Implication for Nursing Approaches: Developing an Theoretical Framework for Patient-Centered Symptom Management in Hemodialysis Patients from the Perspective of Dual-Dimension to Enhancing and Mitigating Coping Strategies: A Cross-Sectional Study

Implication for Nursing Approaches: Developing an Theoretical Framework for Patient-Centered Symptom Management in Hemodialysis Patients from the Perspective of Dual-Dimension to Enhancing and Mitigating Coping Strategies: A Cross-Sectional Study | 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 Implication for Nursing Approaches: Developing an Theoretical Framework for Patient-Centered Symptom Management in Hemodialysis Patients from the Perspective of Dual-Dimension to Enhancing and Mitigating Coping Strategies: A Cross-Sectional Study Xutong ZHENG, Linyu XU, Aiping WANG This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6023205/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Symptom burden among hemodialysis patients significantly impacts their quality of life. Effective symptom management, supported by social support and coping strategies, is critical to improve patient outcomes. However, the role of social support, self-regulatory fatigue, and different coping mechanisms in patient-centered symptom management is not well understood. Methods A cross-sectional study using Partial Least Squares Structural Equation Modeling (PLS-SEM). Data were collected from multiple hemodialysis centers in various regions across China, ensuring a representative sample of diverse geographical areas. Participants were recruited through convenience sampling across different regions, ensuring broad demographic representation. This study used PLS-SEM to develop and validate a theoretical model that describes the relationships among social support, self-regulatory fatigue, adaptation, patient activation, and symptom burden. Results A total of 1,120 patients participated, with a mean age of 51.6 years (SD = 13.8), and 59.1% were male. The Partial Least Squares Structural Equation Modeling (PLS-SEM) results showed that social support had a significant positive effect on both patient activation (β = 0.209, p < 0.001) and adaptation (β = 0.472, p < 0.001), indicating higher levels of social support were associated with increased patient activation and adaptation. Self-regulatory fatigue had a significant negative effect on adaptation (β = -0.131, p < 0.001) but no significant effect on patient activation (β = -0.026, p = 0.455). Patient activation (β = -0.024, p = 0.019) and adaptation (β = -0.023, p = 0.011) both had significant negative effects on symptom burden, indicating that higher levels of activation and adaptation were linked to lower symptom burden. Mediation analysis revealed that social support indirectly reduced symptom burden through both adaptation (β = -0.011, p = 0.011) and patient activation (β = -0.005, p = 0,032). Patient activation demonstrated greater importance in symptom management compared to adaptation based on the importance-performance analysis. Conclusions This study reveals that social support significantly enhances both patient activation and adaptation, leading to a reduction in symptom burden among hemodialysis patients. Self-regulatory fatigue, however, negatively affects adaptation but does not have a significant impact on patient activation. The dual coping strategies—adaptation (passive) and patient activation (proactive)—mediate the relationship between social support and symptom burden, with patient activation showing greater importance in symptom management. These findings emphasize the importance of enhancing social support, reducing self-regulatory fatigue, and fostering duel coping strategies to effectively alleviate the symptom burden in hemodialysis patients. hemodialysis patient-centered care symptom management social support self-regulatory fatigue coping strategies patient activation adaptation partial least squares structural equation modeling cross-sectional study Figures Figure 1 Figure 2 Figure 3 1 Introduction As the population ages and rates of metabolic syndromes and other chronic illnesses rise, the prevalence of Chronic Kidney Disease (CKD) is also increasing, marking it as a significant global health concern. In 2019, CKD accounted for 41.54 million disability-adjusted life years (DALYs) and contributes substantially to cardiovascular disease-related mortality and DALYs annually [ 1 , 2 ]. CKD's position among global causes of death is rising, predicted to be the fifth by 2040 [ 3 , 4 ]. In line with the United Nations Sustainable Development Goals, reducing CKD mortality is essential for achieving a one-third reduction in premature deaths from non-communicable diseases by 2030 [ 5 ]. Limited kidney sources and the constraints of peritoneal dialysis mean hemodialysis remains the key life-sustaining treatment for end-stage renal disease (ESRD) patients [ 6 ]. Although early stages of Chronic Kidney Disease (CKD) involve the majority of patients, stage 5 CKD and dialysis notably impact disability-adjusted life years (YLDs), accounting for 40% and 22% of CKD YLDs in 2017, respectively. Hemodialysis imposes a significant economic burden compared to other chronic disease treatments, with patients facing direct costs and indirect losses from absenteeism, disability, and premature death [ 7 – 9 ]. While immediate cost reductions in direct medical expenses are challenging, promoting patient rehabilitation and social reintegration can mitigate these economic impacts. Hemodialysis patients exhibit diverse symptoms due to the complex pathophysiological nature of their condition, with over 50% experiencing pain, fatigue, itching, and constipation, among other symptoms like bone pain, insomnia, and emotional disorders [ 10 , 11 ]. High symptom burdens can decrease health-related quality of life, increase hospitalizations, and raise mortality risks [ 12 – 15 ]. Prioritizing the alleviation of these symptoms to enhance physiological function and social rehabilitation is crucial for improving the lives of maintenance hemodialysis patients [ 16 ]. The KDIGO Consensus Conference has highlighted that symptom assessment and management are crucial components of quality care for patients with end-stage renal disease, establishing symptom management as a research priority for the chronic kidney disease population [ 17 ]. There is a need to focus on the effectiveness of symptom management strategies, including their impact on outcomes most relevant to patients, such as overall symptom burden, physical function, and health-related quality of life [ 18 ]. Guidelines and consensus statements emphasize the need for symptom management to improve health outcomes [ 17 – 20 ]; (3) the importance of a patient-centered approach, considering patients' values, preferences, and wishes in determining treatment and care plans [ 21 – 23 ]; (4) the importance of the biopsychosocial medical model: symptom assessment and management strategies should consider the biological, psychological, and social factors of patients [ 17 , 24 ]. Effective symptom management in hemodialysis is hindered by significant barriers at healthcare and patient levels. At the healthcare level, there's a lack of awareness among medical professionals about the importance of symptom management, leading to underestimation of symptom severity [ 10 , 25 – 28 ]. The dispersed provision of services across multidisciplinary teams results in continuity gaps and access difficulties [ 29 , 30 ]. Additionally, current models often neglect a patient-centered approach, prioritizing lab results over patient-reported symptoms and failing to address psychological and social needs adequately [ 17 , 23 , 24 , 31 , 32 ].At the patient level, barriers include concealment of symptoms due to fear, lack of knowledge, or low health literacy, leading to poor communication and self-management [ 28 , 33 , 34 ]. Geographic accessibility also affects treatment adherence and survival [ 35 ]. The 2023 KDIGO Consensus Conference highlighted the need for symptom management models that account for national conditions [ 17 ]. Addressing the biological, psychological, and social dimensions of patient health effectively and efficiently, with limited resources, is crucial for reducing symptom burden. The MRC Framework for Complex Interventions emphasizes the importance of developing a program theory to guide complex interventions [ 36 ]. However, many studies on symptom management for hemodialysis patients lack a suitable theory, complicating the understanding of intervention choices and limiting research replicability and scalability. Existing theoretical models have several limitations: (1) They focus on antecedent variables rather than symptom burden itself [ 32 , 37 – 43 ]; (2) They address only one aspect of symptom management, such as a single symptom or outcome [ 44 , 45 ]; (3) They fail to explain how external factors affect intermediary variables, reducing explanatory power [ 32 , 46 , 47 ]; (4) They focus on relationships between symptoms and outcomes rather than pathways to reduce symptom burden [ 32 , 37 , 38 , 48 ]; (5) Their complexity limits practical clinical use [ 32 , 37 , 38 ]; and (6) They do not adjust for covariables [ 38 , 39 , 41 , 44 , 47 – 49 ]. This study addresses these gaps by developing a person-centered symptom management model based on empirical evidence, initially validated using Partial Least Squares Structural Equation Modeling (PLS-SEM), which is ideal for early theory development as it does not require a strong theoretical foundation [ 50 ]. 2 Methods 2.1 Formation of theoretical hypotheses - meta-ethnography This study’s theoretical model was constructed through a meta-ethnographic synthesis of 31 qualitative studies on hemodialysis symptom management, which integrated medical, psychological, and social factors affecting patient experiences and coping mechanisms [ 51 ]. Reciprocal and refutational translations helped synthesize second-order constructs into a unified theoretical framework using a line-of-argument approach, which captured both shared patterns and unique aspects from the studies to guide hypothesis development [ 51 , 52 ]. This comprehensive framework identified essential constructs and their interrelationships, such as social support, chronic self-regulatory cognitive burnout, adaptation, and patient activation, influencing symptom burden, outlined in Table 1 . The method of theoretical substruction was used to derive operational and measurable concepts from these constructs, enhancing the model’s practicality and applicability [ 53 , 54 ]. These relationships underpin the study’s hypotheses. Table 1 Initial hypothesis from meta-ethnography Number of the synthesis and hypothesis Statement synthesis with supporting reference Verifiable hypothesis 1 Support from family members, peers, healthcare providers could strength patients’ ability for physical, psychological and social adaptions [ 27 , 132 , 133 , 145 – 153 ]. Social support→adaption (+) 2 Support from family members, peers, healthcare providers could strength patients’ confidence, skill and knowledge for complex symptom management [ 27 , 133 , 150 – 152 , 154 – 156 ]. Social support→ patient activation (+) 3 Chronic self-regulatory cognitive burnout resulted from physical and mental exhaustion combined from the necessity for constant vigilance and strict adherence to treatment protocols may cause the decline in patients’ ability for physical, psychological and social adaptions [ 130 , 131 , 150 , 153 , 157 , 158 ]. Self-regulatory cognitive burnout→adaption (-) 4 Chronic self-regulatory cognitive burnout resulted from physical and mental exhaustion combined from the necessity for constant vigilance and strict adherence to treatment protocols may cause the decline in patients’ confidence, skill and knowledge for complex symptom management [ 130 , 131 , 149 – 151 , 153 , 158 , 159 ]. Self-regulatory cognitive burnout→patient activation (-) 5 Improved level of patients’ ability for physical, psychological and social adaptions could lower the level of symptom burden [ 131 , 133 , 134 , 149 , 160 , 161 ]. adaption→ symptom burden (-) 6 Improved level of patients’ confidence, skill and knowledge for complex symptom management could lower the level of symptom burden [ 33 , 131 , 133 , 134 , 145 , 156 , 160 , 162 ]. patient activation→ symptom burden (-) 2.2 Study design and study setting This study is a cross-sectional survey that the reporting of results strictly adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (StroBE) statement in terms of research design, data collection, data analysis, and reporting processes (see Supplementary Material 1) [ 55 ]. Convenient sampling was utilized for this study. To enhance the representativeness of the sample, we selected 1–2 cities from each of the northern, southern, western, eastern, and central regions of China for data collection. In northern China, Shenyang, Benxi, and Dalian in Liaoning Province were chosen; in southern China, Ningde, Fuzhou, and Quanzhou in Fujian Province; in western China, Qujing in Yunnan; in eastern China, Huai'an and Suqian in Jiangsu; and in the central region, Taiyuan and Changzhi in Shaanxi. Data collection occurred from August to October 2024. 2.3 Eligibility Criteria Inclusion criteria: (1) Age: Participants must be 18 or older to ensure legal consent and stable chronic conditions. (2) Dialysis frequency: Participants must have undergone regular hemodialysis for at least three months, with a minimum of one session per week, ensuring stable dialysis protocols. (3) Consent: Participants must provide informed consent and voluntarily agree to participate. Exclusion criteria: (1) Pending transplant or alternative dialysis: Individuals scheduled for a kidney transplant or planning peritoneal dialysis within a month are excluded to avoid confounding results. (2) Severe comorbid conditions: Individuals with severe conditions like active malignancies or psychiatric disorders are excluded, as these could distort symptom management outcomes, thereby skewing the interpretation of model. (3) Communication barriers: Individuals unable to give informed consent or complete surveys due to cognitive impairments, severe hearing loss, or language barriers are excluded. 2.4 Patient recruitment and data collection Patient recruitment was facilitated by data coordinators (nurses or interns) at each sub-center. Coordinators underwent standardized training via video conference before distributing the questionnaires. To assess face validity, we pre-filled questionnaires for 30 patients from diverse demographics, making adjustments based on feedback, such as simplifying medical terminology and refining response options [ 56 ]. Data collection involved one-on-one consultations where coordinators explained the survey purpose, duration, and details. Participants who consented were given either a paper questionnaire or a QR code linked to an online survey via Questionnaire Star. Coordinators were on-call to answer questions without influencing responses. For patients without smartphones, paper versions were used, with coordinators immediately checking and correcting any issues. Data was uploaded to the Questionnaire Star platform for centralized management. 2.5 Ethical consideration This study was approved by the Medical Science Research Ethics Committee of the First Affiliated Hospital of China Medical University, with the ethical approval number 2024 − 633. 2.6 Instruments 2.6.1 General demographic data and covariates related to symptom burden The design of this part of the questionnaire was derived from a thorough literature review and discussions with experts. General demographic data primarily includes: age, gender, educational level, whether belonging to a minority group, whether living alone, and income level. Demographic data related to dialysis includes: dialysis frequency and distance to the dialysis center. 2.6.2 Modified Perceived Social Support Scale (PSSS) The Perceived Social Support Scale (PSSS) assesses perceived social support across three dimensions: family, friend, and other supports, with 12 items scored from 1–7, yielding a total score range of 12–84, where higher scores indicate more support [ 57 ]. To address the absence of healthcare provider support in the original scale, we added a dimension from the Chronic Illness Resources Survey [ 58 ], including four items related to healthcare team support. Content validity of this addition was confirmed with expert evaluations, achieving excellent S-CVI and I-CVI scores of 1.Exploratory factor analysis of the combined scale suggested a four-factor structure: family, friend, and healthcare provider support. Confirmatory factor analysis supported this structure with good model fit indices: CFI = 0.982, TLI = 0.978, SRMR = 0.02, and RMSEA = 0.0636, confirming strong structural validity [ 59 , 60 ]. Details of the analysis are in the Supplementary Material 2. 2.6.3 Self-Regulatory Fatigue Scale The Self-Regulatory Fatigue Scale (SRF-S), devised by Nes et al. in 2013 includes dimensions of cognitive, emotional, and behavioral control, comprising 16 items [ 61 ]. It employs a Likert 5-point rating scale, where each item is scored from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating greater self-regulatory fatigue. To align the measurement model with our theoretical presuppositions, we used the cognitive control dimension for theoretical construction and validation. 