Bridge Symptoms Research Articles

Interconnected mental health symptoms: network analysis of depression, anxiety, stress, and burnout among psychiatric nurses in the context of the COVID-19 pandemic

BackgroundMental health symptoms such as anxiety, depression, stress, and burnout are common among healthcare workers. However, the interconnections among them remain under-explored. This study aimed to address the interrelationships among these symptoms in psychiatric nurses.MethodsWe conducted a nationwide survey in the early stage of the COVID-19 pandemic (January to March 2021) to investigate the interconnectedness of depression, anxiety, stress, and burnout among psychiatric nurses. Using network analysis, we identified central symptoms, important bridge symptoms, and the correlations among these central symptoms.ResultsOf the 9,224 psychiatric nurses (79.2% female) included in the statistical analyses, 27.6% reported clinically significant depression, 31.2% anxiety, 14.5% stress, and 23.8% burnout. Network analysis revealed that stress had the highest expected influence (EI) value (0.920) and the highest strength among all nodes. The node for depression scored the highest in both closeness and betweenness. Emotional exhaustion (EE) had the highest bridge expected influence (BEI) of 0.340, with the strongest intergroup association between EE and depression. No significant differences were found in gender or frontline work experience (all p > 0.05).ConclusionsBurnout, depression, anxiety, and stress are relatively common among psychiatric nurses in the context of the COVID-19 pandemic. While anxiety was the most prevalent, stress emerged as the core symptom, and depression as an important bridging node. Interventions targeting the core symptoms and bridging nodes may improve the mental health of psychiatric nurses.

The interplay among burnout, and symptoms of depression, anxiety, and stress in Chinese clinical therapists

Burnout, depression, anxiety, and stress negatively impact the well-being and retention of healthcare professionals. The interplay of these symptoms is understudied. Utilizing network analysis, this study examined the interrelationships among these symptom clusters in clinical therapists in China. An anonymous survey was conducted among clinical therapists from 41 tertiary psychiatric hospitals in China. Burnout was assessed using the Maslach Burnout Inventory-Human Service Survey (MBI-HSS), while symptoms of depression, anxiety, and stress were assessed via the Depression, Anxiety, and Stress Scale-21 (DASS-21). Analyses were performed to identify central symptoms and bridge symptoms of this network. A total of 419 participants were included in this survey. The prevalence rate for burnout, depression, anxiety, and stress was 19.8%, 22.2%, 17.9%, and 8.6%, respectively. Network analysis indicated that stress symptoms had the highest expected influence values, closely followed by emotional exhaustion from MBI-HSS. Notably, emotional exhaustion emerged as the strongest bridge of expected influence. The stability of the expected influence and bridge expected influence was robust, with coefficients at 0.75. The study’s findings underscore the importance of recognizing the central symptoms and bridge symptoms, which could lead to more effective early detection and intervention for burnout, depression, anxiety, and stress among clinical therapists.

Network analysis of correlations between suicide exposure, depression, and anxiety symptoms in adolescents.

Adolescents with a history of suicide exposure, defined as experiencing the suicide death of a family member, friend, or other acquaintances, are more likely to experience mental health issues such as depression or anxiety. This study aimed to explore prevalence rates and the network of adolescents' suicide exposure, depression, and anxiety symptoms, and to clarify the correlations between suicide exposure and symptoms of depression and anxiety. A total of 8,957 adolescents were included in this cross-sectional study. Data regarding general information, symptoms of depression and anxiety, and suicide exposure were collected from mid-September to early October 2021. Network analysis was employed to assess relationships between suicide exposure and individual symptoms of both depression and anxiety. Central symptoms were identified by strength; the flow network was visualized to identify symptoms directly related to suicide exposure. The prevalence rates of suicide exposure, depression, and anxiety were 5.28%, 12.87%, and 10.48%. Results indicated that suicide exposure was associated with both depression and anxiety, and had the strongest positive association with suicidal ideation. Central symptoms of the network were sad mood, nervousness, fatigue, irritability, and uncontrollable worry. Bridge symptoms were suicidal ideation and irritability. Appetite changes, suicidal ideation, uncontrollable worry, sleep difficulties, and irritability were symptoms directly related to suicide exposure. There were significant inter-symptom associations between suicide exposure, depression, and anxiety in adolescents. It is recommended that future studies explore whether targeted interventions and long-term monitoring concerning these inter-symptom associations can protect adolescents with suicide exposure.

