Psychological Adaptation to Chronic Immune-Mediated Disease: A Network Analysis of Fear of Progression, Psychopathology, and Positive Psychological Resources in a Saudi Multicenter Sample

Psychological Adaptation to Chronic Immune-Mediated Disease: A Network Analysis of Fear of Progression, Psychopathology, and Positive Psychological Resources in a Saudi Multicenter Sample | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Psychological Adaptation to Chronic Immune-Mediated Disease: A Network Analysis of Fear of Progression, Psychopathology, and Positive Psychological Resources in a Saudi Multicenter Sample Mogeda El Sayed El Keshky, Ahmed Yasser Samak, Nisreen Yachoub Khalil This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9004507/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 10 You are reading this latest preprint version Abstract Background Chronic immune-mediated diseases are characterized by persistent inflammation, fluctuating symptoms, and illness-related uncertainty, placing patients at elevated risk for psychological distress. Although depression and anxiety are highly prevalent, less is known about how illness-specific fears and protective psychological resources interact within individuals. This study applied a network approach to examine the interrelations among fear of progression, depression, anxiety, resilience, self-compassion, and well-being in patients with immune-mediated diseases. Methods A cross-sectional, multicenter study was conducted among 395 adult patients with physician-diagnosed immune-mediated diseases in Saudi Arabia. Participants completed validated Arabic versions of the PHQ-9, GAD-7, Fear of Progression Questionnaire–Short Form, Brief Resilience Scale, Self-Compassion Scale–Short Form, and WHO-5 Well-Being Index. A regularized Gaussian graphical model was estimated using LASSO with EBIC model selection. Centrality (expected influence) and bridge expected influence were examined. Network stability was evaluated using bootstrapping procedures. Results Clinically significant depression was observed in 56.6% of participants, and clinically significant anxiety in 62.37%, while 85% reported dysfunctional fear of progression. The network revealed strong interconnections between anticipatory anxiety and illness-related fears. Fear of severe medical treatments and catastrophic anticipation emerged as highly central nodes. Resilience (the ability to recover from stress) functioned as a bridge between psychopathology and well-being clusters. Self-compassion components showed inverse associations with depressive self-evaluative symptoms. Conclusion The findings suggest that, in chronic immune-mediated disease, illness-related anticipatory fear may represent a core organizing process in psychological adaptation. Resilience appears to be embedded within the symptom network as a potential regulatory buffer. These results support integrative psychosomatic models emphasizing dynamic interactions among illness-related uncertainty, affective processes, and adaptive resources. well-being resilience self-compassion fear of progression depression anxiety network analysis Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Chronic immune-mediated inflammatory diseases represent a major global health burden and are characterized by persistent immune dysregulation, fluctuating symptom severity, and uncertain long-term trajectories. Conditions such as rheumatoid arthritis, systemic lupus erythematosus, multiple sclerosis, psoriasis, inflammatory bowel disease, and related autoimmune disorders often require lifelong management and expose patients to recurrent cycles of symptom exacerbation and remission. Beyond their somatic manifestations, these illnesses impose substantial psychological demands. A substantial body of epidemiological evidence indicates that depression and anxiety occur at rates two to three times higher among individuals with immune-mediated diseases than in the general population [ 1 – 5 ]. From a psychosomatic perspective, immune-mediated diseases offer a compelling model of bidirectional interactions between biological and psychological processes. Inflammatory activity has been implicated in affective dysregulation, fatigue, cognitive impairment, and behavioral withdrawal, whereas chronic psychological stress may exacerbate immune activation and influence disease progression [ 6 ]. Despite this reciprocal interplay, much of the literature has conceptualized psychiatric symptoms in immune-mediated diseases as parallel comorbidities rather than as embedded components of an integrated adaptation system. Depression and anxiety are frequently examined as isolated diagnostic categories and operationalized using total scale scores, potentially obscuring symptom-level interactions and underlying regulatory mechanisms. A defining psychological feature of chronic immune-mediated diseases is illness-related uncertainty. Patients face unpredictable flares, evolving treatment regimens, and the possibility of progressive disability. This uncertainty may give rise to persistent concerns about worsening health, loss of autonomy, and adverse medical interventions. Fear of progression (FoP) captures these illness-specific anxieties and has been extensively studied in oncology populations [ 7 ]. FoP encompasses worries about disease progression, treatment side effects, social role disruption, and existential threat [ 8 , 9 ]. Although FoP has traditionally been conceptualized within cancer survivorship research, emerging evidence suggests that it is highly prevalent across chronic illnesses, including rheumatoid arthritis [ 10 ] and other immune-mediated conditions [ 11 ]. Importantly, FoP is conceptually distinct from generalized anxiety disorder. Whereas generalized anxiety involves pervasive worry across multiple life domains, FoP is anchored in realistic medical threats and disease-specific concerns [ 12 ]. In chronic immune-mediated diseases, such fears may initially serve adaptive monitoring functions. However, when persistent and dysregulated, they may amplify emotional distress and undermine quality of life. Nevertheless, the relationship between FoP and broader affective symptoms remains insufficiently understood. It remains unclear whether illness-related fear operates independently, overlaps with generalized anxiety, or functions as a central driver of depressive and anxious symptom clusters. In addition to threat-based processes, adaptation to chronic illness involves regulatory and protective capacities. Positive psychological constructs such as resilience and self-compassion have received increasing attention in health psychology and psychosomatic medicine. Resilience, defined as the capacity to recover from stress and adversity [ 13 ], has been associated with reduced psychiatric burden and improved health-related quality of life in immune-mediated diseases [ 6 , 14 ]. Self-compassion, reflecting an attitude of kindness and balanced awareness toward personal suffering [ 15 ], has consistently demonstrated inverse associations with depression and anxiety [ 16 , 17 ]. In chronic illness populations, higher self-compassion is associated with improved emotional adjustment and reduced distress [ 18 ]. Traditional analytic approaches, such as regression and structural equation modeling, typically treat these constructs as distinct latent variables that influence one another [ 19 ]. Although informative, such approaches may not fully capture the dynamic interplay between symptoms and protective traits at the component level. For example, fatigue may reinforce hopelessness, which in turn may intensify fear of disease progression, thereby increasing nervousness and irritability. Protective components such as resilience may attenuate these reinforcing cycles. Understanding these interactions requires analytic strategies capable of modeling direct relationships among specific components. Network theory offers such a framework. Rather than assuming that symptoms are passive indicators of underlying disorders, network models conceptualize psychological phenomena as systems of interacting elements [ 20 ]. From this perspective, symptoms influence one another directly and may form self-sustaining feedback loops. Central nodes are elements with strong and numerous connections to other nodes, potentially exerting substantial influence within the system. Bridge nodes connect clusters, potentially facilitating or buffering comorbidity [ 21 , 22 ]. Importantly, network models are primarily descriptive and do not imply causal direction, particularly in cross-sectional designs [ 23 ]. Nevertheless, they provide valuable insights into structural organization. Applying a network framework to chronic immune-mediated disease populations may advance psychosomatic theory in several ways. First, it enables the integration of illness-specific fears with general affective symptoms and positive psychological resources within a single analytic model. Second, it may clarify whether FoP occupies a peripheral or central position within psychological adaptation. Third, it may elucidate how resilience and self-compassion are embedded within symptom systems rather than functioning solely as distal moderators. To date, no study has examined the joint network structure of depression, anxiety, fear of progression, resilience, self-compassion, and well-being in immune-mediated disease populations in the Middle East. Given cultural, social, and healthcare-system differences that may shape illness perceptions and coping, region-specific investigation is warranted. The present study aimed to examine the network structure of psychopathology and positive psychological constructs among Saudi patients with immune-mediated diseases. Specifically, we sought to (1) estimate the network linking depressive symptoms, anxiety symptoms, FoP components, resilience, self-compassion, and well-being; (2) identify central nodes within this system; and (3) identify bridge nodes connecting distress and protective domains. We hypothesized that illness-related anticipatory fear would occupy central positions and that resilience would function as a bridging protective component within the psychosomatic network. Methods Participants The study included 395 patients diagnosed with immune-mediated diseases. The represented conditions were rheumatoid arthritis (25%), multiple sclerosis (12%), psoriasis (10%), fibromyalgia (8%), lupus (7%), Crohn’s disease (6%), joint stiffness (6%), vitiligo (5%), alopecia (4%), and multiple immune-mediated conditions with comorbid hypertension, type 1 diabetes, or type 2 