Network Analysis Of Symptoms Research Articles

Exploring and comparing central mental health problems between rural primary and secondary school teachers—insight from a large sample symptom network analysis

Exploring and comparing central mental health problems between rural primary and secondary school teachers—insight from a large sample symptom network analysis

Symptom clusters and symptom network analysis during immunotherapy in lung cancer patients.

This study analyzes symptoms in lung cancer patients undergoing immunotherapy to identify core symptom clusters through network analysis and lay a foundation for effective symptom management programs. The sample comprised 240 lung cancer patients receiving immunotherapy. Participants were assessed using the Memorial Symptom Assessment Scale. Exploratory factor analysis was used to extract symptom clusters, and network analysis using JASP 0.17.3 was performed to explore the centrality indices and density of the symptom network. Five symptom clusters were identified, i.e., emotion-related, lung cancer-related, physical, skin, and neural symptom clusters, with a cumulative variance contribution rate of 55.819%. Network analysis revealed that sadness was the most intense symptom (rs = 2.189), dizziness was the most central symptom (rc = 1.388), and fatigue was the most significant bridging symptom (rb = 2.575). This study identified five symptom clusters and a symptom network among lung cancer patients during immunotherapy. The network analysis's centrality indices and network density results can assist healthcare professionals in devising more precise symptom management strategies.

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.

Symptom clusters and network analysis in patients with gynecologic cancer undergoing chemotherapy: A cross-sectional study

ObjectivesThis study aimed to explore the complex relationships among symptoms and symptom clusters in patients with gynecologic cancer receiving chemotherapy using symptom network analysis, and to identify core symptoms and core symptom clusters. MethodsA cross-sectional study was conducted at the Affiliated Hospital of Jiangnan University from December 2023 to June 2024, including 221 patients with gynecologic tumors. Participants completed demographic and clinical information questionnaires and the Chinese version of the MD Anderson Symptom Inventory (MDASI-C). Univariate analysis and multiple linear regression were used to screen covariates, exploratory factor analysis to determine symptom clusters, and network analysis to identify core symptoms and core symptom clusters. ResultsA total of 221 patients were included, with an average age of 58.73 years (SD ​= ​11.50). Fatigue (n ​= ​197, 89.1%) and lack of appetite (n ​= ​192, 86.9%) were the most common symptoms, while fatigue (mean ​= ​4.17, SD ​= ​2.07) and distress (mean ​= ​3.43, SD ​= ​2.20) were the most severe symptoms. Several distinct symptom clusters were identified: sickness behavior, gastrointestinal, psychological, and side-effect clusters. In the constructed network, fatigue emerged as the most central symptom (rs ​= ​1.28), while the sickness behavior cluster was identified as the most central symptom cluster (rs ​= ​1.11). ConclusionsPatients with gynecologic cancer undergoing chemotherapy commonly experience a range of symptoms. Our findings suggest that targeted interventions focusing on the sickness behavior symptom cluster may help reduce the overall symptom burden and assist caregivers in developing more effective symptom management strategies.

Symptom Network Analysis and Unsupervised Clustering of Oncology Patients Identifies Drivers of Symptom Burden and Patient Subgroups With Distinct Symptom Patterns.