A network analysis of voice hearing, emotional distress and subjective recovery before and after cognitive behavioural interventions.

There is a need to identify and to better understand key processes involved in voice hearing, which can inform the targeting and development of psychological interventions for distressing voices. The current study aimed to examine interrelations between the negative impact of voices, voice characteristics, emotional distress and recovery before and after cognitive behavioural interventions for voices (Coping Strategy Enhancement, guided self-help Cognitive Behavioural Therapy, Relating Therapy and Person-Based Cognitive Therapy). The sample consisted of 172 participants from the Sussex Voices Clinic who completed pre- and post-treatment assessments. The negative impact of voices, voice characteristics, emotional distress and recovery were used to estimate two networks, before and after cognitive behavioural interventions, using the graphical lasso method with the extended Bayesian information criterion. Centrality indices were also computed, and the two networks were compared on connectivity, structure and individual edge weights. Depression, anxiety and the negative impact of voices were identified as key central symptoms and acted as bridge symptoms in pre- and post-treatment networks. There were no significant differences in network structure (M = 0.155, p = .57), global strength (S = 0.188, p = .07) and centralities (C = -0.318, p = -.06) between the two networks. Our findings suggest that anxiety and depression are promising treatment targets, that can lead to reductions in voice-related distress, whereas the characteristics of voices and subjective recovery play little role in the network structure. Limitations include the lack of a control group and the lack of diversity within the sample.

"Enhancing post-craniotomy recovery: leveraging AI and network analysis for improved outcomes".

This letter addresses the importance of enhancing post-craniotomy care for primary brain tumor patients by leveraging insights from Rongqing Li et al.'s study on symptom networks. The study identified key central and bridge symptoms, such as sadness and difficulty understanding, which influence post-surgical recovery and quality of life. It also highlighted that patients with noninvasive tumors showed more cohesive symptom networks compared to those with invasive tumors. However, the study had limitations, including a short observation period and reliance on self-reported data, which restricted the depth of the findings.To optimize recovery, integrating artificial intelligence (AI) and machine learning (ML) could revolutionize post-craniotomy care. AI can assist with surgical planning, predict complications, and monitor recovery through wearable devices and real-time alerts. Natural Language Processing (NLP) can improve symptom detection from electronic health records, enhancing clinical decision-making. Despite the potential of these technologies, ethical concerns regarding data privacy and AI-generated report accuracy must be addressed. Future research should focus on long-term outcomes and refining AI applications to improve post-craniotomy symptom management and overall patient outcomes.

Exploring core and bridge symptoms in patients recovering from stroke: a network analysis.

Patients recovering from stroke experience a variety of symptoms that present as a synergistic and mutually reinforcing "symptom cluster," rather than as singular symptoms. In this study, we researched and systematic analyzed these symptom clusters, including core and bridge symptoms, to help determine the relationships between symptoms and to identify key symptom targets, providing a new approach for formulating precise symptom management interventions. Convenience sampling was applied to select 432 stroke recovery patients treated in the Seventh People's Hospital of Changzhou City from August 1, 2023 to April 14, 2024. Subsequently, a cross-sectional survey was conducted using the General Information Questionnaire and Stroke Symptom Experience Scale to extract symptom clusters via exploratory factor analysis. Finally, the "qgraph" and "bootnet" packages in the R language were used to construct a network layout to describe the relationships between symptoms and calculate the centrality index. The average age of the 432 enrolled recovering stroke patients was 68.17 ± 12.14 years, including 268 males (62.04%) and 164 females (37.96%), none of whom underwent surgical intervention. Among this cohort, the 3 symptoms with the highest incidence rates were "limb weakness" (A2, 80.56%), "fatigue" (A5, 77.78%), and "limitations of limb movement" (A1, 68.06%). A total of 5 symptom clusters were extracted: the somatic activity disorder, mood-disorder-related, cognitive-linguistic dysfunction, somatic-pain-related, and foot dysfunction symptom clusters. In the symptom network, the 2 most common symptoms in terms of intensity and expected impact were "fatigue" (A5, rs = 1.14, re = 1.00) and "pessimism about the future" (B3, rs = 1.09, re = 1.02). The symptom with the strongest bridge intensity was "limb pain" (D1, rs = 2.64). This study uses symptom network analysis to explore the symptoms of stroke patients during recovery, identifying core symptoms and bridge symptoms. Based on these findings, we can develop more targeted management plans to improve the accuracy and efficiency of interventions. Through this management approach, we can enhance treatment effectiveness, reduce unnecessary medication, lower adverse drug reactions, and optimize the allocation of medical resources.