2.6.4 Coping and Adaptation Processing Scale Short Form (CAPS-SF) The Coping and Adaptation Processing Scale Short Form (CAPS-SF), developed by nursing theorist Sr. Callista Roy and her team based on the 47-item CAPS scale, is founded on the Coping Adaptation Processing theory and measures the concept of "coping adaptation processes"[ 62 ]. The 2016 revised CAPS-SF consists of 15 items scored on a Likert 4-point scale from "never" to "always" scoring 1–4, with three items reverse-scored [ 63 ]. 2.6.5 Patient Activation Measure 13 (PAM-13) The Patient Activation Measure 13 (PAM-13), a shortened version of the original scale [ 64 ], uses a 5-point Likert scale, with scores ranging from 0 to 100, where higher scores indicate greater patient activation. Since the PAM-13 had not been psychometrically tested for Chinese hemodialysis patients, we conducted an analysis. Exploratory factor analysis identified three dimensions, which were confirmed by confirmatory factor analysis with good fit indices: CFI = 0.964, TLI = 0.954, SRMR = 0.029, and RMSEA = 0.08, meeting established thresholds [ 59 , 60 ]. This confirms the scale's strong structural validity in this population. Detailed analysis is provided in Supplementary Material 2. 2.6.6 Adapted Dialysis Symptom Index (DSI) The Dialysis Symptom Index (DSI), initially developed by Weisbord et al. at the University of Pittsburgh in 2004, assesses the symptom distress of hemodialysis patients, including 25 physical symptoms (such as insomnia, fatigue, decreased sexual desire, decreased appetite, constipation, nausea, vomiting, etc.) and 5 psychological symptoms (such as worry, tension, anxiety, etc.) [ 65 ]. Hao Yanhua expanded it to include symptom frequency and severity, forming the adapted version of the DSI. Each symptom is measured across four dimensions: occurrence, frequency, severity, and distress level, with presence/absence scored as yes/no and the remaining three dimensions scored using a Likert 5-point scale. Previous research indicates that the scale's overall Cronbach’s α coefficient is 0.943. 2.7 Identification and Measures to Control Common Method Bias Common method bias (CMB) arises when the same method or tool is used to measure multiple variables, leading to distorted correlations [ 66 ]. This is common in self-reported surveys, especially cross-sectional studies [ 67 , 68 ]. To control CMB, we ensured anonymity in the questionnaire to reduce social desirability bias and varied question types (such as single-choice, matrix scale items, and multilevel rating sliders) to avoid monotony and order effects [ 66 , 69 ]. During data analysis, we used Harman's single factor method and exploratory factor analysis to detect CMB. If the first factor accounts for less than 50% of the variance, CMB is considered not significant (Fuller et al., 2016). 2.8 Data analysis method 2.8.1 Construction and modification of measurement models We used item parceling to construct measurement models, grouping multiple items from the same scale into a new indicator to enhance communality, reduce error, and improve indicator quality [ 70 , 71 ]. Based on exploratory analysis, we used the mean of items within each dimension as a substitute indicator [ 70 , 71 ]. In the initial structural equation model, we found one item parcel from the CAPS-SF with a factor loading below 0.4, indicating weak performance. Given the reflective nature of the model, this item parcel was removed [ 72 – 74 ]. 2.8.2 K-means clustering to identify patient symptom categories We applied K-means clustering to categorize patient symptom scores for two main reasons: (1) The total symptom burden scores exhibited significant skewness, and transforming the continuous variable into a categorical one helps reduce the impact of outliers, improving model robustness [ 75 , 76 ]. (2) Using categorical variables enhances model interpretability when selecting predictive factors for symptom burden [ 77 , 78 ]. Since the Dialysis Symptom Index lacks predefined cut-off values, K-means clustering was used to determine these values by segmenting samples based on data similarity. After standardizing the scale scores, the algorithm iteratively refined clusters, with cut-off values derived from the centroids and distribution characteristics of each group [ 77 , 78 ]. 2.8.3 Descriptive statistics and preliminary analysis We use IBM SPSS 29.0 for the descriptive statistical analysis of the sample. For categorical variables (such as gender, age), we report frequencies and percentages. In preparation for conducting structural equation modeling, it is necessary to perform a correlation analysis of the core variables involved in the model [ 79 ]. This step helps to evaluate the relationships among the variables and ensures that there is sufficient correlation to support the hypothesized pathways in the model. By analyzing the correlations, we can preliminarily identify potential multicollinearity issues and assess the feasibility of including these variables in the SEM framework [ 79 ]. 2.8.4 Constructing structural equation models using partial least squares Partial Least Squares Structural Equation Modeling (PLS-SEM) uses an iterative algorithm to maximize the explained variance of latent variables, prioritizing predictive capability over global model fit [ 50 , 79 ]. It is ideal for small samples, non-normally distributed data, and exploratory research with immature theory, as it focuses on causal relationships and model prediction. PLS-SEM is well-suited for complex, incomplete theoretical frameworks and is used with SMART-PLS 4 for modeling. In PLS-SEM, the structural equation model consists of a measurement model (inner model) and a structural model (outer model). The measurement model assessment includes evaluating reliability, convergent validity, and discriminant validity [ 50 , 79 , 80 ]. For reliability, we assess internal consistency using Cronbach's alpha, McDonald's omega, and composite reliability. These indicators ensure the measurement model's reliability, with McDonald's omega providing a more accurate reliability estimate by considering the varying contributions of observed variables to latent variables. Composite reliability, calculated from factor loadings and measurement errors, serves as the primary reliability indicator, reflecting the consistency of latent variables. It is particularly suitable for models with uneven loadings. In exploratory research, reliability values between 0.6 and 0.7 are acceptable, while values between 0.7 and 0.9 indicate satisfactory reliability [ 50 , 79 , 80 ]. The measurement model's validity is evaluated through convergent and discriminant validity. Convergent validity is assessed by the loadings of item parcels and the average variance extracted (AVE), with loadings > 0.708 and AVE > 50% indicating adequate communality and variance explanation [ 50 , 79 , 80 ]. Discriminant validity, ensuring distinctiveness between constructs, is evaluated using the square root of AVE. The Fornell-Larcker criterion states that good discriminant validity is achieved if the square root of AVE for a construct exceeds its inter-correlations with other constructs. Additionally, the heterotrait-monotrait (HTMT) ratio is used, with an HTMT > 0.9 indicating a lack of discriminant validity [ 79 , 81 ]. The structural model in PLS-SEM reflects causal relationships between latent factors. Key considerations include collinearity, path coefficient significance, and predictive relevance. Collinearity is assessed using the Variance Inflation Factor (VIF), with VIF > 5 indicating potential issues, prompting factor removal or consolidation. Path significance is determined via bootstrap resampling (5000 iterations), with significant paths having confidence intervals that do not include 0. Predictive relevance is assessed using Q 2 , where values greater than 0 indicate good predictive relevance [ 50 , 79 ]. 2.9 Importance-Performance Map Analysis (IPMA) This study uses Importance-Performance Map Analysis (IPMA) to assess the impact of latent variables on the target variable. IPMA identifies variables that are important but underperforming by analyzing both their path coefficients (importance) and mean values (performance). Importance is determined by path coefficients from Partial Least Squares Path Modeling (PLS-SEM), while performance is measured by the average latent variable scores. The X-axis represents importance, and the Y-axis represents performance. The quadrants help prioritize areas for improvement: top-right for high-performing, important variables; top-left for crucial but underperforming variables; bottom-right for low-impact, high-performance areas; and bottom-left for variables with minimal impact and poor performance [ 79 ]. 3 Results 3.1 Sample characteristics We collected a total of 1,120 samples. Among the survey participants, 59.1% (662 individuals) were male and 40.9% (458 individuals) were female. The age distribution was primarily middle-aged and elderly, with participants under 18 years old making up only 0.8% (9 individuals), 18–25 years old accounting for 1.7% (19 individuals), 26–30 years old 2.1% (24 individuals), 31–35 years old 6.8% (76 individuals), 36–40 years old 9.8% (110 individuals), 41–50 years old 23.9% (268 individuals), 51–60 years old 26.1% (292 individuals), and those over 60 years old comprising 28.7% (322 individuals). The educational level of most respondents was junior college or below, accounting for 87.2% (977 individuals); 11.1% (124 individuals) held a bachelor’s degree, and 1.7% (19 individuals) held a graduate degree or higher. Ethnic minorities (in China, all groups other than Han are considered minorities) made up 7.8% (87 individuals) of the sample, while the Han majority constituted 92.2% (1033 individuals). Those living alone accounted for 16.4% (184 individuals), while those not living alone made up 83.6% (936 individuals). The majority of respondents were at a lower economic level, with 55.1% (617 individuals) having a monthly income of less than 3,000 yuan, 21.3% (238 individuals) earning between 3,000 to 3,999 yuan, 9.2% (103 individuals) between 4,000 to 4,999 yuan, and 14.5% (162 individuals) earning 5,000 yuan or more. The majority of respondents underwent dialysis three times per week, accounting for 84.2% (943 individuals); 7.0% (78 individuals) twice per week, 2.9% (32 individuals) once per week, and 6.0% (67 individuals) five times every two weeks. Regarding travel time to the dialysis center, 41.5% (465 individuals) took less than 30 minutes, 24.9% (279 individuals) took 30–60 minutes, and 33.6% (376 individuals) took more than 60 minutes. 3.2 Result of preliminary analysis Factor analysis revealed that the first component explained 32.75% of the variance, indicating no significant common method bias [ 66 , 82 ]. Pearson’s bivariate correlations among key variables showed that self-regulated cognitive fatigue was negatively correlated with adaption (r = -0.213, p < 0.01) and social support (r = -0.173, p < 0.01), but positively correlated with symptom class (r = 0.169, p 0.05). Adaption was positively correlated with patient activation (r = 0.180, p < 0.01) and social support (r = 0.495, p 0.05). Patient activation was positively correlated with social support (r = 0.214, p < 0.01) and negatively with symptom class (r = -0.086, p 0.05). 3.3 Results of K-Means clustering We applied K-means clustering to divide patients into two groups based on their total symptom burden scores. The choice of two clusters was informed by the 'elbow' method, which showed diminishing returns in variance explanation beyond two clusters [ 83 , 84 ]. Cluster 1 had lower symptom severity, with patients experiencing mild to moderate symptoms less frequently, while Cluster 2 consisted of patients with high symptom severity and frequency. The cutoff value was 63, with cluster centers at 111.51 and 16.32 for the first and second groups, respectively. The clusters stabilized after 11 iterations. Analysis of variance confirmed significant differences between the groups, with an F-value of 1858.364 and a P-value < 0.01, indicating distinct symptom experiences [ 77 ]. Details on the clustering process and iterations are available in Supplementary Material 3. 3.4 Measurement model The measurement model exhibited no multicollinearity issues, with VIFs ranging between 1.0 to 5.0, confirming acceptable levels. Discriminant validity was established through the square root of the average variance extracted (AVE) values, which were higher than the correlations with other variables as shown in Table 2 . As shown in Fig. 1, the HTMT ratios were all below the 0.9 threshold, indicating good discriminant validity between constructs. As in Table 3 , the model's reliability and validity were confirmed with AVE, composite reliability (CR), Cronbach's alpha, and McDonald's omega (ω), all meeting or exceeding recommended thresholds. AVE values indicated substantial variance explanation by latent constructs (> 50%) [ 73 ], with values such as 0.656 for social support, 0.802 for patient activation, and 0.818 for adaption. CR values above 0.70, Cronbach's alpha values ranging from 0.824 to 0.889, and McDonald’s omega values from 0.829 to 0.891 all supported strong internal consistency [ 79 ]. Item loadings were all significant (p < 0.05) and above the 0.70 threshold, affirming item reliability with loadings for social support items from 0.757 to 0.870, patient activation from 0.853 to 0.919, and adaption from 0.862 to 0.926 [ 79 ]. Table 2 Square Root of Construct's AVE and Its Correlation with Any Other Construct Self-regulated cognitive fatigue adaption Patient activation Social support Symptom class Self-regulated cognitive fatigue 1.000 a adaption -0.213** b 0.904 Patient activation -0.063 0.180** 0.896 Social support -0.173** 0.495** 0.214** 0.835 Symptom class 0.169** -0.08** -0.086* -0.056 1.000 a The diagonally bolded number is the square root of the construct AVE b * indicates significance at the 0.05 level and ** indicates significance at the 0.01 level Table 3 Psychometric Properties of Measurement Models Latent variable name Item package Outer model loading AVE C.R. Cronbach’s α McDonald's ω VIF Social support SS1 0.838 ** 0.656 ** 0.884 0.824 0.829 2.023 SS2 0.769 ** 2.040 SS3 0.870 ** 2.669 SS4 0.757** 1.602 Patient activation PAM1 0.853 ** 0.802 ** 0.924 0.877 0.880 2.142 PAM2 0.919 ** 3.048 PAM3 0.913 ** 2.480 adaption ADA1 0.924 ** 0.818 ** 0.931 0.889 0.891 3.109 ADA2 0.926 ** 3.169 ADA3 0.862 ** 2.107 3.5 Structural model: hypothesized model testing First, as indicated in Table 4 and Fig. 2, the results of the direct effects of covariates on symptom class are as follows: Income level had no significant effect on symptom class (β = 0.016, p > 0.05). Minority status (β = 0.005, p > 0.05), gender (β =-0.001, p > 0.05), education level (β = 0.008, p > 0.05), and solitude (β = 0.026, p > 0.05) showed no significant effects on symptom class. Among dialysis-related variables, time travel to dialysis center (β = -0.004, p > 0.05) and dialysis frequency (β = 0.007, p > 0.05) did not show significant effects. For the core variables, the results showed that social support had a significant positive effect on both patient activation (β = 0.209, p < 0.001) and adaption (β = 0.472, p < 0.001), indicating that higher social support is associated with better patient activation and adaption. Self-regulated cognitive fatigue had a significant negative effect on adaption (β = -0.131, p 0.05), meaning higher cognitive fatigue weakens adaption but does not significantly impact patient activation. Patient activation had a significant negative effect on symptom class (β = -0.024, p < 0.05), suggesting that better patient activation is associated with fewer or less severe symptoms. Adaption also had a significant negative effect on symptom class (β = -0.023, p < 0.01), indicating that individuals who adapt better experience fewer or less severe symptoms. The Q² values provide insights into the model’s predictive relevance for each endogenous variable, gauging how well observed values are reconstructed by the model and its constructs [ 79 ]. In our analysis, adaption showed strong predictive relevance with a Q² of 0.211, suggesting the model effectively captures the variance within this construct. Patient activation displayed a lower predictive relevance, documented at a Q² of 0.034. Furthermore, the symptom class also exhibited substantial predictive capacity with a Q² value of 0.041, indicating that our model reliably predicts symptom severity based on the analyzed factors. Next, the results of the mediating effects were presented in Table 5 . Social support had a significant negative indirect effect on symptom class through patient activation (β = -0.005, p < 0.05), and through adaption (β = -0.011, p < 0.01), suggesting that social support can significantly reduce symptoms through these two mediating variables. Self-regulated cognitive fatigue had a significant indirect effect on symptom class through adaption (β = 0.003, p 0.05). Table 4 Direct Path Coefficients and Significant Levels Independent variable Path Coefficient (β) with 95% CI Standard deviation T statistics P values Q2 for dependent variable Dependent variable: Symptom class 0.041 Dialysis_frequency 0.007 [-0.009, 0.022 ] 0.008 0.885 0.376 Income_level 0.016 [-0.003, 0.036] 0.010 1.592 0.112 Time travel to dialysis center -0.004 [-0.021, 0.012] 0.008 0.418 0.676 ★adaption -0.023 [-0.040, -0.005] 0.009 2.536 0.011 age 0.008 [-0.008, 0.025] 0.008 0.966 0.334 education_level 0.008 [-0.009, 0.029] 0.010 0.785 0.432 gender -0.001 [-0.035, 0.035] 0.018 0.075 0.940 minority 0.005 [-0.062, 0.061] 0.032 0.157 0.875 ★ a patient activation -0.024 [-0.044, -0.005] 0.010 2.340 0.019 solitude 0.026 [-0.022, 0.069] 0.024 1.099 0.272 Dependent variable: adaption 0.211 ★Social support 0.472 [0.409, 0.527] 0.030 15.825 0.000 ★Self-regulated cognitive fatigue -0.131 [-0.182, -0.076] 0.027 4.903 0.000 Dependent variable: patient activation 0.034 ★Social support 0.209 [0.125, 0.286] 0.042 5.009 0.000 ★Self-regulated cognitive fatigue -0.026 [-0.092, 0.045] 0.035 0.747 0.455 a ‘★’ means core variable in our study Table 5 Indirect effect (mediating effect) path coefficients and significant levels Path of the model Path Coefficient (β) with 95% confidence interval Standard deviation T statistics P values Social support->patient activation -> Symptom_class -0.005, [-0.010, -0.001] 0.002 2.139 0.032 Social support->adaption -> Symptom_class -0.011, [-0.019, -0.002] 0.004 2.531 0.011 Self-regulated cognitive fatigue->patient activation -> Symptom_class 0.001, [-0.001, 0.003] 0.001 0.633 0.526 Self-regulated cognitive fatigue -> adaption -> Symptom_class 0.003, [0.001, 0.006] 0.001 2.130 0.033 3.6 Result of importance–performance map analysis As indicated in Fig. 3, the importance-performance map analysis reveals that healthcare support is the most influential factor for both adaption and patient activation, with importance values of 0.154 for adaption and 0.068 for patient activation, and a performance score of 79.487. Family support and support from relatives and colleagues also significantly influence these outcomes, with family support having slightly higher importance for adaption (0.147) compared to patient activation (0.065), both with performance scores above 70. Support from relatives and colleagues shows similar importance, but with moderate performance scores. Conversely, self-regulated cognitive fatigue adversely affects both adaption and patient activation, with importance values of -0.131 and − 0.026, respectively, and a performance score of 48.254, suggesting that reducing cognitive fatigue could improve these outcomes. Additional analysis shows solitude as a critical covariate for symptom class due to its substantial impact on symptom severity, despite a low performance score of -16.429. Income and education levels show lower importance and performance, indicating less impact on symptom class. Factors such as dialysis frequency and socioeconomic status have minimal influence. Core variables related to adaption and patient activation show varied influence but generally perform well in the model, suggesting indirect pathways influence symptom severity through these variables, despite their minor direct impact. 