Unraveling the Complex Relationships Between Anxiety, Depression, and Quality of Life in Schizophrenia: A Network Analysis Study

doi:10.21203/rs.3.rs-3609453/v1

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Research Article

Unraveling the Complex Relationships Between Anxiety, Depression, and Quality of Life in Schizophrenia: A Network Analysis Study

https://doi.org/10.21203/rs.3.rs-3609453/v1

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Background

Schizophrenia, a debilitating mental disorder, impacts cognitive, behavioral, and emotional functions. Co-occurring anxiety and depression worsen its complexity and diminish patients' quality of life. This study uses a network analysis approach to explore the relationships among anxiety, depression, and quality of life in hospitalized schizophrenia patients.

Methods

Cross-sectional study on 1328 inpatients with schizophrenia. Data included demographics, clinical details, and self-reported depression (HAMD-17), anxiety (HAMA-14), and quality of life (SQLS-R4). Network analysis employed Gaussian graphical models and Lasso for sparse network estimation.

Results

The analysis revealed hopelessness as the central node in quality of life, emphasizing its role in overall well-being. Somatic anxiety emerged as the central node in depression, highlighting the need to address somatic symptoms. Sleep disturbances were prominent central nodes in anxiety, indicating the need for targeted interventions.

Discussion

This study provides valuable insights into the relationships between anxiety, depression, and quality of life in inpatient schizophrenia populations. Addressing key symptoms such as hopelessness, somatic anxiety, and sleep disturbances can significantly improve overall well-being. Integrated interventions for anxiety and depression, along with comprehensive strategies addressing psychosocial factors, are crucial for optimizing therapeutic outcomes and enhancing quality of life in individuals with schizophrenia.

Anxiety

Depression

Quality of life

Schizophrenia

Network analysis

Schizophrenia is a chronic and severe mental disorder affecting over 21 million individuals worldwide[1]. It is characterized by a complex interplay of cognitive, behavioral, and emotional dysfunctions that significantly hinder the patient's ability to lead a normal life[2]. Given its global health significance, schizophrenia not only affects those afflicted but also imposes a substantial socio-economic burden. Despite progress in pharmacological treatments, the management of schizophrenia remains suboptimal, necessitating a comprehensive understanding of its pathogenesis and treatment interventions[3].

Individuals diagnosed with schizophrenia commonly experience co-occurring emotional disturbances, notably anxiety and depression[4]. The presence of these comorbid conditions exacerbates the complexity of schizophrenia, leading to heightened symptom severity and a poorer prognosis[5]. Furthermore, patients with schizophrenia who also have anxiety or depression often report a diminished quality of life, increased suicidality, and elevated rates of hospitalization[6]. However, the precise interplay between anxiety, depression, and quality of life in individuals with schizophrenia remains incompletely understood, underscoring the necessity for more in-depth research in this domain.

The emergence of the symptom network perspective offers an alternative approach to comprehend and conceptualize the intricate relationships within the human emotional landscape[7]. Unlike traditional statistical methods that typically focus on individual variables, network analysis enables the examination of interdependencies among multiple variables concurrently. While research utilizing symptom network analysis has predominantly concentrated on psychiatric investigations, this methodology is particularly well-suited for comprehending the multidimensional and interconnected nature of symptoms and conditions in psychiatry[8].

Consequently, the purpose of the present study is to harness the potential of network analysis in uncovering the complex relationships between anxiety, depression, and quality of life among hospitalized patients with schizophrenia. By exploring the patterns of their interactions, our objective is to advance our understanding of the complexities inherent in schizophrenia and contribute to the development of more comprehensive and personalized therapeutic strategies for individuals coping with this debilitating disorder.

Study Design and Participants

This cross-sectional study was conducted between November 2019 and October 2021. A comprehensive survey was carried out among inpatients diagnosed with schizophrenia (SZ) at multiple centers in Shenyang, Liaoyang, Dandong, and Wafangdian, Liaoning Province. The study received approval from the Ethics Committee of China Medical University, and informed consent was obtained from all participants or their legal guardians.