Interindividual variability in oncology patients' symptom experiences poses significant challenges in prioritizing symptoms for targeted intervention(s). In this study, computational approaches were used to unbiasedly characterize the heterogeneity of the symptom experience of oncology patients to elucidate symptom patterns and drivers of symptom burden. Severity ratings for 32 symptoms on the Memorial Symptom Assessment Scale from 3088 oncology patients were analyzed. Gaussian Graphical Model symptom networks were constructed for the entire cohort and patient subgroups identified through unsupervised clustering of symptom co-severity patterns. Network characteristics were analyzed and compared using permutation-based statistical tests. Differences in demographic and clinical characteristics between subgroups were assessed using multinomial logistic regression. Network analysis of the entire cohort revealed three symptom clusters: constitutional, gastrointestinal-epithelial, and psychological. Lack of energy was identified as central to the network which suggests that it plays a pivotal role in patients' overall symptom experience. Unsupervised clustering of patients based on shared symptom co-severity patterns identified six patient subgroups with distinct symptom patterns and demographic and clinical characteristics. The centrality of individual symptoms across the subgroup networks differed which suggests that different symptoms need to be prioritized for treatment within each subgroup. Age, treatment status, and performance status were the strongest determinants of subgroup membership. Computational approaches that combine unbiased stratification of patients and in-depth modeling of symptom relationships can capture the heterogeneity in patients' symptom experiences. When validated, the core symptoms for each of the subgroups and the associated clinical determinants may inform precision-based symptom management.

Symptom Network Analysis in a Large Sample of Children and Adults with a Chronic Tic Disorder.

Chronic tic disorders (CTD) are multifaceted disorders characterized by multiple motor and/or vocal tics. They are often associated with complex tics including echophenomena, paliphenomena, and coprophenomena as well as psychiatric comorbidities such as attention deficit/hyperactivity disorder (ADHD) and obsessive-compulsive disorder (OCD). Our goal was to uncover the inter-relational structure of CTD and comorbid symptoms in children and adults and to understand changes in symptom structure across development. We used network and graph analyses to uncover the structure of association of symptoms in childhood/adolescence (n = 529) and adulthood (n = 503) and how this structure might change from childhood to adulthood, pinpointing core symptoms as a main target for interventions. The analysis yielded core symptom networks in young and adult patients with CTD including complex tics and tic-related phenomena as well as touching people and objects. Core symptoms in childhood also included ADHD symptoms, whereas core symptoms in adults included symptoms of OCD instead. Interestingly, self-injurious behavior did not play a core role in the young CTD network, but became one of the central symptoms in adults with CDT. In addition, we found strong connections between complex motor and vocal tics as well as echolalia and echopraxia. Next to other complex tics, echophenomena, paliphenomena, and coprophenomena can be regarded core symptoms of CTD. ADHD symptoms are closely related to CTD in childhood, whereas symptoms of OCD and self-injurious behavior are closely associated with CTD in adults. Our results suggest that a differentiation between motor and vocal tics is somewhat arbitrary.

Symptom network analysis of parent-child depression and anxiety in treatment-seeking youth.

Youth of parents who are experiencing psychopathology are more likely to develop emotional concerns, and these mental health symptoms can have a deleterious impact on parents' mental health. However, the relationship between the presentation of specific symptoms in parents and youth is infrequently examined. Symptom network analysis is an analytic approach that is increasingly being used to better understand the relationship of symptoms within and between disorders and can also be used to examine symptom relations within a dyad. The present study used symptom network analysis to examine bidirectional relationships among depressive and anxiety psychopathology in a transdiagnostic sample of treatment-seeking youth and their primary caregiver. Parental anhedonia and child worthlessness exhibited the greatest centrality within the network, suggesting that these may act as risk and maintenance factors for parent-child psychopathology and may be important intervention targets. Findings support the use of symptom network analysis to inform an understanding of the complex relationships among parent and child depressive and anxiety symptoms. Future research should consider the use of network analytic methods to examine the temporal relationships between parent and child psychopathology and to inform joint parent-child interventions for those with internalizing concerns. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Manic Residual Symptoms Also Deserve Attention: A Symptom Network Analysis of Residual Symptoms in Bipolar Disorder.