A Network Structure of Mental Health and Problematic Mobile Phone Use Among Middle School Students.

Numerous studies have shown that the mental health of middle school students is closely related to problematic mobile phone use. The purpose of this study is to investigate the network structure between the dimensions of the Middle School Students Mental Health Scale and the items of the Self-Rating Questionnaire for Adolescent Problematic Mobile Phone Use by using the network analysis, to clarify the core symptoms and bridge symptoms of the network structure, and to provide ideas and methods for intervening in the mental health and problematic mobile phone use of middle school students. A stratified cluster sampling method was used to select 1637 students from four general middle schools in Xiamen in June 2020 for the survey, and the Middle School Students Mental Health Scale (MSSMHS-60) and the Self-Rating Questionnaire for Adolescent Problematic Mobile Phone Use (SQAPMPU) were used. SPSS28.0 was used for descriptive statistical analysis and R (version 4.2.1) for network analysis. 1. The core symptoms of the network of middle school students' mental health and problematic mobile phone use were "spending more time playing with the phone in order to be satisfied", anxiety, and depression; 2. The bridge symptoms of the network of middle school students' mental health and problematic mobile phone use were academic stress, psychological disequilibrium, and "depression without phone". Reducing the time of smartphone use and relieving anxiety and depression can improve the mental health of middle school students and reduce the incidence of problematic mobile phone use; helping middle school students adjust their study pressure and improving their social support level can reduce the severity of problematic mobile phone use.

Unveiling the hidden connections: network analysis of depressive symptoms, internet addiction, and attachment in Chinese children and adolescents

BackgroundChildren and adolescents diagnosed with major depressive disorder (MDD) often grapple with significant non-suicidal self-injury (NSSI), linked to interpersonal challenges, insecure attachment, and internet addiction. This study explores these relationships in children and adolescents using network analysis, drawing data from seven hospitals in China.ResultsThe study included 162 youths with depressive disorder and 102 healthy controls from seven hospitals. The MDD + NSSI group exhibited severe internet addiction, heightened depressive symptoms, and insecure attachment. Network analysis identified central symptoms (compulsive use) and key bridge symptoms (tolerance symptoms) in the networks related to depressive symptoms, internet addiction, and attachments. These findings suggest potential targets for intervention among children and adolescents at risk for or suffering from depression.ConclusionsThis study sheds light on the intricate relationship between NSSI, attachment issues, and internet addiction in Chinese adolescents with MDD. The network analysis pinpointed central and key bridge symptoms, offering valuable insights for targeted interventions in children and adolescents at risk for or experiencing depression and associated challenges.

Network analysis of Tourette syndrome and attention-deficit/hyperactivity disorder symptoms in children and adolescents

BackgroundWhile Tourette syndrome (TS) and attention-deficit/hyperactivity disorder (ADHD) often co-occur, the nature of the relationship between their symptoms is not well understood. Network analysis of psychopathology allow for detailed examinations of symptom interactions, providing an effective approach to explore the patterns of comorbidity between TS and ADHD symptoms.MethodsThis study included 3,958 participants (male/female = 3,004/954, age mean ± SD = 8.60 ± 2.25 years). We collected data on TS symptoms using the Motor Tic, Obsessions and Compulsions, Vocal Tic Evaluation Survey (MOVES), and ADHD symptoms using the Swanson, Nolan, and Pelham Rating Scale-IV (SNAP-IV). Network analysis was employed to construct a combined network of TS and ADHD symptoms at the symptom level. We utilized the expected influence (EI) and bridge EI metrics to explore the core and bridge symptoms within the network.ResultsThe network structure demonstrated a moderate number of non-zero connections between TS and ADHD symptoms, constituting 23.06% of all potential connections. Core symptoms in the comorbidity network included “Often has difficulty sustaining attention in tasks or play activities,” “Certain bad words or thoughts keep going through my mind,” and “Words come out that I can’t stop or control.” Bridging symptoms identified were “Words come out that I can’t stop or control,” “I do certain things like jumping or clapping over and over,” “I can’t control all my movements,” and “Often talks excessively.”ConclusionThe core and bridging symptoms identified in this study serve as potential therapeutic targets for the treatment of TS and ADHD comorbidity in clinical children and adolescents.