4 Discussion This study utilizes a large sample and rigorous statistical methods, combined with preliminary literature review results, to construct a patient-centered symptom management model. This model uses symptom categories as outcome variables and includes two coping strategies for managing symptom burden as mediating variables: proactive coping (patient activation) and passive coping (adaptation). These coping strategies are strengthened by enhanced social support, where passive coping (adaptation) is reduced by self-depletion, whereas proactive coping (patient activation) is not. This theoretical model helps nurses and other healthcare workers understand the patient-centered symptom management approach, and future interventions can be developed based on this model. Symptom science is integral to nursing, focusing on diagnosing and treating human responses to health issues. Previous models like the Theory of Unpleasant Symptoms [ 85 ] and the Dynamic Symptom model [ 86 ] have examined symptom attributes and their changes, providing frameworks for nurses to manage symptoms in hemodialysis patients. However, these models often lack specificity in intervention strategies and fail to address crucial interactions between healthcare providers and family support systems, as highlighted by the Symptom Science Model 2.0. This model outlines the relationships among symptoms, phenotypes, behaviors, and clinical practices but does not detail patient-centered intervention pathways [ 87 ]. Our developed symptom management model for hemodialysis patients addresses these limitations to some extent. The 2023 KDIGO Controversies Conference suggested Multilevel approaches to enable symptom assessment and management, advocating for person-centered care at the renal team level and patient empowerment management at the individual level [ 17 ]. Symptom Science Model 2.0 also emphasizes the importance of focusing on patient-centered experiences or journeys, underscoring the critical role of the patient themselves in the symptom management process [ 87 ]. This is logical, as in clinical practice, both patient symptom assessment and multidisciplinary team interventions for symptom management need to be patient-centered. We used systematic qualitative research literature focusing on hemodialysis patients to form a preliminary theoretical construction, ensuring that the concepts and relationships in our proposed model are centered around the patient. Symptom Science Model 2.0 also stresses considering determinants of health (such as age, gender, education level) [ 87 ]; in this model, we included these as covariates and adjusted the outcome variable, symptom burden, accordingly. The results showed that, after adjusting for covariates, the core variable relationships remained valid, confirming their robustness. Researchers developing intervention protocols must assess the impact of these covariates on symptom burden and consider cultural and situational factors. This study's findings differ from most previous research, particularly regarding income and education. Unlike studies outside China [ 88 – 92 ], income level did not significantly affect symptom burden, likely due to China's healthcare policies providing nearly full reimbursement for dialysis. Similarly, education level showed no significant effect compared with other studies [ 93 , 94 ], possibly due to the sample's homogeneity, with 87.2% of patients having a high school education or lower. Future studies should use quota sampling to explore education’s influence in the Chinese context. While prior research identified higher symptom burdens in older adults [ 95 , 96 ], certain racial groups [ 97 – 99 ], and women [ 100 – 102 ], this study did not observe these trends, suggesting that interactions between these factors may affect symptom burden collectively. Future research should investigate these interactions to guide health policy. The study also examined dialysis-related factors like dialysis frequency and distance to centers. While some studies suggest more frequent dialysis improves outcomes [ 103 , 104 ], our findings align with studies showing no significant effect [ 105 ], highlighting the need for systematic reviews. No significant impact was found for travel time to dialysis centers, contrary to other studies [ 106 , 107 ]. However, distance may affect referral rates rather than clinical outcomes [ 108 ], suggesting that remote interventions could help address transportation challenges. The symptom burden of our study has been simplified into two distinct clusters. The clinical relevance of these clusters is profound, as they enable healthcare providers to tailor interventions more precisely. For example, patients in cluster 1 (low symptom burden) may benefit from preventive strategies that focus on lifestyle modifications and regular monitoring to maintain their relatively stable condition. Conversely, patients in Cluster 2 (high symptom burden) might require more intensive management approaches, potentially including adjustments to their dialysis regimen, enhanced pharmacological interventions, and more comprehensive support services to address their complex symptomatology. Linking these symptom clusters to potential interventions not only allows for more personalized patient care but also provides a framework for ongoing research into the effectiveness of targeted treatment strategies based on symptom burden. Future studies could explore the impact of specific interventions within these clusters to further refine treatment approaches and improve quality of life for hemodialysis patients. In this model, the mediating variables are adaptation and patient activation, representing the passive and active coping mechanisms of patients in symptom management, respectively. Patient activation refers to the skills, knowledge, and confidence related to a patient's willingness and ability to manage their health [ 109 ]. For the dialysis population, the Centers for Medicare & Medicaid Services (CMS) in the United States have included patient activation as a quality metric in the Kidney Care Choices model [ 110 ]. Previous studies have found that patient activation is associated with health outcomes such as quality of life and symptom burden [ 111 , 112 ]. Our study not only further confirms this correlation (directionally) but also proposes a theoretical model on how patient activation as a mediating variable can reduce symptom burden. Compared to another mediating variable in the model—adaptation—patient activation performs better on the importance-performance map (all three dimensions score above 77, while the highest score for adaptation is 68.9), suggesting that patient activation should be a variable of focus when developing intervention measures. Based on earlier literature, we hypothesized that social support acts as an enhancing variable and self-depletion as a weakening variable for patient activation. However, unexpectedly, the direct pathway from self-depletion to patient activation and the indirect pathway leading to symptom burden were both insignificant, indicating that self-depletion does not lead to a decrease in patient activation levels nor does it result in higher symptom burden. However, whether patient activation levels naturally deplete remains a question. One of the previous studies have dynamically assessed the activation levels of chronic disease patients and found that compared to the baseline, some patients experienced a decline in activation levels [ 113 ]. Since this study is cross-sectional, it currently cannot answer this question. Future studies need to be longitudinal to clarify the overall trajectory of patient activation, the heterogeneity of trajectories, and the factors causing category transitions at each time point. It is also possible to use a Random-Intercept cross-lagged panel design with covariate adjustment to longitudinally track the association between cognitive depletion and patient activation to further validate or refute the relationship between these two variables [ 114 ]. Regardless, nurses need to carefully and dynamically assess the activation levels of patients when developing interventions or intervening, to early identify, respond to, and manage changes in patient activation levels. Social support can enhance patient activation levels, and we have illustrated this using an importance-performance map with patient activation as the outcome variable. We found that support from healthcare professionals performs best in terms of importance and performance. Support from friends has high importance but low performance. Support from family, friends, and colleagues has similar importance but moderate performance. Therefore, when considering the development of interventions, clinical nurses need to focus on mobilizing support from healthcare workers while also enhancing support from friends, as it is a crucial part of social support. However, research indicates that the global distribution of healthcare workers for kidney diseases is extremely uneven, and there is a shortage in many countries [ 115 , 116 ]. Therefore, local health resources and cultural backgrounds should be considered when developing interventions. If healthcare worker support is scarce, other sources of support should be mobilized, and interventions could also consider enhancing support from patients' friends, colleagues, and family. Since hemodialysis patients typically receive treatment at fixed dialysis centers, healthcare workers could consider developing peer education interventions [ 117 , 118 ], selecting key opinion leaders from the same patient group to help others make symptom management decisions [ 119 , 120 ]. Tailored family-centered intervention plans could also be developed after thoroughly assessing patients' family health and support levels, enabling family members to assist effectively in symptom management [ 121 ]. It is important to note that studies have found a dose-response relationship between patient activation and related clinical outcomes [ 110 ]. However, this relationship has a plateau phase, meaning that beyond a certain level, further increases in patient activation do not lead to significant improvements. Therefore, while patient activation can improve patient outcomes, future research needs to clarify the relationship between patient activation and core outcomes in hemodialysis patients. The minimal clinically important difference should be calculated to determine the necessary level of patient activation, and intervention doses and timings should be adjusted accordingly to allocate medical resources efficiently [ 122 , 123 ]. Moreover, it should be noted that this study only provides a broad framework. Future healthcare workers need to use standardized intervention development frameworks, such as the MRC framework [ 36 ], guided by evidence-based evidence, combined with patient benefits and their own intervention conditions, to select the most appropriate intervention components. Preliminary experiments should be conducted on patients to develop rigorous and standardized intervention measures. This study has identified that social support can reduce patient symptom burden by enhancing patient activation levels. This pathway is consistent with the buffering model hypothesis of social support, which suggests that social support can increase an individual's coping abilities, thereby reducing stress events or adverse outcomes [ 124 , 125 ]. Previous research has found that social support can reduce patient symptom burden [ 126 – 128 ], but few have considered patient activation as a mediating variable. Our study enriches and develops the application of the social support theory buffering effect model in the field of reducing symptom burden, enhancing the explanatory power of social support theory in symptom burden research. Cognitive fatigue does not decrease patient activation levels, possibly because patient activation itself is a spontaneous and proactive regulatory mechanism [ 111 , 113 , 129 ]. Social support enhances adaptability in hemodialysis patients, reducing symptom burden. Adaptation, a passive coping mechanism, involves physiological and psychosocial adjustments, with patients balancing medical guidelines against maintaining normalcy in their social and cultural practices [ 130 – 132 ]. This balance creates a dynamic equilibrium where patients make health-aligned choices that reflect their cultural and social identities [ 131 , 133 , 134 ]. Most research on this adaptation is qualitative, with limited quantitative studies and few specific scales measuring adaptation, highlighting a need for clearer definitions and tailored measurement tools. Importance-performance mapping shows that the strongest support for enhancing adaptation comes from healthcare professionals, while the weakest comes from friends. To strengthen adaptation, healthcare workers should focus on health education, symptom management skills, and psychological support for lifestyle adjustments (Saccaro et al., 2024). Multidisciplinary interventions should customize plans to enhance patient decision-making in symptom management [ 135 , 136 ]. Nurses should also address potential misinformation and strengthen patients’ ability to seek reliable symptom assistance through workshops and online programs, boosting confidence and adaptability [ 137 – 140 ]. This study reveals that ego depletion worsens symptom burden by diminishing patients' adaptability but does not significantly impact patient activation levels. Adaptability, which depends heavily on cognitive resources for managing changes and stress, is vulnerable when these resources are depleted [ 141 – 143 ]. In contrast, patient activation, rooted in intrinsic motivation and long-term health management habits, remains stable despite depleted psychological resources, likely supported by external (medical teams, family) and internal factors (beliefs) [ 110 , 111 , 144 ]. The study also found no direct link between self-regulated cognitive fatigue and patient activation, suggesting that factors like resilience, mental health, or patient-physician interactions might be more influential. Thus, while ego depletion directly reduces adaptability and increases symptom burden, activation levels are buffered by more enduring influences. Future research can further explore this phenomenon in several ways to deepen understanding: Firstly, longitudinal studies (such as random intercept cross-lagged, difference-in-differences, cross-lagged network analysis) could be used to examine the dynamic impact of ego depletion on adaptability and activation levels, observing whether there are different long-term trends. Secondly, modifying variables, such as psychological resilience, could be added to structural equation models to test whether these factors can modulate the impact of resource depletion, especially the protective effect on patient adaptability. Additionally, different dimensions of activation levels (such as cognitive and behavioral dimensions) could be distinguished to explore whether these dimensions are differentially affected by ego depletion. Last but not least, research could also focus on designing intervention strategies, targeting patient adaptability with implementation intention plans to create specific coping plans for patients managing symptoms, enhancing the psychological resources needed for adaptability, and reducing the negative effects of ego depletion on symptom burden. While this study specifically addresses symptom management among hemodialysis patients in China, the underlying mechanisms of social support and dual coping strategies may be relevant to other chronic kidney disease stages, patients with different chronic conditions, and even cancer patients, across various cultural and social contexts. Further research is needed to explore how these findings can be adapted and generalized to these broader populations, taking into account the unique clinical, psychological, and sociocultural factors that influence symptom management in diverse healthcare environments 4.1 Limitations This study employed convenience sampling across multiple regions in China, enabling quicker data collection but potentially limiting the representativeness and generalizability to all hemodialysis patients, which may introduce sampling bias. Moreover, while controlling for various covariates, unaccounted residual confounding factors, such as individual differences in coping strategies and disease duration, could affect the study's findings. Also, the design did not fully mitigate interviewer or social desirability biases in self-reported measures. Future research should use anonymous and automated data collection methods to reduce these biases. 