Biopsychosocial factors associated with health-related quality of life in the general Chinese population: Evidence from a nationwide health survey

Biopsychosocial factors associated with health-related quality of life in the general Chinese population: Evidence from a nationwide health survey | 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 Biopsychosocial factors associated with health-related quality of life in the general Chinese population: Evidence from a nationwide health survey xiaoting zhang, yibo wu, Junhao Li, Yuang Dun, Liyang Zhang, yuan chen, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9507474/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 5 You are reading this latest preprint version Abstract Objective To examine how biological, psychological, and social factors are linked to health-related quality of life (HRQoL) among Chinese residents according to the biopsychosocial (BPS) framework. Methods Data were drawn from the 2024 Psychology and Behavior Investigation of Chinese Residents (PBICR), including 25,047 adults from 31 provincial-level regions in China. HRQoL was measured using the EQ-5D-5L. Based on the BPS framework, biological, psychological, and social factors assessed by standardized instruments were specified as formative latent constructs. The BPS model was estimated using Partial Least Squares Structural Equation Modeling (PLS-SEM). Model evaluation consisted of three stages: assessment of the measurement model, evaluation of the structural model, and internal model validation. These analyses focused respectively on indicator relevance and collinearity, structural relationships and model fit, and predictive performance based on PLSpredict with 10-fold cross-validation. Subgroup analyses were conducted to examine heterogeneity in associations across sociodemographic groups defined by education, age, region, sex, income, marital status, residence, smoking status, alcohol consumption, and chronic disease status. Results The mean (SD) EQ-5D-5L utility and EQ-VAS scores of the sample were 0.928 (0.163) and 76.03 (19.16), respectively. Measurement model results showed that nearly all indicator weights were statistically significant with no serious collinearity, indicating the validity of the latent constructs. Among the indicators, sleep stability, general self-efficacy, and social support were the most influential factors. Structural model results indicated that all three types of factors were positively and directly linked to HRQoL. Social factors also showed significant indirect linkages through psychological and biological pathways, supporting patterns consistent with the hypothesized chain-mediated pathway. The BPS model also demonstrated acceptable predictive performance according to PLSpredict results. Subgroup analyses revealed that the strength of associations varied across different characteristics such as education level, age, region, and socioeconomic status. Conclusions The BPS model was estimated and validated, revealing the multidimensional determinants of HRQoL and their interrelated patterns consistent with chain-mediated pathways. Variations in these patterns across different subgroups were also identified. Biopsychosocial Model Health-Related Quality of Life (HRQoL) PLS-SEM PLSpredict Chinese Residents Figures Figure 1 Figure 2 Figure 3 1 Introduction With the increasing global burden of chronic diseases and the aging population, traditional health measurement approaches based on objective indicators—such as mortality and morbidity rates, life expectancy, and physiological measures like blood pressure and BMI, are becoming increasingly inadequate[ 1 ]. Health-Related Quality of Life (HRQoL), a subjective and comprehensive health outcome measure encompassing physical, mental, and social dimensions, has been widely adopted to assess the impact of diseases, injuries, functional limitations, or disabilities[ 2 – 4 ]. Moreover, HRQoL information can be converted into health utility score for use in economic evaluation if it is measured by utility instruments such as the EQ-5D[ 5 , 6 ]. 1.1 Biopsychosocial Model and HRQoL To gain a deeper understanding of the underlying patterns related to HRQoL—particularly within specific cultural contexts—it is essential to adopt robust theoretical frameworks. Engel’s biopsychosocial (BPS) model aligns conceptually with the multidimensional construct of HRQoL. The model posits that health is not determined solely by biological factors but is also closely related to psychological and social influences[ 7 ]. It facilitates the identification of multiple patterns related to health outcomes and potential points of intervention. Moreover, it demonstrates strong cultural adaptability, rendering it particularly suitable in China, in which family structure, rural–urban disparities, and healthcare characteristics differ markedly from Western populations. A growing body of evidence supports the explanatory utility of BPS model in explaining HRQoL and other subjective health outcomes (e.g., pain interference, depression, and perceived discrimination), in which a variety of biological, psychological, and social variables show consistent relationships with HRQoL. For example, self-efficacy and emotional regulation (psychological factors), and the level of social support (social factor), have been identified as significant correlates of HRQoL among older adults and individuals with chronic conditions[ 8 – 10 ]. Comprehensive empirical investigations have further demonstrated the applicability of this framework. For example, Moons et al. demonstrated that psychological health was related to both disease severity and HRQoL through modeled relationships in adults with congenital heart disease, highlighting the explanatory relevance of psychological and social variables[ 11 ]. Thus, the BPS model not only provides a conceptual framework capturing the multidimensional nature of HRQoL, but also helps characterize the underlying patterns underlying variations in HRQoL. 1.2 Literature Review and Mechanistic Pathways The importance of integrating biological, psychological, and social dimensions within a unified theoretical framework is highlighted by previous studies[ 12 , 13 ]. To clarify the pathways through which these dimensions are related to health outcomes and to inform the construction of the analytical model, the explanatory pathways of BPS model was assessed. Evidence indicates that biological, psychological, and social factors not only show independent relationships with health status, but also are interrelated in complex ways[ 8 , 14 ]. Biological variables such as chronic conditions, body mass index (BMI), sleep patterns, and health behaviors are consistently linked to health status. For instance, glycemic control in patients with diabetes is directly associated with improved health outcomes[ 15 ], while high BMI and multimorbidity are shown to significantly impair physical functioning in older adults[ 16 ]. Similarly, disrupted sleep rhythms are correlated with poorer self-perceived health[ 17 ]. Psychological factors such as self-efficacy, personality traits, perceived stress, and Attention-Deficit/Hyperactivity Disorder (ADHD) symptoms emerge as key correlates as well. For example, university students with higher stress levels and lower self-efficacy report significantly poorer mental well-being [ 18 , 19 ]. Evidence among individuals with psychiatric disorders also suggests that a positive psychological profile may be linked to reduced adverse experiences of physical symptoms and improved perceived health[ 20 ]. Social determinants, including education, income, urban–rural residence, and social support, are widely recognized as critical factors. For instance, older adults living with HIV experience worse functional outcomes when faced with social isolation[ 21 ], while access to healthcare services and community resources play an important role in alleviating the disease burden among patients with rheumatoid arthritis[ 22 ]. Longitudinal studies further highlight the cumulative impact of social determinants on well-being throughout the life course[ 8 ]. In recent years, studies have increasingly shifted from examining the isolated effect of the above mentioned factors toward exploring their dynamic and interrelated pathways. Emerging evidence suggests that biological health may statistically mediate the associations between psychological and social factors and health outcomes, and that more complex chain mediation pathways may also exist among these dimensions. For instance, Lingam et al. find that children with chronic illnesses and low socioeconomic status often experience multiple layers of vulnerability, in which unmet physiological needs, persistent psychological distress, and lack of social support interact to exacerbate health risks[ 23 ]. Similarly, Yoo-Jeong et al. report that social isolation among older adults living with HIV is associated with elevated inflammatory markers, which may be linked to emotional dysregulation and cognitive decline, and ultimately which may be related to compromised overall health[ 24 ]. All those imply that structural social disadvantages—such as limited education or low income—may be associated with higher psychological burden and lower psychological resilience, which are in turn associated with poorer physical health and lower HRQoL. This chain-mediated association pattern highlights the cascading effects among social, psychological, and biological domains, revealing the cumulative and systemic nature of health inequalities[ 8 , 14 ]. Accordingly, there is a pressing need to construct integrative models that capture the interactions among biopsychosocial factors, while systematically identifying both mediating and sequential mechanisms, in order to better understand the social determinants of HRQoL—particularly within culturally distinctive and structurally diverse settings such as China. 1.3 Limitations in Scope, Context, and Measurement Although the BPS model has been widely used as a conceptual framework in health-related research, its usage still has several issues in the field. First, existing studies typically focus on specific populations, such as individuals with diabetes[ 15 ], rheumatoid arthritis[ 22 ], older adults living with HIV[ 24 ], or those with mental disorders[ 20 ]; or on specific life stages, such as adolescents[ 25 ], university students[ 18 , 19 ], or older adults[ 21 ]. These studies are also predominantly conducted in high-income countries such as Europe and North America[ 8 , 14 ]. Therefore, the applicability and validity of the BPS model in culturally distinct contexts, such as China, remain underexplored and require further empirical scrutiny. Next, the majority of studies rely on objective indicators, including physiological functioning, clinical diagnoses, or behavioral changes[ 26 ]; while a few address individuals' subjective perceptions of health and overall self-evaluation, such as HRQoL, reflecting individuals' perception on their physical health, perception of pain, functional limitations in daily life, or emotional well-being[ 27 – 29 ]. Lastly, evidence on the pathways through which the biological, psychological, and social dimensions are associated with health outcomes, especially the exploration into chain-mediated pathways is limited. 