Participant Criteria

Participants were required to meet the diagnostic criteria for schizophrenia as outlined in the International Classification of Diseases, 10th Revision. They were 18 years of age or older, capable of comprehending the test content, and willing to cooperate in completing the cognitive assessment. Exclusion criteria encompassed severe physical illnesses or disabilities, comorbid severe mental illnesses, intellectual disability, severe cognitive dysfunction, substance abuse, severe head trauma, and organic brain disease.

Measurements

Demographic information, lifestyle habits, activities, clinical data, and physical measurements were collected. Demographic information included name, age, sex, ethnicity, education, marital status, and annual income. Lifestyle habits and activities included smoking, social activity participation, and engagement in mental activities. Clinical data included illness duration, type and dosage of psychiatric medication, family history of psychiatric disorders, and nonpharmacological treatments. Physical measurements included height, weight, waist circumference, hip circumference, and body fat percentage. Demographic information excluding annual income and education was collected from medical records. Lifestyle habits, activities, and clinical data were collected from medical records or self-reported.

To assess depressive symptom severity, the Hamilton Depression Rating Scale (HAMD-17)[9], a 17-item scale with a score range of 0–50, was utilized. It demonstrated good internal consistency (α = 0.89)[10]. Anxiety symptoms were measured using the Hamilton Anxiety Rating Scale (HAMA-14)[11], a 14-item scale with a score range of 0–56, and exhibited solid internal consistency (α = 0.87). The Schizophrenia Quality of Life Scale Revision 4 (SQLS-R4)[12], a 30-item self-report scale, was employed to evaluate the perceived impact of schizophrenia on patients' lives. This scale yielded a score range of 0-124, with higher scores indicating lower quality of life, and displayed strong internal consistency (α = 0.93).

To ensure data integrity, the research team underwent rigorous training and evaluations to maintain scoring consistency and minimize measurement errors. Regular cross-checking of questionnaires was performed, and any non-cooperating participant's form was appropriately tagged. All collected data was securely archived and processed using Epidata 3.1 after providing necessary training to data entry personnel. The final dataset was carefully scrutinized for logical errors, outliers, missing entries, etc., and corrected based on the original forms.

Statistical Analysis

The R software version 4.0[13] was employed for symptom network estimation using a Gaussian graphical model (GGM) derived from the glasso package, and network visualization using the qgraph package. Each node in the network represented an item from the HAMD-17, HAMA-14, or SQLS-R4, while the edges represented bias correlation coefficients. A combination of the least absolute shrinkage and selection operator (Lasso) and Extended Bayesian Information Criterion (EBIC) was utilized to obtain a sparse regularized network[8].

Network metrics such centrality indices were calculated using the qgraph and networktools packages, respectively. The evaluation of centrality indices for each node encompasses strength, closeness, and betweenness. Strength denotes the absolute sum of the edge weights connected to a particular node. Closeness reflects the average distance between a node and all other nodes within the network. Lastly, betweenness quantifies the frequency with which a node lies on the shortest path between any two other nodes.

The stability and reliability of the network were assessed through a bootstrapping procedure using the bootnet package. A total of 1000 non-parametric bootstrap iterations were conducted to estimate confidence intervals for edge weights and the node stability correlation coefficient (CS-C), which reflects the extent to which network nodes are influenced by other factors within the network[14]. The code for the network analyses is available at https://osf.io/3vy5f/?view_only=fef18ecf8ff54f8fa57b9b2243cdeb69.

The research work, grounded in the network analysis approach, aims to elucidate the interactive landscape of anxiety, depression, and quality-of-life symptoms in patients with schizophrenia. By identifying symptom clusters and inter-relationships, it seeks to enhance our understanding of these psychological comorbidities in schizophrenia, their implications for patient outcomes, and strategies to improve their clinical management.