Lots of patients with bipolar disorder (BD) continue to have residual symptoms after treatment in their remission, BD exhibits intricate characteristics and transformation patterns in its residual symptoms, residual symptoms of different polarities and degrees can mix with and transform to each other. There is a need for further investigation of BD as a comprehensive multivariate disease system. The current research lacks network analyses focusing on BD's residual and subsyndromal symptoms. 242 patients were included with bipolar disorder in remission. We compared demographic data and differences in symptoms between populations with and without residual symptoms using t-tests and chi-square tests, with FDR applied for multiple comparison correction. Logistic regression was used to identify influencing factors for residual symptoms. Symptom networks were compared by network analysis to analyze the relationships between different types of residual symptoms. Depressive residual symptoms (N=111) were more common than manic residual symptoms (n=29) in the patients included. The comparison between two groups with and without residual symptoms shows no difference in demographic data and medical history information. The main influencing factors related to residual symptoms were time from diagnosis to first treatment (OR=0.88), the first(OR=1.51) and second (OR=17.1)factors of the Mood Disorder Questionnaire (MDQ), the Quick Inventory of Depressive Symptomatology Self-Report (QIDS)(OR=5.28), the psychological(OR=0.68) and environment (OR=1.53) subscale of the World Health Organization Quality of Life Short Form (WHOQOL-BREF). There was a significant difference in network structure between the groups with and without residual symptoms (network invariance difference=0.4, p =0.025). At the same time, there was no significant difference between the groups with and without depressive residual symptoms. However, the symptom network in patients with depressive residual symptoms is more loosely structured than in those without, with symptoms exhibiting weaker interconnections. When there is no depressive or manic residual symptom, it can still form a symptom network and cause an impact on social function. This study underscores the complexity of bipolar disorder's residual symptoms. Although it primarily manifests as loosely structured depressive residual symptoms, manic residual symptoms should not be ignored. Future research should explore network-based interventions targeting specific symptom clusters or connections to improve residual symptom management and patient outcomes.

Maternal Depressive Symptoms and Offspring Internalizing Problems: A Cross-Lagged Panel Network Analysis in Late Childhood and Early Adolescence.

The present study investigated the relations between maternal depressive symptoms and internalizing problems in offspring during late childhood and early adolescence, examining sex differences using symptom network analysis. A total of 885 Chinese youths in late childhood (n = 497, 38.6% girls; age = 9.58 years, SD = 0.24) and early adolescence (n = 388, 48.5% girls; age = 11.30 years, SD = 0.24) and their mothers (Mage = 37.34 years, SD = 5.42) were recruited. Cross-lagged panel network (CLPN) analysis was used to explore bridge symptoms (i.e., symptoms connecting two or more mental disorders) and identify transmission pathways between maternal depressive symptoms and offspring's internalizing problems at these two developmental stages. The CLPN results revealed that in late childhood, the bridge connections in the network model were boys feeling worried to mothers feeling distractible, and girls feeling worried to mothers feeling powerless. In early adolescence, the bridge connections were boys experiencing depressed mood to mothers feeling powerless, and mothers feeling bad to girls experiencing depressed mood. These findings highlight the network-level relations between maternal depressive symptoms and offspring internalizing problems. They provide insights into the developmental differences and similarities in symptoms during these periods and suggest ways to break the vicious cycle of psychopathology between mothers and their children.

Sleep Health Analysis Through Sleep Symptoms in 35,808 Individuals Across Age and Sex Differences: Comparative Symptom Network Study.

