and bidirectional. Notably, as the level of personal relative deprivation increased, verbal aggression had a positive conditional effect on anger afterthoughts in Wave 2, and thoughts of revenge had a positive conditional effect on verbal aggression in Wave 2. Moreover, as the first discovery, anger afterthoughts and anger had a negative conditional effect on each other across levels of personal relative deprivation in Wave 2. In addition, network comparison indicates that the MNMs structure was significantly different across timepoints, implying that anger rumination and aggression were inextricably linked in college students during isolation and that this complicated relationship was weakened after isolation. CONCLUSIONS: This study deepens our understanding of the bidirectional relationships between anger rumination and aggression and recognizes the moderating role of personal relative deprivation.

Comparing the centrality symptoms of major depressive disorder samples across junior high school students, senior high school students, college students and elderly adults during city lockdown of COVID-19 pandemic-A network analysis.

INTRODUCTION: Recently, in the view of network analysis, depression has been conceptualized as a complex and dynamic network model combining individual symptoms. To date, no studies have systematically examined and compared depressive symptom networks across different populations. METHODS: A total of 36,105 participants were recruited and asked to complete the Patient Health Questionnaire-9 among junior high school students, senior high school students, college students, and elderly adults who were more susceptible to depression during the COVID-19 lockdown in China. In the analysis, we applied the optimal cutoff score ≥ 8 for students and a score ≥ 6 for elderly adults to identify 5830 participants who were likely to be depressed. The index of "strength" was used to identify central symptoms in the network structure. RESULTS: The results showed that Sad Mood was the most central symptom among junior high school students, senior high school students, and college students, but the most central symptom in the elderly was Guilt. Among the top three central symptoms, Suicide Ideation was unique to senior high school students, while Anhedonia was most prevalent among college students. Guilt - Suicide Ideation, Anhedonia - Energy, Anhedonia - Sad Mood, and Sleep - Energy showed the strongest association among junior and senior high school students, college students, and elderly adults, respectively. NCT (i.e., Network Comparison Test) suggested that the network's global connectivity was ultimately inconsistent, but the network structure remained roughly intact. CONCLUSION: In treatment, targeting central symptoms may be critical to alleviating depression.

Seeking bridge symptoms of anxiety, depression, and sleep disturbance among the elderly during the lockdown of the COVID-19 pandemic-A network approach.

Background: Besides physical changes, elderly adults are prone to have mental disorders such as anxiety, depression, and sleep disturbance, and the pandemic of COVID-19 worsened the situation. However, internal relationships and co-occurrence of psychopathologies were scarcely examined. Therefore, in the current study, through network analysis, we inspected relationships among symptoms of depression, anxiety, and sleep disturbance and identified key symptoms that espoused the disease. Methods: We asked 1,302 elderly adults to fill in Patient Health Questionnaire-2 (depressive symptoms), the Generalized Anxiety Disorder-2 (anxiety symptoms), and the Youth Self-rating Insomnia Scale (sleep disturbance) and then constructed three networks for elderly adults, male elderly, and female elderly. Via network analysis, we accomplished four goals. First, we identified symptom with the highest centrality (i.e., strength) index for each network; then, we found the strongest correlation (i.e., edges) in each network; thirdly, we confirmed specific nodes that could bridge anxiety, depression, and sleep disturbance; the last was to compare networks based on genders. Network stability and accuracy tests were performed. Results: Networks of elderly adults, male elderly, and female elderly were stable, accurate, and intelligible. Among all networks, "Nervousness"- "Excessive worry" (GAD-1- GAD-2) had the strongest correlation, and "Nervousness" (GAD-1) had the highest strength and bridge strength value. When we made a comparison between female elderly's and male elderly's networks, except for the significant difference in the mean value of "Difficulty initiating sleep" (YSIS-3), the findings showed that the two networks were similar. Network stability and accuracy proved to be reliable. Conclusions: In networks of anxiety, depression, and sleep disturbance, anxiety played a conspicuous role in comorbidity, which could be a target for practical intervention and prevention.

Centrality and bridge symptoms of anxiety, depression, and sleep disturbance among college students during the COVID-19 pandemic-a network analysis.

Symptoms of depression and anxiety usually co-occur and are inextricably linked to sleep disturbance. However, little is known about the symptom-to-symptom relationships among these three mental disorders. Hence, to improve our understanding of concurrent depression, anxiety, and sleep disturbance, we used the network analysis approach to construct an interplay relationship among the above three mental disorders and identify which specific symptoms bridge these aggregations. We collected data from a large sample (N = 6710, male = 3074, female = 3636; mean age = 19.28) at a university. We estimated the symptom network structure of depression, anxiety, and sleep disturbance as assessed by the Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and Youth Self-Rating Insomnia Scale during the COVID-19 lockdown. We further investigated four goals: first, identifying the individual core symptoms in the network by the index of "expected influence"; second, determining the bridge symptoms that play roles in linking different mental disorders by the index of bridge expected influence (1-step); third, examining the robustness of all results; and fourth, providing an overall structure that may or may not differ by sex. The network structure was stable, accurate, and predictable. Items referring to sleep dissatisfaction, poor sleep quality, and uncontrollable worry were potentially core symptoms in the interplay among depression, anxiety, and sleep disturbance. Sleep, guilt, restlessness, irritability, and feeling afraid can function as bridges among depression, anxiety, and sleep disturbance, which is clinically relevant and theoretically important. The results suggested that the network structures significantly differed between the female and male networks. Robustness tests also revealed that the results were reliable.