Connecting the dots: Network structures of internalizing and functional symptoms in a population-based cohort

ObjectiveComorbidities between internalizing disorders (IDs) and functional disorders (FDs) are well-documented, indicating shared pathways. However, their symptom-level relationships have been largely unexplored. This exploratory study employs a network approach to investigate symptoms of major depressive disorder (MDD), generalized anxiety disorder (GAD), myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), fibromyalgia (FM), and irritable bowel syndrome (IBS) to identify bridge symptoms explaining comorbidity between the two domains. MethodsWe used cross-sectional data on 72,919 adult subjects from the Lifelines Cohort Study, a Dutch general population sample. A total of 38 symptoms representing diagnostic criteria of IDs and FDs were assessed with validated questionnaires. Network models were estimated using eLasso, based on the Ising model, to identify bridge symptoms. The Network Comparison Test (NCT) was used to test whether there were differences in network structure and strength across sex and age. ResultsSymptoms were moderately connected, with a network density of 52.7%. ID and FD symptoms clustered in their respective domains, but were connected through the bridge symptoms, fatigue, difficulty concentrating, trouble sleeping, and unrefreshing sleep. Fatigue and difficulty concentrating had the most connections, associated with 86.6% and 78.9% of the other symptoms, respectively. NCTs indicated no differences in network connectivity between females versus males or younger versus older adults (>50 years). ConclusionsID and FD symptoms are moderately interconnected. Bridge symptoms displaying strong connections to multiple disorders may play a central role in the mechanisms underpinning the comorbidity between IDs and FDs.

Depression and Anxiety among Migrant Older Adults during the COVID-19 Pandemic in China: Network Analysis of Continuous Cross-Sectional Data.

Many Chinese migrant older adults are more prone to mental health problems due to their "migrant" status. During the COVID-19 pandemic, restrictions on their mobility exacerbated these conditions. Mental health is a crucial dimension of healthy aging. Network analysis offers a novel method for exploring interactions between mental health problems at the symptom level. This study employs network analysis to examine the interactions between comorbid depressive and anxiety symptoms across different stages of the COVID-19 pandemic. Surveys were conducted from September 2019 to January 2020 (T1), September 2020 to January 2021 (T2), and September 2021 onwards (T3). Depression and anxiety symptoms were measured by the Patient Health Questionnaire-9 (PHQ-9) and the Hospital Anxiety and Depression Scale-Anxiety (HADS-A). Expected Influence (EI) and Bridge Expected Influence (Bridge EI) were used to identify central and bridge symptoms in the network. Network stability and accuracy tests were performed. Among the Chinese migrant older adults, the anxiety prevalence was 18.50% at T1, 21.11% at T2, and 9.38% at T3. The prevalence of depression was 26.95% at T1, 55.44% at T2, and 60.24% at T3. The primary central symptoms included 'Afraid something will happen' (A2), 'Irritability' (A6), 'Panic' (A7), 'Feeling of worthlessness' (D6), 'Anhedonia' (D1), and 'Feeling of fear' (A5). The major bridge symptoms included 'Feeling of fear' (A5), 'Panic' (A7), 'Irritability' (A6), 'Fatigue' (D4), 'Anhedonia' (D1), and 'Depressed or sad mood' (D2). Differences in network structure were observed across the periods. The network analysis further revealed the evolving relationships between central and bridge symptoms over time, highlighting the importance of targeted intervention strategies for central and bridge symptoms of comorbid depression and anxiety at different periods.