Sleep health is a multidimensional construct that includes objective and subjective parameters and is influenced by individual sleep-related behaviors and sleep disorders. Symptom network analysis allows modeling of the interactions between variables, enabling both the visualization of relationships between different factors and the identification of the strength of those relationships. Given the known influence of sex and age on sleep health, network analysis can help explore sets of mutually interacting symptoms relative to these demographic variables. This study aimed to study the centrality of symptoms and compare age and sex differences regarding sleep health using a symptom network approach in a large French population that feels concerned about their sleep. Data were extracted from a questionnaire provided by the Réseau Morphée health network. A network analysis was conducted on 39 clinical variables related to sleep disorders and sleep health. After network estimation, statistical analyses consisted of calculating inferences of centrality, robustness (ie, testifying to a sufficient effect size), predictability, and network comparison. Sleep clinical variable centralities within the networks were analyzed by both sex and age using 4 age groups (18-30, 31-45, 46-55, and >55 years), and local symptom-by-symptom correlations determined. Data of 35,808 participants were obtained. The mean age was 42.7 (SD 15.7) years, and 24,964 (69.7%) were women. Overall, there were no significant differences in the structure of the symptom networks between sexes or age groups. The most central symptoms across all groups were nonrestorative sleep and excessive daytime sleepiness. In the youngest group, additional central symptoms were chronic circadian misalignment and chronic sleep deprivation (related to sleep behaviors), particularly among women. In the oldest group, leg sensory discomfort and breath abnormality complaint were among the top 4 central symptoms. Symptoms of sleep disorders thus became more central with age than sleep behaviors. The high predictability of central nodes in one of the networks underlined its importance in influencing other nodes. The absence of structural difference between networks is an important finding, given the known differences in sleep between sexes and across age groups. These similarities suggest comparable interactions between clinical sleep variables across sexes and age groups and highlight the implication of common sleep and wake neural circuits and circadian rhythms in understanding sleep health. More precisely, nonrestorative sleep and excessive daytime sleepiness are central symptoms in all groups. The behavioral component is particularly central in young people and women. Sleep-related respiratory and motor symptoms are prominent in older people. These results underscore the importance of comprehensive sleep promotion and screening strategies tailored to sex and age to impact sleep health.

Prevalence and the interrelationship between depression and anxiety, and its association with problem behaviors among the sample of Chinese rural school-aged children: a symptom network analysis

Prevalence and the interrelationship between depression and anxiety, and its association with problem behaviors among the sample of Chinese rural school-aged children: a symptom network analysis

At-home, telehealth-supported ketamine treatment for depression: Findings from longitudinal, machine learning and symptom network analysis of real-world data

BackgroundImproving safe and effective access to ketamine therapy is of high priority given the growing burden of mental illness. Telehealth-supported administration of sublingual ketamine is being explored toward this goal. MethodsIn this longitudinal study, moderately-to-severely depressed patients received four doses of ketamine at home over four weeks within a supportive digital health context. Treatment was structured to resemble methods of therapeutic psychedelic trials. Patients receiving a second course of treatment were also examined. Symptoms were assessed using the Patient Health Questionnaire (PHQ-9) for depression. We conducted preregistered machine learning and symptom network analyses to investigate outcomes (osf.io/v2rpx). ResultsA sample of 11,441 patients was analyzed, demonstrating a modal antidepressant response from both non-severe (n = 6384, 55.8 %) and severe (n = 2070, 18.1 %) baseline depression levels. Adverse events were detected in 3.0–4.8 % of participants and predominantly neurologic or psychiatric in nature. A second course of treatment helped extend improvements in patients who responded favorably to initial treatment. Improvement was most strongly predicted by lower depression scores and age at baseline. Symptoms of Depressed mood and Anhedonia sustained depression despite ongoing treatment. LimitationsThis study was limited by the absence of comparison or control groups and lack of a fixed-dose procedure for ketamine administration. ConclusionsAt-home, telehealth-supported ketamine administration was largely safe, well-tolerated, and associated with improvement in patients with depression. Strategies for combining psychedelic-oriented therapies with rigorous telehealth models, as explored here, may uniquely address barriers to mental health treatment.

