Search

Abstract Introduction Anxiety and depression often co-occur in adolescents, with factors from family and school playing a significant role in the comorbidity. However, network analysis has not examined and clarified the detailed bridge and central symptoms of this comorbidity caused by online learning and perceived parental relationships across different COVID-19 times. Methods Over four months, 2,356 secondary school students completed the Patient Health Questionnaire-9 and Generalized Anxiety Disorder Scale-7. Participants were divided into harmonious and disharmonious groups based on their answers to a question about parental conflicts. Results The results indicated that adolescents perceiving more parental conflicts showed a denser comorbidity network after four months of online learning. Significant bridge symptoms decreased from three to two across two waves in the harmonious group, while in the disharmonious group, they increased from two to three. The number of central symptoms increased from one in wave 1 to three in wave 2 for the harmonious group, while four in wave 1 decreased to two in wave 2 for the disharmonious group. Furthermore, the CLPN analysis revealed that the strongest positive cross-lagged edge intensity between symptoms was anhedonia—energy in the harmonious group, with anhedonia being the most trigger symptom. In contrast, for the disharmonious group, guilt—suicide and trouble relaxing—excessive worry were the strongest cross-lagged edge, and trouble relaxing was the most trigger symptom. Conclusion These findings may have implications for interventions designed to promote adolescent mental health in the context of online learning and parental conflicts.

Object: Numerous studies show that depression and anxiety have an adverse effect on life satisfaction among college students. Moreover, neuroticism affects depression, anxiety, and life satisfaction. Comparing the low-neuroticism and high-neuroticism groups, the current study used network analysis to examine the relationship between depression, anxiety, and life satisfaction among college students. Methods: A sample consisted of 1233 college students from China who completed the Big Five Inventory-2 (BFI-2), Self-rating Anxiety Scale (SAS), Self-rating Depression Scale (SDS), and Satisfaction With Life Scale (SWLS).All students were divided into two groups according to levels of neuroticism. Depression-anxiety symptom networks and flow networks were formed. Results: “Insomnia” (SAS19) and “Sleep disturbance” (SDS4) are bridge symptoms of groups with varying neuroticism. In addition, compared to the group with low levels of neuroticism, the group with high levels of neuroticism showed more depression symptoms in bridge symptoms and greater global strength. Many depression-anxiety symptoms are negatively associated with life satisfaction, and “Emptiness” (SDS18) is an important symptom in the high-neuroticism group’s flow network. Conclusion: This study contributes to our understanding of the connection between depression, anxiety, neuroticism, and life satisfaction. In addition, the current study identified the essential symptoms to target in depression and anxiety intervention and life satisfaction enhancement among college students.

BackgroundDue to tremendous academic pressure, Chinese high school students suffer from severe depression, anxiety, and sleep disturbances. Moreover, senior high school students commonly face more serious mental health problems than junior high school students. However, the co-occurrence and internal relationships of depression, anxiety, and sleep disturbances clusters are scarcely examined among high students. Therefore, the current study inspected relationships between depression, anxiety, and sleep disturbance symptoms through network analysis and identified key symptoms bolstering the correlation and intensifying the syndromes.MethodsA total of 13,999 junior high school students (Mage = 13.42 years, SDage = 1.35, 50% females) and 12,550 senior high school students (Mage = 16.93 years, SDage = 1.67, 47% females) were recruited in Harbin. We constructed networks for all students, junior high group, and senior high group, including data from the Youth Self-rating Insomnia Scale-3 (YSIS-3), the Generalized Anxiety Disorder-2 (GAD-2), and the Patient Health Questionnaire-2 (PHQ-2). The indices of “strength” was used to identify symptoms' centrality, and “bridge strength” was used to find specific nodes that could bridge anxiety, depression, and sleep disturbance.ResultsThe networks of all students, junior high and senior high students, were stable and accurate. Among all networks, “Nervousness” (GAD1) had the highest strength, and “Nervousness”–“Excessive worry” (GAD1-GAD2) had the strongest correlation. “Nervousness” (GAD1) also functioned as the bridge symptom among junior high students, while “Sad mood” (PHQ2) among senior high students. Senior high students scored higher than junior high students on all items and had a tighter network structure.ConclusionsIn networks consisting of anxiety, depression, and sleep disturbance, anxiety plays a conspicuous role in comorbidity among junior high school students, which transforms into depression among senior high school students. Treatments or interventions should be focused on these critical symptoms.