Posttraumatic stress disorder symptoms in mothers and adolescents after the Wenchuan earthquake: A cross-sectional and longitudinal network analysis

ObjectiveThis study used network analysis to investigate the cross-sectional and longitudinal network between PTSD symptoms within mother-adolescent dyads at 12 and 18 months after the 2008 Wenchuan earthquake. MethodsThe sample was from the Wenchuan Earthquake Adolescent Health Cohort. 399 mother-adolescent dyads completed the Posttraumatic Stress Disorder Self-Rating Scale at 12 and 18 months after the earthquake. We assessed central symptoms (those with the most significant influence on other symptoms) and bridge symptoms (symptoms connecting different communities) in contemporary networks (i.e., cross-sectional networks). Subsequently, cross-lagged panel network analyses (CLPN) were performed to estimate longitudinal relationships among symptoms between dyads. ResultsIn the contemporary networks, symptoms such as “intrusive thoughts” of both dyads and “flashbacks” of adolescents were central, indicating that they are crucial in maintaining the network of PTSD symptoms. Additional symptoms such as maternal “difficulty in concentration” and dyads’ “pessimism and disappointment” should also be considered because of their central roles. Moreover, the temporary network did not directly replicate the contemporary networks, with adolescents’ “nightmares” at 12 months having a high influence on other PTSD symptoms at 18 months. LimitationsSelf-reported tools other than clinical diagnoses were used to collect data. ConclusionsThese symptom-level associations at cross-sectional and longitudinal networks extend our understanding of PTSD symptoms among mother-adolescent dyads by pointing to specific key symptoms of PTSD that may drive the co-occurrence of PTSD among dyads. Recognizing these symptoms is imperative for the development of targeted interventions and treatments aimed at addressing comorbid PTSD in mother-adolescent dyads.

Identifying Bridge Symptoms Between Borderline Personality Disorder and Posttraumatic Stress Disorder: A Network Analysis From a National Cohort.

Identifying Bridge Symptoms Between Borderline Personality Disorder and Posttraumatic Stress Disorder: A Network Analysis From a National Cohort.

The bridge relationships of PTSD and depression symptoms among snakebite victims: a cross-sectional community-based survey

BackgroundThe incidence of comorbid depression and post-traumatic stress disorder (PTSD) symptoms is higher in snakebite victims. However, the present state and contributing factors of depression and PTSD among Chinese snakebite victims remain unclear.MethodsA representative sample of 6837 snakebite victims were assessed with the Post-traumatic Stress Disorder Checklist (Civilian Version) and The Center for Epidemiologic Studies Depression Scale. Multivariate analyses, including network analysis, evaluated the contributing factors of PTSD and depression symptoms caused by snake bites, as well as the bridge symptoms of comorbidity networks.ResultsAmong 6,837 snakebite victims, 79.5% reported PTSD symptoms and 81.4% reported depression symptoms. Comorbidity of PTSD and depression symptoms was found in 75.1%. Key factors included the presence sequelae after snakebite (ORPTSD = 2.31, ORDepression = 1.89), time to medical facilities (6–8 h: ORPTSD = 3.17, ORDepression = 2.46), and marital status (divorced/widowed: ORPTSD = 1.78, ORDepression = 1.76). Symptoms I1 ("Repeated disturbing memories") and D1 ("Bothered by things that don't usually bother me") bridged PTSD and depression networks.ConclusionThe primary psychological challenges for snakebite victims in China are PTSD and depression symptoms, which is concerning. Standardized diagnosis and treatments, timely medical care, and stable marital relationships can reduce risks. Additional psychological support and management of negative memories, especially for those with severe bridge symptoms, can be beneficial. Further research should concentrate on understanding victims' psychological states and developing effective interventions.