A Network Analysis of Symptoms of Post-Traumatic Stress Disorder and Facets of Mindfulness

Post-traumatic Stress Disorder (PTSD) is a mental health concern impacting a large proportion of the population. There is a growing interest in mindfulness-based treatments for PTSD. However, some individuals with PTSD do not respond to treatment. Identifying factors that may provide a more precise treatment approach has the potential to enhance response. Network analysis is a data-driven methodology that has been used to suggest specific targets for treatment. To date, there has not been a network analysis examining the interrelation between PTSD symptoms and trait mindfulness. The current study is an exploratory analysis of N = 214 adults from a non-clinical sample who endorsed a Criterion A traumatic event and completed the PTSD Checklist for DSM-5 and the Five Facet Mindfulness Questionnaire. Both undirected and directed networks were generated to identify links between PTSD symptom clusters and the facets of mindfulness. Both networks highlighted the connection between the PTSD symptom cluster Alterations in Arousal and Reactivity and the Awareness facet of mindfulness; and the PTSD symptom cluster Negative Alterations in Cognitions and Mood with the Non-judging facet of mindfulness. Taken together, these findings provide further evidence that greater mindfulness is generally associated with lower PTSD symptoms. Importantly, the findings highlight Non-judgement and Awareness as facets that might warrant further investigation in the context of mindfulness-based treatment efforts for PTSD.

Effects of childhood trauma on the symptom-level relation between depression, anxiety, stress, and problematic smartphone use: A network analysis

BackgroundChildhood trauma experience is closely associated with depression, anxiety, stress, and problematic smartphone use (PSU). However, few studies have explored the complex symptom-level relations between these variables among people with and without trauma experiences, leaving a gap in treating and alleviating these mental disorders among individuals with childhood trauma. MethodsThe current study used a convenience sampling method and recruited 2708 participants who completed Childhood Trauma Questionnaire-Short Form (CTQ-SF), Depression Anxiety and Stress Scales (DASS-21), and Mobile Phone Addiction Tendency Scale (MPATS), dividing them into trauma (n = 1454, Mean age = 19.67) and no-trauma (n = 1254, Mean age = 19.57) groups according to the cut-off scores of CTQ-SF. Symptom network analysis and network comparison test were conducted to construct and compare the network models between trauma and no-trauma groups. ResultsThe findings indicate that the trauma group and females exhibit greater average levels of DASS-21 and PSU symptoms compared to the no-trauma group and males, respectively. Additionally, the edge between “Stress” and “Anxiety” is the strongest across trauma and no-trauma groups. “Social comfort” is a bridge symptom of the trauma group network and the results of bridge symptoms in the no-trauma group are not stable. LimitationsThis study did not categorize all individuals according to specific types of trauma experiences and it is a cross-sectional design. The prevalences calculated in this study may not be generalizable. ConclusionsInterventions targeting different bridge symptoms in the trauma and no-trauma network models may help reduce the severity of symptoms.

Trajectories and comorbid symptom networks of posttraumatic stress symptoms in frontline rescuers: A longitudinal study

BackgroundPrevious research has largely lacked studies that explore the trajectories of Posttraumatic stress symptoms (PTSS) and the structure of comorbid psychiatric symptom networks following traumatic event, while controlling for the severity of traumatic exposure. The present study aims to explore the characteristic trajectories of PTSS, in the context of ensuring controlled levels of traumatic exposure. Furthermore, the PTSS, depressive, and anxiety comorbid symptom networks of different PTSS trajectory subgroups are also investigated. MethodsA total of 296 frontline rescue personnel were enrolled into our study. In an effort to control for variations in traumatic exposure severity, this study ensured that all participants had same responsibilities and cumulative operational duration at the post-disaster rescue circumstance. Growth mixture models (GMMs) were employed to scrutinize the trajectories of PTSS. Additionally, network analysis was used to examine the comorbid symptom network of PTSS, depression, and anxiety. ResultsFour distinct PTSS trajectories were identified, namely Persisting Symptom, Gradual Recovery, Gradual Aggravation, and Asymptomatic. Although both the Persisting Symptom and Gradual Aggravation groups belong to the high-risk subgroups for persistent PTSS, they exhibit differences in core symptoms within their respective networks. The core symptom for the Persisting Symptom Network is flashbacks, while for the Gradual Aggravation Network, it is sleep disturbances. ConclusionTo the best of our knowledge, the present study represents the first research endeavor to integrate longitudinal trajectory analysis of PTSS with longitudinal symptom network analysis, clarifying the evolving features of PTSS but also offering valuable insights for early screening and intervention strategies.