5 Conclusion This study underscores the complex interactions between coping strategies, social support, and symptom management in hemodialysis patients. It demonstrates that symptoms can be managed through proactive and passive coping strategies, with social support enhancing both. The findings also indicate that self-regulatory fatigue impedes adaptation, highlighting the need for interventions that enhance psychological resilience and support adaptability. From a practical standpoint, the results call for healthcare systems to provide multidisciplinary support to boost patient activation and strengthen coping mechanisms. Tailored interventions should address varying patient needs for more personalized care. Future studies should explore these aspects longitudinally and develop interventions considering individual resilience and health literacy. Addressing the symptom burden in hemodialysis patients not only improves their life quality but also alleviates the economic strain on healthcare systems, suggesting that a holistic approach integrating medical, psychological, and social factors is crucial for sustainable outcomes. This study offers a foundational framework for patient-centered symptom management strategies. Declarations Ethics approval and consent to participate: This study was approved by the Medical Science Research Ethics Committee of the First Affiliated Hospital of China Medical University, with the ethical approval number 2024-633. Informed consent have been gained from all participants. Participation was voluntary, and individuals could freely withdraw from any phase of the process without constraints. The data analysis was rigorously conducted to maintain the anonymity, privacy, and confidentiality of the participating patients. Clinical trial number Not applicable. Consent for publication: Not applicable. Availability of data and material: Research data will be shared with reasonable requests. Funding: Not applicable. Competing interests: None Acknowledgements: Not applicable. ORCID Xutong Zheng: 0000-0002-9236-1764 Author Contribution Xutong ZHENG takes charge of Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Software, Supervision, Validation, Visualization, Roles/Writing - original draft. Linyu XU takes charge of Project administration, Resources, Software, Supervision, Validation, and Visualization. Aiping WANG takes charge of Conceptualization, review & editing. References Bikbov B, Purcell CA, Levey AS, Smith M, Abdoli A, Abebe M, et al. Global, regional, and national burden of chronic kidney disease, 1990–2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395:709–33. Ying M, Shao X, Qin H, Yin P, Lin Y, Wu J, et al. 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J Ren Care. 2014;40:205–15. Tao Y, Liu T, Li P, Lv A, Zhuang K, Ni C. Self-management experiences of haemodialysis patients with self-regulatory fatigue: A phenomenological study. J Adv Nurs. 2023;79:2250–8. Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial1STROBEchecklistcrosssectional.docx SupplementaryMaterial2EFACFA.docx SupplementaryMaterial3normalitytestanditeration.docx Supplementarymaterial4effectsandperformancesvalue.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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16:30:37","extension":"docx","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":16022,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial4effectsandperformancesvalue.docx","url":"https://assets-eu.researchsquare.com/files/rs-6023205/v1/ff3124a90ded84669907baaa.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Implication for Nursing Approaches: Developing an Theoretical Framework for Patient-Centered Symptom Management in Hemodialysis Patients from the Perspective of Dual-Dimension to Enhancing and Mitigating Coping Strategies: A Cross-Sectional Study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eAs the population ages and rates of metabolic syndromes and other chronic illnesses rise, the prevalence of Chronic Kidney Disease (CKD) is also increasing, marking it as a significant global health concern. In 2019, CKD accounted for 41.54\u0026nbsp;million disability-adjusted life years (DALYs) and contributes substantially to cardiovascular disease-related mortality and DALYs annually [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. CKD's position among global causes of death is rising, predicted to be the fifth by 2040 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In line with the United Nations Sustainable Development Goals, reducing CKD mortality is essential for achieving a one-third reduction in premature deaths from non-communicable diseases by 2030 [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLimited kidney sources and the constraints of peritoneal dialysis mean hemodialysis remains the key life-sustaining treatment for end-stage renal disease (ESRD) patients [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. Although early stages of Chronic Kidney Disease (CKD) involve the majority of patients, stage 5 CKD and dialysis notably impact disability-adjusted life years (YLDs), accounting for 40% and 22% of CKD YLDs in 2017, respectively. Hemodialysis imposes a significant economic burden compared to other chronic disease treatments, with patients facing direct costs and indirect losses from absenteeism, disability, and premature death [\u003cspan additionalcitationids=\"CR8\" citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. While immediate cost reductions in direct medical expenses are challenging, promoting patient rehabilitation and social reintegration can mitigate these economic impacts.\u003c/p\u003e \u003cp\u003eHemodialysis patients exhibit diverse symptoms due to the complex pathophysiological nature of their condition, with over 50% experiencing pain, fatigue, itching, and constipation, among other symptoms like bone pain, insomnia, and emotional disorders [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. High symptom burdens can decrease health-related quality of life, increase hospitalizations, and raise mortality risks [\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Prioritizing the alleviation of these symptoms to enhance physiological function and social rehabilitation is crucial for improving the lives of maintenance hemodialysis patients [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe KDIGO Consensus Conference has highlighted that symptom assessment and management are crucial components of quality care for patients with end-stage renal disease, establishing symptom management as a research priority for the chronic kidney disease population [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. There is a need to focus on the effectiveness of symptom management strategies, including their impact on outcomes most relevant to patients, such as overall symptom burden, physical function, and health-related quality of life [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Guidelines and consensus statements emphasize the need for symptom management to improve health outcomes [\u003cspan additionalcitationids=\"CR18 CR19\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]; (3) the importance of a patient-centered approach, considering patients' values, preferences, and wishes in determining treatment and care plans [\u003cspan additionalcitationids=\"CR22\" citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]; (4) the importance of the biopsychosocial medical model: symptom assessment and management strategies should consider the biological, psychological, and social factors of patients [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEffective symptom management in hemodialysis is hindered by significant barriers at healthcare and patient levels. At the healthcare level, there's a lack of awareness among medical professionals about the importance of symptom management, leading to underestimation of symptom severity [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. The dispersed provision of services across multidisciplinary teams results in continuity gaps and access difficulties [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Additionally, current models often neglect a patient-centered approach, prioritizing lab results over patient-reported symptoms and failing to address psychological and social needs adequately [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].At the patient level, barriers include concealment of symptoms due to fear, lack of knowledge, or low health literacy, leading to poor communication and self-management [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Geographic accessibility also affects treatment adherence and survival [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. The 2023 KDIGO Consensus Conference highlighted the need for symptom management models that account for national conditions [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Addressing the biological, psychological, and social dimensions of patient health effectively and efficiently, with limited resources, is crucial for reducing symptom burden.\u003c/p\u003e \u003cp\u003eThe MRC Framework for Complex Interventions emphasizes the importance of developing a program theory to guide complex interventions [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. However, many studies on symptom management for hemodialysis patients lack a suitable theory, complicating the understanding of intervention choices and limiting research replicability and scalability. Existing theoretical models have several limitations: (1) They focus on antecedent variables rather than symptom burden itself [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan additionalcitationids=\"CR38 CR39 CR40 CR41 CR42\" citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]; (2) They address only one aspect of symptom management, such as a single symptom or outcome [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]; (3) They fail to explain how external factors affect intermediary variables, reducing explanatory power [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]; (4) They focus on relationships between symptoms and outcomes rather than pathways to reduce symptom burden [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]; (5) Their complexity limits practical clinical use [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]; and (6) They do not adjust for covariables [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e, \u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. This study addresses these gaps by developing a person-centered symptom management model based on empirical evidence, initially validated using Partial Least Squares Structural Equation Modeling (PLS-SEM), which is ideal for early theory development as it does not require a strong theoretical foundation [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e].\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Formation of theoretical hypotheses - meta-ethnography\u003c/h2\u003e \u003cp\u003eThis study\u0026rsquo;s theoretical model was constructed through a meta-ethnographic synthesis of 31 qualitative studies on hemodialysis symptom management, which integrated medical, psychological, and social factors affecting patient experiences and coping mechanisms [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Reciprocal and refutational translations helped synthesize second-order constructs into a unified theoretical framework using a line-of-argument approach, which captured both shared patterns and unique aspects from the studies to guide hypothesis development [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. This comprehensive framework identified essential constructs and their interrelationships, such as social support, chronic self-regulatory cognitive burnout, adaptation, and patient activation, influencing symptom burden, outlined in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The method of theoretical substruction was used to derive operational and measurable concepts from these constructs, enhancing the model\u0026rsquo;s practicality and applicability [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. These relationships underpin the study\u0026rsquo;s hypotheses.\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\u003eInitial hypothesis from meta-ethnography\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of the synthesis and hypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStatement synthesis with supporting reference\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVerifiable hypothesis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSupport from family members, peers, healthcare providers could strength patients\u0026rsquo; ability for physical, psychological and social adaptions [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e, \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e, \u003cspan additionalcitationids=\"CR146 CR147 CR148 CR149 CR150 CR151 CR152\" citationid=\"CR145\" class=\"CitationRef\"\u003e145\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e153\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSocial support\u0026rarr;adaption (+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSupport from family members, peers, healthcare providers could strength patients\u0026rsquo; confidence, skill and knowledge for complex symptom management [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e, \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e, \u003cspan additionalcitationids=\"CR151\" citationid=\"CR150\" class=\"CitationRef\"\u003e150\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR152\" class=\"CitationRef\"\u003e152\u003c/span\u003e, \u003cspan additionalcitationids=\"CR155\" citationid=\"CR154\" class=\"CitationRef\"\u003e154\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e156\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSocial support\u0026rarr; patient activation (+)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChronic self-regulatory cognitive burnout resulted from physical and mental exhaustion combined from the necessity for constant vigilance and strict adherence to treatment protocols may cause the decline in patients\u0026rsquo; ability for physical, psychological and social adaptions [\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e, \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e, \u003cspan citationid=\"CR150\" class=\"CitationRef\"\u003e150\u003c/span\u003e, \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e153\u003c/span\u003e, \u003cspan citationid=\"CR157\" class=\"CitationRef\"\u003e157\u003c/span\u003e, \u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e158\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-regulatory cognitive burnout\u0026rarr;adaption (-)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eChronic self-regulatory cognitive burnout resulted from physical and mental exhaustion combined from the necessity for constant vigilance and strict adherence to treatment protocols may cause the decline in patients\u0026rsquo; confidence, skill and knowledge for complex symptom management [\u003cspan citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e, \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e, \u003cspan additionalcitationids=\"CR150\" citationid=\"CR149\" class=\"CitationRef\"\u003e149\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR151\" class=\"CitationRef\"\u003e151\u003c/span\u003e, \u003cspan citationid=\"CR153\" class=\"CitationRef\"\u003e153\u003c/span\u003e, \u003cspan citationid=\"CR158\" class=\"CitationRef\"\u003e158\u003c/span\u003e, \u003cspan citationid=\"CR159\" class=\"CitationRef\"\u003e159\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSelf-regulatory cognitive burnout\u0026rarr;patient activation (-)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImproved level of patients\u0026rsquo; ability for physical, psychological and social adaptions could lower the level of symptom burden [\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e, \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e, \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e, \u003cspan citationid=\"CR149\" class=\"CitationRef\"\u003e149\u003c/span\u003e, \u003cspan citationid=\"CR160\" class=\"CitationRef\"\u003e160\u003c/span\u003e, \u003cspan citationid=\"CR161\" class=\"CitationRef\"\u003e161\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eadaption\u0026rarr; symptom burden (-)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImproved level of patients\u0026rsquo; confidence, skill and knowledge for complex symptom management could lower the level of symptom burden [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e, \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e, \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e, \u003cspan citationid=\"CR145\" class=\"CitationRef\"\u003e145\u003c/span\u003e, \u003cspan citationid=\"CR156\" class=\"CitationRef\"\u003e156\u003c/span\u003e, \u003cspan citationid=\"CR160\" class=\"CitationRef\"\u003e160\u003c/span\u003e, \u003cspan citationid=\"CR162\" class=\"CitationRef\"\u003e162\u003c/span\u003e].