Network analysis of depressive and anxiety symptoms in adolescents during and after the COVID-19 outbreak peak

The association between adaptability and the symptoms of depression and anxiety in early adolescents: a network analysis in a longitudinal design

BACKGROUND

The co-occurrence of depression and anxiety is relatively more common among adolescents. Emerging evidence suggests that adaptability might affect this phenomenon. Network analysis can provide insight into the dynamics between symptoms of mental disorders. Therefore, we used network analysis 1) to explore symptom networks and 2) to investigate the association between adaptability and symptoms based on a longitudinal design.

METHODS

833 Chinese adolescents (449 males and 384 female) were recruited, with adaptability, depressive and anxiety symptoms measured at T1, 426 of them were followed up one year later at T2. Symptom networks were constructed for all participants and for the two groups based on their adaptability scores at T1. Furthermore, mediation analysis was performed to examine the relationship between adaptability and bridge symptoms at both timepoints.

RESULTS

Irritable and Guilty showed the highest expected value at T1 (p < 0.05). The high adaptability group's symptom network (HGN) was less connected than the low adaptability group's symptom network (LGN) (p < 0.001). Furthermore, Irritable (T1) and adaptability (T2) were sequential mediators (p < 0.001) between adaptability (T1) and Irritable (T2).

CONCLUSIONS

These findings suggest that adaptability might affect the network dynamics, underscoring its importance to the occurrence of depression and anxiety among adolescents. Irritable and Guilty being the bridge symptoms may indicate the interventions to target in adolescents with comorbid depression and anxiety.

Negative emotions among Chinese primary and middle school students and association with screen time: A network analysis

BACKGROUND

Psychological health is crucial for the physical and mental well-being of primary and secondary school students, however, the research on the interrelationships among negative emotions is still limited. This study aims to investigate the network structure of depression, anxiety, and stress, and explore their correlation with screen time in these populations.

METHOD

This study was conducted from March to July 2022 in 157 counties across 18 cities in Henan Province. The Depression Anxiety Stress Scale (DASS-21) was used to assess the negative emotions experienced by students. Firstly, "Expected Influence" and "Bridge Expected Influence" were considered as key indicators within the symptom network to depict the structure of depressive, anxiety and stress symptoms. Secondly. a case-dropping bootstrap procedure was applied to assess the stability of the network. Finally, this study identified the central and bridge symptoms in the network, and explored their relationships with screen time.

RESULTS

The study included 52,782 students with an average age of 11.23±2.56 years. Network analysis showed that feelings of worthlessness, panic, and agitation are the predominant symptoms in the negative emotional networks. Depression, anxiety, and stress were interconnected through feelings of low mood, overreaction, and trembling. Additionally, lack of initiative and dry mouth exhibited the most significant direct associations with screen time.

CONCLUSION

The central and bridge symptoms identified in the negative emotion networks can serve as potential focal points for future research on negative emotions among primary and secondary school students.

Examining the effects of school-vacation transitions on depression and anxiety in adolescents: network analysis

BACKGROUND

The school-vacation cycle may have impacts on the psychological states of adolescents. However, little evidence illustrates how transition from school to vacation impacts students' psychological states (e.g. depression and anxiety).

AIMS

To explore the changing patterns of depression and anxiety symptoms among adolescent students within a school-vacation transition and to provide insights for prevention or intervention targets.

METHOD

Social demographic data and depression and anxiety symptoms were measured from 1380 adolescent students during the school year (age: 13.8 ± 0.88) and 1100 students during the summer vacation (age: 14.2 ± 0.93) in China. Multilevel mixed-effect models were used to examine the changes in depression and anxiety levels and the associated influencing factors. Network analysis was used to explore the symptom network structures of depression and anxiety during school and vacation.

RESULTS

Depression and anxiety symptoms significantly decreased during the vacation compared to the school period. Being female, higher age and with lower mother's educational level were identified as longitudinal risk factors. Interaction effects were found between group (school versus vacation) and the father's educational level as well as grade. Network analyses demonstrated that the anxiety symptoms, including 'Nervous', 'Control worry' and 'Relax' were the most central symptoms at both times. Psychomotor disturbance, including 'Restless', 'Nervous' and 'Motor', bridged depression and anxiety symptoms. The central and bridge symptoms showed variation across the school vacation.

CONCLUSIONS

The school-vacation transition had an impact on students' depression and anxiety symptoms. Prevention and intervention strategies for adolescents' depression and anxiety during school and vacation periods should be differentially developed.

Identification of Core Symptom Cluster in Patients With Digestive Cancer: A Network Analysis

BACKGROUND

A lack of identified core symptom clusters in digestive cancer patients hinders achieving precision symptom intervention. There are few studies on identifying digestive cancer symptom clusters based on network analysis.

OBJECTIVES

The aims of this study were to construct the symptom network of digestive cancer patients and identify the core symptom cluster.

METHODS

A cross-sectional study was conducted among 202 digestive cancer patients. The Chinese version of the MD Anderson Symptom Inventory for gastrointestinal cancer scale was used to assess the symptoms by convenience sampling. R software was used to construct a symptom network and identify core symptom clusters. Edge weight and centrality difference tests were used to test the accuracy of core symptom cluster identification.

RESULTS

The most common symptoms were distress, poor appetite, and sadness. The most serious symptoms were poor appetite, disturbed sleep, and fatigue. The core symptom cluster of the psychoemotional symptom group was distress, sadness, and numbness. The centrality index showed that the top 3 in strength were distress (Rs = 1.11), fatigue (Rs = 1.09), and sadness (Rs = 1.04). The edge weight difference test showed that the psychoemotional symptom group had high stability.

CONCLUSIONS

The psychoemotional symptoms of digestive cancer patients should be given priority for intervention. Network analysis must be extended to the symptom research of cancer patients as soon as possible to provide a scientific basis for symptom management.

IMPLICATIONS FOR PRACTICE

Nurses must perform comprehensive psychological and emotional assessments, initiate referrals for psychoemotional symptom management and psychological services, and administer pharmacologic and nonpharmacologic interventions to improve appetite loss in digestive cancer patients.