Participant Characteristics

A total of 1328 patients who met the enrollment criteria completed the assessment. Among them, 807 (60.8%) were male inpatients with schizophrenia. The mean age of the sample was 47.87 years (SD = 11.42). A family history of schizophrenia was reported by 130 participants (9.8%), and 463 participants (34.9%) had a high school education. The mean scores for HAMD-17, HAMA-14, and SQLS-r4 were 5.22 (SD = 5.24), 5.05 (SD = 5.91), and 55.8 (SD = 15.5).

Network Analysis

Network Structure

Figure 1 presents the network structure consisting of 17 nodes of HAMD-17, 14 nodes of HAMA-14, and 3 nodes of SQLS-r4. This network structure elucidates the intricate relationships among the anxiety, depression, and quality of life communities in hospitalized patients with schizophrenia. The network comprised 555 (98.9%) non-zero edges, of which 412 (74.2%) exhibited positive associations. The model's edge weight matrix revealed that "Retardation" and "Work and Activities" had the strongest positive edges within the depression community (WaA-Ret, edge weight = 0.192). In the anxiety community, "Tension" and "Fears" exhibited the strongest positive edge (Ten-Fea, edge weight = 0.453). Within the quality of life community, "Symptoms and side-effects" and "Psychosocial" displayed the strongest positive edge (SaS-Psy, edge weight = 0.444). Notably, the strongest positive connections between the depression and anxiety communities were observed between "Depressed Mood" and "Depressed Mood" (DMo-DeM, edge weight = 0.773). Between the depression and quality of life communities, the strongest positive connection was found between "Psychosocial" and "Anxiety - Psychic" (Psy-AnP, edge weight = 0.076). Between the anxiety and quality of life communities, a weak positive association was detected between "Psychosocial" and "Depressed Mood" (Psy-DeM, edge weight = 0.027). Edge weights with 95% confidence intervals are shown in the Supplementary information (https://osf.io/3vy5f/?view_only=fef18ecf8ff54f8fa57b9b2243cdeb69).

Central Symptoms, Bridging Symptoms, and Predicted Mediators

Figure 2A illustrates the intensity centrality index (Z-score transformation) of the network. Within the depression community, "Somatic Anxiety" (Anxiety - Somatic) (AnS, Strength = 1.83) emerged as the most central node. In the anxiety community, "Insomnia" (Ins, Strength = 2.28) took the central node position, while in the quality of life community, "Psychosocial" (Psy, Strength = 1.68) held the most central node position. Figure 2B portrays the mediated centrality strength (Z-score transformation) of the network. The strongest bridging symptom within the depression community was "Anxiety - Somatic" (AnS, Betweenness = 2.31). In the anxiety community, "Behavior at Interview" (BeI, Betweenness = 1.93) acted as the strongest bridging symptom, while within the quality of life community, "Psychosocial" (Psy, Betweenness = 2.19) played the strongest bridging role. Figure 2C shows the predictive mediability (Z-score transformation) of each node in the network, which reflects the extent to which a node is influenced by neighboring nodes. Within the depression community, "Somatic Anxiety" (AnS) exhibited the strongest predictability (Predictability = 1.85). In the anxiety community, "Insomnia" (Ins) emerged as the strongest predictor (Predictability = 2.30), and within the quality of life community, "Psychosocial" (Psy) showed the highest predictability (Predictability = 1.70). Detailed information on the centrality measure can be found in the Supplementary Information (https://osf.io/3vy5f/?view_only=fef18ecf8ff54f8fa57b9b2243cdeb69).