2 Study Objectives and Hypotheses Drawing upon the BPS framework, the study aimed to systematically examine how biological, psychological, and social factors are jointly associated with HRQoL in general Chinese population by developing a culturally contextualized structural equation model (SEM). The model seeks to capture both direct associations and to identify underlying mediated association pathways, as well as subgroup-specific variations within the Chinese sociocultural context. The hypotheses are as follows: H1 Favorable biological health indicators—including an optimal body mass index (BMI), absence of multimorbidity, regular physical activity, non-smoking status, consistent sleep patterns, appropriate age, and non-drinking—are expected to be directly positively associated with HRQoL. H2 Psychological resources, such as higher self-efficacy, lower perceived stress, positive personality traits (such as high conscientiousness, low neuroticism, high extraversion, high agreeableness, and low openness), and fewer symptoms of Attention Deficit Hyperactivity Disorder (ADHD), are expected to be directly positively associated with higher HRQoL. H3 Social structural factors — including higher education levels, higher household income, urban residency, stronger health literacy, more positive family communication, social support, geographic location, and employment status — are expected to be directly positively associated with better HRQoL. H4 : Biological and psychological health are expected to statistically mediate the associations between social factors and HRQoL. Specifically, social and psychological factors are expected to be indirectly associated with HRQoL through biological health (H4a: Social → Biological → HRQoL; H4b: Psychological → Biological → HRQoL); and social factors are also expected to be indirectly associated with HRQoL through psychological health (H4c: Social → Psychological → HRQoL). H5 A chain-mediated association pattern is also anticipated, whereby social disadvantage is associated with higher psychological distress and lower psychological resilience, which are in turn associated with poorer biological health and lower HRQoL (Social → Psychological → Biological → HRQoL). Subgroup analyses were further conducted to examine whether the strength and structure of direct and indirect associations differ significantly across different subgroups such as urban and rural residents, different socioeconomic status (SES) subgroups, etc. 3 Methods 3.1 Survey Design and Data Collection This study utilized data from the 2024 Psychology and Behavior Investigation of Chinese Residents (PBICR) survey, a nationwide cross-sectional health survey conducted annually. It was conducted from June to September, 2024, covering 22 provinces, 5 autonomous regions, and 4 municipalities directly under the central government. A total of 150 cities, 202 districts/counties, 390 townships/towns/streets, and 800 communities were sampled. Both stratified and quota sampling methods were employed to obtain a nationally representative sample, and details of the PBICR-2024 survey design and protocol, including sampling procedures, investigator training, and interview settings, have been described previously (PBICR-2024 Study Protocol, 2025). Trained investigators or investigation teams were assigned to each city to conduct face-to-face interviews. The inclusion criteria were as follows: (1) aged ≥ 18 years, (2) Chinese nationality, (3) permanent residency in China (with a maximum absence of 1 month), (4) voluntary participation in the study, (5) ability to complete the online questionnaire independently or with assistance from the investigator, and (6) ability to understand the meaning of the survey questions. The exclusion criteria were: (1) mental disorders or psychiatric conditions, (2) cognitive impairment, (3) participation in other similar health surveys, and (4) unwillingness to cooperate. To ensure data quality, the survey implemented a rigorous screening process. Initially, 38,793 questionnaires were distributed, and after excluding invalid or non-consenting responses, 38,424 valid questionnaires were obtained, with a response rate of 99.05%. Subsequently, low-quality samples, including those with ambiguous consent, underage participants, non-Chinese residents, and responses completed in under 5 minutes, were excluded, leaving 36,240 valid samples. After logical consistency checks, 35,861 qualified samples were retained, which may be related to a qualification rate of 98.95%. Finally, after quota filtering, 25,047 samples were retained from the qualified responses. The attrition rate at each stage of the data cleaning process remained below 2%, demonstrating the high reliability of the data and strong participant compliance. The PBICR-2024 survey encompasses a wide range of aspects, including sociodemographic attributes, health status, family structure, social environment, childhood experiences, behavioral patterns, psychological states, and contemporary social issues, in which several internationally and domestically standardized instruments were employed. The adopted measurement tools in the study are described in the following sections. 3.2 Measures EQ-5D-5L The EQ-5D-5L is a new version of the widely used HRQoL instrument EQ-5D. It assesses individuals’ HRQoL in terms of five dimensions: Mobility, self-care, usual activities, pain/discomfort, and anxiety/depression . Each dimension has five functioning levels, ranging from "no problems" (level 1) to "extreme problems" (level 5), yielding 3,125 distinct health states. Each health state can be expressed using a 5-digit number (eg, “no problems” in any dimension is “11111” and “extreme problems” in every dimension is “55555”) and can be assigned a utility score using a value set derived from a valuation study. In the analysis, the Chinese 5L value set was adopted, ranging from − 0.391(the worst health state) to 1.0 (full health). The Visual Analog Scale (VAS) is often used alongside the EQ-5D-5L utility and measures overall health perception on a scale from 0 (worst) to 100 (best), providing a subjective health measure in addition to its health-state descriptive system. The Chinese version of the EQ-5D-5L utility has been culturally adapted and validated in various populations[ 30 ]. Biological Dimension Measurement The biological dimension was assessed using the International Physical Activity Questionnaire (IPAQ-7) , inquiring participants' physical activity levels over the past 7 days using 7 items. Each item is divided into three main categories: vigorous physical activity (duration: minutes, frequency: days), moderate-intensity physical activity (duration: minutes, frequency: days), and walking time of at least 10 minutes (frequency: days). The duration of each activity is converted into the metabolic equivalent (MET) corresponding to the basal metabolic rate, and the total physical activity score (MET-minutes/week) is calculated. Specifically, the MET for walking is calculated as 3.3 × average daily walking time × weekly walking days; the MET for moderate-intensity activity is calculated as 4.0 × average daily time spent in moderate-intensity activity × days engaged in moderate-intensity activity per week; the MET for vigorous activity is calculated as 8.0 × average daily time spent in vigorous activity × days engaged in vigorous activity per week. Thus, the total basal metabolic time (in minutes) per week is the sum of the walking MET, moderate-intensity activity MET, and vigorous activity MET. According to the classification criteria, < 600 MET-min/week is defined as low activity, 600–3000 MET-min/week as moderate activity, and ≥ 3000 MET-min/week as high activity; higher values indicate greater frequency, duration, or intensity of physical activity, which are generally associated with better fitness and health status[ 31 ]. Psychological Dimension Measurements The psychological dimension was evaluated using the Perceived Stress Scale (PSS-4), General Self-Efficacy Scale (NGSES-SF), Big Five Personality Inventory (BFI-10), and Attention Deficit Hyperactivity Disorder Scale (ASRS). The PSS-4 is based on 4 questions to assess individuals' perception of stressful situations in their lives. The questionnaire is divided into 2 dimensions: sense of loss of control (items 1 and 2) and tension (items 3 and 4). The Likert method is used to score items, with a range from 1 to 5 (from "never" to "always"). The total score is calculated by summing the scores of all 4 items, with a score range from 4 to 20. A higher score indicates that the individual felt more unpredictable, uncontrollable, or overloaded in their life during the past month[ 32 ]. In this study, the Cronbach α coefficient for the PSS-4 scale was 0.934. NGSES-SF is used to assess an individual's self-efficacy. The questionnaire comprises three dimensions: the level or degree of self-efficacy, its intensity , and its generality . A Likert scale is employed to score each item, with a range from 1 to 5 (1 = strongly disagree, 5 = strongly agree). The total score is calculated by summing the scores of all three items, yielding a score range of 3 to 15 points, with higher scores indicating stronger self-efficacy[ 33 ]. In this study, the Cronbach’s α coefficient for the NGSES-SF scale was 0.928. The BFI-10 (Ten-Item Big Five Inventory) is designed to assess individual personality traits across five dimensions: extraversion, agreeableness, conscientiousness, neuroticism , and openness using 10 items. It employs a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). Extraversion scores are calculated by summing the scores of items 1R and 6, agreeableness scores are calculated by summing the scores of items 2 and 7R, conscientiousness scores are derived by summing the scores of items 3R and 8, neuroticism scores are calculated by summing the scores of items 4R and 9, and openness scores are obtained by summing the scores of items 5R and 10 (R indicates reverse scoring). The total score of the questionnaire ranges from 10 to 50, with higher scores indicating stronger expression of the corresponding personality traits[ 34 ]. In this study, the Cronbach’s alpha coefficients for extraversion, agreeableness, conscientiousness, neuroticism , and openness were 0.815, 0.733, 0.752, 0.770, and 0.726, respectively, all exceeding the threshold of 0.7, which meets the reliability standards for personality measurement in psychological assessments. The Adult ADHD Self-Report Scale V1.1 (ASRS-V1.1) is a six-item instrument used for screening Attention Deficit Hyperactivity Disorder (ADHD) in adults. It employs a 5-point Likert scale, with response options ranging from "never" to "very often." For all items, the "never" response is scored as 0. The maximum score for each item varies: item 3 has a maximum of 6 points, items 1 and 2 have a maximum of 5 points, item 5 has a maximum of 4 points, item 6 has a maximum of 3 points, and item 4 has a maximum of 2 points, which may be related to a total score range from 0 to 25. Higher total scores indicate greater levels of attention deficit or hyperactivity disorder[ 35 ]. In this study, the Cronbach's α coefficient for the ASRS-V1.1 scale was 0.915. Social Dimension Measurement The social dimension was assessed adopting the Health Literacy Scale (HLS-SF4), Family Communication Scale (FCS-SF), and Social Support Scale (PSSS-SF). HLS-SF4 is a brief health literacy assessment tool consisting of four Likert items. It encompasses three dimensions: healthcare, disease prevention , and health promotion . The scoring range for each item is from 0 to 3 (from very difficult to very easy), with the total score ranging from 0 to 12. Higher scores indicate a higher level of health literacy. Owing to its shorter completion time and broad applicability across various populations, it is convenient to use it in large-scale cross-sectional studies[ 36 , 37 ]. In this study, the Cronbach's α coefficient for the HLS-SF4 scale was 0.899. The FCS-SF assesses the quality, openness, and effectiveness of communication among family members based on four items. It covers various aspects of family communication and uses a Likert-type scoring method, with ratings ranging from 1 to 5 (strongly disagree to strongly agree). Its total score is calculated by summing the scores of all four items, with a range from 4 to 20 points. Higher scores indicate a higher level of family communication[ 38 ]. In this study, the Cronbach's α coefficient for the FCS-SF scale was 0.948. The PSSS-SF assesses individuals' perceived social support based on several items. The questionnaire comprises three main dimensions: family support (items 1–3), friend support (items 4–6), and other support (items 7–9). Each item is scored using a Likert scale ranging from 1 to 7 (from "strongly disagree" to "strongly agree"). Higher scores indicate a stronger perception of social support [ 39 ]. In this study, the Cronbach's α coefficient for the PSSS-SF scale was 0.903. 3.3 Statistical Analysis 3.3.1 Descriptive Statistics Descriptive statistics were used to describe participants' biological, psychological, and social characteristics and HRQoL. Specifically, mean, standard deviation, and median were used to describe the distribution of EQ-5D-5L utility and EQ-VAS scores across categorical variables; and the differences in EQ-5D-5L utility and EQ-VAS scores between different subgroups were tested using independent samples t-test or analysis of variance. 3.3.2 Model construction Model construction began with data preprocessing, in which missing values were imputed using mode imputation. Given that most variables were categorical and the proportion of missing data was low, this approach was considered appropriate for the present analysis[ 40 – 42 ]. The next step was to calculate the score for each measurement scale according to the respective scoring methods, ensuring suitability for modeling and enhancing model interpretability. Categorical variables such as age, BMI, and number of chronic diseases were also dummy-coded prior to modeling. It should be noted that the formal BPS model did not take gender into account, as it remained unclear whether gender should be classified under the biological dimension or the social dimension[ 13 , 43 – 45 ]. In SEM, latent variables were treated as abstract concepts or traits that could not be directly observed but could be indirectly measured through multiple observable indicators. For example, psychological well-being could not be directly measured but could be represented by indicators such as anxiety, depression, and life satisfaction. In the analysis, the biological, psychological, and social dimensions were modeled as latent variables, each represented by relevant observable indicators. To examine the model fit of the biological, psychological, and social latent variables, as well as their path relationships and mediated association structures with the dependent variables (i.e., EQ-5D-5L utility values and EQ-VAS scores), the study employed Partial Least Squares Structural Equation Modeling (PLS-SEM), a variant of SEM (Fig. 1 ). It is particularly suitable for exploratory and predictive research because it could effectively analyze relationships among variables in cross-sectional data and simultaneously handle ordinal categorical and continuous variables. All PLS-SEM analyses were conducted using SmartPLS version 4.0 software. 3.3.3 Model Evaluation The BPS model was evaluated based on both the Measurement Model and Structural Model, and was then internally validated according to robustness check using PLSpredict. Measurement Model The measurement model assessed the relationship between latent variables and their corresponding indicators. The selection of indicators was based on the theoretical framework of biological, psychological, and social constructs to ensure conceptual completeness, semantic clarity, and consistency with the formative measurement approach. The study adopted a formative measurement model, focusing on multicollinearity diagnostics and the statistical significance of indicator weights. Collinearity was assessed using the Variance Inflation Factor (VIF), where VIF ≥ 5, 3–5, and < 3 indicated the presence of collinearity, the possibility of collinearity, and no collinearity problems, respectively[ 46 , 47 ]. The statistical significance of each indicator’s outer weight was tested using bootstrapping. If the t-value of an indicator exceeded 1.96 (corresponding to a 5% confidence interval), or if the p-value was less than 0.05, the indicator was considered statistically significant. If an indicator’s weight was not statistically significant but was theoretically justified, the indicator was still retained in the model. Structural Model The structural model was used to examine the relationships among latent variables and their associations with endogenous variables, focusing on path coefficients and their statistical significance, VIF, overall model fit, and R². The relationships were categorized into direct, indirect, and total associations. As stated above, the hypothesis tested primarily examined the direct and indirect associations of biological, psychological, and social factors on HRQoL. For direct associations, path coefficients represented the direction and magnitude of the relationships among latent variables; coefficients below 0.1 were considered weak (insufficient to explain variance), between 0.1 and 0.3 moderate, and above 0.3 strong[ 46 ]. For indirect effects, the analysis emphasized on statistical significance rather than the coefficient magnitude, as indirect effects arose from the combined associations of multiple mediating pathways. All path coefficients were evaluated for statistical significance using bootstrapping, based on their corresponding t-values and p-values, ensuring robustness and reliability of the results. VIF values were also used to assess potential multicollinearity among exogenous latent variables when predicting an endogenous variable, thereby ensuring the stability of the path coefficients. Model fit was assessed using the Standardized Root Mean Square Residual (SRMR) and the Normed Fit Index (NFI), in which SRMR values below 0.05 and NFI values above 0.90 were generally considered indicative of good model fit. R² was used to assess the extent to which exogenous variables explain the variance of endogenous latent variables: an R² value of approximately 0.20 was generally considered an acceptable level of explanatory power[ 46 , 48 ]. Robustness Check using PLSpredict The PLSpredict method with tenfold cross-validation, repeated ten iterations, was adopted to evaluate the predictive performance of the PLS-SEM model. First, Q² was used to assess predictive relevance, in which values greater than zero indicated that the model had predictive ability beyond random chance[ 49 , 50 ]. To further examine predictive accuracy, the method used a linear regression model (LM) as the benchmark, comparing the prediction errors of PLS-SEM and LM for the endogenous variables EQ-5D-5L and EQ-VAS. Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) served as measures of prediction errors. According to the guidelines of Shmueli et al. and Hair et al., if at least half of the indicators yielded lower errors than LM, the model was considered to demonstrate moderate predictive power; otherwise, it did not[ 47 , 51 ]. 3.3.4 Subgroups analysis The sample was stratified based on 10 variables: education (low, middle, high), age (young, middle, old), region (eastern, central, western), sex (male, female), income (≤ 4000, > 4000), marriage (single, married, other), residence (urban, rural), smoking (yes, no), drinking (yes, no), chronic disease (yes, no). The model was then independently applied within each subgroup to assess the consistency of path-based associations and differences in model fit. Consistent with those of the structural model, the significance, direction, and strength of each path were determined by the path coefficients and p-values, while SRMR and NFI were used to assess the model fit. 3.4 Ethics The study was supported by the National Health Commission Key Laboratory of Health Economics and Policy Research under its Key Scientific Research Project (NHC-HEPR202401). The study protocol was approved by the Ethics Review Committee of Shanghai Jiao Tong University (Approval No. H20240237I) and was registered in the Chinese Clinical Trial Registry (Registration No. ChiCTR2400085016). The cover page of the questionnaire will explain the study’s purpose and assure anonymity, confidentiality, and the right to refuse to participate in the study. Informed consent was obtained from all subjects involved in the study. 