Comparing network structures of depressive and anxiety symptoms between demographically-matched heart disease and heart disease free samples using propensity score matching: Findings from the Chinese Longitudinal Healthy Longevity Survey (CLHLS)

BackgroundOlder adults with heart disease often experience higher rates of comorbid anxiety and depression. This study examined depression and anxiety network structures among older adults with heart disease and their heart disease free peers. MethodsNetwork analyses of secondary cross-sectional data from the 2017 to 2018 wave of CLHLS were used to construct groups of older adults with and without heart disease using propensity score matching. Depression and anxiety symptoms were assessed using Center for Epidemiological Studies Depression Scale and Generalized Anxiety Disorder Scale, respectively. Central symptoms and bridge symptoms were identified using expected influence. Results1689 older adults with heart disease and matched control sample of 1689 older adults without heart disease were included. The prevalence and severity of depression and anxiety were significantly higher in older adults with heart disease compared to the control group. There was no significant difference in overall structures of depression and anxiety network models between two the groups. Key central symptoms and bridge symptoms within these groups were highly similar; GAD 2 “Uncontrollable worrying” and GAD 4 “Trouble relaxing” were identified as the most central symptoms, while GAD 1 “Nervousness” and CESD 1 “Feeling bothered” were identified as key bridge symptoms across both network models. ConclusionDepression and anxiety are more prevalent in older adults with heart disease than demographically-matched heart disease free controls. However, network structures of these symptoms do not differ between two groups. Accordingly, depression and anxiety psychosocial interventions developed for older adults without heart disease may also benefit older adults with heart disease.

Symptom network connectivity and interaction among people with HIV in China: secondary analysis based on a cross-sectional survey

BackgroundThe symptom burden in people with HIV (PWH) is considerable. Nonetheless, the identification of a central symptom, or bridge symptom, among the myriad symptoms experienced by PWH remains unclear. This study seeks to establish networks of symptom experiences within different clusters and investigate the relationships and interconnectedness between these symptoms in PWH.MethodsA multicenter, cross-sectional descriptive design was carried out in China over two periods: November 2021 to January 2022 and April 2022 to May 2022. A total of 711 PWH completed online questionnaires, providing information on demographics and the 27-item Self-Report Symptom Scale. The symptom network was analyzed using Network/Graph theory, allowing for the exploration of connections between physical, cognitive, and psychological symptoms. This analysis was based on data from a subset of 493 individuals out of the total 711 PWH.ResultsA total of 493 PWH who exhibited symptoms out of a total of 711 PWH were analyzed. The average number of symptoms reported was 5.367. The most prevalent symptom was sleep disturbance (37.98%). In the node centrality analysis, a cognitive symptom, ‘becoming confusing’, emerged as the most central symptom with significant values for node centrality (strength = 1.437, betweenness = 140.000, closeness = 0.003). Fever was identified as the bridge symptom with the highest bridge strength (0.547), bridge closeness (0.053), lower bridge betweenness (23.000), and bridge expectedinfluence (0.285). Overall, our network displayed good accuracy and stability.ConclusionEarly identification and assessment of the central or bridge symptoms should be emphasized in clinical practice. According to the findings from network analysis, healthcare providers should proactively explore intervention strategies or bundle care to alleviate the burden of symptoms and enable anticipatory care.

Associations between personality and problematic internet use among Chinese adolescents and young adults: A network analysis

BackgroundWith the number of internet users booming, problematic internet use (PIU) has become a public health threat. This study aims to figure out the inter-relationships between PIU symptoms and personality traits with network-based analysis among young people and to discuss the gender difference in the above networks. MethodsBased on a national cross-sectional study in 2022, 4655 Chinese adolescents and young adults aged 14 to 25 were included. We adopted the 6-item Short-Form Problematic Internet Use Questionnaire (PIUQ-SF-6) and the 10-item version of the Big Five Inventory (BFI-10) to measure PIU and personality traits, respectively. Network analysis was used to identify influential nodes and edges and compare the network models between male and female participants. ResultsThe mean age of 4655 participants was 19.84, and 52.1 % (2424) of them were females. There are differences in age and gender between participants with and without PIU (P < 0.05). The network of personality and PIU showed that 22 out of the 28 edges were estimated to be nonzero, and “obsession-neuroticism” was the strongest positive edge between the two communities. Central symptoms (i.e., “obsession” and “control disorder”) and bridge symptoms (i.e., “obsession” and “neuroticism”) have been identified. Gender differences existed in network global strength: female = 3.71, male = 3.18 (p < 0.001). LimitationsThe cross-sectional study needs more evidence to build causal inference. ConclusionsThe results of PIU-personality networks may contribute to the personalized prevention and treatment of PIU. The gender difference in PIU-personality networks also requires more attention and discussion.