Expanded Network Structure

To gain further insights, we constructed a more complex network structure Fig. 3 by incorporating all 30 entries of SQLS-r4 as nodes, resulting in a total of 61 nodes encompassing anxiety, depression, and quality of life domains. In the quality of life community, the strongest positive edge exists between the “motivation and energy dimension”, that is, “I am able to carry out daily activities” and “I participate in activities I enjoy” (M3-M4, edge weight = 0.45). The strongest positive edge connecting the “psychosocial” dimension and the “symptoms and side-effects” dimension is “I feel angry” and “My muscles twitch” (P1-S7, edge weight = 0.161). The strongest edge between the depression community and the quality of life community appears between “Difficulty falling asleep” and the “symptoms and side-effects” dimension of “My sleep is disturbed” (InE-S6, edge weight = 0.14). The strongest edge between the anxiety community and the quality of life community appears between “Insomnia” and the “symptoms and side-effects” dimension of “My sleep is disturbed” (InE-S6, edge weight = 0.14).

Figure 4 highlights the most central node, the strongest bridging symptom, and the most predictive node within the quality of life community. "I feel hopeless" (P4) demonstrated the highest values across all three measures: Strength = 1.91, Betweenness = 2.60, and Predictability = 1.81.

Network Stability

We have conducted a bootstrap analysis to examine the stability of the network. The visualized images show relatively narrow 95% confidence intervals for the weighted values of each edge, indicating the accuracy of the estimated network. The coefficients (CS-coefficients) for strength centrality, bridging strength, and predicted mediability are all 0.75, surpassing the suggested threshold of 0.25. Further details can be found in the Supplementary Information (https://osf.io/3vy5f/?view_only=fef18ecf8ff54f8fa57b9b2243cdeb69).

In this study, we aimed to elucidate the complex relationships between anxiety, depression, and quality of life in hospitalized schizophrenia patients using an exploratory network analysis approach. The identification of key central nodes, bridging symptoms, and predictability patterns enhances our understanding of the complex interplay between these domains, providing a foundation for targeted clinical interventions.

As the most central node within the quality of life community, "I feel hopeless" emerges as a critical factor shaping the well-being of individuals with schizophrenia. Hopelessness has been linked to increased symptom severity, functional impairment, and reduced treatment adherence[15]. By fostering hope, providing psychoeducation, and promoting optimism, interventions may enhance overall quality of life in this population[16–18]. One notable finding was the strong positive edge between the motivation and energy dimension. This result aligns with previous research highlighting the importance of motivation and energy in patients' engagement in enjoyable activities and overall quality of life[19]. Furthermore, the network analysis identified significant connections between emotional and physical symptoms. This finding supports prior research suggesting that addressing both emotional and physical symptoms may be crucial for improving patients' well-being[20, 21].

In the depression community, "Somatic Anxiety" emerged as the most central node. This finding suggests that somatic anxiety symptoms, such as physical discomfort or restlessness, may have a central role in the network and contribute significantly to the experience of depression in individuals with schizophrenia. Previous studies have highlighted the relevance of somatic symptoms in depression, emphasizing the importance of addressing these symptoms in treatment interventions[22, 23]. Depression symptoms' strong associations with impaired work and activities underscore the necessity of interventions targeting motivational deficits and functional impairments. It is possible that the negative impact of depression on work and activities further exacerbates depressive symptoms, creating a vicious cycle. Behavioral activation therapy, a treatment modality that emphasizes engaging in rewarding activities and enhancing motivation, has shown promise in ameliorating both depressive symptoms and functional outcomes.It emphasizes engagement in rewarding activities and motivation enhancement, may improve both depressive symptoms and functional outcomes[24, 25].

Within the anxiety community, the strong positive edge between "Tension" and "Fears" suggests that these symptoms often co-occur and reinforce each other. Anxiety disorders are known to involve excessive worry and fear, which can manifest across various domains of life[26]. Cognitive restructuring techniques, which aim to challenge and modify maladaptive thoughts and beliefs, offer promise in alleviating these distressing symptoms[18, 27, 28]. Additionally, exposure-based interventions, such as systematic desensitization, have demonstrated effectiveness in reducing anxiety symptoms by gradually exposing individuals to feared situations or stimuli, ultimately fostering adaptive coping mechanisms[29]. In the anxiety community, "Behavior at Interview" emerged as the strongest bridging symptom. This finding suggests that observable behavioral indicators may act as bridges between different anxiety symptoms. Behavioral manifestations, such as avoidance behaviors or hyperarousal, may represent common underlying processes in anxiety disorders[30, 31]. These evidence-based interventions provide a comprehensive approach to mitigating anxiety-related distress and improving overall well-being in individuals with schizophrenia.