\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003epatient activation\u0026rarr; symptom burden (-)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study design and study setting\u003c/h2\u003e \u003cp\u003eThis study is a cross-sectional survey that the reporting of results strictly adheres to the Strengthening the Reporting of Observational Studies in Epidemiology (StroBE) statement in terms of research design, data collection, data analysis, and reporting processes (see Supplementary Material 1) [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Convenient sampling was utilized for this study. To enhance the representativeness of the sample, we selected 1\u0026ndash;2 cities from each of the northern, southern, western, eastern, and central regions of China for data collection. In northern China, Shenyang, Benxi, and Dalian in Liaoning Province were chosen; in southern China, Ningde, Fuzhou, and Quanzhou in Fujian Province; in western China, Qujing in Yunnan; in eastern China, Huai'an and Suqian in Jiangsu; and in the central region, Taiyuan and Changzhi in Shaanxi. Data collection occurred from August to October 2024.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Eligibility Criteria\u003c/h2\u003e \u003cp\u003eInclusion criteria: (1) Age: Participants must be 18 or older to ensure legal consent and stable chronic conditions. (2) Dialysis frequency: Participants must have undergone regular hemodialysis for at least three months, with a minimum of one session per week, ensuring stable dialysis protocols. (3) Consent: Participants must provide informed consent and voluntarily agree to participate. Exclusion criteria: (1) Pending transplant or alternative dialysis: Individuals scheduled for a kidney transplant or planning peritoneal dialysis within a month are excluded to avoid confounding results. (2) Severe comorbid conditions: Individuals with severe conditions like active malignancies or psychiatric disorders are excluded, as these could distort symptom management outcomes, thereby skewing the interpretation of model. (3) Communication barriers: Individuals unable to give informed consent or complete surveys due to cognitive impairments, severe hearing loss, or language barriers are excluded.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Patient recruitment and data collection\u003c/h2\u003e \u003cp\u003ePatient recruitment was facilitated by data coordinators (nurses or interns) at each sub-center. Coordinators underwent standardized training via video conference before distributing the questionnaires. To assess face validity, we pre-filled questionnaires for 30 patients from diverse demographics, making adjustments based on feedback, such as simplifying medical terminology and refining response options [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e]. Data collection involved one-on-one consultations where coordinators explained the survey purpose, duration, and details. Participants who consented were given either a paper questionnaire or a QR code linked to an online survey via Questionnaire Star. Coordinators were on-call to answer questions without influencing responses. For patients without smartphones, paper versions were used, with coordinators immediately checking and correcting any issues. Data was uploaded to the Questionnaire Star platform for centralized management.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Ethical consideration\u003c/h2\u003e \u003cp\u003eThis study was approved by the Medical Science Research Ethics Committee of the First Affiliated Hospital of China Medical University, with the ethical approval number 2024\u0026thinsp;\u0026minus;\u0026thinsp;633.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Instruments\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.6.1 General demographic data and covariates related to symptom burden\u003c/h2\u003e \u003cp\u003eThe design of this part of the questionnaire was derived from a thorough literature review and discussions with experts. General demographic data primarily includes: age, gender, educational level, whether belonging to a minority group, whether living alone, and income level. Demographic data related to dialysis includes: dialysis frequency and distance to the dialysis center.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.6.2 Modified Perceived Social Support Scale (PSSS)\u003c/h2\u003e \u003cp\u003eThe Perceived Social Support Scale (PSSS) assesses perceived social support across three dimensions: family, friend, and other supports, with 12 items scored from 1\u0026ndash;7, yielding a total score range of 12\u0026ndash;84, where higher scores indicate more support [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. To address the absence of healthcare provider support in the original scale, we added a dimension from the Chronic Illness Resources Survey [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], including four items related to healthcare team support. Content validity of this addition was confirmed with expert evaluations, achieving excellent S-CVI and I-CVI scores of 1.Exploratory factor analysis of the combined scale suggested a four-factor structure: family, friend, and healthcare provider support. Confirmatory factor analysis supported this structure with good model fit indices: CFI\u0026thinsp;=\u0026thinsp;0.982, TLI\u0026thinsp;=\u0026thinsp;0.978, SRMR\u0026thinsp;=\u0026thinsp;0.02, and RMSEA\u0026thinsp;=\u0026thinsp;0.0636, confirming strong structural validity [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Details of the analysis are in the Supplementary Material 2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.6.3 Self-Regulatory Fatigue Scale\u003c/h2\u003e \u003cp\u003eThe Self-Regulatory Fatigue Scale (SRF-S), devised by Nes et al. in 2013 includes dimensions of cognitive, emotional, and behavioral control, comprising 16 items [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. It employs a Likert 5-point rating scale, where each item is scored from 1 (strongly disagree) to 5 (strongly agree), with higher scores indicating greater self-regulatory fatigue. To align the measurement model with our theoretical presuppositions, we used the cognitive control dimension for theoretical construction and validation.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.6.4 Coping and Adaptation Processing Scale Short Form (CAPS-SF)\u003c/h2\u003e \u003cp\u003eThe Coping and Adaptation Processing Scale Short Form (CAPS-SF), developed by nursing theorist Sr. Callista Roy and her team based on the 47-item CAPS scale, is founded on the Coping Adaptation Processing theory and measures the concept of \"coping adaptation processes\"[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. The 2016 revised CAPS-SF consists of 15 items scored on a Likert 4-point scale from \"never\" to \"always\" scoring 1\u0026ndash;4, with three items reverse-scored [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e2.6.5 Patient Activation Measure 13 (PAM-13)\u003c/h2\u003e \u003cp\u003eThe Patient Activation Measure 13 (PAM-13), a shortened version of the original scale [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e], uses a 5-point Likert scale, with scores ranging from 0 to 100, where higher scores indicate greater patient activation. Since the PAM-13 had not been psychometrically tested for Chinese hemodialysis patients, we conducted an analysis. Exploratory factor analysis identified three dimensions, which were confirmed by confirmatory factor analysis with good fit indices: CFI\u0026thinsp;=\u0026thinsp;0.964, TLI\u0026thinsp;=\u0026thinsp;0.954, SRMR\u0026thinsp;=\u0026thinsp;0.029, and RMSEA\u0026thinsp;=\u0026thinsp;0.08, meeting established thresholds [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. This confirms the scale's strong structural validity in this population. Detailed analysis is provided in Supplementary Material 2.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e2.6.6 Adapted Dialysis Symptom Index (DSI)\u003c/h2\u003e \u003cp\u003eThe Dialysis Symptom Index (DSI), initially developed by Weisbord et al. at the University of Pittsburgh in 2004, assesses the symptom distress of hemodialysis patients, including 25 physical symptoms (such as insomnia, fatigue, decreased sexual desire, decreased appetite, constipation, nausea, vomiting, etc.) and 5 psychological symptoms (such as worry, tension, anxiety, etc.) [\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e]. Hao Yanhua expanded it to include symptom frequency and severity, forming the adapted version of the DSI. Each symptom is measured across four dimensions: occurrence, frequency, severity, and distress level, with presence/absence scored as yes/no and the remaining three dimensions scored using a Likert 5-point scale. Previous research indicates that the scale's overall Cronbach\u0026rsquo;s α coefficient is 0.943.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Identification and Measures to Control Common Method Bias\u003c/h2\u003e \u003cp\u003eCommon method bias (CMB) arises when the same method or tool is used to measure multiple variables, leading to distorted correlations [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. This is common in self-reported surveys, especially cross-sectional studies [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. To control CMB, we ensured anonymity in the questionnaire to reduce social desirability bias and varied question types (such as single-choice, matrix scale items, and multilevel rating sliders) to avoid monotony and order effects [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e]. During data analysis, we used Harman's single factor method and exploratory factor analysis to detect CMB. If the first factor accounts for less than 50% of the variance, CMB is considered not significant (Fuller et al., 2016).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e2.8 Data analysis method\u003c/h2\u003e \u003cdiv id=\"Sec17\" class=\"Section3\"\u003e \u003ch2\u003e2.8.1 Construction and modification of measurement models\u003c/h2\u003e \u003cp\u003eWe used item parceling to construct measurement models, grouping multiple items from the same scale into a new indicator to enhance communality, reduce error, and improve indicator quality [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Based on exploratory analysis, we used the mean of items within each dimension as a substitute indicator [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. In the initial structural equation model, we found one item parcel from the CAPS-SF with a factor loading below 0.4, indicating weak performance. Given the reflective nature of the model, this item parcel was removed [\u003cspan additionalcitationids=\"CR73\" citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section3\"\u003e \u003ch2\u003e2.8.2 K-means clustering to identify patient symptom categories\u003c/h2\u003e \u003cp\u003eWe applied K-means clustering to categorize patient symptom scores for two main reasons: (1) The total symptom burden scores exhibited significant skewness, and transforming the continuous variable into a categorical one helps reduce the impact of outliers, improving model robustness [\u003cspan citationid=\"CR75\" class=\"CitationRef\"\u003e75\u003c/span\u003e, \u003cspan citationid=\"CR76\" class=\"CitationRef\"\u003e76\u003c/span\u003e]. (2) Using categorical variables enhances model interpretability when selecting predictive factors for symptom burden [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e]. Since the Dialysis Symptom Index lacks predefined cut-off values, K-means clustering was used to determine these values by segmenting samples based on data similarity. After standardizing the scale scores, the algorithm iteratively refined clusters, with cut-off values derived from the centroids and distribution characteristics of each group [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e, \u003cspan citationid=\"CR78\" class=\"CitationRef\"\u003e78\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section3\"\u003e \u003ch2\u003e2.8.3 Descriptive statistics and preliminary analysis\u003c/h2\u003e \u003cp\u003eWe use IBM SPSS 29.0 for the descriptive statistical analysis of the sample. For categorical variables (such as gender, age), we report frequencies and percentages. In preparation for conducting structural equation modeling, it is necessary to perform a correlation analysis of the core variables involved in the model [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. This step helps to evaluate the relationships among the variables and ensures that there is sufficient correlation to support the hypothesized pathways in the model. By analyzing the correlations, we can preliminarily identify potential multicollinearity issues and assess the feasibility of including these variables in the SEM framework [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section3\"\u003e \u003ch2\u003e2.8.4 Constructing structural equation models using partial least squares\u003c/h2\u003e \u003cp\u003ePartial Least Squares Structural Equation Modeling (PLS-SEM) uses an iterative algorithm to maximize the explained variance of latent variables, prioritizing predictive capability over global model fit [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. It is ideal for small samples, non-normally distributed data, and exploratory research with immature theory, as it focuses on causal relationships and model prediction. PLS-SEM is well-suited for complex, incomplete theoretical frameworks and is used with SMART-PLS 4 for modeling.\u003c/p\u003e \u003cp\u003eIn PLS-SEM, the structural equation model consists of a measurement model (inner model) and a structural model (outer model). The measurement model assessment includes evaluating reliability, convergent validity, and discriminant validity [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. For reliability, we assess internal consistency using Cronbach's alpha, McDonald's omega, and composite reliability. These indicators ensure the measurement model's reliability, with McDonald's omega providing a more accurate reliability estimate by considering the varying contributions of observed variables to latent variables. Composite reliability, calculated from factor loadings and measurement errors, serves as the primary reliability indicator, reflecting the consistency of latent variables. It is particularly suitable for models with uneven loadings. In exploratory research, reliability values between 0.6 and 0.7 are acceptable, while values between 0.7 and 0.9 indicate satisfactory reliability [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe measurement model's validity is evaluated through convergent and discriminant validity. Convergent validity is assessed by the loadings of item parcels and the average variance extracted (AVE), with loadings\u0026thinsp;\u0026gt;\u0026thinsp;0.708 and AVE\u0026thinsp;\u0026gt;\u0026thinsp;50% indicating adequate communality and variance explanation [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR80\" class=\"CitationRef\"\u003e80\u003c/span\u003e]. Discriminant validity, ensuring distinctiveness between constructs, is evaluated using the square root of AVE. The Fornell-Larcker criterion states that good discriminant validity is achieved if the square root of AVE for a construct exceeds its inter-correlations with other constructs. Additionally, the heterotrait-monotrait (HTMT) ratio is used, with an HTMT\u0026thinsp;\u0026gt;\u0026thinsp;0.9 indicating a lack of discriminant validity [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e, \u003cspan citationid=\"CR81\" class=\"CitationRef\"\u003e81\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe structural model in PLS-SEM reflects causal relationships between latent factors. Key considerations include collinearity, path coefficient significance, and predictive relevance. Collinearity is assessed using the Variance Inflation Factor (VIF), with VIF\u0026thinsp;\u0026gt;\u0026thinsp;5 indicating potential issues, prompting factor removal or consolidation. Path significance is determined via bootstrap resampling (5000 iterations), with significant paths having confidence intervals that do not include 0. Predictive relevance is assessed using Q\u003csup\u003e2\u003c/sup\u003e, where values greater than 0 indicate good predictive relevance [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec21\" class=\"Section2\"\u003e \u003ch2\u003e2.9 Importance-Performance Map Analysis (IPMA)\u003c/h2\u003e \u003cp\u003eThis