diabetes (17%). Participants were 50.85% female. Regarding education, 2.8% had less than a high school education, 18.6% had completed high school, 49.5% held a college degree, and 29.1% had postgraduate education. In terms of marital status, 51.18% were married, 37.29% had never married, and 11.53% were divorced, separated, or widowed. Monthly income was distributed as follows: 11.2% earned less than 5,000 SR, 14.5% earned 5,000–10,000 SR, 15.0% earned 10,000–15,000 SR, 26.4% earned 15,000–20,000 SR, and 7.8% earned more than 20,000 SR. Self-rated disease severity on a 1–7 scale was distributed as follows: 10.8% (1), 4.8% (2), 15.2% (3), 20.3% (4), 14.9% (5), 21.3% (6), and 12.5% (7). The mean duration since diagnosis was 9.2 years (SD = 6.2). Procedure This multicenter, cross-sectional study was conducted between July and October 2024. Participants were recruited from King Abdulaziz University Hospital (Jeddah), King Abdullah University Hospital (Riyadh), the Immunodeficiency Prevention Association (MANAAH), and other specialized immune-mediated disease clinics in Jeddah and Riyadh. Patients received oral and written information regarding the study objectives, confidentiality, and voluntary participation. Inclusion criteria were: (1) age ≥ 18 years, (2) a confirmed diagnosis of an immune-mediated disease at any stage, and (3) intact cognitive functioning. Exclusion criteria included severe mental illness, cognitive disorders, or intellectual impairment. Self-report questionnaires were administered by trained researchers and PhD-level psychologists. Standardized procedures and quality-control measures were implemented to minimize bias. Measures Participants completed the following validated instruments: Fear of Progression Questionnaire–Short Form The Fear of Progression Questionnaire–Short Form[ 24 ], derived from[ 25 ], includes 12 items rated from 1 (never) to 5 (very often), with total scores ranging 12–60. Higher scores indicate stronger fear of disease progression. The scale has demonstrated validity and discriminant properties relative to anxiety [ 26 ]. Internal consistency in this study was excellent (α = 0.93). Self-Compassion Scale–Short Form The Self-Compassion Scale–Short Form [ 27 ] comprises 12 items rated from 1 (almost never) to 5 (almost always). Six items are reverse scored. Total scores range from 12–60, with higher values indicating greater self-compassion. The Arabic version validated in Saudi Arabia [ 28 ] showed good reliability (α = 0.87). Brief Resilience Scale The Brief Resilience Scale [ 13 ] includes 6 items rated from 1 (strongly disagree) to 5 (strongly agree), with three reverse-scored items. Total scores range from 6–30, reflecting recovery capacity from stress. The Arabic validated version [ 29 ] demonstrated good internal consistency (α = 0.85). Patient Health Questionnaire (PHQ-9) The PHQ-9 [ 30 ] consists of 9 items reflecting DSM-IV criteria for depression, rated from 0 (not at all) to 3 (nearly every day), with total scores ranging from 0–27. Higher scores indicate greater depressive severity. The Arabic version validated in Saudi Arabia [ 31 ] was used. Internal consistency in this study was excellent (α = 0.87). WHO-5 Well-being Index The WHO-5 [ 32 ] measures subjective well-being over the past two weeks using five items rated 0–5. Raw scores are transformed to a 0–100 scale, with higher scores indicating greater well-being. The scale has demonstrated validity in clinical samples [ 33 ]. Internal consistency in this study was excellent (α = 0.90). Generalized Anxiety Disorder Screener (GAD-7) The GAD-7[ 34 ]includes 7 items assessing DSM-IV anxiety symptoms, rated 0–3, yielding total scores between 0–21. Higher scores reflect greater anxiety severity. The Arabic validated version [ 35 ] demonstrated excellent reliability in this sample (α = 0.90). Translation and Pilot Testing of the Fear of Progression Scale All measures had validated Arabic versions except the Fear of Progression scale. This instrument was translated following World Health Organization guidelines [ 36 ]. Two bilingual professors translated the original English version into Arabic. Two independent translators, blinded to the original, conducted back-translation. After comparison and consensus, a final version was established. The translated scale was pilot tested in 55 patients with rheumatoid arthritis and fibromyalgia. Items were reported as clear and comprehensible. The Arabic FoP demonstrated moderate correlations with GAD-7 (r = 0.43, p < 0.001) and PHQ-9 (r = 0.39, p < 0.001), and showed good internal consistency (α = 0.89). This validated version was used in the main study. Data analysis Data analyses were conducted in R [ 37 ]. Descriptive statistics, skewness, and kurtosis were computed for all items and total scores. Network analysis was performed using the bootnet package [ 20 ]. A Gaussian graphical model was estimated using LASSO regularization [ 38 ] with EBIC model selection [ 39 ]. Network visualization was conducted using qgraph[ 40 ]. Node redundancy was evaluated using the goldbricker procedure in the networktools package[41] . Nodes represented individual questionnaire items, and edges represented partial correlations after controlling for all other nodes [ 23 ]. The Fruchterman–Reingold algorithm was applied to position strongly connected nodes closer together. Centrality was assessed using expected influence, and bridge expected influence was used to identify nodes linking clusters. Network stability was evaluated using nonparametric bootstrapping with 1,000 samples and 95% confidence intervals. Correlation stability coefficients of ≥ 0.70 were considered acceptable [ 20 ]. Results Descriptive Statistics Table 1 presents the descriptive statistics, including skewness and kurtosis. Table 1 Descriptive statistics, skewness, and kurtosis Variable Mean SD Range Skewness Kurtosis WHO1 3.09 1.14 0–5 -0.40 2.37 WHO2 3.08 1.28 0–5 -0.08 2.13 WHO3 2.82 1.11 0–5 -0.07 2.39 WHO4 2.85 1.26 0–5 -0.10 2.39 WHO5 2.95 1.15 0–5 -0.17 2.54 Well-being score 14.8 5.03 0–25 -0.32 2.23 SCS1R 2.29 1.16 1–5 0.73 2.79 SCS2 3.79 0.93 1–5 -0.41 2.42 SCS3 3.90 0.91 1–5 -0.51 2.92 SCS4R 2.32 1.29 1–5 0.80 2.54 SCS5 3.86 0.97 1–5 -0.66 2.98 SCS6 3.70 1.01 1–5 -0.79 3.31 SCS7 3.93 0.90 1–5 -0.52 2.75 SCS8R 2.10 1.04 1–5 0.89 3.34 SCS9R 2.18 1.24 1–5 0.92 2.87 SCS10 3.83 0.98 1–5 -0.92 3.75 SCS11R 2.86 1.24 1–5 0.07 1.99 SCS12R 2.95 1.39 1–5 -0.04 1.72 Self-compassion score 37.7 5.19 12–60 1.53 6.43 BRS1 3.91 0.84 1–5 -0.60 3.66 BRS2R 2.4 1.08 1–5 0.48 2.55 BRS3 3.56 1.08 1–5 -0.39 2.61 BRS4R 2.6 1.11 1–5 0.35 2.52 BRS5 3.74 0.94 1–5 -0.44 2.96 BRS6R 2.45 1.08 1–5 0.46 2.72 Resilience score 18.6 2.80 6–30 0.13 3.03 FoP1 3.74 1.21 1–5 -1.05 3.22 FoP2 3.72 1.28 1–5 -0.92 2.81 FoP3 3.86 1.16 1–5 -1.06 3.45 FoP4 3.97 1.07 1–5 -1.17 3.97 FoP5 4.03 1.11 1–5 -1.28 4.12 FoP6 3.97 1.24 1–5 -1.28 3.65 FoP7 3.78 1.14 1–5 -0.99 3.37 FoP8 3.88 1.24 1–5 -1.23 3.57 FoP9 3.95 1.13 1–5 -1.24 3.98 FoP10 3.88 1.11 1–5 -1.17 3.87 FoP11 3.86 1.20 1–5 -1.03 3.25 FoP12 3.88 1.24 1–5 -1.14 3.34 Fear of disease progression 46.5 10.8 12–60 -1.36 3.98 PHQ1 1.30 0.80 0–3 0.27 2.64 PHQ2 1.38 1.06 0–3 0.10 1.77 PHQ3 1.61 0.98 0–3 -0.03 1.96 PHQ4 1.60 0.99 0–3 0.05 1.90 PHQ5 1.56 0.99 0–3 -0.006 1.94 PHQ6 1.09 0.83 0–3 0.16 2.15 PHQ7 1.27 0.86 0–3 -0.007 2.15 PHQ8 1.24 0.96 0–3 0.17 1.98 PHQ9 0.93 0.81 0–3 0.34 2.13 Depression score 12.01 5.88 0–27 0.29 2.05 GAD1 1.43 0.86 0–3 0.15 2.35 GAD2 1.45 0.89 0–3 -0.11 2.22 GAD3 1.46 0.96 0–3 0.09 2.05 GAD4 1.46 0.94 0–3 0.17 2.11 GAD5 1.31 0.87 0–3 -0.01 2.18 GAD6 1.48 0.96 0–3 0.06 2.05 GAD7 1.41 1.00 0–3 0.11 1.94 Anxiety score 10.04 5.19 0–21 0.20 2.05 Notes : SD = standard deviation As shown in Table 1 , skewness and kurtosis values fell within acceptable ranges, indicating an approximately normal distribution of the network nodes. Total scale scores were calculated for all variables. The mean depression score was 12.01 (SD = 5.88, range = 0–27), with 56.6% of participants scoring at or above the clinical cut-off of 10. The mean anxiety score was 10.04 (SD = 5.19, range = 0–21), and 62.37% of participants scored above the cut-off of 8. The mean fear of progression score was 46.5 (SD = 10.8, range = 12–60); using a cut-off of 34, 85% of participants demonstrated dysfunctional levels of fear. The mean well-being score was 14.82 (SD = 5.03, range = 0–25), with 30.7% of participants scoring below the cut-off of 13, indicating poor well-being. The mean self-compassion score was 37.7 (SD = 5.19, range = 12–60), and the mean resilience score was 18.6 (SD = 2.80, range = 6–30). Psychological Network Structure Redundant nodes were examined using the goldbricker procedure. No node pairs exceeded the 25% redundancy threshold; therefore, all 51 nodes were retained in the network. Stability analyses indicated adequate robustness of the edge weights and centrality indices (Fig. 1 ). Visual inspection of the network (Fig. 2 ) revealed a clear clustering of psychological well-being nodes (resilience, self-compassion, and well-being items) and psychopathology nodes (depression, anxiety, and fear of progression). Despite this clustering, numerous cross-domain connections were observed, indicating substantial integration between protective and distress-related processes. Negative Associations Between Well-being and Psychopathology The strongest negative edges primarily linked depressive fatigue and hopelessness with resilience and well-being components. For example, feeling tired or having little energy (PHQ4) showed strong inverse associations with resilience indicators (e.g., the ability to recover from stress; BRS1, BRS3) and with feeling active and vigorous (WHO3). Similarly, depressed mood (PHQ2) was negatively connected with resilience (BRS1), and low interest (PHQ1) was inversely associated with perceived daily engagement (WHO5). Self-compassion items reflecting a balanced perspective and reduced self-criticism were also negatively connected with depressive self-evaluative symptoms. For instance, feeling bad about oneself (PHQ6) showed strong inverse associations with self-compassion components such as understanding disliked aspects of oneself (SCS2) and reduced feelings of inadequacy (SCS1R, SCS4R). These findings suggest that self-compassion may counteract negative self-evaluation within the depressive symptom cluster. Interactions Between Self-Compassion and Fear of Progression Notably, several negative edges linked lower self-compassion to fear of progression. Items reflecting social comparison and perceived isolation (e.g., SCS4R, SCS8R) were connected to fears of