Sleep disturbances emerged as a central issue in both anxiety and depression domains. The connection between sleep disturbances and anxiety, depression, and impaired quality of life underline the importance of targeted interventions addressing sleep issues in schizophrenia patients. Interventions such as sleep hygiene education, relaxation techniques, and cognitive-behavioral therapy for insomnia (CBT-I) have demonstrated the potential to improve sleep quality and alleviate associated symptoms[32, 33]. By targeting the underlying factors contributing to sleep disturbances, these interventions not only improve sleep outcomes but also have a positive impact on overall mental health and quality of life.

Given the substantial impact of psychosocial factors on quality of life, comprehensive treatment approaches should prioritize the integration of psychosocial interventions alongside symptom management[34]. Social support interventions, including peer support programs and family involvement, can enhance social functioning and mitigate symptoms' detrimental effects on quality of life[35, 36]. Moreover, vocational rehabilitation programs focusing on employment support and skill development may improve psychosocial functioning and overall well-being[37, 38].

The overlapping symptomatology between anxiety and depression calls for integrated interventions targeting both domains simultaneously. Cognitive-behavioral therapy (CBT), which addresses anxiety and depressive symptoms, has proven effective for individuals with comorbid conditions[17, 39, 40]. Pharmacological interventions, such as selective serotonin reuptake inhibitors (SSRIs), have also shown efficacy in treating both anxiety and depressive symptoms, making them valuable options in the management of comorbidities[39, 41]. By addressing the underlying psychological processes and neurotransmitter imbalances associated with both anxiety and depression, these interventions offer a comprehensive approach to symptom relief and improved overall well-being. Collaborative care models, involving coordinated efforts between mental health professionals and primary care providers, present a promising avenue for integrated treatment delivery in individuals with schizophrenia. These models facilitate a holistic approach to patient care, ensuring that both psychiatric and physical health needs are addressed in a coordinated manner[42, 43]. By fostering effective communication and collaboration among healthcare providers, collaborative care models enhance the delivery of integrated interventions, ultimately leading to improved outcomes for individuals with schizophrenia.

Several limitations of this study should also be taken into consideration. Firstly, the focus of the study on hospitalized patients with schizophrenia may limit the generalizability of the findings to individuals in community settings. To enhance the applicability of the results, future research should replicate these findings in diverse settings and populations, including outpatient and community-based samples. Secondly, the cross-sectional nature of the study precludes making causal inferences. To establish causality and unravel the temporal dynamics of the identified relationships, longitudinal research designs are warranted. Longitudinal studies would allow for a more comprehensive understanding of how changes in anxiety, depression, and quality of life influence each other over time, shedding light on the directionality of these relationships. Lastly, it is worth noting that the study relied on self-reported measures, which may be susceptible to reporting biases and inaccuracies. Future research could incorporate more objective measures, such as clinician-rated assessments or behavioral observations, to complement the self-report data and provide a more robust understanding of the studied variables.

In conclusion, the network analysis conducted in this study yields valuable insights into the intricate relationships among anxiety, depression, and quality of life in hospitalized patients with schizophrenia. The implications of these findings are highly relevant for clinical practice, highlighting the need for integrated interventions that address anxiety and depressive symptoms, psychosocial factors, sleep disturbances, and functional outcomes. By comprehending the complex network of symptoms and connections, healthcare professionals can develop tailored and comprehensive treatment approaches to effectively improve the well-being and quality of life of individuals with schizophrenia. However, future research should aim to overcome the limitations of this study by expanding the sample to diverse settings, employing longitudinal designs, and incorporating objective measures to further advance our understanding of these critical relationships.