Abstract 7440: Symptom network analysis and unsupervised clustering of a cohort of oncology patients identifies drivers of symptom burden and patient subgroups with distinct symptom patterns

Abstract Purpose: Inter-individual variability in oncology patients’ symptom experiences poses significant challenges in prioritizing symptoms for targeted intervention(s). In this study, computational approaches were used to unbiasedly characterize the heterogeneity of the symptom experience of oncology patients to elucidate their symptom patterns and drivers of symptom burden. Methods: Severity ratings for 32 symptoms on the Memorial Symptom Assessment Scale from 3088 oncology patients were analyzed. Gaussian Graphical Model symptom networks were constructed for the entire cohort and subgroups identified through unsupervised clustering of symptom co-severity patterns. Symptom network characteristics were analyzed and compared using permutation-based statistical tests. Demographic and clinical characteristic differences between subgroups were assessed using multinomial logistic regression. Results: Network analysis of the entire cohort revealed three core symptom clusters: constitutional, gastrointestinal-epithelial, and psychological. Lack of energy was identified as central to the network, suggesting that it plays a crucial role in driving overall symptom burden and may be the most salient to target clinically. Unsupervised clustering of patients based on shared symptom co-severity patterns identified six patient subgroups with distinct symptom patterns and demographic and clinical characteristics. The first subgroup represented a baseline cohort of patients who are most affected by common constitutional symptoms of cancer care. The second subgroup represented patients with severe overall symptom burden. The third subgroup represented patients primarily experiencing neuropathy. The fourth subgroup represented patients with advanced disease and treatment-related symptoms but relatively high quality of life. The fifth subgroup represented younger patients with pronounced psychological disease burden. The sixth subgroup represented patients with minimal symptom burden. Age, treatment status, and Karnofsky Performance Score were the strongest determinants of subgroup membership, but their interplay (in one case even with ethnicity) reaffirms the complex multi-factorial nature of the symptom experience. For each subgroup, symptoms with the highest network centralities that should be prioritized for management were identified. Conclusions: This study provides a translational research framework for clinicians to better understand and address the symptom care needs of oncology patients. The framework combines unbiased stratification of patients and in-depth modeling of symptom relationships to capture the heterogeneity in their experiences. When validated, the identified core symptoms and associated clinical determinants may inform precision-based symptom management. Citation Format: Brandon H. Bergsneider, Terri S. Armstrong, Yvette P. Conley, Bruce Cooper, Marilyn Hammer, Jon D. Levine, Steven Paul, Christine Miaskowski, Orieta Celiku. Symptom network analysis and unsupervised clustering of a cohort of oncology patients identifies drivers of symptom burden and patient subgroups with distinct symptom patterns [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7440.

Sex differences in symptom network structure of depression, anxiety, and self-efficacy among people with diabetes: a network analysis.

The present study aims to explore the relations between symptoms of depression and anxiety and self-efficacy among people with diabetes. At the same time, we also examined the sex difference between network structures. This study recruited 413 participants with diabetes, and they completed Generalized Anxiety Disorder Scale (GAD-7), Patient Health Questionnaire (PHQ-9), and the Self-efficacy for Diabetes (SED). Symptom network analysis and network comparison test were used to construct and compare the depression-anxiety symptom network models of the female and male groups. Finally, we conducted flow diagrams to explore the symptoms directly or indirectly related to self-efficacy. The strongest edges in the depression-anxiety symptom networks are the edge between "GAD3" (Excessive worry) and "GAD4" (Trouble relaxing) and the edge between "PHQ1" (Anhedonia) and "PHQ4" (Energy) in the female and male groups, respectively. Most of the symptoms with the highest EI and bridge EI are related to worry and nervousness. Additionally, in the flow diagram of the female group, "PHQ6" (Guilt) has a high negative association with self-efficacy. Females with diabetes are more vulnerable to depression and anxiety. Interventions targeting key symptoms in the network may be helpful in relieving the psychological problems among people with diabetes.