disease progression (FoP1) and anxiety before medical appointments (FoP2). Additionally, fear of relying on others for daily activities (FoP7) was negatively associated with resilience-related recovery capacity (BRS6R). These patterns suggest that lower self-compassion and resilience may intensify illness-related fears. Centrality and Bridge Centrality Centrality indices are presented in Fig. 3 . Expected influence values indicated that the most central nodes in the network were fear of severe medical treatments (FoP9), catastrophic anticipation (“being afraid that something awful might happen”; GAD7), intolerance toward disliked aspects of life (SCS12R), nervousness (GAD1), irritability (GAD6), and fear of being unable to pursue hobbies (FoP8). These nodes exhibited the strongest overall connections to other components in the network, suggesting that anticipatory threat and emotional reactivity occupy structurally influential positions within the psychological system of patients with immune-mediated diseases. Notably, both generalized anxiety and illness-specific fear items appeared among the most central nodes, indicating substantial integration between diffuse anxiety processes and disease-related concerns. In addition, intolerance of unwanted life experiences (a reverse-scored self-compassion item) emerged as highly central, underscoring the potential relevance of experiential intolerance and self-critical tendencies within the broader network. Bridge centrality analyses (Fig. 4 ) identified nodes that connected the psychological well-being cluster (resilience, self-compassion, and well-being) with the psychopathology cluster (depression, anxiety, and fear of progression). The strongest bridge nodes were catastrophic anticipation (GAD7), concentration difficulties (PHQ7), and resilience recovery capacity (BRS5). Catastrophic anticipation (GAD7) served as a key connector between anxiety and illness-specific fears, whereas concentration difficulties (PHQ7) linked depressive symptoms with broader distress domains. Resilience (BRS5), reflecting the perceived ability to recover from difficult experiences, functioned as a protective bridge between the well-being and psychopathology clusters. This structural positioning suggests that resilience may play an integrative role within the network rather than operating independently of distress-related processes. Discussion This study examined the interrelations among fear of progression, depressive symptoms, anxiety symptoms, resilience, self-compassion, and well-being in a sample of Saudi patients with chronic immune-mediated diseases using a network analytic approach. By integrating threat-based processes and protective psychological resources within a single model, the findings contribute to a more comprehensive psychosomatic understanding of adaptation to chronic illness. One of the most prominent findings was the centrality of illness-related anticipatory fear, particularly fear of severe medical treatments and catastrophic expectations that “something awful might happen.” These nodes demonstrated strong connections with multiple affective and cognitive symptoms. From a psychosomatic perspective, this suggests that psychological adaptation to chronic immune-mediated disease may be organized around future-oriented threat appraisal. Illness-related uncertainty is inherent in immune-mediated diseases, in which symptom flares, treatment side effects, and potential disability are realistic concerns. Fear of progression may therefore represent a psychologically meaningful response to ongoing biological unpredictability. However, when persistent and amplified, such fear may sustain broader emotional dysregulation. Rather than being secondary to depression or generalized anxiety, illness-related fear may function as an organizing component that integrates somatic awareness, cognitive appraisal, and affective arousal. This interpretation aligns with conceptual distinctions between FoP and generalized anxiety [ 12 ]. Whereas generalized anxiety involves diffuse worry, FoP is specifically anchored in illness trajectory. The network findings suggest that illness-specific fears may not merely coexist with anxiety and depression, but may also structurally interconnect them within the context of chronic disease. Nervousness and irritability also emerged as influential nodes. Irritability may reflect sustained physiological arousal associated with chronic stress and inflammatory processes. Emerging research suggests that inflammatory cytokines can influence mood regulation and emotional reactivity [ 6 ]. In immune-mediated diseases, physical discomfort, fatigue, and uncertainty may interact with inflammatory pathways to heighten emotional volatility. Within the network, irritability may represent a behavioral expression of sustained anticipatory fear. Chronic hypervigilance toward disease progression may maintain heightened arousal, which in turn may manifest as nervousness and frustration. These processes may contribute to interpersonal strain and reduced quality of life, thereby reinforcing depressive cognitions. Concentration difficulties emerged as a bridge node connecting the psychopathology and well-being clusters. Cognitive impairment in immune-mediated diseases may arise through multiple pathways, including inflammatory activity, fatigue, and depressive rumination. Positioned between clusters, concentration difficulties may reflect an interface between somatic and psychological processes. From a psychosomatic perspective, cognitive symptoms may signal the convergence of biological and psychological strain. When attentional resources are compromised, patients may struggle to engage in valued activities or adaptive coping strategies, potentially amplifying distress. Resilience, specifically the perceived ability to recover from stress, functioned as a bridge component linking protective and distress domains. Rather than operating outside the symptom network, resilience appeared to be embedded within it. This suggests that adaptive recovery capacity may influence how activation spreads across the system. Resilience has been associated with improved outcomes in immune-mediated diseases [ 6 , 14 ]. Within a network framework, resilience may attenuate connections between fear-based nodes and depressive symptoms, potentially reducing network density. Although causal inferences cannot be drawn from cross-sectional data[ 23 ], the structural positioning of resilience supports psychosomatic models that emphasize regulatory flexibility as central to adaptation to chronic illness. Components reflecting low self-compassion, particularly intolerance toward disliked aspects of life, demonstrated notable connectivity. Chronic illness often involves functional limitations and changes in identity. Patients who respond to these changes with self-criticism or experiential avoidance may intensify emotional distress. Conversely, self-compassion may foster adaptive acceptance, thereby mitigating the amplification of fear and depressive self-evaluation. These findings align with research linking self-compassion to reduced depression and anxiety [ 16 , 17 ]. Within chronic illness contexts, cultivating compassionate self-relating may buffer against shame and hopelessness associated with perceived physical decline. The findings have important implications for psychosomatic care. Interventions targeting illness-specific anticipatory fear may be particularly relevant. Traditional cognitive-behavioral approaches that focus solely on generalized anxiety or depressive symptoms may overlook the central role of fears related to disease progression. Incorporating FoP-focused components, resilience training, and compassion-based strategies may provide a more integrative approach. By strengthening regulatory capacities and addressing threat-appraisal processes, such interventions may influence multiple interconnected symptoms. The study was conducted in Saudi Arabia, where cultural norms, religious beliefs, and family structures may shape illness perception and coping. Collective support systems may buffer distress, yet concerns about dependency and role disruption may intensify fear of progression. Future research should examine cultural moderators of network structure. Several limitations warrant consideration. The cross-sectional design precludes causal interpretation, and network centrality does not imply temporal precedence. Longitudinal and intensive time-series designs are needed to clarify dynamic activation patterns. In addition, the sample included heterogeneous immune-mediated conditions, and disease-specific mechanisms may produce distinct network structures. Future studies should therefore examine condition-specific networks and incorporate biological markers of inflammation. Despite these limitations, the study advances psychosomatic understanding by demonstrating that illness-related anticipatory fear occupies a structurally influential position within psychological adaptation to chronic immune-mediated disease, while resilience operates as a regulatory bridge. Conclusions Chronic immune-mediated diseases involve more than co-occurring psychiatric symptoms; they reflect dynamic interactions among illness-related uncertainty, affective processes, cognitive symptoms, and adaptive resources. By applying a network framework, this study highlights the central role of anticipatory fear and the embedded regulatory function of resilience. These findings support integrative psychosomatic models that emphasize the dynamic interplay between biological vulnerability and psychological regulation in adaptation to chronic illness. Declarations Ethics approval and consent to participate Ethical approval was obtained from the Scientific Research Ethics Committee, Faculty of Arts and Humanities, King Abdulaziz University, Saudi Arabia and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants. Clinical trial number: not applicable Consent for publication Not applicable. Availability of data and materials The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors have no conflicts of interest to declare that are relevant to the content of this article. Funding This study was funded by the Deanship of Scientific Research (DSR), at King Abdulaziz University, Jeddah, under grant no. (IPP:120-246-2025). Authors' contributions Mogeda El Keshky: Conceptualization, Methodology, Data curation, Formal analysis, Investigation, Supervision, Visualization, Writing—original draft, Funding acquisition. Ahmed Samak: Methodology, Validation, Resources, Writing—review & editing. Nisreen Yachoub: Data curation, Resources, Writing—review & editing. All authors read and approved the final manuscript. Acknowledgements This study was funded by the Deanship of Scientific Research (DSR), at King Abdulaziz University, Jeddah, under grant no. (IPP :120-246-2025). 