study uses Importance-Performance Map Analysis (IPMA) to assess the impact of latent variables on the target variable. IPMA identifies variables that are important but underperforming by analyzing both their path coefficients (importance) and mean values (performance). Importance is determined by path coefficients from Partial Least Squares Path Modeling (PLS-SEM), while performance is measured by the average latent variable scores. The X-axis represents importance, and the Y-axis represents performance. The quadrants help prioritize areas for improvement: top-right for high-performing, important variables; top-left for crucial but underperforming variables; bottom-right for low-impact, high-performance areas; and bottom-left for variables with minimal impact and poor performance [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e"},{"header":"3 Results","content":"\u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Sample characteristics\u003c/h2\u003e \u003cp\u003eWe collected a total of 1,120 samples. Among the survey participants, 59.1% (662 individuals) were male and 40.9% (458 individuals) were female. The age distribution was primarily middle-aged and elderly, with participants under 18 years old making up only 0.8% (9 individuals), 18\u0026ndash;25 years old accounting for 1.7% (19 individuals), 26\u0026ndash;30 years old 2.1% (24 individuals), 31\u0026ndash;35 years old 6.8% (76 individuals), 36\u0026ndash;40 years old 9.8% (110 individuals), 41\u0026ndash;50 years old 23.9% (268 individuals), 51\u0026ndash;60 years old 26.1% (292 individuals), and those over 60 years old comprising 28.7% (322 individuals). The educational level of most respondents was junior college or below, accounting for 87.2% (977 individuals); 11.1% (124 individuals) held a bachelor\u0026rsquo;s degree, and 1.7% (19 individuals) held a graduate degree or higher. Ethnic minorities (in China, all groups other than Han are considered minorities) made up 7.8% (87 individuals) of the sample, while the Han majority constituted 92.2% (1033 individuals). Those living alone accounted for 16.4% (184 individuals), while those not living alone made up 83.6% (936 individuals). The majority of respondents were at a lower economic level, with 55.1% (617 individuals) having a monthly income of less than 3,000 yuan, 21.3% (238 individuals) earning between 3,000 to 3,999 yuan, 9.2% (103 individuals) between 4,000 to 4,999 yuan, and 14.5% (162 individuals) earning 5,000 yuan or more. The majority of respondents underwent dialysis three times per week, accounting for 84.2% (943 individuals); 7.0% (78 individuals) twice per week, 2.9% (32 individuals) once per week, and 6.0% (67 individuals) five times every two weeks. Regarding travel time to the dialysis center, 41.5% (465 individuals) took less than 30 minutes, 24.9% (279 individuals) took 30\u0026ndash;60 minutes, and 33.6% (376 individuals) took more than 60 minutes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Result of preliminary analysis\u003c/h2\u003e \u003cp\u003eFactor analysis revealed that the first component explained 32.75% of the variance, indicating no significant common method bias [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e, \u003cspan citationid=\"CR82\" class=\"CitationRef\"\u003e82\u003c/span\u003e]. Pearson\u0026rsquo;s bivariate correlations among key variables showed that self-regulated cognitive fatigue was negatively correlated with adaption (r = -0.213, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and social support (r = -0.173, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), but positively correlated with symptom class (r\u0026thinsp;=\u0026thinsp;0.169, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). It was not significantly correlated with patient activation (r = -0.063, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Adaption was positively correlated with patient activation (r\u0026thinsp;=\u0026thinsp;0.180, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and social support (r\u0026thinsp;=\u0026thinsp;0.495, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), but had a non-significant negative correlation with symptom class (r = -0.08, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Patient activation was positively correlated with social support (r\u0026thinsp;=\u0026thinsp;0.214, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) and negatively with symptom class (r = -0.086, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting higher activation is linked to lower symptom severity. Social support showed a weak, non-significant correlation with symptom class (r = -0.056, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Results of K-Means clustering\u003c/h2\u003e \u003cp\u003eWe applied K-means clustering to divide patients into two groups based on their total symptom burden scores. The choice of two clusters was informed by the 'elbow' method, which showed diminishing returns in variance explanation beyond two clusters [\u003cspan citationid=\"CR83\" class=\"CitationRef\"\u003e83\u003c/span\u003e, \u003cspan citationid=\"CR84\" class=\"CitationRef\"\u003e84\u003c/span\u003e]. Cluster 1 had lower symptom severity, with patients experiencing mild to moderate symptoms less frequently, while Cluster 2 consisted of patients with high symptom severity and frequency. The cutoff value was 63, with cluster centers at 111.51 and 16.32 for the first and second groups, respectively. The clusters stabilized after 11 iterations. Analysis of variance confirmed significant differences between the groups, with an F-value of 1858.364 and a P-value\u0026thinsp;\u0026lt;\u0026thinsp;0.01, indicating distinct symptom experiences [\u003cspan citationid=\"CR77\" class=\"CitationRef\"\u003e77\u003c/span\u003e]. Details on the clustering process and iterations are available in Supplementary Material 3.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Measurement model\u003c/h2\u003e \u003cp\u003eThe measurement model exhibited no multicollinearity issues, with VIFs ranging between 1.0 to 5.0, confirming acceptable levels. Discriminant validity was established through the square root of the average variance extracted (AVE) values, which were higher than the correlations with other variables as shown in Table \u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. As shown in Fig.\u0026nbsp;1, the HTMT ratios were all below the 0.9 threshold, indicating good discriminant validity between constructs. As in Table \u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the model's reliability and validity were confirmed with AVE, composite reliability (CR), Cronbach's alpha, and McDonald's omega (ω), all meeting or exceeding recommended thresholds. AVE values indicated substantial variance explanation by latent constructs (\u0026gt;\u0026thinsp;50%) [\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e], with values such as 0.656 for social support, 0.802 for patient activation, and 0.818 for adaption. CR values above 0.70, Cronbach's alpha values ranging from 0.824 to 0.889, and McDonald\u0026rsquo;s omega values from 0.829 to 0.891 all supported strong internal consistency [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. Item loadings were all significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and above the 0.70 threshold, affirming item reliability with loadings for social support items from 0.757 to 0.870, patient activation from 0.853 to 0.919, and adaption from 0.862 to 0.926 [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSquare Root of Construct's AVE and Its Correlation with Any Other Construct\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=\"left\" 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=\"char\" char=\".\" 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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-regulated cognitive fatigue\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eadaption\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePatient activation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSocial support\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSymptom class\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-regulated cognitive fatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eadaption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.213**\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient activation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.180**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.896\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.173**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.495**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.214**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptom class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.169**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.08**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.086*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003eThe diagonally bolded number is the square root of the construct AVE\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003eb\u003c/sup\u003e* indicates significance at the 0.05 level and ** indicates significance at the 0.01 level\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePsychometric Properties of Measurement Models\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"char\" char=\".\" 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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatent variable name\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eItem package\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOuter model loading\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eC.R.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eCronbach\u0026rsquo;s α\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMcDonald's ω\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.838\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.656\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.824\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.829\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.023\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.769\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.040\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.870\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.669\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.757**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.602\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePatient activation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePAM1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.853\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.802\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.924\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.142\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePAM2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.919\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePAM3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.913\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.480\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eadaption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADA1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.924\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.818\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.931\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADA2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.926\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3.169\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eADA3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.862\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2.107\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Structural model: hypothesized model testing\u003c/h2\u003e \u003cp\u003eFirst, as indicated in Table \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and Fig.\u0026nbsp;2, the results of the direct effects of covariates on symptom class are as follows: Income level had no significant effect on symptom class (β\u0026thinsp;=\u0026thinsp;0.016, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05). Minority status (β\u0026thinsp;=\u0026thinsp;0.005, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), gender (β =-0.001, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), education level (β\u0026thinsp;=\u0026thinsp;0.008, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), and solitude (β\u0026thinsp;=\u0026thinsp;0.026, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) showed no significant effects on symptom class. Among dialysis-related variables, time travel to dialysis center (β = -0.004, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) and dialysis frequency (β\u0026thinsp;=\u0026thinsp;0.007, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05) did not show significant effects.\u003c/p\u003e \u003cp\u003eFor the core variables, the results showed that social support had a significant positive effect on both patient activation (β\u0026thinsp;=\u0026thinsp;0.209, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and adaption (β\u0026thinsp;=\u0026thinsp;0.472, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating that higher social support is associated with better patient activation and adaption. Self-regulated cognitive fatigue had a significant negative effect on adaption (β = -0.131, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), but no significant effect on patient activation (β = -0.026, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05), meaning higher cognitive fatigue weakens adaption but does not significantly impact patient activation. Patient activation had a significant negative effect on symptom class (β = -0.024, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), suggesting that better patient activation is associated with fewer or less severe symptoms. Adaption also had a significant negative effect on symptom class (β = -0.023, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), indicating that individuals who adapt better experience fewer or less severe symptoms.\u003c/p\u003e \u003cp\u003eThe Q\u0026sup2; values provide insights into the model\u0026rsquo;s predictive relevance for each endogenous variable, gauging how well observed values are reconstructed by the model and its constructs [\u003cspan citationid=\"CR79\" class=\"CitationRef\"\u003e79\u003c/span\u003e]. In our analysis, adaption showed strong predictive relevance with a Q\u0026sup2; of 0.211, suggesting the model effectively captures the variance within this construct. Patient activation displayed a lower predictive relevance, documented at a Q\u0026sup2; of 0.034. Furthermore, the symptom class also exhibited substantial predictive capacity with a Q\u0026sup2; value of 0.041, indicating that our model reliably predicts symptom severity based on the analyzed factors.\u003c/p\u003e \u003cp\u003eNext, the results of the mediating effects were presented in Table \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. Social support had a significant negative indirect effect on symptom class through patient activation (β = -0.005, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), and through adaption (β = -0.011, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), suggesting that social support can significantly reduce symptoms through these two mediating variables. Self-regulated cognitive fatigue had a significant indirect effect on symptom class through adaption (β\u0026thinsp;=\u0026thinsp;0.003, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), indicating that cognitive fatigue worsens symptoms by negatively affecting adaption. However, its indirect effect through patient activation was not significant (β\u0026thinsp;=\u0026thinsp;0.001, p\u0026thinsp;\u0026gt;\u0026thinsp;0.05).