Unraveling the core and bridge menopausal symptoms of perimenopausal women: a network analysis.

Perimenopause is the period from the early menopausal transition to 12 months after the final menstrual period. The clustering of menopausal symptoms poses a challenge for perimenopausal management. Core symptoms are targets for interventions that can alleviate other related symptoms. Bridge symptoms are connectors that link related symptom clusters and can improve the effectiveness of interventions. This study aims to construct a network structure of menopausal symptoms and to identify core and bridge symptoms as a reference for future management. Two hundred forty-two Chinese perimenopausal women were included in the survey. The structure and associations of the menopausal symptoms assessed by the Greene Climacteric Scale were analyzed using a network analysis. We generated the network structure graph using R software and checked its accuracy and stability. In the menopausal transition, the most prevalent symptoms were feeling tired or lacking in energy, excitability, and irritability. Sexual dysfunction was common among early postmenopausal women. Irritability (S = 7.16, C = 0.0167, B = 8) was a core symptom of the network. The depressive symptom cluster was a core symptom cluster, most of which have high centrality indices. Excitability (B = 6) was a bridge symptom connecting the anxiety and depressive symptom clusters. Our study has highlighted the crucial significance of irritability and excitability in perimenopausal management. Overcoming the challenges of perimenopausal management requires the public to ameliorate the prejudice and stigma associated with emotional symptoms.

Uncovering the symptom relationshipamongsleep quality, anxiety, and depression in Chinese patients with breast cancer: multidimensional data validation using PSQI versus actigraphy.

The interplay between sleep quality, anxiety, and depression among breast cancer patients remains poorly understood. This study aimed to investigate and compare thesymptoms relationships among these three factors in Chinese breast cancer patients, utilizing two sleep assessments. Our study encompassed 288 participants diagnosed with breast cancer, from whom we collected demographic information through questionnaires. Sleep quality symptoms were assessed using the Pittsburgh Sleep Quality Index (PSQI) and wrist actigraphy, while anxiety and depression symptoms were measured using the Hospital Anxiety and Depression Scale (HADS). Network analyses were conducted using R to calculate the centrality (strength) and further identify central symptoms and bridge symptoms in two networks that differed by sleep assessments. Central symptoms are closely related to other symptoms, whereas bridge symptoms indicate that symptoms may increasespread riskbetween different conditions. In the network using PSQI data, "I have lost interest in my appearance" had the highest strength centrality (rs = 2.417), followed by "sleep duration" (rs = 1.068) and "sleep efficiency" (rs = 0.955). In the network using wrist actigraphy data, "wake after sleep onset" had the highest strength value (rs = 2.437), followed by "sleep efficiency" (rs = 2.397) and "sleep latency" (rs = 1.506). Two bridge symptoms were identified: "I feel cheerful" and "I look forward with enjoyment to things" in both networks. Depressive symptoms played a leading role in the sleep-anxiety-depression network, underscoring the need for targeted intervention tailored to survivors' specific needs. Health workers can give priority to symptom-specific screening and therapies, incorporating psychological support into standard cancer care.

Assessing relinquishment of positivity as a central symptom bridging anxiety and depression

Anxiety and depression are often comorbid and chronic disorders. Previous research indicates that positivity relinquishment is a moderator of anxiety and depression, such that only anxious individuals who endorsed relinquishing positivity were also depressed. We sought to extend those findings by conducting three network analyses with self-report measures of anxiety, depression, activity avoidance, and perceived positivity of avoided activities (N = 104). We pre-registered our hypothesis for the first two networks that relinquishment of positivity would emerge as a central bridge symptom between anxiety and depressive symptoms. After combining redundant nodes, we estimated three networks and investigated the bridge symptoms in each network. Relinquishment of positivity bridged the symptom clusters in the first network, and avoidance of positivity was found to bridge the two symptom clusters of anxiety and depression in networks two and three. Additionally, an anhedonia circuit was uncovered in all three networks in which loss of interest/worthlessness, loss of energy, and loss of pleasure/pessimism connected to anxiety through relinquishment or avoidance. Our findings suggest that both relinquishment of positivity as well as avoidance of positivity could be potential pathways explaining the development and maintenance of anxiety and depression and should be properly targeted in treatment.