4 Results 4.1 Descriptive statistics The mean age of study participants was 40.7 years, with females accounting for 50.6%. Regarding biological variables, approximately four-fifths of the participants (80.1%) were young and middle-aged adults (< 55 years). More than half of the participants (59.4%) had a BMI within the normal range. The majority of participants did not consume alcohol (72.2%) or smoke (80.4%), and 83.8% had stable sleep patterns. Approximately one-quarter (24.8%) reported having at least one chronic condition (Table 1 and Appendix Table A1). Table 1 Participant Characteristics and Distribution of EQ-5D-5L Utility and EQ-VAS 1 Biological Variable 2 N (%) EQ-5D-5L utility EQ-VAS Mean (SD) Median P value 1 Mean (SD) Median P value Age group <0.001 <0.001 18–34 10244(40.9) 0.947(0.126) 1.000 78.00(18.66) 81.00 35–54 9830(39.2) 0.936(0.149) 1.000 76.28(19.10) 80.00 ≥ 55 4973(19.9) 0.872(0.229) 0.951 71.49(19.54) 76.00 BMI group <0.001 <0.001 < 18.5 2394(9.6) 0.901(0.213) 1.000 74.84(20.15) 80.00 18.5–23.9 14872(59.4) 0.934(0.150) 1.000 76.49(18.62) 80.00 ≥ 24 7780(31.1) 0.925(0.166) 1.000 75.52(19.82) 80.00 Number of chronic diseases 3 <0.001 <0.001 0 18823(75.2) 0.944(0.152) 1.000 78.14(18.71) 81.00 1 4111(16.4) 0.901(0.164) 0.951 71.31(18.81) 76.00 2 1404(5.6) 0.862(0.174) 0.893 68.16(18.27) 70.00 ≥ 3 712(2.8) 0.771(0.250) 0.831 63.04(20.60) 63.00 Smoking <0.001 <0.001 Yes 4914(19.6) 0.919(0.162) 1.000 74.24(20.44) 80.00 No 20133(80.4) 0.930(0.163) 1.000 76.47(18.81) 80.00 Sleep variability <0.001 <0.001 Highly stable 8176(32.6) 0.956(0.114) 1.000 79.54(19.02) 82.00 Relatively stable 12812(51.2) 0.939(0.116) 1.000 76.40(17.13) 80.00 Relatively unstable 3242(12.9) 0.858(0.264) 0.951 69.01(21.57) 73.00 Very unstable 817(3.3) 0.747(0.383) 0.942 63.03(26.45) 66.00 Alcohol drinking <0.001 <0.001 Yes 6962(27.8) 0.937(0.123) 1.000 76.81(18.26) 80.00 No 18085(72.2) 0.924(0.175) 1.000 75.73(19.49) 80.00 Social Variable N (%) EQ-5D-5L utility EQ-VAS Mean (SD) Median P value 1 Mean (SD) Median P value Educational level 4 <0.001 <0.001 Primary Education 3159(12.6) 0.889(0.196) 0.951 70.64(19.60) 75.00 Secondary Education 8742(34.9) 0.933(0.151) 1.000 75.69(19.14) 80.00 Higher Education 13146(52.5) 0.933(0.160) 1.000 77.56(18.82) 81.00 Monthly household income (RMB) <0.001 6000 8266(33.0) 0.937(0.157) 1.000 78.50(17.95) 81.00 Residence <0.001 <0.001 Urban 18815(75.1) 0.935(0.148) 1.000 76.89(18.58) 80.00 Rural 6232(24.9) 0.905(0.200) 1.000 73.43(20.59) 79.00 Region <0.001 <0.001 Eastern 10591(42.3) 0.912(0.199) 1.000 76.09(19.84) 80.00 Central 8983(35.9) 0.943(0.115) 1.000 77.08(17.88) 80.00 Western 5473(21.9) 0.933(0.147) 1.000 74.19(19.72) 79.00 Employment <0.001 <0.001 Yes 15766(62.9) 0.939(0.153) 1.000 76.70(19.11) 80.00 No 9281(37.1) 0.909(0.177) 1.000 74.89(19.19) 80.00 1 Table 1 presents the proportions of categorical variables and the distribution of EQ-5D-5L utility and EQ-VAS scores. The mean and standard deviation of continuous variables are provided in Appendix Table 1 . 2 Independent sample t-test was used for 2 sample groups, and analysis of variance (ANOVA) was used for 3 or more sample groups. 3 Chronic diseases include hypertension, diabetes, hyperlipidemia, coronary heart disease, stroke, respiratory diseases, urinary system diseases, digestive 3 system diseases, osteoporosis, arthritis, tumors, rare diseases, and other conditions. 4 Primary education and below is low education, junior and senior high school (including vocational) is intermediate, and bachelor's degree and above (including associate degree) is higher education. In terms of psychological variables, the participants' mean PSS-4 score was at a moderate level (8.07, SD: 3.82), while the mean NGSES-SF score was relatively high (10.97, SD: 2.59). Among BFI-10, extraversion (3.64, SD: 0.96), agreeableness (3.76, SD: 0.86), and conscientiousness (3.80, SD: 0.88) had relatively high scores, while neuroticism (3.56, SD: 0.94) and openness (3.43, SD: 0.98) were at moderate levels. The distribution of ADHD score was quite wide (12.06, SD: 5.44), which suggested that some participants might have experienced significant attention-related issues (Appendix Table A1). With regard to social variables, more than half of the participants (52.5%) received higher education, and nearly two-thirds (60.2%) reported a monthly income greater than 2,000 RMB. Approximately three-quarters (75.1%) lived in urban areas, and more than half (57.8%) were from the central and western regions. The mean score of HLS-SF4 was 11.18 (SD: 2.782), and the mean score for FCS-SF was 14.68 (SD: 3.80), both of which were relatively high (Table 1 and Appendix Table A1). The mean (SD) EQ-5D-5L utility and EQ-VAS scores of the sample were 0.928 (0.163) and 76.03 (19.16), respectively. Those with lower EQ-5D-5L utility scores included older adults (aged 55 and above), those with multiple chronic conditions, smokers, individuals with lower education levels and monthly income, rural residents, participants with abnormal BMI, non-drinkers, those from the eastern region, individuals with poor sleep quality, and unemployed participants (all p < 0.05, Table 1 ). The results for EQ-VAS scores were generally consistent, except for the participants from the western region had lower EQ-VAS scores (p < 0.05) (Table 1 and Appendix Table A1). 4.2 Evaluation of measurement model The results of measurement model were presented in Table 2 and Fig. 2 & 3 . Among all the indicators, sleep variability (EQ-5D-5L utility: 0.743; EQ-VAS: 0.732), general self-efficacy (EQ-5D-5L utility: 0.569; EQ-VAS: 0.559), and social support (EQ-5D-5L utility: 0.601; EQ-VAS: 0.593) had the weights exceeding 0.5. The other indicators generally exhibited external weights within the range of 0.1 to 0.3. All indicators had the VIF values less than or close to 3, indicating no collinearity issues. According to p-values and t-values, almost all indicators showed statistical significance, except for Conscientiousness (BFI-10) (t = 1.049, p = 0.294) and Secondary Education (t = 0.604, p = 0.546) in the EQ-5D-5L utility model; and Conscientiousness (BFI-10) (t = 0.367, p = 0.713) and Eastern (t = 0.381, p = 0.703) in the EQ-VAS model (Table 2 ). Table 2 Measurement model results Constructs of EQ-5D-5L utility Code Indicators Convergent validity Collinearity Statistical significance of weights Outer weights VIF T-statistics P-value Biological B1(1) Age(18–34) 0.451 2.267 11.970 0.000 B1(2) Age(35–54) 0.393 2.012 12.278 0.000 B1(3) Age(> 54) (Reference) B2(1) BMI( 23.9) 0.180 3.042 5.619 0.000 B3(1) Number of chronic diseases(≥ 3) -0.328 1.078 14.526 0.000 B3(2) Number of chronic diseases(2) -0.213 1.122 11.243 0.000 B3(3) Number of chronic diseases(1) -0.186 1.135 10.122 0.000 B3(4) Number of chronic diseases(0) (Reference) B4 Smoking -0.034 1.146 2.103 0.036 B5 Sleep variability 0.743 1.014 35.404 0.000 B6 Physical activity level( IPAQ-7) 0.056 1.009 2.771 0.006 B7 Alcohol drinking 0.099 1.153 7.152 0.000 Physiological P1 Perceived stress (PSS-4) 0.202 1.773 18.855 0.000 P2 General Self-Efficacy(NGSES-SF) 0.569 1.498 53.111 0.000 P3 Extraversion(BFI-10) 0.038 2.764 3.151 0.002 P4 Agreeableness(BFI-10) 0.263 3.136 19.621 0.000 P5 Conscientiousness(BFI-10) -0.014 2.533 1.049 0.294 P6 Neuroticism(BFI-10) 0.047 2.507 3.988 0.000 P7 Openness(BFI-10) 0.179 1.711 11.993 0.000 P8 ADHD(ASRS) 0.132 1.754 12.989 0.000 Social S1(1) Primary Education -0.096 1.348 6.780 0.000 S1(2) Secondary Education -0.006 1.166 0.604 0.546 S1(3) Higher Education (Reference) S2(1) Monthly household income(≤ 2,000) -0.038 1.350 4.470 0.000 S2(2) Monthly household income(2001–4000) -0.033 1.446 3.954 0.000 S2(3) Monthly household income (4001–6000) -0.018 1.364 2.334 0.020 S2(4) Monthly household income(>6000) (Reference) S3 Health Literacy(HLS-SF4) 0.397 1.236 38.991 0.000 S4 Social support(PSSS-SF) 0.601 1.409 54.551 0.000 S5 Family Communication(FCS-SF) 0.272 1.364 26.098 0.000 S6 Residence(urban) 0.040 1.124 5.390 0.000 S7(1) Eastern 0.041 1.226 5.124 0.000 S7(2) Central 0.078 1.236 10.270 0.000 S7(3) Western (Reference) S8 Employment 0.094 1.036 11.033 0.000 Constructs of EQ-VAS Code Indicators Convergent validity Collinearity Statistical significance of weights Outer weights VIF T-statistics P-value Biological B1(1) Age(18–34) 0.302 2.267 6.985 0.000 B1(2) Age(35–54) 0.249 2.012 7.590 0.000 B1(3) Age(> 54) (Reference) B2(1) BMI( 23.9) 0.094 3.042 2.907 0.004 B3(1) Number of chronic diseases(≥ 3) -0.276 1.078 14.163 0.000 B3(2) Number of chronic diseases(2) -0.253 1.122 13.324 0.000 B3(3) Number of chronic diseases(1) -0.282 1.135 14.845 0.000 B3(4) Number of chronic diseases(0) (Reference) B4 Smoking -0.102 1.146 5.183 0.000 B5 Sleep variability 0.732 1.014 37.096 0.000 B6 Physical activity level( IPAQ-7) 0.262 1.009 12.976 0.000 B7 Alcohol drinking 0.105 1.153 5.920 0.000 Physiological P1 Perceived stress (PSS-4) 0.179 1.000 17.565 0.000 P2 General Self-Efficacy(NGSES-SF) 0.559 1.773 55.237 0.000 P3 Extraversion(BFI-10) 0.051 1.498 4.502 0.000 P4 Agreeableness(BFI-10) 0.253 2.764 20.162 0.000 P5 Conscientiousness(BFI-10) 0.005 3.136 0.367 0.713 P6 Neuroticism(BFI-10) 0.059 2.533 5.183 0.000 P7 Openness(BFI-10) 0.173 2.507 12.078 0.000 P8 ADHD(ASRS) 0.133 1.711 13.853 0.000 Social S1(1) Primary Education -0.090 1.754 6.986 0.000 S1(2) Secondary Education -0.021 1.348 2.133 0.033 S1(3) Higher Education (Reference) S2(1) Monthly household income(≤ 2,000) -0.043 1.350 5.434 0.000 S2(2) Monthly household income(2001–4000) -0.052 1.446 6.549 0.000 S2(3) Monthly household income (4001–6000) -0.035 1.364 4.695 0.000 S2(4) Monthly household income(>6000) (Reference) S3 Health Literacy(HLS-SF4) 0.400 1.236 40.753 0.000 S4 Social support(PSSS-SF) 0.593 1.409 57.638 0.000 S5 Family Communication(FCS-SF) 0.287 1.364 28.116 0.000 S6 Residence(urban) 0.023 1.124 3.332 0.001 S7(1) Eastern -0.003 1.226 0.381 0.703 S7(2) Central 0.049 1.236 6.651 0.000 S7(3) Western (Reference) S8 Employment 0.053 1.036 6.377 0.000 4.3 Evaluation of structural model In the structural model, all path coefficients were positive and statistically significant (p < 0.001) (Table 3 and Appendix Table A2). The coefficients of the direct paths were all above 0.1, indicating significant and practically meaningful relationships among the variables. The indirect paths were also positive and statistically significant, confirming robust mediated association patterns. All these were consistent with the five hypotheses. All VIF values were less than 3, indicating acceptable multicollinearity between the latent variables (Table 3 ). SRMR values were all less than 0.05, and NFI values were close to 0.9 (EQ-5D-5L utility model: SRMR = 0.046, NFI = 0.884; EQ-VAS model: SRMR = 0.043, NFI = 0.890), indicating good model fit (Table 3 ). The R² values for the EQ-5D-5L utility and EQ-VAS scores were 0.188 and 0.222, indicating reasonable variance explained by the model (Fig. 2 ). Table 3 Path analysis verification Hypothesis Path Analysis 1 Effect Type Path Coefficient VIF T Value p Value Hypothesis supported or not H1 Biological→EQ-5D-5L utility Direct 0.263 1.085 33.793 0.000 Supported H2 Psychological→EQ-5D-5L utility Direct 0.196 2.068 22.574 0.000 Supported H3 Social→EQ-5D-5L utility Direct 0.104 2.079 11.475 0.000 Supported H4a Social→Biological→EQ-5D-5L utility Indirect 0.044 — 8.667 0.000 Supported H4b Psychological→Biological→EQ-5D-5L utility Indirect 0.035 — 8.007 0.000 Supported H4c Social→Psychological→EQ-5D-5L utility Indirect 0.140 — 22.458 0.000 Supported H5 Social→Psychological→Biological→EQ-5D-5L utility Chain Indirect 0.025 — 7.951 0.000 Supported Fit Indices Saturated Model Estimated Model Model Fit SRMR 0.046 0.046 NFI 0.884 0.884 Hypothesis Path Analysis 2 Effect Type Path Coefficient VIF T Value p Value Hypothesis supported or not H1 Biological→EQ-VAS utility Direct 0.178 1.090 26.960 0.000 Supported H2 Psychological→EQ-VAS utility Direct 0.238 2.107 26.047 0.000 Supported H3 Social→EQ-VAS utility Direct 0.180 2.105 20.198 0.000 Supported H4a Social→Biological→EQ-VAS utility Indirect 0.027 — 8.023 0.000 Supported H4b Psychological→Biological→EQ-VAS utility Indirect 0.028 — 9.414 0.000 Supported H4c Social→Psychological→EQ-VAS utility Indirect 0.171 — 25.641 0.000 Supported H5 Social→Psychological→Biological→EQ-VAS utility Chain Indirect 0.020 — 9.368 0.000 Supported Fit Indices Saturated Model Estimated Model Model Fit SRMR 0.043 0.043 NFI 0.890 0.890 1 Model 1 takes EQ-5D-5L utility as the dependent variable. 