Data Availability Statement:

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Funding:

This work was supported by the National Key R&D Program of China [grant numbers 2018YFC1311600, 2016YFC1306900 to Yanqing Tang]; STI2030-Major Projects-2021ZD0200700; the Natural Science Foundation of Liaoning Province [grant number 2020-MS-176 to Xiaowei Jiang]; the Applied Fundamental Research Program of Liaoning Province[grant number 2023JH2/101300031 to Yanqing Tang]; Shenyang Science and Technology Planning Project [grant number #22-321-32-06 to Yanqing Tang], Basic Scientific Research Projects of Universities of Liaoning Province [grant number LJKMZ20221214 to Yifang Zhou]; the Natural Science Foundation of Liaoning Province [grant number 2022-YGJC-40 to Lingtao Kong].

Conflicts of interest:

The authors declare that they have no conflicts of interest.

Ethics approval statement:

The Human Ethics Committee of China Medical University approved

the study protocol ([2018]083).

Declaration of Interest Statement

Declarations of interest: none.

Patient consent statement:

Informed consent was obtained from all participants included in the study.

Permission to reproduce material from other sources

Not applicable to this study.

Clinical trial registration

Not applicable to this study.

CRediT authorship contribution statement

Yucheng Wang: Conceptualization, Methodology, Software, Investigation, Writing - Original Draft. Wei Deng: Investigation, Writing - Original Draft. Huanrui Zhang: Writing - Review & Editing, Validation, Formal analysis. Yang Zhou: Formal analysis. Zijia Li: Investigation. Peiyi Wu: Writing - Original Draft. Yide Xin: Investigation. Yixiao Xu: Investigation, Supervision. Yanqing Tang: Writing - Review & Editing, Supervision, Project administration, Funding acquisition.