Network analysis of depressive and anxiety symptoms with well-being in students during the COVID-19 pandemic: a repeated cross-sectional study

The university student population is particularly vulnerable to depression, which was identified during the COVID-19 pandemic. Understanding how depressive symptoms are interrelated with mental and physical health in students is essential. The aim of this study was to reveal the network of depressive and anxiety symptoms with respect to well-being (life satisfaction, physical health, physical activity, and perceived stress) during a difficult situation-the COVID-19 pandemic-at two measurement points of different pandemic severities. A repeated cross-sectional study was conducted in June 2020 (T1) (lower pandemic severity) and March 2021 (T2) (higher pandemic severity) among 592 and 1230 Czech university students, respectively. The measurements used were the PHQ-9, GAD-7, PSS-10, SWLS, self-rated physical health (SRH), and sociodemographic survey. The network analysis approach was utilized. For the significance of differences, the χ2 test, Student's t test, and ANOVA were performed. The results revealed that scale-level depression, stress, and worse SRH increased over time, whereas life satisfaction decreased. Scale-level anxiety and physical activity were stable over time. PHQ2 Sad mood was the most central and influential node at T1 and T2. PHQ9 Suicidal Ideation was closely related to other variables at T1, whereas PHQ1 Anhedonia was closely related to other variables at T2. The most influential risk factors were the PHQ-2 score and the GAD-2 score, which are associated with uncontrollable worrying, whereas life satisfaction, physical health, and physical activity were the most protective factors. It is crucial to recognize and decrease the PHQ2 score and increase life satisfaction to improve the mental health of university students.

Using the Complex Network Model to Associate Nutritional, Psychological, and Physical Parameters and Aspects of Sleep with Depression Symptoms

Background/Objectives: Major depressive disorder is a significant public health concern linked to factors such as a low-quality diet, a sedentary lifestyle, and poor sleep quality, all of which contribute to its development; nevertheless, the existing literature lacks a comprehensive framework to effectively integrate these interrelated influences. Methods: To address this gap, we conducted a questionnaire-based study involving 411 individuals aged 18 to 74 and employed a weighted complex network model to clarify the associations among nutritional factors, physical activity levels, psychological parameters, and sleep profiles and depression. In addition to constructing networks that encompass distinct subgroups based on general context, sex differences (female vs. male), and four age categories, our network was designed with a clearly defined target: the score from the Beck Depression Inventory. Results: In all networks studied, psychological parameters (e.g., tension, depression, hostility, fatigue, confusion, and total mood disturbance) emerged as the most influential nodes in relation to the targeted node (Eigenvector centrality of approximately 0.30). Additionally, sleep quality was identified as the next most relevant parameter for the general network (Eigenvector = 0.25), while nutritional factors-particularly carbohydrates-demonstrated greater prestige within the male network (Eigenvector = 0.06). Nutritional parameters had a stronger influence on depressive symptoms among individuals aged 29-39 years (Eigenvectors = 0.09, 0.09, and 0.14 for energy, carbohydrates, and fat, respectively). Conclusions: This novel approach allowed for a clearer visualization of how the studied parameters impact depressive symptoms, revealing significant differences when certain aspects are examined independently across distinct groups.

Which comes first? Comorbidity of depression and anxiety symptoms: A cross-lagged network analysis

Depression and anxiety significantly impact college students, leading to various negative outcomes. While numerous studies have investigated the relationship between these two conditions, their temporal sequence remains unresolved. Many previous studies have concentrated on broad latent variables, often neglecting the nuanced symptomatology perspective, which may offer deeper insight into the clinical characteristics of these disorders. In this study, we collected questionnaire data from a college in South China using a cluster random sampling method. Data collection occurred over two time points, with the first round completed in November 2022 and May 2023, with a six-month interval. A total of 689 participants successfully completed the questionnaires during both rounds. Employing cross-lagged network analysis from a symptom-focused perspective, this research examines the interactions and predictive relationships between symptoms of depression and anxiety. The findings identified key symptoms-specifically "Irritability", "Guilty" and "Sad mood"- as critical bridging nodes of connection within the depression and anxiety symptom network. Our analysis revealed both bidirectional predictive relationships between certain symptoms nodes of depression and anxiety, as well as unidirectional ones. By highlighting these core nodes and their directional relationships, this study offers valuable insights that can inform targeted intervention and treatment strategies for enhancing mental health among college students.

Mapping of the network connection between sleep quality symptoms, depression, generalized anxiety, and burnout in the general population of Peru and El Salvador

BACKGROUND

A meta-analysis of randomized controlled trials has suggested a bidirectional relationship between sleep problems and mental health issues. Despite these findings, there is limited conclusive evidence on the relationship between sleep quality, depression, anxiety, and burnout.

OBJECTIVE

The current study aimed to evaluate the relationships between sleep quality symptoms, anxiety, depression, and burnout in samples of adult individuals from two Latin American countries, Peru and El Salvador, through network analysis and to identify key symptoms that reinforce the correlation and intensify the syndromes.

METHODS

A total of 1012 individuals from El Salvador and Peru participated, with an average age of 26.5 years (SD = 9.1). Symptom networks were constructed for both countries based on data from the Jenkins Sleep Scale, Patient Health Questionnaire-2, General Anxiety Disorder-2, and a single burnout item.

RESULTS

The results indicated that Depressed Mood, Difficulty Falling Asleep, and Nervousness were the most central symptoms in a network in the participating countries. The strongest conditional associations were found between symptoms belonging to the same construct, which were similar in both countries. Thus, there is a relationship between Nervousness and Uncontrollable Worry, Anhedonia and Depressed Mood, and Nighttime Awakenings and Difficulty in Staying Asleep. It was observed that burnout is a bridge symptom between both countries and presents stronger conditional associations with Tiredness on Awakening, Depressed Mood, and Uncontrollable Worry. Other bridge symptoms include a Depressed Mood and Nervousness. The network structure did not differ between the participants from Peru and El Salvador.

CONCLUSION

The networks formed by sleep quality, anxiety, depression, and burnout symptoms play a prominent role in the comorbidity of mental health problems among the general populations of Peru and El Salvador. The symptom-based analytical approach highlights the different diagnostic weights of these symptoms. Treatments or interventions should focus on identifying central and bridge symptoms.