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Health Psychol Rev. 2023;17. https://doi.org/10.1080/17437199.2022.2039744 . Dinkel A, Herschbach P. Fear of Progression in Cancer Patients and Survivors. In: Goerling U, Mehnert A, editors. Recent Results in Cancer Research. Berlin: Springer International Publishing; 2018. https://doi.org/10.1007/978-3-319-64310-6_2 . Smith BW, Dalen J, Wiggins K, Tooley E, Christopher P, Bernard J. The brief resilience scale: Assessing the ability to bounce back. Int J Behav Med. 2008;15:194–200. https://doi.org/10.1080/10705500802222972 . Nakazawa K, Noda T, Ichikura K, Okamoto T, Takahashi Y, Yamamura T, et al. Resilience and depression/anxiety symptoms in multiple sclerosis and neuromyelitis optica spectrum disorder. Mult Scler Relat Disord. 2018;25:309–15. https://doi.org/10.1016/j.msard.2018.08.023 . Neff K. Self-Compassion: An Alternative Conceptualization of a Healthy Attitude Toward Oneself. Self Identity. 2003;2:85–101. https://doi.org/10.1080/15298860309032 . Pinto-Gouveia J, Duarte C, Matos M, Fráguas S. The protective role of self-compassion in relation to psychopathology symptoms and quality of life in chronic and in cancer patients. Clin Psychol Psychother. 2014;21:311–23. https://doi.org/10.1002/cpp.1838 . van der Donk LJ, Fleer J, Tovote A, Ranchor AV, Smink A, Mul VEM et al. The role of mindfulness and self-compassion in depressive symptoms and affect: A Comparison between Cancer Patients and Healthy Controls. Mindfulness (N Y). 2020;11:883–94. https://doi.org/10.1007/s12671-019-01298-1 Neiman N, Boothroyd D, Anjur K, Bensen R, Yeh AM, Wren AVA. Self-Compassion in Adolescents and Young Adults With Inflammatory Bowel Disease: Relationship of Self-Compassion to Psychosocial and Physical Outcomes. Inflamm Bowel Dis. 2024. https://doi.org/10.1093/ibd/izae170 . Ordóñez-Carrasco JL, Sayans-Jiménez P, Rojas-Tejada AJ. Ideation-to-action framework variables involved in the development of suicidal ideation: A network analysis. Curr Psychol. 2023;42:4053–64. https://doi.org/10.1007/s12144-021-01765-w . Epskamp S, Borsboom D, Fried EI. Estimating psychological networks and their accuracy: A tutorial paper. Behav Res Methods. 2018;50:195–212. https://doi.org/10.3758/s13428-017-0862-1 . Jones PJ, Ma R, McNally RJ. Bridge Centrality: A Network Approach to Understanding Comorbidity. Multivar Behav Res. 2019;56:353–67. https://doi.org/10.1080/00273171.2019.1614898 . Robinaugh DJ, Millner AJ, Mcnally RJ. Identifying Highly Influential Nodes in the Complicated Grief Network. J Abnorm Psychol. 2016;125:747–57. https://doi.org/10.1037/abn0000181 . Epskamp S, Waldorp LJ, Mõttus R, Borsboom D. The Gaussian Graphical Model in Cross-Sectional and Time-Series Data. Multivar Behav Res. 2018;53:453–80. https://doi.org/10.1080/00273171.2018.1454823 . Mehnert A, Herschbach P, Berg P, Henrich G, Koch U. Progredienzangst bei Brustkrebspatientinnen - Validierung der Kurzform des Progredienzangstfragebogens PA-F-KF/ Fear of progression in breast cancer patients – validation of the short form of the Fear of Progression Questionnaire (FoP-Q-SF). Z Psychosom Med Psychother. 2006;52:274–88. https://doi.org/10.13109/zptm.2006.52.3.274 . Raes F, Pommier E, Neff KD, Van Gucht D. Construction and factorial validation of a short form of the Self-Compassion Scale. Clin Psychol Psychother. 2011;18:250–5. Spitzer RL, Kroenke K, Williams JBW. Validation and utility of a self-report version of PRIME-MD: The PHQ primary care study. JAMA. 1999;282:1737. https://doi.org/10.1001/jama.282.18.1737 . World Health Organization. Who-Five Well-being Index (WHO-5)​. 1998. https://www.psykiatri-regionh.dk/who-5/Pages/default.aspx Spitzer RL, Kroenke K, Williams JBW, Löwe B. A Brief Measure for Assessing Generalized Anxiety Disorder. Arch Intern Med. 2006;166:1092–7. https://doi.org/10.1001/archinte.166.10.1092 . Mahendran R, Liu J, Kuparasundram S, Griva K. Validation of the English and simplified Mandarin versions of the Fear of Progression Questionnaire – Short Form in Chinese cancer survivors. BMC Psychol. 2020;8:4–10. https://doi.org/10.1186/s40359-020-0374-0 . Alabdulaziz H, Alquwez N, Almazan JU, Albougami A. Nurse Education Today The Self-Compassion Scale Arabic version for baccalaureate nursing students: A validation study. Nurse Educ Today. 2020;89:104420. https://doi.org/10.1016/j.nedt.2020.104420 . Baattaiah BA, Alharbi MD, Khan F, Aldhahi MI. Translation and population-based validation of the Arabic version of the brief resilience scale. Ann Med. 2023;55:2230887. https://doi.org/10.1080/07853890.2023.2230887 . Khalifa AFM, Ahmed Hussamuldin AB, Alkhathran RM, Alghamdi AA, Rifaey AA, Alabdullah AM, et al. PHQ-9 to screen for depression in Riyadh, Saudi Arabia. Med Sci. 2023;27:1–7. https://doi.org/10.54905/disssi/v27i135/e204ms2974 . Fekih-Romdhane F, Dahdouh FAMGAO, Hallit S. Validation and optimal cut-off score of the World Health Organization Well-being Index (WHO-5) as a screening tool for depression among patients with schizophrenia. BMC Psychiatry. 2024;24:291. https://doi.org/10.1186/s12888-024-05814-z . Alghadir A, Anwer S, Albougami A, Salahuddin M. Psychometric Properties of the Generalized Anxiety Disorder Scale Among Saudi University Male Students. Neuropsychiatr Dis Treat. 2020;16:1427–32. https://doi.org/10.2147/NDT.S246526 . WHO. WHO | Process of translation and adaptation of instruments. 2011. Core Team R. R. R: A language and environment for statistical computing. R Foundation for Statistical Computing. 2024. https://cran.rstudio.com/manuals.html Friedman J, Hastie T, Tibshirani R. Sparse inverse covariance estimation with the graphical lasso. Biostatistics. 2008;9:432–41. https://doi.org/10.1093/biostatistics/kxm045 . Foygel R, Drton M. Extended Bayesian Information Criteria for Gaussian Graphical Models. In: Proceedings of the 23rd International Conference on Neural Information Processing Systems. 2010. pp. 604–612. Epskamp S, Cramer AOJ, Waldorp LJ, Schmittmann VD, Borsboom D. qgraph: Network Visualizations of Relationships in Psychometric Data. J Stat Softw. 2012;48. https://doi.org/10.18637/jss.v048.i04 . Jones PJ, Ma R, McNally RJ. Bridge Centrality: A Network Approach to Understanding Comorbidity. Multivar Behav Res. 2021;56:353–67. https://doi.org/10.1080/00273171.2019.1614898 . Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9004507","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":621348346,"identity":"4e151e18-7bc8-4412-8611-a3883f848b28","order_by":0,"name":"Mogeda El Sayed El Keshky","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYDCCA2CSmYEfRCUUkKJFsgGkxYAULQZgBjFa+G6fTnz4pcJa3vj86sQPDwwY5PnFDuDXInkud7OxzJl0w2033m6WADrMcObsBPxaDM7wbpOWbDvMuO3G2Q0gLQkGt4nS8u+w/eYZZzf/IFqL5MeGw4kb+Hu3EWeL5BnezcYMx9KTZ9zg3WaRYCBB2C98Z3g3PvxRY23b3392880fFTby/NIEtIAAMw+IlACrlCCsHAQYf4BI/gPEqR4Fo2AUjIKRBwCf3EkbYKMMwQAAAABJRU5ErkJggg==","orcid":"","institution":"Assiut University","correspondingAuthor":true,"prefix":"","firstName":"Mogeda","middleName":"El Sayed El","lastName":"Keshky","suffix":""},{"id":621348347,"identity":"6c8d2f4f-4dbe-464b-8114-603eb7efb2d6","order_by":1,"name":"Ahmed Yasser Samak","email":"","orcid":"","institution":"Armed Forces College of Medicine","correspondingAuthor":false,"prefix":"","firstName":"Ahmed","middleName":"Yasser","lastName":"Samak","suffix":""},{"id":621348348,"identity":"1c883a88-021f-4ae3-ac46-b024339a9e20","order_by":2,"name":"Nisreen Yachoub Khalil","email":"","orcid":"","institution":"King Abdulaziz University","correspondingAuthor":false,"prefix":"","firstName":"Nisreen","middleName":"Yachoub","lastName":"Khalil","suffix":""}],"badges":[],"createdAt":"2026-03-02 01:23:22","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9004507/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9004507/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":107254254,"identity":"ae60d337-7901-4fef-bccd-59f628303759","added_by":"auto","created_at":"2026-04-19 12:00:49","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":208117,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork stability\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-9004507/v1/bf7b7b917daa23e870ccbd91.png"},{"id":107254256,"identity":"684fa47c-2dea-4121-ae03-66cdbf2497c1","added_by":"auto","created_at":"2026-04-19 12:00:49","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":275130,"visible":true,"origin":"","legend":"\u003cp\u003ePsychological network of psychological well-being and psychopathology in patients with immune system\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-9004507/v1/ae641a1fa6e6a2b5c2bad039.png"},{"id":107254257,"identity":"e415c08c-6565-4df4-9f2b-6b2e8bfcd66d","added_by":"auto","created_at":"2026-04-19 12:00:49","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":386928,"visible":true,"origin":"","legend":"\u003cp\u003eExpected influence\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-9004507/v1/3b2523f1505a388fa214653d.png"},{"id":107254255,"identity":"4a59c783-61fb-4278-9fca-a9b2a15baed1","added_by":"auto","created_at":"2026-04-19 12:00:49","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":14447,"visible":true,"origin":"","legend":"\u003cp\u003eBridge expected influence\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-9004507/v1/e9d792a8b8a14d952dc85be4.png"},{"id":107483190,"identity":"9b2bee6e-b224-4fdf-ab9a-53994bc16a7b","added_by":"auto","created_at":"2026-04-22 02:26:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1435640,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9004507/v1/5ca77671-05b7-4049-8b17-b124804e2670.