Network analysis of symptoms, physiological, psychological and environmental risk factors based on unpleasant symptom theory in patients with chronic heart failure.

Somatic symptoms and related factors in patients with chronic heart failure have been extensively researched. However, more insight into the complex interconnections among these constructs is needed, as most studies focus on them independently from each other. The aim of this study is to gain a comprehensive understanding of how somatic symptoms and related factors are interconnected among patients with chronic heart failure. A total of 379 patients were enrolled. Network analysis was used to explore the interconnections among the somatic symptoms and related risk factors. The four core symptoms of chronic heart failure were daytime dyspnea, dyspnea when lying down, fatigue and difficulty sleeping. Within the network, the edge weights of depression-anxiety, subjective social support-objective social support, and subjective social support-social support availability were more significant than others. Among physiological, psychological and environmental factors, the edge weights of NYHA-dyspnea, depression-difficulty sleeping, and social support availability-dyspnea when lying down were more significant than others. Depression and anxiety had the highest centrality, indicating stronger and closer connections with other nodes. Psychological and environmental factors stood out in the network, suggesting the potential value of interventions targeting these factors to improve overall health.

Symptom network analysis and comparison of the sleep disorders diagnostic criteria based on the ICSD-3 and the DSM-5 diagnostic manuals

Symptom network analysis and comparison of the sleep disorders diagnostic criteria based on the ICSD-3 and the DSM-5 diagnostic manuals

Pre-hospital symptom clusters and symptom network analysis in decompensated cirrhotic patients: A cross-sectional study.

To generate pre-hospital symptom networks, explore core, bridge and sentinel symptoms, identify pre-hospital symptom clusters and analyse relationship between influencing factors and symptom clusters in decompensated cirrhosis patients. A cross-sectional study design using the Strengthening the Reporting of Observational Studies in Epidemiology checklist. Demographical, physiological, psychological and sociological characteristics and the pre-hospital symptoms of 292 decompensated cirrhotic patients were collected from October 2021 to March 2023 in China. Frequencies, percentages, means, standard deviations, independent samples t-tests, one-way analysis of variance, exploratory factor analysis, multiple stepwise regression analysis and network analysis were used for data analysis. 'I don't look like myself' and itching were core and bridge symptoms, while bloating and lack of energy were sentinel symptoms in decompensated cirrhotic patients. Monthly family income, anxiety, depression, social support and disease duration influenced the neuropsychological symptom cluster, with worrying as the strongest predictor symptom. Influential factors for cirrhosis-specific symptom cluster included Child-Pugh class, monthly family income, disease duration, anxiety and depression, with itching being the strongest predictor symptom. Monthly family income, disease duration and depression were influential factors for gastrointestinal symptom cluster, with loss of appetite as the strongest predictor symptom. Neuropsychological, cirrhosis-specific and gastrointestinal symptom clusters were formed in decompensated cirrhotic patients. Through network analysis, direct connections between symptoms, symptom clusters and their influencing factors were revealed, thereby offering clinicians a foundation for effectively managing patients' pre-hospital symptoms. Decompensated cirrhosis patients commonly have multiple symptoms, while the management of pre-hospital symptoms is often suboptimal. This study identified neuropsychological, cirrhosis-specific, gastrointestinal symptom clusters and recognized core, bridge and sentinel symptoms in these patients. It also revealed the most prominent symptoms within each cluster. This provides insight into the hierarchy of symptoms, improving symptom management in decompensated cirrhosis. There was no patient or public involvement.