BackgroundBesides 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.MethodsWe 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.ResultsNetworks 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.ConclusionsIn networks of anxiety, depression, and sleep disturbance, anxiety played a conspicuous role in comorbidity, which could be a target for practical intervention and prevention.

Background: The COVID-19 pandemic and the shift to online learning have increased the risk of Internet addiction (IA) among adolescents, especially those who are depressed. This study aims to identify the core symptoms of IA among depressed adolescents using a cross-lagged panel network framework, offering a fresh perspective on understanding the interconnectedness of IA symptoms. Methods: Participants completed the Internet addiction test and the Patient Health Questionnaire-9. A total of 2415 students were initially included, and after matching, only 342 students (a cutoff score of 8) were retained for the final data analysis. A cross-lagged panel network analysis was conducted to examine the autoregressive and cross-lagged trajectories of IA symptoms over time. Results: The incidence rate of depression rose remarkably from 14.16% (N = 342) to 17.64% (N = 426) after the four-month online learning. The symptom of “Anticipation” exhibited the highest out-expected influence within the IA network, followed by “Stay online longer” and “Job performance or productivity suffer”. Regarding the symptom network of depression, “Job performance or productivity suffer” had the highest in-expected influence, followed by “Life boring and empty”, “Snap or act annoyed if bothered”, “Check email/SNS before doing things”, and “School grades suffer”. No significant differences were found in global network strength and network structure between waves 1 and 2. Conclusion: These findings prove the negative effects of online learning on secondary students’ mental health and have important implications for developing more effective interventions and policies to mitigate IA levels among depressed adolescents undergoing online learning.

Loneliness and depression are significant mental health challenges among college students; however, the intricate relationship between these phenomena remains unclear, particularly in the context of self-compassion. In this comprehensive study, we employ a cross-lagged panel network (CLPN) analysis to investigate the symptom-level association between depression and loneliness while exploring the potential moderating influence of self-compassion. Our sample consisted of 2785 college students, who were categorized into high- and low-self-compassion groups based on scores from the Self-Compassion Scale. Depressive symptoms were assessed using the Patient Health Questionnaire-9, while the UCLA Loneliness Scale-8 measured loneliness expressions. Our findings indicate that self-compassion plays a crucial role in the relationship between depression and loneliness. Specifically, we observed distinctive patterns within the high and low-self-compassion groups. In the low-self-compassion group, “energy” emerged as the most influential symptom, whereas “motor function” exhibited the highest influence in the high-self-compassion group. Furthermore, among individuals with high self-compassion, the pathway from depression to loneliness was characterized by “guilt—being alone when desired,” while the reverse path from loneliness to depression encompassed “left out—feeling sad” and “left out—anhedonia.” Conversely, in the low-self-compassion group, depression and loneliness demonstrated a more intricate mutual triggering relationship, suggesting that self-compassion effectively moderates the association between these variables. This study provides valuable insights into the underlying mechanisms driving the interplay between depression and loneliness, shedding light on the pivotal role of self-compassion in this intricate dynamic.

Identification of protective factors to prevent firefighters' anxiety and depression is meaningful. We explored whether emotion-regulation strategies mediate the relationship between personality traits and anxiety and depression among Chinese firefighters. Approximately, 716 Chinese firefighters were recruited and completed the Emotion Regulation Questionnaire (ERQ), Self-Rating Anxiety Scale (SAS), Self-Rating Depression Scale (SDS), and Big Five Inventory−2 (BFI-2) Scale. Results (N = 622) indicated that only negative emotionality traits could predict anxiety symptoms. Meanwhile, the multilevel mediation effect analyses showed that conscientiousness through cognitive reappraisal could reduce anxiety and depression symptoms in Chinese firefighters. Our findings clarify Chinese firefighters' underlying emotion-regulation process between personality traits and anxiety and depression. Implications, limitations, and future directions are discussed.

Perceived organizational support (POS) in the relationship between neuroticism and job burnout among firefighters received little attention in China. A sampling of 716 firefighters in China, we drew on perceived organization support theory and the notion of support as a buffer in job burnout, examining moderating effects of POS on the relationship between neuroticism and three components of burnout (emotional exhaustion, depersonalization and reduced personal accomplishment). Also, this study explored the mediating effect of burnout on the relationship between neuroticism and mental health (i.e., anxiety and depression). We found that two components (depersonalization and emotional exhaustion) of burnout have significantly mediated the relationship between neuroticism and anxiety and depression. At the same time, POS reinforced the relationship between neuroticism and depersonalization and emotional exhaustion. Therefore, organizations can take our analysis into account when taking actions to improve firefighters' mental health. The implications of these findings were discussed.