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Psychological Adaptation to Chronic Immune-Mediated Disease: A Network Analysis of Fear of Progression, Psychopathology, and Positive Psychological Resources in a Saudi Multicenter Sample","fulltext":[{"header":"Introduction","content":"\u003cp\u003eChronic immune-mediated inflammatory diseases represent a major global health burden and are characterized by persistent immune dysregulation, fluctuating symptom severity, and uncertain long-term trajectories. Conditions such as rheumatoid arthritis, systemic lupus erythematosus, multiple sclerosis, psoriasis, inflammatory bowel disease, and related autoimmune disorders often require lifelong management and expose patients to recurrent cycles of symptom exacerbation and remission. Beyond their somatic manifestations, these illnesses impose substantial psychological demands. A substantial body of epidemiological evidence indicates that depression and anxiety occur at rates two to three times higher among individuals with immune-mediated diseases than in the general population [\u003cspan additionalcitationids=\"CR2 CR3 CR4\" citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFrom a psychosomatic perspective, immune-mediated diseases offer a compelling model of bidirectional interactions between biological and psychological processes. Inflammatory activity has been implicated in affective dysregulation, fatigue, cognitive impairment, and behavioral withdrawal, whereas chronic psychological stress may exacerbate immune activation and influence disease progression [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Despite this reciprocal interplay, much of the literature has conceptualized psychiatric symptoms in immune-mediated diseases as parallel comorbidities rather than as embedded components of an integrated adaptation system. Depression and anxiety are frequently examined as isolated diagnostic categories and operationalized using total scale scores, potentially obscuring symptom-level interactions and underlying regulatory mechanisms.\u003c/p\u003e \u003cp\u003eA defining psychological feature of chronic immune-mediated diseases is illness-related uncertainty. Patients face unpredictable flares, evolving treatment regimens, and the possibility of progressive disability. This uncertainty may give rise to persistent concerns about worsening health, loss of autonomy, and adverse medical interventions. Fear of progression (FoP) captures these illness-specific anxieties and has been extensively studied in oncology populations [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. FoP encompasses worries about disease progression, treatment side effects, social role disruption, and existential threat [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Although FoP has traditionally been conceptualized within cancer survivorship research, emerging evidence suggests that it is highly prevalent across chronic illnesses, including rheumatoid arthritis [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e] and other immune-mediated conditions [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eImportantly, FoP is conceptually distinct from generalized anxiety disorder. Whereas generalized anxiety involves pervasive worry across multiple life domains, FoP is anchored in realistic medical threats and disease-specific concerns [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In chronic immune-mediated diseases, such fears may initially serve adaptive monitoring functions. However, when persistent and dysregulated, they may amplify emotional distress and undermine quality of life. Nevertheless, the relationship between FoP and broader affective symptoms remains insufficiently understood. It remains unclear whether illness-related fear operates independently, overlaps with generalized anxiety, or functions as a central driver of depressive and anxious symptom clusters.\u003c/p\u003e \u003cp\u003eIn addition to threat-based processes, adaptation to chronic illness involves regulatory and protective capacities. Positive psychological constructs such as resilience and self-compassion have received increasing attention in health psychology and psychosomatic medicine. Resilience, defined as the capacity to recover from stress and adversity [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], has been associated with reduced psychiatric burden and improved health-related quality of life in immune-mediated diseases [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Self-compassion, reflecting an attitude of kindness and balanced awareness toward personal suffering [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], has consistently demonstrated inverse associations with depression and anxiety [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. In chronic illness populations, higher self-compassion is associated with improved emotional adjustment and reduced distress [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTraditional analytic approaches, such as regression and structural equation modeling, typically treat these constructs as distinct latent variables that influence one another [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Although informative, such approaches may not fully capture the dynamic interplay between symptoms and protective traits at the component level. For example, fatigue may reinforce hopelessness, which in turn may intensify fear of disease progression, thereby increasing nervousness and irritability. Protective components such as resilience may attenuate these reinforcing cycles. Understanding these interactions requires analytic strategies capable of modeling direct relationships among specific components.\u003c/p\u003e \u003cp\u003eNetwork theory offers such a framework. Rather than assuming that symptoms are passive indicators of underlying disorders, network models conceptualize psychological phenomena as systems of interacting elements [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. From this perspective, symptoms influence one another directly and may form self-sustaining feedback loops. Central nodes are elements with strong and numerous connections to other nodes, potentially exerting substantial influence within the system. Bridge nodes connect clusters, potentially facilitating or buffering comorbidity [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Importantly, network models are primarily descriptive and do not imply causal direction, particularly in cross-sectional designs [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Nevertheless, they provide valuable insights into structural organization.\u003c/p\u003e \u003cp\u003eApplying a network framework to chronic immune-mediated disease populations may advance psychosomatic theory in several ways. First, it enables the integration of illness-specific fears with general affective symptoms and positive psychological resources within a single analytic model. Second, it may clarify whether FoP occupies a peripheral or central position within psychological adaptation. Third, it may elucidate how resilience and self-compassion are embedded within symptom systems rather than functioning solely as distal moderators.\u003c/p\u003e \u003cp\u003eTo date, no study has examined the joint network structure of depression, anxiety, fear of progression, resilience, self-compassion, and well-being in immune-mediated disease populations in the Middle East. Given cultural, social, and healthcare-system differences that may shape illness perceptions and coping, region-specific investigation is warranted.\u003c/p\u003e \u003cp\u003eThe present study aimed to examine the network structure of psychopathology and positive psychological constructs among Saudi patients with immune-mediated diseases. Specifically, we sought to (1) estimate the network linking depressive symptoms, anxiety symptoms, FoP components, resilience, self-compassion, and well-being; (2) identify central nodes within this system; and (3) identify bridge nodes connecting distress and protective domains. We hypothesized that illness-related anticipatory fear would occupy central positions and that resilience would function as a bridging protective component within the psychosomatic network.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eThe study included 395 patients diagnosed with immune-mediated diseases. The represented conditions were rheumatoid arthritis (25%), multiple sclerosis (12%), psoriasis (10%), fibromyalgia (8%), lupus (7%), Crohn\u0026rsquo;s disease (6%), joint stiffness (6%), vitiligo (5%), alopecia (4%), and multiple immune-mediated conditions with comorbid hypertension, type 1 diabetes, or type 2 diabetes (17%).\u003c/p\u003e \u003cp\u003eParticipants were 50.85% female. Regarding education, 2.8% had less than a high school education, 18.6% had completed high school, 49.5% held a college degree, and 29.1% had postgraduate education. In terms of marital status, 51.18% were married, 37.29% had never married, and 11.53% were divorced, separated, or widowed. Monthly income was distributed as follows: 11.2% earned less than 5,000 SR, 14.5% earned 5,000\u0026ndash;10,000 SR, 15.0% earned 10,000\u0026ndash;15,000 SR, 26.4% earned 15,000\u0026ndash;20,000 SR, and 7.8% earned more than 20,000 SR.\u003c/p\u003e \u003cp\u003eSelf-rated disease severity on a 1\u0026ndash;7 scale was distributed as follows: 10.8% (1), 4.8% (2), 15.2% (3), 20.3% (4), 14.9% (5), 21.3% (6), and 12.5% (7). The mean duration since diagnosis was 9.2 years (SD\u0026thinsp;=\u0026thinsp;6.2).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eProcedure\u003c/h3\u003e\n\u003cp\u003eThis multicenter, cross-sectional study was conducted between July and October 2024. Participants were recruited from King Abdulaziz University Hospital (Jeddah), King Abdullah University Hospital (Riyadh), the Immunodeficiency Prevention Association (MANAAH), and other specialized immune-mediated disease clinics in Jeddah and Riyadh.\u003c/p\u003e \u003cp\u003e Patients received oral and written information regarding the study objectives, confidentiality, and voluntary participation. Inclusion criteria were: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years, (2) a confirmed diagnosis of an immune-mediated disease at any stage, and (3) intact cognitive functioning. Exclusion criteria included severe mental illness, cognitive disorders, or intellectual impairment.\u003c/p\u003e \u003cp\u003eSelf-report questionnaires were administered by trained researchers and PhD-level psychologists. Standardized procedures and quality-control measures were implemented to minimize bias.\u003c/p\u003e\n\u003ch3\u003eMeasures\u003c/h3\u003e\n\u003cp\u003eParticipants completed the following validated instruments:\u003c/p\u003e\n\u003ch3\u003eFear of Progression Questionnaire–Short Form\u003c/h3\u003e\n\u003cp\u003eThe Fear of Progression Questionnaire\u0026ndash;Short Form[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], derived from[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], includes 12 items rated from 1 (never) to 5 (very often), with total scores ranging 12\u0026ndash;60. Higher scores indicate stronger fear of disease progression. The scale has demonstrated validity and discriminant properties relative to anxiety [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. Internal consistency in this study was excellent (α\u0026thinsp;=\u0026thinsp;0.93).