Charlson FJ, Ferrari AJ, Santomauro DF, Diminic S, Stockings E, Scott JG, et al. Global Epidemiology and Burden of Schizophrenia: Findings From the Global Burden of Disease Study 2016. Schizophr Bull. 2018;44(6):1195–203.
Stępnicki P, Kondej M, Kaczor AA. Current Concepts and Treatments of Schizophrenia. Molecules. 2018;23(8).
Jauhar S, Johnstone M, McKenna PJ, Schizophrenia. Lancet (London England). 2022;399(10323):473–86.
Braga RJ, Reynolds GP, Siris SG. Anxiety comorbidity in schizophrenia. Psychiatry Res. 2013;210(1):1–7.
Upthegrove R, Marwaha S, Birchwood M. Depression and Schizophrenia: Cause, Consequence, or Trans-diagnostic Issue? Schizophr Bull. 2017;43(2):240–4.
Wetherell JL, Palmer BW, Thorp SR, Patterson TL, Golshan S, Jeste DV. Anxiety symptoms and quality of life in middle-aged and older outpatients with schizophrenia and schizoaffective disorder. J Clin Psychiatry. 2003;64(12):1476–82.
Borsboom D, Cramer AO. Network analysis: an integrative approach to the structure of psychopathology. Ann Rev Clin Psychol. 2013;9:91–121.
Epskamp S, Fried EI. A tutorial on regularized partial correlation networks. Psychol Methods. 2018;23(4):617–34.
Hamilton M. A rating scale for depression. J Neurol Neurosurg Psychiatry. 1960;23(1):56–62.
Carrozzino D, Patierno C, Fava GA, Guidi J. The Hamilton Rating Scales for Depression: A Critical Review of Clinimetric Properties of Different Versions. Psychother Psychosom. 2020;89(3):133–50.
Maier W, Buller R, Philipp M, Heuser I. The Hamilton Anxiety Scale: reliability, validity and sensitivity to change in anxiety and depressive disorders. J Affect Disord. 1988;14(1):61–8.
Wilkinson G, Hesdon B, Wild D, Cookson R, Farina C, Sharma V, et al. Self-report quality of life measure for people with schizophrenia: the SQLS. Br J psychiatry: J mental Sci. 2000;177:42–6.
Team RC. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria UR. 2022.
Armour C, Fried EI, Deserno MK, Tsai J, Pietrzak RH. A network analysis of DSM-5 posttraumatic stress disorder symptoms and correlates in U.S. military veterans. J Anxiety Disord. 2017;45:49–59.
Birchwood M, Iqbal Z, Chadwick P, Trower P. Cognitive approach to depression and suicidal thinking in psychosis. 1. Ontogeny of post-psychotic depression. Br J psychiatry: J mental Sci. 2000;177:516–21.
Ropaj E, Jones A, Dickson JM, Gill Z, Taylor PJ. Are negative beliefs about psychosis associated with emotional distress in adults and young people with such experiences? A meta-analysis. Psychol Psychother. 2021;94(Suppl 2):242–67.
Hynninen MJ, Bjerke N, Pallesen S, Bakke PS, Nordhus IH. A randomized controlled trial of cognitive behavioral therapy for anxiety and depression in COPD. Respir Med. 2010;104(7):986–94.
Penn DL, Uzenoff SR, Perkins D, Mueser KT, Hamer R, Waldheter E, et al. A pilot investigation of the Graduated Recovery Intervention Program (GRIP) for first episode psychosis. Schizophr Res. 2011;125(2–3):247–56.
Fervaha G, Foussias G, Agid O, Remington G. Motivational and neurocognitive deficits are central to the prediction of longitudinal functional outcome in schizophrenia. Acta psychiatrica Scandinavica. 2014;130(4):290–9.
Critchley HD, Eccles J, Garfinkel SN. Interaction between cognition, emotion, and the autonomic nervous system. Handb Clin Neurol. 2013;117:59–77.
Scheewe TW, Backx FJ, Takken T, Jörg F, van Strater AC, Kroes AG, et al. Exercise therapy improves mental and physical health in schizophrenia: a randomised controlled trial. Acta psychiatrica Scandinavica. 2013;127(6):464–73.
Kanchanatawan B, Sirivichayakul S, Thika S, Ruxrungtham K, Carvalho AF, Geffard M, et al. Physio-somatic symptoms in schizophrenia: association with depression, anxiety, neurocognitive deficits and the tryptophan catabolite pathway. Metab Brain Dis. 2017;32(4):1003–16.
Weiss EM, Singewald E, Baldus C, Hofer E, Marksteiner J, Nasrouei S, et al. Differences in psychological and somatic symptom cluster score profiles between subjects with Idiopathic environmental intolerance, major depression and schizophrenia. Psychiatry Res. 2017;249:187–94.
Dobson KS, Hollon SD, Dimidjian S, Schmaling KB, Kohlenberg RJ, Gallop RJ, et al. Randomized trial of behavioral activation, cognitive therapy, and antidepressant medication in the prevention of relapse and recurrence in major depression. J Consult Clin Psychol. 2008;76(3):468–77.
Mueser KT, Gingerich S. Treatment of co-occurring psychotic and substance use disorders. Social work in public health. 2013;28(3–4):424–39.