2 Model 2 takes EQ-VAS as the dependent variable. 4.4 Robustness Check using PLSpredict The predictive performance of the model was assessed using Q², RMSE, and MAE (Table 4 ). The Q² values for EQ-5D-5L utility and EQ-VAS were 0.098 and 0.158, respectively. Based on the 10-fold cross-validation, the predictive errors of PLS-SEM were identical to those of the linear model (RMSE: 0.15; MAE: 0.09) for the EQ-5D-5L utility model, indicating moderate predictive performance. For the EQ-VAS model, the predictive errors of PLS-SEM were similar with those of the LM (RMSE: 17.58 vs. 17.53; MAE: 13.59 vs. 13.57). All these indicated that the BPS model demonstrated acceptable predictive performance (Table 4 ). Table 4 Results of the Robustness Check using PLSpredict Constructs Q 2 Predict PLSPredict PLS-SEM RMSE PLS-SEM MAE LM RMSE 1 LM MAE Result of Predictive Relevance (Q2 Predict) and K-fold Cross-Validation (ten fold, ten iterations) EQ-5D-5L utility 0.098 0.15 0.09 0.15 0.09 EQ-VAS 0.158 17.58 13.59 17.53 13.57 1 The full form of LM is Linear Model. 4.5 Subgroup analysis The results of subgroup analysis according to PLS-SEM path-based associations were presented in Table 5 . For direct associations, most path coefficients were greater than 0.1 in the EQ-5D-5L utility model. Meanwhile, the path from Social to EQ-5D-5L utility showed coefficients below 0.1 among the following subgroups: low education level , younger and older age groups , central and western regions, female, income ≤ 4000, single, urban residents, smokers, drinkers , and individuals with chronic diseases . In the EQ-VAS model, all direct associations coefficients were greater than 0.1.With regard to statistical significance, certain direct paths did not reach significance. In the EQ-5D-5L utility model, those included Social → EQ-5D-5L utility ( low education level and smokers ) and Biological → EQ-5D-5L utility ( younger age group, female , and drinkers ); in the EQ-VAS model, the insignificance of path Biological → EQ-VAS utility was observed among the subgroups with low education level and those from the central region . Table 5 Model of PLS-SEM path analysis diagram in different groups EQ-5D Path Analysis 3 Education 1 Age 2 Region Sex income Low Middle High Young Middle Old Eastern Central Western Male Female ≤ 4000 >4000 Biological→EQ-5D-5L utility 0.354*** 0.241*** 0.246*** -0.194** 0.240*** 0.261*** 0.267*** -0.319*** -0.315*** 0.274*** 0.266* 0.289*** 0.243*** Psychological→EQ-5D-5L utility 0.277*** 0.194*** 0.179*** 0.168*** 0.191*** 0.353*** 0.239*** 0.145*** 0.174*** 0.181*** 0.206*** 0.192*** 0.197*** Social→EQ-5D-5L utility -0.001 0.105*** 0.136*** 0.086*** 0.113*** 0.087*** 0.135*** 0.052*** 0.038*** 0.114*** 0.093*** 0.084*** 0.118*** Social→Biological→EQ-5D-5L utility 0.034*** 0.012*** 0.018*** 0.003* 0.006* 0.021*** 0.035*** 0.107*** 0.063*** 0.040*** 0.049*** 0.041*** 0.033*** Psychological→Biological→EQ-5D-5L utility 0.069*** 0.053*** 0.051*** 0.047*** 0.063*** 0.074*** 0.057*** -0.004 0.022** 0.041*** 0.034*** 0.044*** 0.037*** Social→Psychological→EQ-5D-5L utility 0.182*** 0.136*** 0.132*** 0.123*** 0.136*** 0.248*** 0.177*** 0.096*** 0.121*** 0.131*** 0.146*** 0.138*** 0.140*** Social→Psychological→Biological→EQ-5D-5L utility 0.045*** 0.037*** 0.038*** 0.035*** 0.045*** 0.052*** 0.042*** -0.002 0.015** 0.030*** 0.024*** 0.032*** 0.027*** VAS Path Analysis 3 Education Age Region Sex income Low Middle High Young Middle Old Eastern Central Western Male Female ≤ 4000 >4000 Biological→EQ-VAS 0.229* 0.152*** 0.157*** 0.148*** 0.153*** 0.180*** 0.184*** 0.183* 0.190*** 0.183*** 0.179*** 0.183*** 0.174*** Psychological→EQ-VAS 0.278*** 0.231*** 0.264*** 0.243*** 0.264*** 0.299*** 0.253*** 0.238*** 0.192*** 0.236*** 0.239*** 0.254*** 0.231*** Social→EQ-VAS 0.168*** 0.171*** 0.159*** 0.144*** 0.173*** 0.182*** 0.189*** 0.138*** 0.210*** 0.181*** 0.176*** 0.161*** 0.179*** Social→Biological→EQ-VAS utility 0.015** 0.005** 0.010*** 0.004* 0.007*** 0.017*** 0.027*** 0.033*** 0.039*** 0.024*** 0.031*** 0.021*** 0.021*** Psychological→Biological→EQ-VAS utility 0.050*** 0.038*** 0.039*** 0.037*** 0.040*** 0.047*** 0.039*** 0.022*** 0.015** 0.033*** 0.026*** 0.034*** 0.032*** Social→Psychological→EQ-VAS utility 0.187*** 0.164*** 0.195*** 0.177*** 0.190*** 0.217*** 0.188*** 0.165*** 0.134*** 0.172*** 0.170*** 0.184*** 0.165*** Social→Psychological→Biological→EQ-VAS utility 0.034*** 0.027*** 0.028*** 0.027*** 0.029*** 0.034*** 0.029*** 0.015*** 0.010** 0.024*** 0.019*** 0.025*** 0.023*** EQ-5D Path Analysis 3 Marriage Residence Smoking Drinking Chronic disease Single Married Other 4 Urban Rural Yes No Yes No Yes No Biological→EQ-5D-5L utility 0.254*** 0.275*** 0.253*** 0.243*** 0.296*** 0.385*** 0.248*** -0.285* 0.268*** 0.163*** 0.210*** Psychological→EQ-5D-5L utility 0.195*** 0.188*** 0.300*** 0.199*** 0.185*** 0.156*** 0.199*** 0.171*** 0.204*** 0.272*** 0.185*** Social→EQ-5D-5L utility 0.088*** 0.123*** 0.136*** 0.098*** 0.115*** 0.025* 0.125*** 0.055*** 0.115*** 0.067*** 0.123*** Social→Biological→EQ-5D-5L utility 0.039*** 0.053*** 0.020* 0.031*** 0.044*** 0.069*** 0.040*** 0.029*** 0.049*** 0.001 0.011*** Psychological→Biological→EQ-5D-5L utility 0.037*** 0.031*** 0.075*** 0.035*** 0.058*** 0.046*** 0.034*** 0.047*** 0.035*** 0.040*** 0.046*** Social→Psychological→EQ-5D-5L utility 0.140*** 0.135*** 0.203*** 0.141*** 0.137*** 0.111*** 0.142*** 0.116*** 0.148*** 0.181*** 0.136*** Social→Psychological→Biological→EQ-5D-5L utility 0.026*** 0.022*** 0.051*** 0.025*** 0.043*** 0.033*** 0.024*** 0.032*** 0.026*** 0.027*** 0.034*** VAS Path Analysis 3 Marriage Residence Smoking Drinking Chronic disease Single Married Other Urban Rural Yes No Yes No Yes No Biological→EQ-VAS 0.167*** 0.192*** 0.200*** 0.179*** 0.167*** 0.226*** 0.162*** 0.197*** 0.170*** 0.081*** 0.117*** Psychological→EQ-VAS 0.236*** 0.238*** 0.257*** 0.236*** 0.251*** 0.249*** 0.234*** 0.218*** 0.247*** 0.289*** 0.258*** Social→EQ-VAS 0.179*** 0.174*** 0.256*** 0.169*** 0.191*** 0.135*** 0.192*** 0.144*** 0.191*** 0.154*** 0.168*** Social→Biological→EQ-VAS utility 0.024*** 0.033*** 0.017* 0.018*** 0.030*** 0.027*** 0.026*** 0.014*** 0.030*** -0.002* 0.006*** Psychological→Biological→EQ-VAS utility 0.028*** 0.027*** 0.057*** 0.033*** 0.029*** 0.043*** 0.025*** 0.042*** 0.024*** 0.022*** 0.028*** Social→Psychological→EQ-VAS utility 0.170*** 0.172*** 0.179*** 0.168*** 0.188*** 0.180*** 0.169*** 0.147*** 0.181*** 0.195*** 0.190*** Social→Psychological→Biological→EQ-VAS utility 0.020*** 0.019*** 0.039*** 0.023*** 0.022*** 0.031*** 0.018*** 0.029*** 0.018*** 0.015*** 0.021*** 1 Primary education and below is low education, junior and senior high school (including vocational) is intermediate, and bachelor's degree and above (including associate degree) is higher education. 2 18–34 years old are considered young adults, 35–54 years old are middle-aged, and 55 years and above are elderly. 3 * indicates p-value < 0.5, ** indicates p-value < 0.1, *** indicates p-value < 0.05. The table shows the path coefficients(β) and their significance. 4 Others include divorce or widowhood. For indirect associations, several subgroup paths did not reach statistical significance. In the EQ-5D-5L utility model, those included Social → Biological → EQ-5D-5L utility ( younger age group, middle age group, other marital status , and individuals with chronic diseases ), Psychological → Biological → EQ-5D-5L utility ( central and western regions ), and Social → Psychological → Biological → EQ-5D-5L utility ( central and western regions ). In the EQ-VAS model, they were Social → Biological → EQ-VAS utility ( low education level, middle education level, younger age group, other marital status , and individuals with chronic diseases ), as well as Social → Psychological → Biological → EQ-VAS utility ( western region ). Across all the subgroups, SRMR values were below 0.05, and NFI values were all above or close to 0.9 (see Appendix Table A2), indicating that the BPS model fit well across all subgroups. 5 Discussion 5.1 Research Findings and Innovations The study, grounded in the BPS model, investigated the associations of biological, psychological, and social factors with the HRQoL of Chinese residents. We found that all the three kinds of factors were directly and positively associated with HRQoL. Moreover, psychological and social factors also served as statistical mediators of the associations between biological, psychological, and social factors and HRQoL. The study also revealed a chain-mediated association pattern, in which social disadvantage was associated with higher psychological burden, which in turn was associated with biological health status, ultimately being associated with lower HRQoL. In terms of subgroup analysis, the BPS model demonstrated acceptable predictive performance and performed well across different subgroups; while certain path-based associations—particularly the associations between social factors and HRQoL—were weaker or non-significant among disadvantaged groups such as individuals with lower education, lower income, or chronic diseases. Compared to prior studies, our study had three advantages. First, it used the largest sample size among existing BPS model studies, and was the first investigation in the general Chinese population. By exploring the BPS model within the context of China’s background, this study uncovered the pathway-consistent association patterns through which biological, psychological, and social factors were associated with HRQoL in the population. Second, the analyses demonstrated the model’s robustness across different subgroups and examined variations in the association patterns related to HRQoL among these groups. These findings provide important insights for developing more targeted public health interventions and policies tailored to the specific needs of different subpopulations. Third, it revealed complex interconnections and mediated association structures among biological, psychological, and social factors, providing new empirical evidence. These findings deepen the understanding of the intricate interrelationships within the BPS framework and promote the integration of multidisciplinary approaches in future health research. 