Examining Anxiety and Insomnia in Internship Students and Their Association with Internet Gaming Disorder

Background: Internships are a mandatory graduation requirement to help medical students transition to the work environment. Some individuals are prone to anxiety in an unfamiliar environment, which is a public concern among young adults. Here, we investigated the mechanism between internet gaming disorder and anxiety and insomnia among internship students. Methods: A convenient sample of 267 internship students was collected in a cross-sectional study module between 17 July and 27 December 2022. The survey contained a 7-item Generalized Anxiety Disorder (GAD-7), Athens Insomnia Scale (AIS), and Internet Gaming Disorder Scale-Short-Form (IGDS9-SF). The association was estimated using Pearson's correlations, and network analysis was performed to characterize these associations. Results: Our results indicate that about 60% of participants exhibited mild to severe anxiety and insomnia, while 2.28% showed symptoms of internet gaming disorder. Also, we found a moderate association between anxiety and insomnia. An item-level analysis indicated that GAD_1 "feeling anxious" and GAD_5 "unable to sit still" are essential for gaming, and that GAD_2 "uncontrollable worrying" is crucial for insomnia. This indicated an interplay between these items, supported by our centrality analysis, where we found that GAD_1 and GAD_2 depicted high centrality. Conclusions: We found high rates of anxiety and insomnia in internship students and the association between selected symptoms of anxiety and insomnia. At the same time, low rates of internet gaming disorder could be attributed to a lack of time for entertainment and an increased awareness of its risks. Given these findings, an awareness of anxiety and insomnia risk should be emphasized.

Network analysis of suicide ideation and depression-anxiety symptoms among Chinese adolescents

Background

The co-occurrence of depression and anxiety among adolescents is typically associated with suicide ideation.

Aims

The study aimed to investigate the symptom-level relationship between suicide ideation and the comorbidity of depression and anxiety.

Methods

1501 adolescents aged 12-19 years were assessed using the Patient Health Questionnaire (PHQ-9) and the Generalized Anxiety Disorder Scale, and 716 adolescents who scored ≥5 on both scales were selected as participants. Network analysis was used to identify the network structure of depressive symptoms and anxiety symptoms. Participants were categorised into either the suicide ideation or non-suicide ideation groups based on their scoring on the suicide-related item in PHQ-9. A comparison was made between the depression-anxiety symptom networks of the two groups.

Results

'Restlessness', 'sad mood' and 'trouble relaxing' were the most prominent central symptoms in the depression-anxiety symptom network, and 'restlessness', 'nervousness' and 'reduced movement' were the bridge symptoms in this network. 'Sad mood' was found to be directly related to 'suicide ideation' with the highest variance. The network structure was significantly different in properties between the suicide ideation group and the non-suicide ideation group, with 'restlessness' and 'sad mood' exhibiting significantly higher influence in the network of the suicide ideation group than that in the non-suicide ideation group.

Conclusion

Restlessness and sad mood could be targeted for the intervention of depression-anxiety symptoms among adolescents with suicide ideation.

A network model of depressive and anxiety symptoms: a statistical evaluation

BACKGROUND

Although network analysis studies of psychiatric syndromes have increased in recent years, most have emphasized centrality symptoms and robust edges. Broadening the focus to include bridge symptoms within a systematic review could help to elucidate symptoms having the strongest links in network models of psychiatric syndromes. We conducted this systematic review and statistical evaluation of network analyses on depressive and anxiety symptoms to identify the most central symptoms and bridge symptoms, as well as the most robust edge indices of networks.

METHODS

A systematic literature search was performed in PubMed, PsycINFO, Web of Science, and EMBASE databases from their inception to May 25, 2022. To determine the most influential symptoms and connections, we analyzed centrality and bridge centrality rankings and aggregated the most robust symptom connections into a summary network. After determining the most central symptoms and bridge symptoms across network models, heterogeneity across studies was examined using linear logistic regression.

RESULTS

Thirty-three studies with 78,721 participants were included in this systematic review. Seventeen studies with 23 cross-sectional networks based on the Patient Health Questionnaire (PHQ) and Generalized Anxiety Disorder (GAD-7) assessments of clinical and community samples were examined using centrality scores. Twelve cross-sectional networks based on the PHQ and GAD-7 assessments were examined using bridge centrality scores. We found substantial variability between study samples and network features. 'Sad mood', 'Uncontrollable worry', and 'Worrying too much' were the most central symptoms, while 'Sad mood', 'Restlessness', and 'Motor disturbance' were the most frequent bridge centrality symptoms. In addition, the connection between 'Sleep' and 'Fatigue' was the most frequent edge for the depressive and anxiety symptoms network model.

CONCLUSION

Central symptoms, bridge symptoms and robust edges identified in this systematic review can be viewed as potential intervention targets. We also identified gaps in the literature and future directions for network analysis of comorbid depression and anxiety.

Uncovering the symptom relationship between anxiety, depression, and internet addiction among left-behind children: A large-scale purposive sampling network analysis

Facing long-term separation from their parents, left-behind children are at risk of the co-occurrence of internalizing and externalizing problems. Although previous research has gained substantial information examining the relationship between anxiety, depression, and internet addiction at the aggregate level of variables, little is known about the heterogeneity and interactions between these components at the symptom level with a large-scale purposive sample. Adopting the network approach, two network pathways, depression and anxiety, and associations between these variables and internet addiction were constructed. Our sample included 5367 left-behind children (Mage = 13.57; SDage = 1.37; 50.07% females). Relevant bridging, central symptoms, and network stability were identified. Two relatively stable networks were obtained. For the network of anxiety and depression, sleep problems and tachycardia were vital bridging symptoms. Central symptoms, including tachycardia, restlessness, fatigue, and emptiness, were symptoms of depression. For the network of symptoms of anxiety, depression, and internet addiction, the bridging symptoms remained the same, and the central symptoms included tachycardia, restlessness, loss of control, and emptiness. By identifying relevant bridging and central symptoms, those with higher levels of these symptoms could be regarded as intervention targets, providing a reference for the current issue of valuing diagnosis over prevention in left-behind children.