\u003c/p\u003e\n\u003ch3\u003eSelf-Compassion Scale–Short Form\u003c/h3\u003e\n\u003cp\u003eThe Self-Compassion Scale\u0026ndash;Short Form [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e] comprises 12 items rated from 1 (almost never) to 5 (almost always). Six items are reverse scored. Total scores range from 12\u0026ndash;60, with higher values indicating greater self-compassion. The Arabic version validated in Saudi Arabia [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e] showed good reliability (α\u0026thinsp;=\u0026thinsp;0.87).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBrief Resilience Scale\u003c/h2\u003e \u003cp\u003eThe Brief Resilience Scale [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] includes 6 items rated from 1 (strongly disagree) to 5 (strongly agree), with three reverse-scored items. Total scores range from 6\u0026ndash;30, reflecting recovery capacity from stress. The Arabic validated version [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e] demonstrated good internal consistency (α\u0026thinsp;=\u0026thinsp;0.85).\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003ePatient Health Questionnaire (PHQ-9)\u003c/h3\u003e\n\u003cp\u003eThe PHQ-9 [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e] consists of 9 items reflecting DSM-IV criteria for depression, rated from 0 (not at all) to 3 (nearly every day), with total scores ranging from 0\u0026ndash;27. Higher scores indicate greater depressive severity. The Arabic version validated in Saudi Arabia [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e] was used. Internal consistency in this study was excellent (α\u0026thinsp;=\u0026thinsp;0.87).\u003c/p\u003e\n\u003ch3\u003eWHO-5 Well-being Index\u003c/h3\u003e\n\u003cp\u003eThe WHO-5 [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e] measures subjective well-being over the past two weeks using five items rated 0\u0026ndash;5. Raw scores are transformed to a 0\u0026ndash;100 scale, with higher scores indicating greater well-being. The scale has demonstrated validity in clinical samples [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. Internal consistency in this study was excellent (α\u0026thinsp;=\u0026thinsp;0.90).\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eGeneralized Anxiety Disorder Screener (GAD-7)\u003c/h2\u003e \u003cp\u003eThe GAD-7[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]includes 7 items assessing DSM-IV anxiety symptoms, rated 0\u0026ndash;3, yielding total scores between 0\u0026ndash;21. Higher scores reflect greater anxiety severity. The Arabic validated version [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] demonstrated excellent reliability in this sample (α\u0026thinsp;=\u0026thinsp;0.90).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eTranslation and Pilot Testing of the Fear of Progression Scale\u003c/h2\u003e \u003cp\u003eAll measures had validated Arabic versions except the Fear of Progression scale. This instrument was translated following World Health Organization guidelines [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Two bilingual professors translated the original English version into Arabic. Two independent translators, blinded to the original, conducted back-translation. After comparison and consensus, a final version was established.\u003c/p\u003e \u003cp\u003eThe translated scale was pilot tested in 55 patients with rheumatoid arthritis and fibromyalgia. Items were reported as clear and comprehensible. The Arabic FoP demonstrated moderate correlations with GAD-7 (r\u0026thinsp;=\u0026thinsp;0.43, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and PHQ-9 (r\u0026thinsp;=\u0026thinsp;0.39, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and showed good internal consistency (α\u0026thinsp;=\u0026thinsp;0.89). This validated version was used in the main study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eData analyses were conducted in R [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Descriptive statistics, skewness, and kurtosis were computed for all items and total scores.\u003c/p\u003e \u003cp\u003eNetwork analysis was performed using the bootnet package [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. A Gaussian graphical model was estimated using LASSO regularization [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] with EBIC model selection [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Network visualization was conducted using qgraph[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e]. Node redundancy was evaluated using the goldbricker procedure in the networktools package[41] .\u003c/p\u003e \u003cp\u003eNodes represented individual questionnaire items, and edges represented partial correlations after controlling for all other nodes [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The Fruchterman\u0026ndash;Reingold algorithm was applied to position strongly connected nodes closer together. Centrality was assessed using expected influence, and bridge expected influence was used to identify nodes linking clusters.\u003c/p\u003e \u003cp\u003eNetwork stability was evaluated using nonparametric bootstrapping with 1,000 samples and 95% confidence intervals. Correlation stability coefficients of \u0026ge;\u0026thinsp;0.70 were considered acceptable [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive Statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the descriptive statistics, including skewness and kurtosis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics, skewness, and kurtosis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRange\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.39\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWHO5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWell-being score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e14.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCS1R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.79\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCS4R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.32\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.54\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.66\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCS6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.31\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCS7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCS8R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCS9R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCS10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.75\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCS11R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSCS12R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-compassion score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e37.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.43\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.66\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRS2R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.61\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRS4R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBRS6R\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.72\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResilience score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e18.6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6\u0026ndash;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.03\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.72\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.81\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.45\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.97\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.97\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.65\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoP8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoP9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoP10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.87\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoP11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.25\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFoP12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026ndash;5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.34\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFear of disease progression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12\u0026ndash;60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-1.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e3.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.64\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.77\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.61\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.03\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.96\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.90\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.83\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.98\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePHQ9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.93\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.13\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDepression score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.43\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.35\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.45\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.22\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.11\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.87\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.01\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.18\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.96\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGAD7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.94\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAnxiety score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e10.04\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0\u0026ndash;21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2.05\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003cem\u003eNotes\u003c/em\u003e: SD\u0026thinsp;=\u0026thinsp;standard deviation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, skewness and kurtosis values fell within acceptable ranges, indicating an approximately normal distribution of the network nodes. Total scale scores were calculated for all variables. The mean depression score was 12.01 (SD\u0026thinsp;=\u0026thinsp;5.88, range\u0026thinsp;=\u0026thinsp;0\u0026ndash;27), with 56.6% of participants scoring at or above the clinical cut-off of 10. The mean anxiety score was 10.04 (SD\u0026thinsp;=\u0026thinsp;5.19, range\u0026thinsp;=\u0026thinsp;0\u0026ndash;21), and 62.37% of participants scored above the cut-off of 8.