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDirect Path Coefficients and Significant Levels\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=\"left\" 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=\"left\" 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\u003eIndependent variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath Coefficient (β) with 95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP values\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ2 for dependent variable\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eDependent variable: Symptom class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDialysis_frequency\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.007 [-0.009, 0.022 ]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.376\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncome_level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.016 [-0.003, 0.036]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTime travel to dialysis center\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.004 [-0.021, 0.012]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.418\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.676\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e★adaption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.023 [-0.040, -0.005]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.008 [-0.008, 0.025]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeducation_level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.008 [-0.009, 0.029]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003egender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.001 [-0.035, 0.035]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.940\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eminority\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.005 [-0.062, 0.061]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.157\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.875\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e★\u003csup\u003ea\u003c/sup\u003epatient activation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.024 [-0.044, -0.005]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003esolitude\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.026 [-0.022, 0.069]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eDependent variable: adaption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.211\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e★Social support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.472 [0.409, 0.527]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15.825\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e★Self-regulated cognitive fatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.131 [-0.182, -0.076]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.903\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003eDependent variable: patient activation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e★Social support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.209 [0.125, 0.286]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e★Self-regulated cognitive fatigue\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.026 [-0.092, 0.045]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.747\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e\u003csup\u003ea\u003c/sup\u003e\u0026lsquo;★\u0026rsquo; means core variable in our study\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eIndirect effect (mediating effect) path coefficients and significant levels\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePath of the model\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath Coefficient (β) with 95% confidence interval\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStandard deviation\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eT statistics\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP values\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial support-\u0026gt;patient activation -\u0026gt; Symptom_class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.005,\u003c/p\u003e \u003cp\u003e[-0.010, -0.001]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial support-\u0026gt;adaption -\u0026gt; Symptom_class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.011,\u003c/p\u003e \u003cp\u003e[-0.019, -0.002]\u003c/p\u003e\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.531\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-regulated cognitive fatigue-\u0026gt;patient activation -\u0026gt; Symptom_class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.001,\u003c/p\u003e \u003cp\u003e[-0.001, 0.003]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.633\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.526\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-regulated cognitive fatigue -\u0026gt; adaption -\u0026gt; Symptom_class\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003, [0.001, 0.006]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Result of importance\u0026ndash;performance map analysis\u003c/h2\u003e \u003cp\u003eAs indicated in Fig.\u0026nbsp;3, the importance-performance map analysis reveals that healthcare support is the most influential factor for both adaption and patient activation, with importance values of 0.154 for adaption and 0.068 for patient activation, and a performance score of 79.487. Family support and support from relatives and colleagues also significantly influence these outcomes, with family support having slightly higher importance for adaption (0.147) compared to patient activation (0.065), both with performance scores above 70. Support from relatives and colleagues shows similar importance, but with moderate performance scores. Conversely, self-regulated cognitive fatigue adversely affects both adaption and patient activation, with importance values of -0.131 and \u0026minus;\u0026thinsp;0.026, respectively, and a performance score of 48.254, suggesting that reducing cognitive fatigue could improve these outcomes.\u003c/p\u003e \u003cp\u003eAdditional analysis shows solitude as a critical covariate for symptom class due to its substantial impact on symptom severity, despite a low performance score of -16.429. Income and education levels show lower importance and performance, indicating less impact on symptom class. Factors such as dialysis frequency and socioeconomic status have minimal influence. Core variables related to adaption and patient activation show varied influence but generally perform well in the model, suggesting indirect pathways influence symptom severity through these variables, despite their minor direct impact.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study utilizes a large sample and rigorous statistical methods, combined with preliminary literature review results, to construct a patient-centered symptom management model. This model uses symptom categories as outcome variables and includes two coping strategies for managing symptom burden as mediating variables: proactive coping (patient activation) and passive coping (adaptation). These coping strategies are strengthened by enhanced social support, where passive coping (adaptation) is reduced by self-depletion, whereas proactive coping (patient activation) is not. This theoretical model helps nurses and other healthcare workers understand the patient-centered symptom management approach, and future interventions can be developed based on this model.\u003c/p\u003e \u003cp\u003eSymptom science is integral to nursing, focusing on diagnosing and treating human responses to health issues. Previous models like the Theory of Unpleasant Symptoms [\u003cspan citationid=\"CR85\" class=\"CitationRef\"\u003e85\u003c/span\u003e] and the Dynamic Symptom model [\u003cspan citationid=\"CR86\" class=\"CitationRef\"\u003e86\u003c/span\u003e] have examined symptom attributes and their changes, providing frameworks for nurses to manage symptoms in hemodialysis patients. However, these models often lack specificity in intervention strategies and fail to address crucial interactions between healthcare providers and family support systems, as highlighted by the Symptom Science Model 2.0. This model outlines the relationships among symptoms, phenotypes, behaviors, and clinical practices but does not detail patient-centered intervention pathways [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. Our developed symptom management model for hemodialysis patients addresses these limitations to some extent. The 2023 KDIGO Controversies Conference suggested Multilevel approaches to enable symptom assessment and management, advocating for person-centered care at the renal team level and patient empowerment management at the individual level [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Symptom Science Model 2.0 also emphasizes the importance of focusing on patient-centered experiences or journeys, underscoring the critical role of the patient themselves in the symptom management process [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]. This is logical, as in clinical practice, both patient symptom assessment and multidisciplinary team interventions for symptom management need to be patient-centered. We used systematic qualitative research literature focusing on hemodialysis patients to form a preliminary theoretical construction, ensuring that the concepts and relationships in our proposed model are centered around the patient. Symptom Science Model 2.0 also stresses considering determinants of health (such as age, gender, education level) [\u003cspan citationid=\"CR87\" class=\"CitationRef\"\u003e87\u003c/span\u003e]; in this model, we included these as covariates and adjusted the outcome variable, symptom burden, accordingly.\u003c/p\u003e \u003cp\u003eThe results showed that, after adjusting for covariates, the core variable relationships remained valid, confirming their robustness. Researchers developing intervention protocols must assess the impact of these covariates on symptom burden and consider cultural and situational factors. This study's findings differ from most previous research, particularly regarding income and education. Unlike studies outside China [\u003cspan additionalcitationids=\"CR89 CR90 CR91\" citationid=\"CR88\" class=\"CitationRef\"\u003e88\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e], income level did not significantly affect symptom burden, likely due to China's healthcare policies providing nearly full reimbursement for dialysis. Similarly, education level showed no significant effect compared with other studies [\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e], possibly due to the sample's homogeneity, with 87.2% of patients having a high school education or lower. Future studies should use quota sampling to explore education\u0026rsquo;s influence in the Chinese context. While prior research identified higher symptom burdens in older adults [\u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e], certain racial groups [\u003cspan additionalcitationids=\"CR98\" citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e], and women [\u003cspan additionalcitationids=\"CR101\" citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR102\" class=\"CitationRef\"\u003e102\u003c/span\u003e], this study did not observe these trends, suggesting that interactions between these factors may affect symptom burden collectively. Future research should investigate these interactions to guide health policy. The study also examined dialysis-related factors like dialysis frequency and distance to centers. While some studies suggest more frequent dialysis improves outcomes [\u003cspan citationid=\"CR103\" class=\"CitationRef\"\u003e103\u003c/span\u003e, \u003cspan citationid=\"CR104\" class=\"CitationRef\"\u003e104\u003c/span\u003e], our findings align with studies showing no significant effect [\u003cspan citationid=\"CR105\" class=\"CitationRef\"\u003e105\u003c/span\u003e], highlighting the need for systematic reviews. No significant impact was found for travel time to dialysis centers, contrary to other studies [\u003cspan citationid=\"CR106\" class=\"CitationRef\"\u003e106\u003c/span\u003e, \u003cspan citationid=\"CR107\" class=\"CitationRef\"\u003e107\u003c/span\u003e]. However, distance may affect referral rates rather than clinical outcomes [\u003cspan citationid=\"CR108\" class=\"CitationRef\"\u003e108\u003c/span\u003e], suggesting that remote interventions could help address transportation challenges.\u003c/p\u003e \u003cp\u003eThe symptom burden of our study has been simplified into two distinct clusters. The clinical relevance of these clusters is profound, as they enable healthcare providers to tailor interventions more precisely. For example, patients in cluster 1 (low symptom burden) may benefit from preventive strategies that focus on lifestyle modifications and regular monitoring to maintain their relatively stable condition. Conversely, patients in Cluster 2 (high symptom burden) might require more intensive management approaches, potentially including adjustments to their dialysis regimen, enhanced pharmacological interventions, and more comprehensive support services to address their complex symptomatology. Linking these symptom clusters to potential interventions not only allows for more personalized patient care but also provides a framework for ongoing research into the effectiveness of targeted treatment strategies based on symptom burden. Future studies could explore the impact of specific interventions within these clusters to further refine treatment approaches and improve quality of life for hemodialysis patients.\u003c/p\u003e \u003cp\u003eIn this model, the mediating variables are adaptation and patient activation, representing the passive and active coping mechanisms of patients in symptom management, respectively. Patient activation refers to the skills, knowledge, and confidence related to a patient's willingness and ability to manage their health [\u003cspan citationid=\"CR109\" class=\"CitationRef\"\u003e109\u003c/span\u003e]. For the dialysis population, the Centers for Medicare \u0026amp; Medicaid Services (CMS) in the United States have included patient activation as a quality metric in the Kidney Care Choices model [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. Previous studies have found that patient activation is associated with health outcomes such as quality of life and symptom burden [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e, \u003cspan citationid=\"CR112\" class=\"CitationRef\"\u003e112\u003c/span\u003e]. Our study not only further confirms this correlation (directionally) but also proposes a theoretical model on how patient activation as a mediating variable can reduce symptom burden. Compared to another mediating variable in the model\u0026mdash;adaptation\u0026mdash;patient activation performs better on the importance-performance map (all three dimensions score above 77, while the highest score for adaptation is 68.9), suggesting that patient activation should be a variable of focus when developing intervention measures. Based on earlier literature, we hypothesized that social support acts as an enhancing variable and self-depletion as a weakening variable for patient activation. However, unexpectedly, the direct pathway from self-depletion to patient activation and the indirect pathway leading to symptom burden were both insignificant, indicating that self-depletion does not lead to a decrease in patient activation levels nor does it result in higher symptom burden. However, whether patient activation levels naturally deplete remains a question. One of the previous studies have dynamically assessed the activation levels of chronic disease patients and found that compared to the baseline, some patients experienced a decline in activation levels [\u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e]. Since this study is cross-sectional, it currently cannot answer this question. Future studies need to be longitudinal to clarify the overall trajectory of patient activation, the heterogeneity of trajectories, and the factors causing category transitions at each time point. It is also possible to use a Random-Intercept cross-lagged panel design with covariate adjustment to longitudinally track the association between cognitive depletion and patient activation to further validate or refute the relationship between these two variables [\u003cspan citationid=\"CR114\" class=\"CitationRef\"\u003e114\u003c/span\u003e]. Regardless, nurses need to carefully and dynamically assess the activation levels of patients when developing interventions or intervening, to early identify, respond to, and manage changes in patient activation levels.