Multidimensional stressors and depressive and anxiety symptoms in adolescents: A network analysis through simulations

BACKGROUND

Existing research has established associations between various stressors and adolescent mental health, primarily from a variable-level perspective. However, a symptom-level understanding about which stressors and symptoms might play a important role is scarce.

METHODS

The sample consisted of 15,570 adolescents aged 10 to 19. Participants completed questionnaires which assessed multidimensional stressors, depressive symptoms, anxiety symptoms, and demographic information. Network analysis was conducted to explore the relationships between stressors and depressive and anxiety symptoms. Additionally, to identify effective targets for the treatment and prevention of adolescent mental health issues, symptom-specific intervention simulations were performed on the network to investigate changes in symptom values in response to the alleviation and aggravation of specific stressors and symptoms.

RESULTS

Findings revealed that academic stressors exhibited stronger associations with anxiety symptoms than other stressors, particularly nervousness. Family relationships were more closely linked to depressive symptoms than other stressors, particularly suicidal ideation. Academic stressors emerged as an effective intervention target, and uncontrollable worry as an important prevention target. With the exception of academic stressors, simulating aggravation interventions on symptoms resulted in more changes in overall symptom activation than alleviation interventions.

LIMITATIONS

A cross-sectional design did not uncover network changes over time and the sample was non-clinical.

CONCLUSIONS

This study highlights the importance of addressing academic stressors to alleviate adolescents' depressive and anxiety symptoms and reveals that uncontrollable worry is a key prevention target. The findings are helpful for clinicians and educators to develop effective strategies to protect adolescents' mental health.

Relationships between psychopathological symptoms, pandemic-related stress, perceived social support, and COVID-19 infection history: a network analysis in Chinese college students

Introduction

Previous coronavirus, 2019 (COVID-19) research has applied network analysis to examine relationships between psychopathological symptoms but rarely extended to potential risk and protective factors or the influence of COVID-19 infection history. This study examined complex inter-relationships between psychopathological symptoms, COVID-19-related stressors, perceived social support, and COVID-19 infection history among Chinese university/college students during the peak of fifth pandemic wave using a network analysis approach.

Methods

A Least Absolute Shrinkage and Selection Operator-regularized partial correlation network using Gaussian graphical model was constructed in 1,395 Chinese university/college students in Hong Kong who completed a survey between 15 March and 3 April, 2022. Depressive, anxiety, and acute/traumatic stress symptoms were measured by Patient Health Questionnaire-9, Generalized Anxiety Disorder-7, and Impact of Event Scale-6, respectively. COVID-19-related stressors and perceived social support were measured. Network differences by COVID-19 infection history (COVID-network vs. no_COVID-network) and network communities were examined.

Results

Our results showed that the most influential nodes were depressed mood, uncontrollable worries, and uncontrollable thoughts about COVID-19. The main bridging symptoms were concentration problems and psychomotor problems. The COVID-network, comprising participants with a history of COVID-19 infection only, was significantly stronger than the no_COVID-network. Perceived social support and stress from conflicts with family/friends formed a unique community with negative cognition and suicidal idea in the COVID-network only.

Conclusion

Our findings indicate that specific interventions targeting interpersonal conflicts and concentration problems as well as facilitating stress buffering effects of social support may represent effective strategies to reduce psychological distress in university/college students during COVID-19 and should be considered for future pandemic preparedness.

Depression and anxiety among caregivers of psychiatric patients during the late stage of the COVID-19 pandemic: A perspective from network analysis

BACKGROUND

Depressive and anxiety symptoms (depression and anxiety hereafter) are common among psychiatric patients and their caregivers during the COVID-19 pandemic. Network analysis is a novel method to assess the associations between psychiatric syndromes/disorders at the symptom level. This study examined depression and anxiety among caregivers of psychiatric inpatients during the late stage of the COVID-19 pandemic from the perspective of network analysis.

METHODS

A total of 1101 caregivers of psychiatric inpatients were included in this study. The severity of depression was assessed using the nine-item Patient Health Questionnaire (PHQ-9), while anxiety was assessed with the seven-item Generalized Anxiety Disorder Scale (GAD-7). The expected index (EI) and bridge EI index were used to identify the central and bridge symptoms, respectively. The stability of the network was evaluated via a case-dropping bootstrap procedure.

RESULTS

The prevalence of depression and anxiety were 32.4 % (95%CI: 29.7 %-35.3 %) and 28.0 % (95%CI: 25.4 %-30.7 %), respectively while the prevalence of comorbid depression and anxiety was 24.9 % (95%CI: 22.4 %-27.6 %). The most central symptom was "Fatigue", followed by "Trouble Relaxing" and "Restlessness". The highest bridge symptom was "Restlessness", followed by "Uncontrollable worry" and "Suicide ideation". The bootstrap test indicated that the whole network model was stable, and no network difference was detected between genders and between different education levels.

CONCLUSIONS

Depression, anxiety, and comorbid depression and anxiety were common among caregivers of psychiatric inpatients during the late stage of the COVID-19 pandemic. Central and bridge symptoms identified in this network analysis should be considered key target symptoms to address in caregivers of patients.

Mapping network connection and direction between anxiety and depression symptoms across the early, middle, and late adolescents: Insights from a large Chinese sample

Depression and anxiety are two mental disorders prevailing among adolescents. However, issues regarding the trajectory of depression and anxiety are still controversial on both the disease and symptom dimensions. The novel method of network analysis was used to provide insight into the symptom dimension. 20,544 adolescents (female = 10,743, 52.3%) aged between 14 and 24 years (age mean ± sd = 16.9 ± 2.94) were divided into three subgroups according to age so that the course of depression and anxiety could be traced. Network analysis and the Bayesian network model were used in the current study. The results indicated that uncontrollable worry - excessive worry was the most significant edge for all adolescents, whereas concentration - motor had the highest edge weights for early adolescents, and anhedonia - energy was the most critical pairwise symptom for middle and late adolescents. Irritability can bridge anxiety and depression in the early and middle stages of adolescence, while suicide plays a bridging role in the early and late stages of adolescence. Restlessness and guilt can bridge anxiety and depression in middle- and late-stage adolescents, and feeling afraid plays a unique role in middle-stage adolescents. Except for sad mood, which can trigger middle adolescents' anxiety and depression, the other three subgroups were mainly triggered by nervousness. In addition, all results in our current study were shown to be stable and accurate. In treatment, targeting central and triggering symptoms at different stages of adolescence may be critical to alleviating the comorbidity of anxiety and depression.