5.2 Model Selection This study explored the complex pathways associated with the HRQoL of Chinese residents. In line with this objective, the BPS model fit well with the research needs because of its strong theoretical foundation, which aligned closely with the multidimensional concept of HRQoL. Meanwhile, several theoretical frameworks have also been adopted in HRQoL research, among which the Ferrans model is one of the most representative. Although it has been widely applied to explain the factors associated with HRQoL among specific populations, including the general population, it primarily emphasizes relatively unidirectional relationships and thus may not fully capture the complex interrelationships among biological, psychological, and social dimensions. Empirically, it has often been used to verify predefined structural relationships, with limited attention to the context-dependent and culturally embedded associations across different social settings[ 52 ]. Furthermore, although the model has been widely applied, empirical findings regarding its hypothesized pathways have been mixed, with some relationships showing limited or non-significant associations across studies. For example, Duangchan and Matthews reviewed 31 empirical studies and reported that certain links, such as the pathway from environmental characteristics to biological function, were not supported in any study[ 53 ]. Early studies applying the BPS model mostly relied on linear regression methods, as they typically involved a limited number of variables and relatively simple analytical structures[ 7 , 54 ]. In contrast, the present study incorporated latent variable modeling, numerous observed indicators, and complex path-based association structures, for which traditional regression-based approaches were inadequate. Accordingly, Structural Equation Modeling (SEM), which moves beyond simple linear regression by allowing the simultaneous estimation of multiple relationships among latent constructs, was considered more appropriate. Among different SEM approaches, variance-based Partial Least Squares SEM (PLS-SEM) offered several advantages over covariance-based SEM (CB-SEM): it is robust to non-normal data distributions, accommodates both categorical and continuous variables, and supports prediction-oriented model assessment through the PLSpredict procedure for out-of-sample validation [ 46 , 51 , 55 ]. 5.3 Measurement Model Analysis The analyses showed that sleep variability, general self-efficacy, and social support had strong explanatory relevance according to external weights. Sleep variability has been shown to be associated with circadian rhythm stability and the regulation of the hypothalamic–pituitary–adrenal (HPA) axis, thereby being linked to immune function and stress responses and being associated with individuals’ health from both physiological and psychological perspectives. In light of recent empirical evidence on sleep regularity and health outcomes[ 56 ], our results were consistent with the growing emphasis on sleep regularity in East Asian societies and provided empirical support for the relevance of this health concern. Self-efficacy was strongly associated with individuals’ perceived control over stressors and their environment, and was related to stronger coping and self-regulatory capacities, lower psychological distress and helplessness, and more favorable patterns of health behaviors and mental well-being. Scholz et al., based on cross-cultural samples from Germany, Poland, and South Africa, also found that general self-efficacy significantly predicted psychological health and life satisfaction, serving as an important personal resource for fostering positive mental states[ 57 ]. Social support not only provided emotional comfort and practical assistance but also has been shown to be associated with buffering the negative impacts of stressful events at the psychological level, thereby being linked to greater psychological resilience and social integration. In the Chinese context, which emphasizes collectivism and interpersonal connectedness, the positive associations between social support and health and well-being appeared particularly pronounced. Similarly, Zimet et al., using a sample of US university students, validated the structural validity of the Multidimensional Scale of Perceived Social Support (MSPSS) and demonstrated that perceived social support from family, friends, and significant others was significantly associated with better social adaptation and well-being[ 58 ]. Although most indicators passed the significance test, conscientiousness (one of the Big Five personality traits) and secondary education in the EQ-5D-5L utility model, as well as the Eastern region in the EQ-VAS model, did not reach statistical significance. In the Chinese context, conscientiousness tended to emphasize responsibility toward family members, close friends, and organizations, rather than personal health management. Consequently, its association with individual health self-regulation might have been less direct or less prominent[ 59 , 60 ]. Meanwhile, the non-significance of secondary education and the Eastern region may reflect structural or contextual factors. For instance, individuals with secondary education exhibited considerable socioeconomic diversity—ranging from urban blue-collar workers to upwardly mobile young adults—which may be related to heterogeneous health outcomes. Regarding the Eastern region, although its overall economic development level was relatively high, residents there tended to have better access to healthcare and health resources, which may have reduced health disparities and led to more homogeneous health outcomes. This concentration effect could have weakened the statistical significance of the regional variable[ 61 ]. Additionally, we observed a positive association between alcohol consumption and the biological dimension, which contrasts with certain research findings. GBD 2020 Alcohol Collaborators found that higher levels of alcohol consumption were significantly associated with increased risks of all-cause, cardiovascular, cancer, and digestive disease mortality, suggesting potential adverse biological consequences of alcohol use[ 62 ]. Meanwhile, some studies have shown that moderate alcohol consumption may be associated with improved cardiovascular health and potential benefits for brain health [ 63 , 64 ]. Therefore, future research is warranted to further refine the classification and measurement of alcohol consumption levels to better understand its heterogeneous associations with health outcomes. 5.4 Structural Model Analysis Our study identified multiple path-based association structures among biological, psychological, and social factors in relation to HRQoL. First, the validation of the Biological → HRQoL, Psychological → HRQoL, and Social → HRQoL pathways suggested that these factors were directly and significantly associated with HRQoL. These findings were consistent with prior empirical evidence. For example, a study on multiple sclerosis in Germany emphasized that neuropathic pain and disease-related physiological status were closely associated with HRQoL[ 65 ]. Furthermore, the Social → Biological → HRQoL pathway indicated that social factors were indirectly associated with HRQoL through their associations with biological health. This pattern is consistent with evidence from a study on older adults living with HIV in the United States, which reported that social conditions were related to biological health indicators such as immune functioning, and subsequently associated with HRQoL[ 66 ]. Similarly, the Psychological → Biological → HRQoL pathway highlighted that psychological health was associated with biological functioning, particularly through the association between depressive symptoms and inflammatory markers, which were in turn linked to HRQoL, as shown in studies on rheumatoid arthritis[ 67 ]. Finally, the Social → Psychological → HRQoL pathway suggested that social factors were associated with psychological health, which was subsequently related to HRQoL, a pattern consistent with evidence from a UK study on child health that emphasized the important role of social support in mental health and HRQoL [ 23 ]. Moreover, compared with previous studies, the analysis revealed a more complex chain-mediated association pattern—“social → psychological → physiological → HRQoL”. This pathway suggested that social disadvantage was associated with higher psychological distress and lower psychological resilience, which were subsequently associated with poorer physiological health and, in turn, lower HRQoL. Limited social resources—such as insufficient social support, weak family communication, or socioeconomic deprivation—may be associated with higher perceived stress and negative emotions, which are commonly linked to adverse psychological states such as anxiety, depression, or helplessness. These psychological states have been widely documented to be associated with the activation of physiological stress systems, particularly the hypothalamic–pituitary–adrenal (HPA) axis, which may be related to elevated cortisol levels, immune dysregulation, and metabolic imbalance. Over time, this cumulative psychophysiological burden may be associated with reduced physical health and functional capacity, ultimately corresponding to lower HRQoL. Notably, among the path analysis results, the association between social factors and psychological factors had the largest coefficient (EQ-5D-5L utility score: β = 0.715; EQ-VAS score: β = 0.721), indicating that this relationship was relatively stronger compared with other pathways. This pattern may reflect the sociocultural context of China, where dense social support networks and family-oriented social structures are closely linked to psychological well-being, potentially amplifying the strength of the association between social factors and psychological health. 