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.

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.

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.

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.

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.