Anxiety and depression played a central role in the COVID-19 mental distress: A network analysis

INTRODUCTION

Psychological, socio-demographics, and clinical factors play an important role in patients with COVID-19, but their relationship is complex. The network approach might be used to disentangle complex interactions in different systems. Using data from a multicentre, cross-sectional, survey among patients with COVID-19 in Spain (July-November 2020), we investigated the network structure of mental disorders symptoms, social support, and psychological resilience, and changes in network structures according to the presence of a pre-existing mental disorder or hospitalization for COVID-19.

METHODS

Subjects completed a survey to evaluate sociodemographic characteristics, COVID-19 infection status, resilience, social support, and symptoms of depression, anxiety disorders, post-traumatic stress disorder, panic attacks, and substance use disorder. 2084 patients with COVID-19 were included in the analysis. Network analysis was conducted to evaluate network and bridge centrality, and the network properties were compared between COVID-19 patients with and without a history of lifetime mental disorder, and between hospitalized and non-hospitalized patients.

LIMITATIONS

Generalization of our findings may be difficult since differences in network connectivity may exist in different populations or samples.

RESULTS

Anxiety and depression showed high centrality in patients with COVID-19 and anxiety showed the highest bridge influence in the network. Resilience and social support showed a low influence on mental disorder symptoms. Global network estimations show no statistically significant changes between patients with and without pre-existing mental disorders or between hospitalized and non-hospitalized patients.

CONCLUSIONS

Anxiety might be a key treatment target in patients with COVID-19 since its treatment might prevent other mental health adverse outcomes.

Network analysis of cold cognition and depression in middle-aged and elder population: the moderation of grandparenting

Background

Cognitive decline and negative emotions are common in aging, especially decline in cold cognition which often co-occurred with depression in middle-aged and older adults. This study analyzed the interactions between cold cognition and depression in the middle-aged and elder populations using network analysis and explored the effects of grandparenting on the cold cognition-depression network.

Methods

The data of 6,900 individuals (≥ 45 years) from the China Health and Retirement Longitudinal Study (CHARLS) were used. The Minimum Mental State Examination (MMSE) and the Epidemiology Research Center Depression Scale-10 (CESD-10) were used to assess cold cognition and depressive symptoms, respectively. Centrality indices and bridge centrality indices were used to identify central nodes and bridge nodes, respectively.

Results

Network analysis showed that nodes "language ability" and "depressed mood" were more central nodes in the network of cold cognition and depression in all participants. Meantime, nodes "attention," "language ability" and "hopeless" were three key bridge nodes connecting cold cognition and depressive symptoms. Additionally, the global connectivity of the cold cognition and depression network was stronger in the non-grandparenting than the grandparenting.

Conclusion

The findings shed a light on the complex interactions between cold cognition and depression in the middle-aged and elder populations. Decline in language ability and depressed mood can serve as predictors for the emergence of cold cognitive dysfunction and depression in individuals during aging. Attention, language ability and hopelessness are potential targets for psychosocial interventions. Furthermore, grandparenting is effective in alleviating cold cognitive dysfunction and depression that occur during individual aging.

A network analysis of subjective well-being in Chinese high school students

BACKGROUND

The psychological situation of high school students during adolescence is not promising, and the most obvious manifestation is the lack of subjective well-being (SWB). This network analysis presents a model of the interaction and correlation between different items of SWB, identifying the most central items for high school students.

METHODS

Through offline and online surveys, 4,378 questionnaires were sent out and finally 4,282 Chinese high school students were available. The response rate was 97.807%. The study used the eLASSO method to estimate the network structure and centrality measures. This algorithm used the EBIC to select the best neighbor factor for each node.

RESULTS

The average age for high school students was 16.320 years old and the average SWB score was 76.680. The distribution of SWB between male and female students was significant different (P < 0.001). S8 (Have you been anxious, worried, or upset) was the node with the highest strength and expected influence. The network structure and centrality remained stable after discarding 75% of the sample at random. Except for S15 (How concerned or worried about your health have you been), all nodes were positively correlated with each other (P < 0.01). The network structure of SWB was similar for female and male students (network strength: 8.482 for male participants; 8.323 for female participants; P = 0.159), as well as for rural and urban students (network strength: 8.500 for rural students; 8.315 for urban students; P = 0.140).

CONCLUSION

Targeting S8 (Have you been anxious, worried, or upset) as a potential intervention target may increase high school students' SWB effectively.

Bridge connection between depression and anxiety symptoms and lifestyles in Chinese residents from a network perspective

Background

Lifestyle habits are vital components of the culture of mental health treatment settings. We examined the bridge connection between depressive and anxiety symptoms and lifestyles from a network perspective using a population-based study.

Methods

Face-to-face interviews were conducted with a provincially representative sample of 13,768 inhabitants from the Guangdong Sleep and Psychosomatic Health Survey based on standardized evaluation techniques. We identified the central symptoms by expected influence. The interconnection between depression and anxiety symptoms, as well as the bridge connectivity linking depression-anxiety symptoms and lifestyle factors, were assessed using the bridge centrality index. Network stability and sensibility analyses were performed using a case-dropping bootstrap procedure.

Results

The core symptom that exhibited the highest expected influence was fatigue or little energy, followed by uncontrollable worry, trouble relaxing, and sad mood in the depression-anxiety symptoms network, while guilt was the most interconnected symptom and had the highest bridge strength. Surrounding nodes of each node explained an average variance of 57.63%. Additionally, suicidal thoughts were recognized as collective bridging symptoms connecting lifestyle variables in the network integrating depression-anxiety symptoms with lifestyle factors. Current tobacco and alcohol consumption were positively associated with suicidal thoughts and irritability. Habitual diet rhythm and physical exercise frequency were linked to suicidal thoughts, guilt, and poor appetite or overeating. Suicidal thoughts, irritability, and guilt indicated the greatest connectivity with lifestyle factors. All networks had high stability and accuracy.