5.5 PLS-Predict Validation Analysis We observed that the prediction errors of PLS-SEM were comparable to those of the linear model (LM) for several indicators. Such findings are not uncommon in the existing literature [ 51 ]. In fact, the methodological guidelines of PLSpredict emphasize that it is expected that LM may show lower prediction errors under certain conditions [ 68 ]. Nevertheless, this does not imply that linear regression is inherently superior, as relying on a single predictive indicator is insufficient for a comprehensive assessment of predictive adequacy[ 69 ]. The predictive performance of the LM benchmark was comparable to that of PLS-SEM, possibly because of the relatively low level of multicollinearity in the data. As a result, the strength of PLS-SEM in handling multicollinearity may not have been fully reflected in this specific application. Nevertheless, PLS-SEM’s ability to accommodate both continuous and categorical variables and to estimate complex path-based association structures offers advantages beyond traditional linear regression. Thus, PLS-SEM remains an appropriate and robust analytical approach for the present study. 5.6 Subgroup Analysis Social factors showed weaker associations with HRQoL among the low-education group but exhibited stronger associations among the medium- and high-education groups. This pattern was consistent with studies from Europe and the US, suggesting that education is associated with higher health literacy and greater efficiency in translating social and institutional resources into health outcomes [ 70 , 71 ]. The indirect psychological and biological pathways were more pronounced among highly educated individuals, further indicating that education may function as an important amplifier of health-related resources. In China, education not only reflects individual capacity differences but also determines access to medical and digital resources within the urban–rural dual structure [ 72 ], suggesting that structural inequality is closely associated with how social factors relate to health outcomes. Age differences followed a similar pattern. Among young adults (18–34 years), the psychological → biological and social → psychological → biological pathways were not statistically significant or were weak, whereas they were significantly positive among middle-aged and older adults. This finding was consistent with evidence on “youth subhealth” [ 1 , 20 ]. In the Chinese context, the pressures of involution and excessively long working hours may be associated with a weaker translation of psychological resilience into physical health. By contrast, middle-aged and older adults, who generally exhibit greater emotional regulation and social stability, showed stronger coordination among biopsychosocial dimensions. Regional and urban–rural differences revealed clear patterns consistent with structural inequality. In western China, the social → biological and psychological → biological pathways were substantially weaker, which aligned with existing evidence on regional poverty and health inequality [ 8 ]. This suggests that traditional kinship networks in western China may not sufficiently compensate for the shortage of medical and technological infrastructure[ 73 ]. Urban residents were more likely to exhibit stronger associations between social resources and health-related outcomes, whereas rural residents appeared to benefit less, potentially due to weakened social networks and limited institutional coverage. These findings are consistent with international evidence showing that the health-related associations of urban social capital are generally stronger. In the Chinese context, the urban–rural dual structure—through differences in institutional coverage and the spatial concentration of resources—may further weaken the linkage between rural social capital and formal support systems, thereby amplifying urban–rural disparities in the health-related returns of social resources. In terms of gender differences, the biological → HRQoL pathway was significantly associated with HRQoL among men but weaker or non-significant among women, a pattern consistent with the “gender health paradox” [ 43 ]. Women’s dual roles in employment and caregiving may be associated with higher psychological stress and lower subjective health perception, even when biological conditions are similar. This pattern appears particularly pronounced in China, suggesting that societal expectations regarding gender roles may be linked to poorer mental health among women, thereby contributing to disparities in health perception. Among individuals with chronic diseases, psychological factors showed stronger associations with biological health, whereas the associations between social factors and biological health were weaker. This pattern was consistent with findings from studies conducted in Europe and North America[ 67 , 74 ]. In the Chinese context, insufficient family caregiving capacity and limited community-based services may lead patients to rely more heavily on psychological self-regulation, forming a pattern of “individualized compensation” within an imperfect social support system (see Appendix Table A3). 5.7 Policy Implications The identified key factors and chain-mediated patterns could have several policy implications. First, social resources such as health literacy and social support should be addressed as upstream determinants of psychological health. Strengthening community-based support and health literacy programs may therefore improve HRQoL by enhancing psychological resilience and reducing perceived stress. Second, given the identified psychological-to-biological pathway, stress management and sleep health interventions could be integrated into primary healthcare services to improve biological health indicators and, in turn, HRQoL. Third, the weaker social–HRQoL associations observed among socioeconomically disadvantaged and chronically ill groups indicate that interventions tailored to these groups may be necessary to reduce inequalities in HRQoL. This approach may help ensure that health improvements are more evenly distributed across different population groups. 5.8 Limitations The main limitation lies in the use of a cross-sectional design, which precludes the examination of temporal dynamics and causal relationships among variables. Future research may adopt panel or longitudinal designs, allowing for a more accurate assessment of changes and directional associations over time. Furthermore, although the study systematically examined subgroup differences, it did not further test potential interaction or moderation effects among variables. Future research could employ appropriate methods such as multi-group structural equation modeling or hierarchical regression analyses to explore these interactive and moderating association patterns. 5.9 Conclusion This study, based on the BPS model, developed and validated a framework for examining the associations of biological, psychological, and social factors with the HRQoL of Chinese residents. According to the framework, key factors (i.e., sleep variability, self-efficacy, and social support) associated with HRQoL were identified, and the direct and indirect chain-mediated association structures linking these factors to HRQoL were demonstrated. In addition, variations in these association patterns across different subgroups were also identified. Declarations Ethics approval and consent to participate This study was approved by the Ethics Review Committee of Shanghai Jiao Tong University (Approval No. H20240237I). According to the ethics approval document (page 1), the committee formally approved the study and permitted its implementation, with follow-up monitoring during the study period. All procedures were conducted in accordance with relevant guidelines and regulations. Informed consent was obtained from all participants prior to data collection. Consent for publication Not applicable. Clinical trial number: Not applicable Availability of data and materials The data used in this study were derived from the Psychological and Behavioral Investigation of Chinese Residents (PBICR) and are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. Funding This study has been sponsored by the National Natural Science Foundation of China (Project No.:72274037) awarded to Dr. Pei Wang. Authors’ contributions Xiaoting Zhang : Conceptualization, Methodology, Formal analysis, Writing – original draft, Writing – review & editing. Yibo Wu : Data curation, Investigation, Writing – review & editing. Junhao Li : Formal analysis consultation and investigation. Yuang Dun : Investigation. Liyang Zhang : Investigation. Yuan Chen : Conceptualization, Methodology. Pei Wang : Conceptualization, Methodology, Supervision, Funding acquisition, Validation, Writing – review & editing. Yuanyuan Gu : Conceptualization, Writing – review & editing. All authors read and approved the final manuscript. Acknowledgements The authors sincerely thank all participants and investigators involved in the survey. References World Health Organization. WHO guidelines on integrated care for older people. Geneva: World Health Organization; 2018. The World Health Organization quality of life assessment (WHOQOL). Position paper from the World Health Organization. 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Lancet. 2008;372:1661–9. https://doi.org/10.1016/S0140-6736(08)61690-6 . Lynch J, Smith GD, Hillemeier M, Shaw M, Raghunathan T, Kaplan G. Income inequality, the psychosocial environment, and health: comparisons of wealthy nations. Lancet. 2001;358:194–200. https://doi.org/10.1016/S0140-6736(01)05407-1 . Jia C, Long Y, Luo X, Li X, Zuo W, Wu Y. Inverted U-shaped relationship between education and family health: The urban-rural gap in Chinese dual society. Front Public Health. 2023;10:1071245. https://doi.org/10.3389/fpubh.2022.1071245 . Li C, Tang C. Income-related health inequality among rural residents in western China. Front Public Health. 