\u003c/p\u003e \u003cp\u003eThe mean fear of progression score was 46.5 (SD\u0026thinsp;=\u0026thinsp;10.8, range\u0026thinsp;=\u0026thinsp;12\u0026ndash;60); using a cut-off of 34, 85% of participants demonstrated dysfunctional levels of fear. The mean well-being score was 14.82 (SD\u0026thinsp;=\u0026thinsp;5.03, range\u0026thinsp;=\u0026thinsp;0\u0026ndash;25), with 30.7% of participants scoring below the cut-off of 13, indicating poor well-being. The mean self-compassion score was 37.7 (SD\u0026thinsp;=\u0026thinsp;5.19, range\u0026thinsp;=\u0026thinsp;12\u0026ndash;60), and the mean resilience score was 18.6 (SD\u0026thinsp;=\u0026thinsp;2.80, range\u0026thinsp;=\u0026thinsp;6\u0026ndash;30).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003ePsychological Network Structure\u003c/h2\u003e \u003cp\u003eRedundant nodes were examined using the goldbricker procedure. No node pairs exceeded the 25% redundancy threshold; therefore, all 51 nodes were retained in the network. Stability analyses indicated adequate robustness of the edge weights and centrality indices (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eVisual inspection of the network (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) revealed a clear clustering of psychological well-being nodes (resilience, self-compassion, and well-being items) and psychopathology nodes (depression, anxiety, and fear of progression). Despite this clustering, numerous cross-domain connections were observed, indicating substantial integration between protective and distress-related processes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eNegative Associations Between Well-being and Psychopathology\u003c/h2\u003e \u003cp\u003eThe strongest negative edges primarily linked depressive fatigue and hopelessness with resilience and well-being components. For example, feeling tired or having little energy (PHQ4) showed strong inverse associations with resilience indicators (e.g., the ability to recover from stress; BRS1, BRS3) and with feeling active and vigorous (WHO3). Similarly, depressed mood (PHQ2) was negatively connected with resilience (BRS1), and low interest (PHQ1) was inversely associated with perceived daily engagement (WHO5).\u003c/p\u003e \u003cp\u003eSelf-compassion items reflecting a balanced perspective and reduced self-criticism were also negatively connected with depressive self-evaluative symptoms. For instance, feeling bad about oneself (PHQ6) showed strong inverse associations with self-compassion components such as understanding disliked aspects of oneself (SCS2) and reduced feelings of inadequacy (SCS1R, SCS4R). These findings suggest that self-compassion may counteract negative self-evaluation within the depressive symptom cluster.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eInteractions Between Self-Compassion and Fear of Progression\u003c/h2\u003e \u003cp\u003eNotably, several negative edges linked lower self-compassion to fear of progression. Items reflecting social comparison and perceived isolation (e.g., SCS4R, SCS8R) were connected to fears of disease progression (FoP1) and anxiety before medical appointments (FoP2). Additionally, fear of relying on others for daily activities (FoP7) was negatively associated with resilience-related recovery capacity (BRS6R). These patterns suggest that lower self-compassion and resilience may intensify illness-related fears.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003eCentrality and Bridge Centrality\u003c/h2\u003e \u003cp\u003eCentrality indices are presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Expected influence values indicated that the most central nodes in the network were fear of severe medical treatments (FoP9), catastrophic anticipation (\u0026ldquo;being afraid that something awful might happen\u0026rdquo;; GAD7), intolerance toward disliked aspects of life (SCS12R), nervousness (GAD1), irritability (GAD6), and fear of being unable to pursue hobbies (FoP8). These nodes exhibited the strongest overall connections to other components in the network, suggesting that anticipatory threat and emotional reactivity occupy structurally influential positions within the psychological system of patients with immune-mediated diseases.\u003c/p\u003e \u003cp\u003eNotably, both generalized anxiety and illness-specific fear items appeared among the most central nodes, indicating substantial integration between diffuse anxiety processes and disease-related concerns. In addition, intolerance of unwanted life experiences (a reverse-scored self-compassion item) emerged as highly central, underscoring the potential relevance of experiential intolerance and self-critical tendencies within the broader network.\u003c/p\u003e \u003cp\u003eBridge centrality analyses (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) identified nodes that connected the psychological well-being cluster (resilience, self-compassion, and well-being) with the psychopathology cluster (depression, anxiety, and fear of progression). The strongest bridge nodes were catastrophic anticipation (GAD7), concentration difficulties (PHQ7), and resilience recovery capacity (BRS5).\u003c/p\u003e \u003cp\u003eCatastrophic anticipation (GAD7) served as a key connector between anxiety and illness-specific fears, whereas concentration difficulties (PHQ7) linked depressive symptoms with broader distress domains. Resilience (BRS5), reflecting the perceived ability to recover from difficult experiences, functioned as a protective bridge between the well-being and psychopathology clusters. This structural positioning suggests that resilience may play an integrative role within the network rather than operating independently of distress-related processes.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study examined the interrelations among fear of progression, depressive symptoms, anxiety symptoms, resilience, self-compassion, and well-being in a sample of Saudi patients with chronic immune-mediated diseases using a network analytic approach. By integrating threat-based processes and protective psychological resources within a single model, the findings contribute to a more comprehensive psychosomatic understanding of adaptation to chronic illness.\u003c/p\u003e \u003cp\u003eOne of the most prominent findings was the centrality of illness-related anticipatory fear, particularly fear of severe medical treatments and catastrophic expectations that \u0026ldquo;something awful might happen.\u0026rdquo; These nodes demonstrated strong connections with multiple affective and cognitive symptoms. From a psychosomatic perspective, this suggests that psychological adaptation to chronic immune-mediated disease may be organized around future-oriented threat appraisal.\u003c/p\u003e \u003cp\u003eIllness-related uncertainty is inherent in immune-mediated diseases, in which symptom flares, treatment side effects, and potential disability are realistic concerns. Fear of progression may therefore represent a psychologically meaningful response to ongoing biological unpredictability. However, when persistent and amplified, such fear may sustain broader emotional dysregulation. Rather than being secondary to depression or generalized anxiety, illness-related fear may function as an organizing component that integrates somatic awareness, cognitive appraisal, and affective arousal.\u003c/p\u003e \u003cp\u003eThis interpretation aligns with conceptual distinctions between FoP and generalized anxiety\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Whereas generalized anxiety involves diffuse worry, FoP is specifically anchored in illness trajectory. The network findings suggest that illness-specific fears may not merely coexist with anxiety and depression, but may also structurally interconnect them within the context of chronic disease.\u003c/p\u003e \u003cp\u003eNervousness and irritability also emerged as influential nodes. Irritability may reflect sustained physiological arousal associated with chronic stress and inflammatory processes. Emerging research suggests that inflammatory cytokines can influence mood regulation and emotional reactivity [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. In immune-mediated diseases, physical discomfort, fatigue, and uncertainty may interact with inflammatory pathways to heighten emotional volatility.\u003c/p\u003e \u003cp\u003eWithin the network, irritability may represent a behavioral expression of sustained anticipatory fear. Chronic hypervigilance toward disease progression may maintain heightened arousal, which in turn may manifest as nervousness and frustration. These processes may contribute to interpersonal strain and reduced quality of life, thereby reinforcing depressive cognitions.\u003c/p\u003e \u003cp\u003eConcentration difficulties emerged as a bridge node connecting the psychopathology and well-being clusters. Cognitive impairment in immune-mediated diseases may arise through multiple pathways, including inflammatory activity, fatigue, and depressive rumination. Positioned between clusters, concentration difficulties may reflect an interface between somatic and psychological processes.\u003c/p\u003e \u003cp\u003eFrom a psychosomatic perspective, cognitive symptoms may signal the convergence of biological and psychological strain. When attentional resources are compromised, patients may struggle to engage in valued activities or adaptive coping strategies, potentially amplifying distress.\u003c/p\u003e \u003cp\u003eResilience, specifically the perceived ability to recover from stress, functioned as a bridge component linking protective and distress domains. Rather than operating outside the symptom network, resilience appeared to be embedded within it. This suggests that adaptive recovery capacity may influence how activation spreads across the system.\u003c/p\u003e \u003cp\u003eResilience has been associated with improved outcomes in immune-mediated diseases\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Within a network framework, resilience may attenuate connections between fear-based nodes and depressive symptoms, potentially reducing network density. Although causal inferences cannot be drawn from cross-sectional data[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], the structural positioning of resilience supports psychosomatic models that emphasize regulatory flexibility as central to adaptation to chronic illness.