Conclusion

These highlighted core and bridge symptoms could serve as latent targets for the prevention and intervention of comorbid depression and anxiety. It might be crucial for clinical practitioners to design effective and targeted treatment and prevention strategies aiming at specific lifestyles and behaviors.

Internet addiction and residual depressive symptoms among clinically stable adolescents with major psychiatric disorders during the COVID-19 pandemic: a network analysis perspective

To assess the inter-relationships between residual depressive symptoms (RDS) and Internet addiction (IA) using network analysis among clinically stable adolescents with major psychiatric disorders during the COVID-19 pandemic. RDS and IA were assessed using the Patient Health Questionnaire-9 (PHQ-9) and the Internet Addiction Test (IAT), respectively. Central symptoms and bridge symptoms in the network model were examined. A total of 1,454 adolescents met the study criteria and were included in the analyses. The prevalence of IA was 31.2% (95% CI: 28.8%-33.6%). In the network analysis, the nodes IAT15 ("Preoccupation with the Internet"), PHQ2 ("Sad mood"), and PHQ1 ("Anhedonia") were the most central symptoms in the IA-RDS network model. Bridge symptoms included IAT10 ("Sooth disturbing about your Internet use"), PHQ9 ("Suicide ideation"), and IAT3 ("Prefer the excitement online to the time with others"). Additionally, PHQ2 ("Sad mood") was the main node linking "Anhedonia" to other IA clusters. Internet addiction was common among clinically stable adolescents with major psychiatric disorders during the COVID-19 pandemic. Core and bridge symptoms identified in this study could be prioritized as targets for the prevention and treatment of IA in this population.

Depression and anxiety among Macau residents during the COVID-19 outbreak: A network analysis perspective

Background

The 2019 novel coronavirus disease (COVID-19) outbreak affected people's lifestyles and increased their risk for depressive and anxiety symptoms (depression and anxiety, respectively hereafter). We assessed depression and anxiety in residents of Macau during "the 6.18 COVID-19 outbreak" period and explored inter-connections of different symptoms from the perspective of network analysis.

Methods

In this cross-sectional study, 1,008 Macau residents completed an online survey comprising the nine-item Patient Health Questionnaire (PHQ-9) and seven-item Generalized Anxiety Disorder Scale (GAD-7) to measure depression and anxiety, respectively. Central and bridge symptoms of the depression-anxiety network model were evaluated based on Expected Influence (EI) statistics, while a bootstrap procedure was used to test the stability and accuracy of the network model.

Results

Descriptive analyses indicated the prevalence of depression was 62.5% [95% confidence interval (CI) = 59.47-65.44%], the prevalence of anxiety was 50.2% [95%CI = 47.12-53.28%], and 45.1% [95%CI = 42.09-48.22%] of participants experienced comorbid depression and anxiety. "Nervousness-Uncontrollable worry" (GADC) (EI = 1.15), "Irritability" (GAD6) (EI = 1.03), and "Excessive worry" (GAD3) (EI = 1.02) were the most central symptoms, while "Irritability" (GAD6) (bridge EI = 0.43), "restlessness" (GAD5) (bridge EI = 0.35), and "Sad Mood" (PHQ2) (bridge EI = 0.30) were key bridge symptoms that emerged in the network model.

Conclusion

Nearly half of residents in Macau experienced comorbid depression and anxiety during the 6.18 COVID-19 outbreak. Central and bridge symptoms identified in this network analysis are plausible, specific targets for treatment and prevention of comorbid depression and anxiety related to this outbreak.

The relationship between irritability, depression and anxiety among Chinese college students during the COVID-19 pandemic: A network analysis

Introduction

Irritability, a common symptom included in the 5th Edition of Diagnostic and Statistical Manual of Mental Disorders (DSM-5), is thought to be associated with multiple emotional disorders. It is commonly seen among college students in isolation during the COVID-19 pandemic. However, its relation with anxiety and depression remains unclear. We aim to study the relation of irritability, anxiety and depression in Chinese college students during the COVID-19 pandemic by using network analysis to understand the co-occurrence of these three disorders.

Methods

During the COVID-19 pandemic, we recruited 1516 college students from five general universities in China to complete the Irritability, Depression and Anxiety Scale (IDA-S) to analyze the symptom network of irritability, depression and anxiety. Specifically, we assessed the indices of strength centrality and bridge strength for each node in the network.

Results

Some strongest linkages were found among anxiety symptoms "nervous" and "panic" (weight = 0.36), depression symptoms "sad mood" and "amused" (weight = 0.32), inward irritability items "self-hurt" and "self-harm" (weight = 0.32) and outward items "rough" and "aggressive" (weight = 0.28). The anxiety symptom "panic" had the highest strength value, followed by the inward irritability symptom "annoyed". The nodes "ease" and "sleep" had the lowest strength value. In addition, the anxiety symptom "relax" had the highest bridge strength value, followed by inward irritability symptom "annoyed".

Conclusion

This study explored the characteristics of a network of irritability, depression and anxiety symptoms among Chinese college students during the COVID-19 pandemic. We found that anxiety and irritability symptoms played an important role in the network. The findings provide evidence for prevention and intervention for college students' mental health problems during the COVID-19 pandemic.

Loneliness, affective disorders, suicidal ideation, and the use of psychoactive substances in a sample of adolescents during the COVID-19 pandemic: A cross-sectional study

PROBLEM

The global coronavirus (COVID-19) pandemic has been challenging for adolescents. Indeed, with the closure of schools and social centers and reduction of extracurricular activities, increased social isolation has compounded difficulties in and with school performance, loneliness, and social networking. Increased risk of mental health problems, substance abuse, affective disorders, suicidal ideation, and suicide has been reported in adolescents.

METHODS

This cross-sectional study assesses the association between loneliness, depression, anxiety, suicidal ideation, the use of social networks, and school achievement in a sample of Italian adolescents during the COVID-19 pandemic. This study also explores emotional dysregulation through the association between affective disorders (depression and anxiety), substance use, and social networks. The sample comprises adolescents in the first and second grades of high school during the pandemic; participants received an email explaining the purpose of the e-research. Data were collected using the Strengths and Difficulties Questionnaire, the Achenbach System of Empirically Based Assessment, and the Loneliness Scale.