\u003c/p\u003e \u003cp\u003eSocial support can enhance patient activation levels, and we have illustrated this using an importance-performance map with patient activation as the outcome variable. We found that support from healthcare professionals performs best in terms of importance and performance. Support from friends has high importance but low performance. Support from family, friends, and colleagues has similar importance but moderate performance. Therefore, when considering the development of interventions, clinical nurses need to focus on mobilizing support from healthcare workers while also enhancing support from friends, as it is a crucial part of social support. However, research indicates that the global distribution of healthcare workers for kidney diseases is extremely uneven, and there is a shortage in many countries [\u003cspan citationid=\"CR115\" class=\"CitationRef\"\u003e115\u003c/span\u003e, \u003cspan citationid=\"CR116\" class=\"CitationRef\"\u003e116\u003c/span\u003e]. Therefore, local health resources and cultural backgrounds should be considered when developing interventions. If healthcare worker support is scarce, other sources of support should be mobilized, and interventions could also consider enhancing support from patients' friends, colleagues, and family. Since hemodialysis patients typically receive treatment at fixed dialysis centers, healthcare workers could consider developing peer education interventions [\u003cspan citationid=\"CR117\" class=\"CitationRef\"\u003e117\u003c/span\u003e, \u003cspan citationid=\"CR118\" class=\"CitationRef\"\u003e118\u003c/span\u003e], selecting key opinion leaders from the same patient group to help others make symptom management decisions [\u003cspan citationid=\"CR119\" class=\"CitationRef\"\u003e119\u003c/span\u003e, \u003cspan citationid=\"CR120\" class=\"CitationRef\"\u003e120\u003c/span\u003e]. Tailored family-centered intervention plans could also be developed after thoroughly assessing patients' family health and support levels, enabling family members to assist effectively in symptom management [\u003cspan citationid=\"CR121\" class=\"CitationRef\"\u003e121\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is important to note that studies have found a dose-response relationship between patient activation and related clinical outcomes [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e]. However, this relationship has a plateau phase, meaning that beyond a certain level, further increases in patient activation do not lead to significant improvements. Therefore, while patient activation can improve patient outcomes, future research needs to clarify the relationship between patient activation and core outcomes in hemodialysis patients. The minimal clinically important difference should be calculated to determine the necessary level of patient activation, and intervention doses and timings should be adjusted accordingly to allocate medical resources efficiently [\u003cspan citationid=\"CR122\" class=\"CitationRef\"\u003e122\u003c/span\u003e, \u003cspan citationid=\"CR123\" class=\"CitationRef\"\u003e123\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, it should be noted that this study only provides a broad framework. Future healthcare workers need to use standardized intervention development frameworks, such as the MRC framework [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e], guided by evidence-based evidence, combined with patient benefits and their own intervention conditions, to select the most appropriate intervention components. Preliminary experiments should be conducted on patients to develop rigorous and standardized intervention measures. This study has identified that social support can reduce patient symptom burden by enhancing patient activation levels. This pathway is consistent with the buffering model hypothesis of social support, which suggests that social support can increase an individual's coping abilities, thereby reducing stress events or adverse outcomes [\u003cspan citationid=\"CR124\" class=\"CitationRef\"\u003e124\u003c/span\u003e, \u003cspan citationid=\"CR125\" class=\"CitationRef\"\u003e125\u003c/span\u003e]. Previous research has found that social support can reduce patient symptom burden [\u003cspan additionalcitationids=\"CR127\" citationid=\"CR126\" class=\"CitationRef\"\u003e126\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR128\" class=\"CitationRef\"\u003e128\u003c/span\u003e], but few have considered patient activation as a mediating variable. Our study enriches and develops the application of the social support theory buffering effect model in the field of reducing symptom burden, enhancing the explanatory power of social support theory in symptom burden research. Cognitive fatigue does not decrease patient activation levels, possibly because patient activation itself is a spontaneous and proactive regulatory mechanism [\u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e, \u003cspan citationid=\"CR113\" class=\"CitationRef\"\u003e113\u003c/span\u003e, \u003cspan citationid=\"CR129\" class=\"CitationRef\"\u003e129\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSocial support enhances adaptability in hemodialysis patients, reducing symptom burden. Adaptation, a passive coping mechanism, involves physiological and psychosocial adjustments, with patients balancing medical guidelines against maintaining normalcy in their social and cultural practices [\u003cspan additionalcitationids=\"CR131\" citationid=\"CR130\" class=\"CitationRef\"\u003e130\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR132\" class=\"CitationRef\"\u003e132\u003c/span\u003e]. This balance creates a dynamic equilibrium where patients make health-aligned choices that reflect their cultural and social identities [\u003cspan citationid=\"CR131\" class=\"CitationRef\"\u003e131\u003c/span\u003e, \u003cspan citationid=\"CR133\" class=\"CitationRef\"\u003e133\u003c/span\u003e, \u003cspan citationid=\"CR134\" class=\"CitationRef\"\u003e134\u003c/span\u003e]. Most research on this adaptation is qualitative, with limited quantitative studies and few specific scales measuring adaptation, highlighting a need for clearer definitions and tailored measurement tools. Importance-performance mapping shows that the strongest support for enhancing adaptation comes from healthcare professionals, while the weakest comes from friends. To strengthen adaptation, healthcare workers should focus on health education, symptom management skills, and psychological support for lifestyle adjustments (Saccaro et al., 2024). Multidisciplinary interventions should customize plans to enhance patient decision-making in symptom management [\u003cspan citationid=\"CR135\" class=\"CitationRef\"\u003e135\u003c/span\u003e, \u003cspan citationid=\"CR136\" class=\"CitationRef\"\u003e136\u003c/span\u003e]. Nurses should also address potential misinformation and strengthen patients\u0026rsquo; ability to seek reliable symptom assistance through workshops and online programs, boosting confidence and adaptability [\u003cspan additionalcitationids=\"CR138 CR139\" citationid=\"CR137\" class=\"CitationRef\"\u003e137\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR140\" class=\"CitationRef\"\u003e140\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThis study reveals that ego depletion worsens symptom burden by diminishing patients' adaptability but does not significantly impact patient activation levels. Adaptability, which depends heavily on cognitive resources for managing changes and stress, is vulnerable when these resources are depleted [\u003cspan additionalcitationids=\"CR142\" citationid=\"CR141\" class=\"CitationRef\"\u003e141\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR143\" class=\"CitationRef\"\u003e143\u003c/span\u003e]. In contrast, patient activation, rooted in intrinsic motivation and long-term health management habits, remains stable despite depleted psychological resources, likely supported by external (medical teams, family) and internal factors (beliefs) [\u003cspan citationid=\"CR110\" class=\"CitationRef\"\u003e110\u003c/span\u003e, \u003cspan citationid=\"CR111\" class=\"CitationRef\"\u003e111\u003c/span\u003e, \u003cspan citationid=\"CR144\" class=\"CitationRef\"\u003e144\u003c/span\u003e]. The study also found no direct link between self-regulated cognitive fatigue and patient activation, suggesting that factors like resilience, mental health, or patient-physician interactions might be more influential. Thus, while ego depletion directly reduces adaptability and increases symptom burden, activation levels are buffered by more enduring influences.\u003c/p\u003e \u003cp\u003eFuture research can further explore this phenomenon in several ways to deepen understanding: Firstly, longitudinal studies (such as random intercept cross-lagged, difference-in-differences, cross-lagged network analysis) could be used to examine the dynamic impact of ego depletion on adaptability and activation levels, observing whether there are different long-term trends. Secondly, modifying variables, such as psychological resilience, could be added to structural equation models to test whether these factors can modulate the impact of resource depletion, especially the protective effect on patient adaptability. Additionally, different dimensions of activation levels (such as cognitive and behavioral dimensions) could be distinguished to explore whether these dimensions are differentially affected by ego depletion. Last but not least, research could also focus on designing intervention strategies, targeting patient adaptability with implementation intention plans to create specific coping plans for patients managing symptoms, enhancing the psychological resources needed for adaptability, and reducing the negative effects of ego depletion on symptom burden. While this study specifically addresses symptom management among hemodialysis patients in China, the underlying mechanisms of social support and dual coping strategies may be relevant to other chronic kidney disease stages, patients with different chronic conditions, and even cancer patients, across various cultural and social contexts. Further research is needed to explore how these findings can be adapted and generalized to these broader populations, taking into account the unique clinical, psychological, and sociocultural factors that influence symptom management in diverse healthcare environments\u003c/p\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Limitations\u003c/h2\u003e \u003cp\u003eThis study employed convenience sampling across multiple regions in China, enabling quicker data collection but potentially limiting the representativeness and generalizability to all hemodialysis patients, which may introduce sampling bias. Moreover, while controlling for various covariates, unaccounted residual confounding factors, such as individual differences in coping strategies and disease duration, could affect the study's findings. Also, the design did not fully mitigate interviewer or social desirability biases in self-reported measures. Future research should use anonymous and automated data collection methods to reduce these biases.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study underscores the complex interactions between coping strategies, social support, and symptom management in hemodialysis patients. It demonstrates that symptoms can be managed through proactive and passive coping strategies, with social support enhancing both. The findings also indicate that self-regulatory fatigue impedes adaptation, highlighting the need for interventions that enhance psychological resilience and support adaptability. From a practical standpoint, the results call for healthcare systems to provide multidisciplinary support to boost patient activation and strengthen coping mechanisms. Tailored interventions should address varying patient needs for more personalized care. Future studies should explore these aspects longitudinally and develop interventions considering individual resilience and health literacy. Addressing the symptom burden in hemodialysis patients not only improves their life quality but also alleviates the economic strain on healthcare systems, suggesting that a holistic approach integrating medical, psychological, and social factors is crucial for sustainable outcomes. This study offers a foundational framework for patient-centered symptom management strategies.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Medical Science Research Ethics Committee of the First Affiliated Hospital of China Medical University, with the ethical approval number 2024-633. Informed consent have been gained from all participants. Participation was voluntary, and individuals could freely withdraw from any phase of the process without constraints. The data analysis was rigorously conducted to maintain the anonymity, privacy, and confidentiality of the participating patients.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and material:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eResearch data will be shared with reasonable requests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eORCID\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXutong Zheng: 0000-0002-9236-1764\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXutong ZHENG takes charge of Conceptualization, Data curation, Formal analysis, Methodology, Project administration, Software, Supervision, Validation, Visualization, Roles/Writing - original draft. Linyu XU takes charge of Project administration, Resources, Software, Supervision, Validation, and Visualization. Aiping WANG takes charge of Conceptualization, review \u0026amp; editing.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eBikbov B, Purcell CA, Levey AS, Smith M, Abdoli A, Abebe M, et al. Global, regional, and national burden of chronic kidney disease, 1990\u0026ndash;2017: a systematic analysis for the Global Burden of Disease Study 2017. Lancet. 2020;395:709\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eYing M, Shao X, Qin H, Yin P, Lin Y, Wu J, et al. Disease Burden and Epidemiological Trends of Chronic Kidney Disease at the Global, Regional, National Levels from 1990 to 2019. Nephron. 2024;148:113\u0026ndash;23.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKovesdy CP. Epidemiology of chronic kidney disease: an update 2022. 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J Ren Nutrition: Official J Council Ren Nutr Natl Kidney Foundation. 2020;30:556\u0026ndash;60.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003ePicariello F, Moss-Morris R, Macdougall IC, Chilcot J. It\u0026rsquo;s when you\u0026rsquo;re not doing too much you feel tired: A qualitative exploration of fatigue in end-stage kidney disease. Brit J Health Psych. 2018;23:311\u0026ndash;33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRezaei Z, Jalali A, Jalali R, Khaledi-Paveh B. Psychological problems as the major cause of fatigue in clients undergoing hemodialysis: A qualitative study. Int J Nurs Sci. 2018;5:262\u0026ndash;7.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eStevenson J, Tong A, Campbell KL, Craig JC, Lee VW. Perspectives of healthcare providers on the nutritional management of patients on haemodialysis in Australia: an interview study. 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J Ren Care. 2014;40:205\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTao Y, Liu T, Li P, Lv A, Zhuang K, Ni C. Self-management experiences of haemodialysis patients with self-regulatory fatigue: A phenomenological study. J Adv Nurs. 2023;79:2250\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"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":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"hemodialysis, patient-centered care, symptom management, social support, self-regulatory fatigue, coping strategies, patient activation, adaptation, partial least squares structural equation modeling, cross-sectional study","lastPublishedDoi":"10.21203/rs.3.rs-6023205/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6023205/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eSymptom burden among hemodialysis patients significantly impacts their quality of life. Effective symptom management, supported by social support and coping strategies, is critical to improve patient outcomes. However, the role of social support, self-regulatory fatigue, and different coping mechanisms in patient-centered symptom management is not well understood.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA cross-sectional study using Partial Least Squares Structural Equation Modeling (PLS-SEM). Data were collected from multiple hemodialysis centers in various regions across China, ensuring a representative sample of diverse geographical areas. Participants were recruited through convenience sampling across different regions, ensuring broad demographic representation. This study used PLS-SEM to develop and validate a theoretical model that describes the relationships among social support, self-regulatory fatigue, adaptation, patient activation, and symptom burden.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 1,120 patients participated, with a mean age of 51.6 years (SD\u0026thinsp;=\u0026thinsp;13.8), and 59.1% were male. The Partial Least Squares Structural Equation Modeling (PLS-SEM) results showed that social support had a significant positive effect on both patient activation (β\u0026thinsp;=\u0026thinsp;0.209, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and adaptation (β\u0026thinsp;=\u0026thinsp;0.472, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating higher levels of social support were associated with increased patient activation and adaptation. Self-regulatory fatigue had a significant negative effect on adaptation (β = -0.131, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) but no significant effect on patient activation (β = -0.026, p\u0026thinsp;=\u0026thinsp;0.455). Patient activation (β = -0.024, p\u0026thinsp;=\u0026thinsp;0.019) and adaptation (β = -0.023, p\u0026thinsp;=\u0026thinsp;0.011) both had significant negative effects on symptom burden, indicating that higher levels of activation and adaptation were linked to lower symptom burden. Mediation analysis revealed that social support indirectly reduced symptom burden through both adaptation (β = -0.011, p\u0026thinsp;=\u0026thinsp;0.011) and patient activation (β = -0.005, p\u0026thinsp;=\u0026thinsp;0,032). Patient activation demonstrated greater importance in symptom management compared to adaptation based on the importance-performance analysis.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study reveals that social support significantly enhances both patient activation and adaptation, leading to a reduction in symptom burden among hemodialysis patients. Self-regulatory fatigue, however, negatively affects adaptation but does not have a significant impact on patient activation. The dual coping strategies\u0026mdash;adaptation (passive) and patient activation (proactive)\u0026mdash;mediate the relationship between social support and symptom burden, with patient activation showing greater importance in symptom management. These findings emphasize the importance of enhancing social support, reducing self-regulatory fatigue, and fostering duel coping strategies to effectively alleviate the symptom burden in hemodialysis patients.\u003c/p\u003e","manuscriptTitle":"Implication for Nursing Approaches: Developing an Theoretical Framework for Patient-Centered Symptom Management in Hemodialysis Patients from the Perspective of Dual-Dimension to Enhancing and Mitigating Coping Strategies: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-02-21 16:22:32","doi":"10.21203/rs.3.rs-6023205/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"62e70f0d-708a-4a8a-a493-a5f3d16f1338","owner":[],"postedDate":"February 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-02-21T16:22:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-02-21 16:22:32","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6023205","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6023205","identity":"rs-6023205","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}