\u003c/p\u003e \u003cp\u003eComponents reflecting low self-compassion, particularly intolerance toward disliked aspects of life, demonstrated notable connectivity. Chronic illness often involves functional limitations and changes in identity. Patients who respond to these changes with self-criticism or experiential avoidance may intensify emotional distress. Conversely, self-compassion may foster adaptive acceptance, thereby mitigating the amplification of fear and depressive self-evaluation.\u003c/p\u003e \u003cp\u003eThese findings align with research linking self-compassion to reduced depression and anxiety\u003c/p\u003e \u003cp\u003e[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Within chronic illness contexts, cultivating compassionate self-relating may buffer against shame and hopelessness associated with perceived physical decline.\u003c/p\u003e \u003cp\u003eThe findings have important implications for psychosomatic care. Interventions targeting illness-specific anticipatory fear may be particularly relevant. Traditional cognitive-behavioral approaches that focus solely on generalized anxiety or depressive symptoms may overlook the central role of fears related to disease progression.\u003c/p\u003e \u003cp\u003eIncorporating FoP-focused components, resilience training, and compassion-based strategies may provide a more integrative approach. By strengthening regulatory capacities and addressing threat-appraisal processes, such interventions may influence multiple interconnected symptoms.\u003c/p\u003e \u003cp\u003eThe study was conducted in Saudi Arabia, where cultural norms, religious beliefs, and family structures may shape illness perception and coping. Collective support systems may buffer distress, yet concerns about dependency and role disruption may intensify fear of progression. Future research should examine cultural moderators of network structure.\u003c/p\u003e \u003cp\u003eSeveral limitations warrant consideration. The cross-sectional design precludes causal interpretation, and network centrality does not imply temporal precedence. Longitudinal and intensive time-series designs are needed to clarify dynamic activation patterns. In addition, the sample included heterogeneous immune-mediated conditions, and disease-specific mechanisms may produce distinct network structures. Future studies should therefore examine condition-specific networks and incorporate biological markers of inflammation.\u003c/p\u003e \u003cp\u003eDespite these limitations, the study advances psychosomatic understanding by demonstrating that illness-related anticipatory fear occupies a structurally influential position within psychological adaptation to chronic immune-mediated disease, while resilience operates as a regulatory bridge.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eChronic immune-mediated diseases involve more than co-occurring psychiatric symptoms; they reflect dynamic interactions among illness-related uncertainty, affective processes, cognitive symptoms, and adaptive resources. By applying a network framework, this study highlights the central role of anticipatory fear and the embedded regulatory function of resilience. These findings support integrative psychosomatic models that emphasize the dynamic interplay between biological vulnerability and psychological regulation in adaptation to chronic illness.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEthical approval was obtained from the Scientific Research Ethics Committee, Faculty of Arts and Humanities, King Abdulaziz University, Saudi Arabia and conducted in accordance with the Declaration of Helsinki. Written informed consent was obtained from all participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003enot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have no conflicts of interest to declare that are relevant to the content of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Deanship of Scientific Research (DSR), at King Abdulaziz University, Jeddah, under grant no. (IPP:120-246-2025).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMogeda El Keshky: Conceptualization, Methodology, Data curation, Formal analysis, Investigation, Supervision, Visualization, Writing\u0026mdash;original draft, Funding acquisition.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Ahmed Samak: Methodology, Validation, Resources, Writing\u0026mdash;review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Nisreen Yachoub: Data curation, Resources, Writing\u0026mdash;review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis study was funded by the Deanship of Scientific Research (DSR), at King Abdulaziz University, Jeddah, under grant no. (IPP :120-246-2025). The authors acknowledge with thanks to the Deanship of Scientific Research (DSR), at King Abdulaziz University, Jeddah for their technical and financial support.\u003c/p\u003e\n"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eMarrie RA, Graff L, Walker JR, Fisk JD, Patten SB, Hitchon CA, et al. Effects of psychiatric comorbidity in immune-mediated inflammatory disease: Protocol for a prospective study. JMIR Res Protoc. 2018;7. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2196/resprot.8794\u003c/span\u003e\u003cspan address=\"10.2196/resprot.8794\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCheng L, Gao W, Xu Y, Yu Z, Wang W, Zhou J, et al. 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J Stat Softw. 2012;48. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18637/jss.v048.i04\u003c/span\u003e\u003cspan address=\"10.18637/jss.v048.i04\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eJones PJ, Ma R, McNally RJ. Bridge Centrality: A Network Approach to Understanding Comorbidity. Multivar Behav Res. 2021;56:353\u0026ndash;67. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1080/00273171.2019.1614898\u003c/span\u003e\u003cspan address=\"10.1080/00273171.2019.1614898\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"well-being, resilience, self-compassion, fear of progression, depression, anxiety, network analysis","lastPublishedDoi":"10.21203/rs.3.rs-9004507/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9004507/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eChronic immune-mediated diseases are characterized by persistent inflammation, fluctuating symptoms, and illness-related uncertainty, placing patients at elevated risk for psychological distress. Although depression and anxiety are highly prevalent, less is known about how illness-specific fears and protective psychological resources interact within individuals. This study applied a network approach to examine the interrelations among fear of progression, depression, anxiety, resilience, self-compassion, and well-being in patients with immune-mediated diseases.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional, multicenter study was conducted among 395 adult patients with physician-diagnosed immune-mediated diseases in Saudi Arabia. Participants completed validated Arabic versions of the PHQ-9, GAD-7, Fear of Progression Questionnaire\u0026ndash;Short Form, Brief Resilience Scale, Self-Compassion Scale\u0026ndash;Short Form, and WHO-5 Well-Being Index. A regularized Gaussian graphical model was estimated using LASSO with EBIC model selection. Centrality (expected influence) and bridge expected influence were examined. Network stability was evaluated using bootstrapping procedures.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eClinically significant depression was observed in 56.6% of participants, and clinically significant anxiety in 62.37%, while 85% reported dysfunctional fear of progression. The network revealed strong interconnections between anticipatory anxiety and illness-related fears. Fear of severe medical treatments and catastrophic anticipation emerged as highly central nodes. Resilience (the ability to recover from stress) functioned as a bridge between psychopathology and well-being clusters. Self-compassion components showed inverse associations with depressive self-evaluative symptoms.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe findings suggest that, in chronic immune-mediated disease, illness-related anticipatory fear may represent a core organizing process in psychological adaptation. Resilience appears to be embedded within the symptom network as a potential regulatory buffer. These results support integrative psychosomatic models emphasizing dynamic interactions among illness-related uncertainty, affective processes, and adaptive resources.\u003c/p\u003e","manuscriptTitle":"Psychological Adaptation to Chronic Immune-Mediated Disease: A Network Analysis of Fear of Progression, Psychopathology, and Positive Psychological Resources in a Saudi Multicenter Sample","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-19 12:00:45","doi":"10.21203/rs.3.rs-9004507/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-04-27T15:01:03+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-04-25T15:38:21+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"64120542308775562092696101328023379090","date":"2026-04-18T11:48:49+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"307591786047675918857501862121320971073","date":"2026-04-17T13:28:58+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"108583660472461153104421791485191765936","date":"2026-04-17T09:18:21+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-09T07:01:20+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-03-10T08:53:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-03-07T06:59:39+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-03-07T06:59:08+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Psychology","date":"2026-03-02T01:12:53+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-psychology","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"psyo","sideBox":"Learn more about [BMC Psychology](http://bmcpsychology.biomedcentral.com/)","snPcode":"","submissionUrl":"","title":"BMC Psychology","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"e631c8a5-f294-415f-b3f5-008bdba1e4d4","owner":[],"postedDate":"April 19th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-04-19T12:00:45+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-19 12:00:45","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9004507","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9004507","identity":"rs-9004507","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}