FINDINGS

A total of 505 adolescents completed the web survey. Data revealed that students experienced difficulties with loneliness, problems with school achievement, and extracurricular activities. The mean scores for depression and anxiety were close to the borderline range. A total of 14.3% of adolescents intentionally harmed themselves or attempted suicide.

CONCLUSIONS

This study raises concerns about the impacts of the pandemic on adolescents that require the attention of adult reference figures who deal with adolescents, such as parents, teachers, and healthcare professionals. Results indicate the necessity of providing early interventions aimed at the prevention of psychopathologies and the promotion of adolescent mental health due to the pandemic.

Psychological impact of COVID-19: A cross-lagged network analysis from the English Longitudinal Study of Aging COVID-19 database

Background

The COVID-19 pandemic and its subsequent health restrictions had an unprecedented impact on mental health, contributing to the emergence and reinforcement of various psychopathological symptoms. This complex interaction needs to be examined especially in a vulnerable population such as older adults.

Objective

In the present study we analyzed network structures of depressive symptoms, anxiety, and loneliness from the English Longitudinal Study of Aging COVID-19 Substudy over two waves (Months of June-July and November-December 2020).

Methods

For this purpose, we use measures of centrality (expected and bridge-expected influence) in addition to the Clique Percolation method to identify overlapping symptoms between communities. We also use directed networks to identify direct effects between variables at the longitudinal level.

Results

UK adults aged >50 participated, Wave 1: 5,797 (54% female) and Wave 2: 6,512 (56% female). Cross-sectional findings indicated that difficulty relaxing, anxious mood, and excessive worry symptoms were the strongest and similar measures of centrality (Expected Influence) in both waves, while depressive mood was the one that allowed interconnection between all networks (bridge expected influence). On the other hand, sadness and difficulty sleeping were symptoms that reflected the highest comorbidity among all variables during the first and second waves, respectively. Finally, at the longitudinal level, we found a clear predictive effect in the direction of the nervousness symptom, which was reinforced by depressive symptoms (difficulties in enjoying life) and loneliness (feeling of being excluded or cut off from others).

Conclusion

Our findings suggest that depressive, anxious, and loneliness symptoms were dynamically reinforced as a function of pandemic context in older adults in the UK.

Differences in central symptoms of anxiety and depression between college students with different academic performance: A network analysis

Objective

The prevalence of anxiety and depressive symptoms for Chinese college students are high. Academic pressure is one of the prominent risk factors of psychological well-beings for Chinese college students. The application of network analysis provides researchers a more comprehensive understanding of symptom-symptom interaction in mental disorders. This study aims to find out whether there is a difference in central symptoms between students with different academic performance.

Method

A total sample of 1,291 college students was included in our study. Anxiety and depressive symptoms were measured by PHQ-9 and GAD-7. Central symptoms were identified through centrality indices. Network stability was examined using the case-dropping method.

Results

For the poor academic group, the most central symptom is PHQ-2 (feeling depressed). The most central symptom of the good academic group is GAD-2 (uncontrolled worry). The least central symptom for both groups is PHQ-9 (suicidal thought). Network structure is statistically different between two groups, global strength is not statistically different between two groups.

Conclusion

The pertinent symptom is feeling depressed, followed by uncontrolled worry and poor appetite, and for the good academic group, the pertinent symptom is an uncontrolled worry, theoretical explanation and clinical implications is discussed.

Social anxiety and depression symptoms in Chinese left-behind children after the lifting of COVID-19 lockdown: A network analysis

BACKGROUND

Returning to social life after the lifting of COVID-19 lockdown may increase risk of social anxiety, which is highly co-morbid with depression. However, few studies have reported the association between them.

AIMS

To explore the complex relationship between social anxiety and depression symptoms in left-behind children after the lifting of the COVID-19 lockdown.

METHODS

A cross-sectional survey was conducted 6 months after the lockdown removal. A total of 3,107 left-behind children completed the survey with a mean age of 13.33 and a response rate of 87.77%. Depression and social anxiety severity were assessed by the DSM-5 Patient Health Questionnaire for Adolescents and the DSM-5 Social Anxiety Disorder Questionnaire, respectively. The symptom-level association between the two disorders was examined using network analysis.

RESULTS

After the lifting of COVID-19 lockdown, the prevalence of depression and social anxiety in left-behind children was 19.57% and 12.36%, respectively, with a co-morbidity rate of 8.98%. Network analysis showed that "Social tension" and "Social avoidance" had the greatest expected influence; "Humiliation" and "Motor" were bridge symptom nodes in the network. The directed acyclic graph indicated that "Social fright" was at the upstream of all symptoms.

CONCLUSION

Attention should be paid to social anxiety symptoms in left-behind children after the lifting of COVID-19 lockdown. Prevention and intervention measures should be taken promptly to reduce the comorbidity of social anxiety and depression symptoms in the left-behind children after the lifting of lockdown.

Network structure of insomnia and depressive symptoms among shift workers in China

A bidirectional relationship between insomnia and depression has been observed. However, few studies have used network analysis to explore the interaction patterns in that association at the symptom level. This study aimed to estimate network structures of insomnia and depressive symptoms among shift workers, as well as to compare the differences in network properties between individuals without and with insomnia symptoms and/or at risk of depression. A total of 1883 shift workers were included in our study. Insomnia symptoms were evaluated by three items based on the criteria of the Diagnostic and Statistical Manual of Mental Disorders, and depressive symptoms were assessed by the Center for Epidemiologic Studies Depression Scale. Network analyses were used for the statistical analysis. "Difficulty initiating sleep", "Hard to get started", and "Depressed mood" with higher expected influence (EI) values were identified as the most central symptoms within the insomnia-depressive networks among shift workers. The significant differences between individuals without and with insomnia symptoms and/or at risk of depression were observed in symptoms of "Difficulty initiating sleep" and "Hard to get started". "Depressed mood", "Difficulty initiating sleep", or "Hard to get started" were the most key symptoms that trigger and sustain the structure of insomnia and depressive symptom among shift workers. Hence, timely intervention for the above three symptoms in future research or clinical practice (e.g., cognitive behavioral therapy for insomnia) may be crucial in alleviating insomnia and depressive symptoms among shift workers.