Hofmann SG, Sawyer AT, Witt AA, Oh D. The effect of mindfulness-based therapy on anxiety and depression: A meta-analytic review. J Consult Clin Psychol. 2010;78(2):169–83.
Penn DL, Roberts DL, Combs D, Sterne A. Best practices: The development of the Social Cognition and Interaction Training program for schizophrenia spectrum disorders. Psychiatric Serv (Washington DC). 2007;58(4):449–51.
Hofmann SG, Asnaani A, Vonk IJ, Sawyer AT, Fang A. The Efficacy of Cognitive Behavioral Therapy: A Review of Meta-analyses. Cogn therapy Res. 2012;36(5):427–40.
Kehle-Forbes SM, Ackland PE, Spoont MR, Meis LA, Orazem RJ, Lyon A, et al. Divergent experiences of U.S. veterans who did and did not complete trauma-focused therapies for PTSD: A national qualitative study of treatment dropout. Behav Res Ther. 2022;154:104123.
Asmundson GJ, Taylor S, Smits JA. Panic disorder and agoraphobia: an overview and commentary on DSM-5 changes. Depress Anxiety. 2014;31(6):480–6.
Moser JS, Durbin CE, Patrick CJ, Schmidt NB. Combining neural and behavioral indicators in the assessment of internalizing psychopathology in children and adolescents. Journal of clinical child and adolescent psychology: the official journal for the Society of Clinical Child and Adolescent Psychology, American Psychological Association, Division 53. 2015;44(2):329 – 40.
Riemann D, Baglioni C, Bassetti C, Bjorvatn B, Dolenc Groselj L, Ellis JG, et al. European guideline for the diagnosis and treatment of insomnia. J Sleep Res. 2017;26(6):675–700.
Waters F, Faulkner D, Naik N, Rock D. Effects of polypharmacy on sleep in psychiatric inpatients. Schizophr Res. 2012;139(1–3):225–8.
Swildens W, van Busschbach JT, Michon H, Kroon H, Koeter MW, Wiersma D, et al. Effectively working on rehabilitation goals: 24-month outcome of a randomized controlled trial of the Boston psychiatric rehabilitation approach. Can J psychiatry Revue canadienne de psychiatrie. 2011;56(12):751–60.
Lucksted A, McFarlane W, Downing D, Dixon L. Recent developments in family psychoeducation as an evidence-based practice. J Marital Fam Ther. 2012;38(1):101–21.
Shalaby RAH, Agyapong VIO. Peer Support in Mental Health: Literature Review. JMIR mental health. 2020;7(6):e15572.
Kopelowicz A, Liberman RP, Zarate R. Recent advances in social skills training for schizophrenia. Schizophr Bull. 2006;32(Suppl 1):12–23.
Wykes T, Steel C, Everitt B, Tarrier N. Cognitive behavior therapy for schizophrenia: effect sizes, clinical models, and methodological rigor. Schizophr Bull. 2008;34(3):523–37.
Cuijpers P, Sijbrandij M, Koole SL, Andersson G, Beekman AT, Reynolds CF 3. The efficacy of psychotherapy and pharmacotherapy in treating depressive and anxiety disorders: a meta-analysis of direct comparisons. World psychiatry: official journal of the World Psychiatric Association (WPA). 2013;12(2):137–48.
Haddock G, Lewis S, Bentall R, Dunn G, Drake R, Tarrier N. Influence of age on outcome of psychological treatments in first-episode psychosis. Br J psychiatry: J mental Sci. 2006;188:250–4.
Cuijpers P, van Straten A, van Oppen P, Andersson G. Are psychological and pharmacologic interventions equally effective in the treatment of adult depressive disorders? A meta-analysis of comparative studies. J Clin Psychiatry. 2008;69(11):1675–85. quiz 839 – 41.
Archer J, Bower P, Gilbody S, Lovell K, Richards D, Gask L, et al. Collaborative care for depression and anxiety problems. Cochrane Database Syst Rev. 2012;10:Cd006525.
Lester H, Birchwood M, Freemantle N, Michail M, Tait L. REDIRECT: cluster randomised controlled trial of GP training in first-episode psychosis. Br J Gen practice: J Royal Coll Gen Practitioners. 2009;59(563):e183–90.

No competing interests reported.

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Unraveling the Complex Relationships Between Anxiety, Depression, and Quality of Life in Schizophrenia: A Network Analysis Study

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Abstract

Background

Methods

Results

Discussion

Figures

Introduction

Methods

Study Design and Participants

Participant Criteria

Measurements

Statistical Analysis

Results

Participant Characteristics

Network Analysis

Network Structure

Central Symptoms, Bridging Symptoms, and Predicted Mediators

Expanded Network Structure

Network Stability

Discussion

Declarations

References

Additional Declarations

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