Adjusting intervention strategies for mental health of COVID-19 patients: A network analysis based on a survey in Omicron-infected patients

Background

The COVID-19 pandemic had a major impact on people's mental health. As the SAS-Cov-2 evolves to become less virulent, the number of asymptomatic patients increases. It remains unclear if the mild symptoms are associated with mild perceived stress and mental illness, and the interventions to improve the mental health of the patients are rarely reported.

Methods

This cross-sectional study investigated the level of depression, anxiety and perceived stress of 1,305 COVID-19 patients who received treatment in the Fangcang shelter hospitals in Shanghai, China. Network analysis was used to explore the relationship among depression, anxiety and perceived stress.

Results

The prevalence of depression, anxiety and perceived stress in the patients with Omicron infection were 9.03, 4.60, and 17.03%, respectively, lower than the prevalence reported during the initial outbreak of COVID-19. "Restlessness (A5)," "Uncontrollable worry (A2)," "Trouble relaxing (A4)" and "Fatigue (D4)" had the highest expected influence values. "Irritability (A6)" and "Uncontrollable (S1)" were bridge symptoms in the network. Comparative analysis of the network identified differences in the network structures between symptomatic and asymptomatic patients.

Conclusion

This study investigated the prevalence of depression, anxiety and perceived stress and the correlation among them in Omicron-infected patients in Fangcang shelter hospital, in Shanghai, China. The core symptoms identified in the study provide insight into targeted clinical prevention and intervention of mental health in non-severe Omicron-infected patients.

Anxiety, depression, stress, worry about COVID-19 and fear of loneliness during COVID-19 lockdown in Peru: A network analysis approach

This study aims to examine the relationships between symptoms of anxiety, depression, stress, worry about COVID-19 and fear of loneliness during COVID-19 lockdown in Peru using network analysis. There were 854 participants aged 18 to 50 years (Mean = 36.54; SD = 9.23); 634 females (74.20%) and 220 males (25.80%), who completed the Generalized Anxiety Disorder Scale (GAD-7), Patient Health Questionnaire (PHQ-9), Perceived Stress Scale (PSS-10), Preoccupation with COVID-19 Contagion (PRE-COVID-19), Brief Scale of Fear of Loneliness (BSFL). A partial unregularized network was estimated through the ggmModSelect function. Expected influence (EI) and bridging EI values were calculated to identify central symptoms and bridging symptoms respectively. The results reveal those two symptoms of depression-stress and anxiety-were the most central symptoms in the network. Depressive symptoms are at the same time the most comorbid and it is shown that there are no differences in the network when compared between those who left home and those who did not leave home during lockdown. Depressive symptoms are concluded to be central and bridging in the network and interconnected with some symptoms of stress and anxiety. These findings may be important to understand the experience of COVID-19 lockdown in Peru.

Network and Exploratory Factorial Analysis of the Depression Clinical Evaluation Test

Depression is a highly prevalent disorder with a wide range of symptomatology. Existing instruments for its assessment have only a few items for each factor. The Depression Clinical Evaluation Test (DCET) has been created to cover all depression symptoms at different times (month, year, and always) with several items for each facet. The content validity of this instrument has been judged by experts and, in this paper, we analyse its factorial structure and make a network analysis of it. The test (196 items) was administered to 602 adults without psychological disorders (M_age = 24.7, SD = 8.38, 72% women) both online and on paper. A network was estimated for each time point, using the absolute minimum selection and shrinkage operator. From the factor analysis, 12 factors were established for month, 11 for year, and 10 for always, leaving 94 items. The network analysis showed that the facets of depressive mood, anhedonia, and thoughts of Death, are central to all the estimated networks. The DCET is proposed as a valid and reliable multifactorial instrument to detect the variability of depressive symptoms in adults, guaranteeing its diagnostic usefulness.

[Psychological and behavioral problems in children and adolescents during the coronavirus disease 2019 epidemic: a Scoping review]

OBJECTIVES

To investigate the psychological and behavioral problems and related influencing factors in children and adolescents during the coronavirus disease 2019 (COVID-19) epidemic.

METHODS

China National Knowledge Infrastructure, Wanfang Data, PubMed, and Web of Science were searched using the method of subject search for articles published up to March 31, 2022, and related data were extracted for Scoping review.

RESULTS

A total of 3 951 articles were retrieved, and 35 articles from 12 countries were finally included. Most of the articles were from the journals related to pediatrics, psychiatry, psychology, and epidemiology, and cross-sectional survey was the most commonly used research method. Psychological and behavioral problems in children and adolescents mainly included depression/anxiety/stress, sleep disorder, internet behavior problems, traumatic stress disorder, and self-injury/suicide. Influencing factors were analyzed from the three aspects of socio-demographic characteristics, changes in living habits, and ways of coping with COVID-19.

CONCLUSIONS

During the COVID-19 epidemic, the psychological and behavioral problems of children and adolescents in China and overseas are severe. In the future, further investigation and research can be carried out based on relevant influencing factors to improve the psychological and behavioral problems.

Depressive Symptoms and Negative Experiences in School: A Network Analysis

Negative experiences in school predict adolescents' depression. However, the dynamic interactions of depressive symptoms with adolescents' negative experiences in school remain unclear. This study aims to applied network analysis to detect the complex relationships between early adolescents' depressive symptoms and negative experiences in school. We adopted the data from a Chinese national survey conducted in 2018. Eight hundred ninety-seven adolescents from 10 to 15 years old were included. The measurements include an 8-item depression screener scale and a 14-item scale assessing negative experiences in school. The centrality analysis suggests that Chinese early adolescents' core depressive symptoms are negative affections. Regarding "negative experiences in the school," the most central nodes are poor academic performance and peer relationships. The bridge-centrality results show negative emotions in school and difficulties in peer relationships are significantly linked to depression. Educators should pay attention to adolescents' negative affection and difficulties in peer relationships in school settings because they are potential signs of depression. Relevant peer support intervention aiming for adolescents' social development should be underscored.