2022;10:1065808. https://doi.org/10.3389/fpubh.2022.1065808 . Nicassio PM, Kay MA, Custodio MK, Irwin MR, Olmstead R, Weisman MH. An evaluation of a biopsychosocial framework for health-related quality of life and disability in rheumatoid arthritis. J Psychosom Res. 2011;71:79–85. https://doi.org/10.1016/j.jpsychores.2011.01.008 . Additional Declarations No competing interests reported. Supplementary Files 05Supplementarymaterial.docx Cite Share Download PDF Status: Under Review Version 1 posted Reviewers agreed at journal 06 May, 2026 Reviewers invited by journal 30 Apr, 2026 Editor assigned by journal 25 Apr, 2026 Submission checks completed at journal 25 Apr, 2026 First submitted to journal 23 Apr, 2026 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-9507474","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":635849813,"identity":"217f6f08-f42e-49a3-8251-46d4b5a0b86d","order_by":0,"name":"xiaoting zhang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"xiaoting","middleName":"","lastName":"zhang","suffix":""},{"id":635849814,"identity":"5d416259-2357-4232-81f5-439fb6ae5eec","order_by":1,"name":"yibo wu","email":"","orcid":"","institution":"Zhejiang University","correspondingAuthor":false,"prefix":"","firstName":"yibo","middleName":"","lastName":"wu","suffix":""},{"id":635849815,"identity":"69c05eb7-a2b5-4614-8622-187f6425903c","order_by":2,"name":"Junhao Li","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Junhao","middleName":"","lastName":"Li","suffix":""},{"id":635849816,"identity":"689078d0-9623-4c2e-a82f-3a23d075538b","order_by":3,"name":"Yuang Dun","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Yuang","middleName":"","lastName":"Dun","suffix":""},{"id":635849817,"identity":"1ff610a9-7497-4244-8b63-1b3672751758","order_by":4,"name":"Liyang Zhang","email":"","orcid":"","institution":"Fudan University","correspondingAuthor":false,"prefix":"","firstName":"Liyang","middleName":"","lastName":"Zhang","suffix":""},{"id":635849818,"identity":"20759020-797d-40c8-a77c-808a81c55658","order_by":5,"name":"yuan chen","email":"","orcid":"","institution":"Shanghai Normal University","correspondingAuthor":false,"prefix":"","firstName":"yuan","middleName":"","lastName":"chen","suffix":""},{"id":635849820,"identity":"3c488c81-6490-466a-bb3c-beacbe0a759a","order_by":6,"name":"Pei wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIie3NMQuCQBTA8XsIuUitT6LvcCBYYNRXOQlyORqjIeKmW/020SgIuVy5Ci1N0eDg1Fak0HzaFnT/6T14Px4hJtPvNh0QwprB6kyWrviWpF8Qmp3SOxxypNnidiWbIBT2KdETtVpOQF2QqtuYEhWFwlkxLfET7lOQlx0tmI8g01CgQ/UkLxtyRlpED4RXF1Jw7woyqQmvv4gOZF6UPgG1QFeVa2THyJMO1xM35l4Fhxn2s2iP1TYYxbbSk7re8Ck+I2vWtvs6qwLRemQymUz/3BtBJUZOcmBaiwAAAABJRU5ErkJggg==","orcid":"","institution":"Fudan University","correspondingAuthor":true,"prefix":"","firstName":"Pei","middleName":"","lastName":"wang","suffix":""},{"id":635849822,"identity":"d3a95b30-85c6-4657-b977-d33186924760","order_by":7,"name":"yuanyuan gu","email":"","orcid":"","institution":"Macquarie University","correspondingAuthor":false,"prefix":"","firstName":"yuanyuan","middleName":"","lastName":"gu","suffix":""}],"badges":[],"createdAt":"2026-04-23 13:39:33","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-9507474/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9507474/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":109081069,"identity":"63784b2e-e67b-4963-9814-439627818d7b","added_by":"auto","created_at":"2026-05-12 11:56:54","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":182332,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual Framework and Measurement Indicators of the biopsychosocial (BPS) Model\u003c/p\u003e\n\u003cp\u003eNotes: EQ-5D-5L, EuroQol Five-Dimension Five-Level questionnaire; EQ-VAS, EuroQol Visual Analog Scale; IPAQ-7, International Physical Activity Questionnaire; BMI, Body Mass Index; PSS-4, Perceived Stress Scale; NGSES-SF, New General Self-Efficacy Scale–Short Form; BFI-10, Ten-Item Big Five Inventory; ASRS, Adult ADHD Self-Report Scale; HLS-SF4, Short-Form Health Literacy Scale; FCS-SF, Family Communication Scale–Short Form; PSSS-SF, Perceived Social Support Scale–Short Form.\u003c/p\u003e","description":"","filename":"Figure1ConceptualFrameworkandMeasurementIndicatorsoftheBPSModel.png","url":"https://assets-eu.researchsquare.com/files/rs-9507474/v1/b921bffedd96ee4b8afe6b56.png"},{"id":108976828,"identity":"e3952d42-73db-4285-bf9a-8edbae55ea4e","added_by":"auto","created_at":"2026-05-11 11:29:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":95554,"visible":true,"origin":"","legend":"\u003cp\u003eModel of PLS-SEM of EQ-5D-5L utility path analysis.\u003c/p\u003e\n\u003cp\u003eNotes: B1(1): Age (18-34); B1(2): Age (35-54); B1(3): Age (\u0026gt;54); B2(1): BMI (\u0026lt;18.5); B2(2): BMI (18.5-23.9); B2(3): BMI (\u0026gt;23.9); B3(1): Number of chronic diseases (≥3); B3(2): Number of chronic diseases (2); B3(3): Number of chronic diseases (1); B3(4): Number of chronic diseases (0); B4: Smoking; B5: Sleep variability; B6: Physical activity level (IPAQ-7); B7: Alcohol drinking; P1: Perceived stress (PSS-4); P2: General Self-Efficacy (NGSES-SF); P3: Extraversion (BFI-10); P4: Agreeableness (BFI-10); P5: Conscientiousness (BFI-10); P6: Neuroticism (BFI-10); P7: Openness (BFI-10); P8: ADHD (ASRS); S1(1): Primary Education; S1(2): Secondary Education; S1(3): Higher Education; S2(1): Monthly household income (≤2,000); S2(2): Monthly household income (2001-4000); S2(3): Monthly household income (4001-6000); S2(4): Monthly household income (\u0026gt;6000); S3: Health Literacy (HLS-SF4); S4: Social support (PSSS-SF); S5: Family Communication (FCS-SF); S6: Residence (urban); S7(1): Eastern; S7(2): Central; S7(3): Western; S8: Employment.\u003c/p\u003e","description":"","filename":"Figure2ModelofPLSSEMofEQ5D5Lutilitypathanalysis.png","url":"https://assets-eu.researchsquare.com/files/rs-9507474/v1/595e424224bafbf46922699f.png"},{"id":108827774,"identity":"d56e13e8-3d70-4249-bf26-107c65668125","added_by":"auto","created_at":"2026-05-08 18:19:52","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96836,"visible":true,"origin":"","legend":"\u003cp\u003eModel of PLS-SEM of EQ-VAS path analysis.\u003c/p\u003e\n\u003cp\u003eNotes: The full forms of the abbreviations are presented in Figure 2.\u003c/p\u003e","description":"","filename":"Figure3ModelofPLSSEMofEQVASpathanalysis.png","url":"https://assets-eu.researchsquare.com/files/rs-9507474/v1/c0143dffb7f9e89b6edfdb9d.png"},{"id":109082391,"identity":"cdbc353f-f2dd-4446-833a-1d0d54d513b3","added_by":"auto","created_at":"2026-05-12 12:38:10","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1480434,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9507474/v1/8325ec80-1013-4897-bee2-c439c4a149fb.pdf"},{"id":108827772,"identity":"5f1ee7db-de21-483e-9217-7ea356a01ae5","added_by":"auto","created_at":"2026-05-08 18:19:52","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":23624,"visible":true,"origin":"","legend":"","description":"","filename":"05Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-9507474/v1/ca7e6f874ab493b19f93849c.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Biopsychosocial factors associated with health-related quality of life in the general Chinese population: Evidence from a nationwide health survey","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eWith the increasing global burden of chronic diseases and the aging population, traditional health measurement approaches based on objective indicators\u0026mdash;such as mortality and morbidity rates, life expectancy, and physiological measures like blood pressure and BMI, are becoming increasingly inadequate[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Health-Related Quality of Life (HRQoL), a subjective and comprehensive health outcome measure encompassing physical, mental, and social dimensions, has been widely adopted to assess the impact of diseases, injuries, functional limitations, or disabilities[\u003cspan additionalcitationids=\"CR3\" citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Moreover, HRQoL information can be converted into health utility score for use in economic evaluation if it is measured by utility instruments such as the EQ-5D[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Biopsychosocial Model and HRQoL\u003c/h2\u003e \u003cp\u003eTo gain a deeper understanding of the underlying patterns related to HRQoL\u0026mdash;particularly within specific cultural contexts\u0026mdash;it is essential to adopt robust theoretical frameworks. Engel\u0026rsquo;s biopsychosocial (BPS) model aligns conceptually with the multidimensional construct of HRQoL. The model posits that health is not determined solely by biological factors but is also closely related to psychological and social influences[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. It facilitates the identification of multiple patterns related to health outcomes and potential points of intervention. Moreover, it demonstrates strong cultural adaptability, rendering it particularly suitable in China, in which family structure, rural\u0026ndash;urban disparities, and healthcare characteristics differ markedly from Western populations.\u003c/p\u003e \u003cp\u003eA growing body of evidence supports the explanatory utility of BPS model in explaining HRQoL and other subjective health outcomes (e.g., pain interference, depression, and perceived discrimination), in which a variety of biological, psychological, and social variables show consistent relationships with HRQoL. For example, self-efficacy and emotional regulation (psychological factors), and the level of social support (social factor), have been identified as significant correlates of HRQoL among older adults and individuals with chronic conditions[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]. Comprehensive empirical investigations have further demonstrated the applicability of this framework. For example, Moons et al. demonstrated that psychological health was related to both disease severity and HRQoL through modeled relationships in adults with congenital heart disease, highlighting the explanatory relevance of psychological and social variables[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Thus, the BPS model not only provides a conceptual framework capturing the multidimensional nature of HRQoL, but also helps characterize the underlying patterns underlying variations in HRQoL.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e1.2 Literature Review and Mechanistic Pathways\u003c/h2\u003e \u003cp\u003eThe importance of integrating biological, psychological, and social dimensions within a unified theoretical framework is highlighted by previous studies[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. To clarify the pathways through which these dimensions are related to health outcomes and to inform the construction of the analytical model, the explanatory pathways of BPS model was assessed. Evidence indicates that biological, psychological, and social factors not only show independent relationships with health status, but also are interrelated in complex ways[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Biological variables such as chronic conditions, body mass index (BMI), sleep patterns, and health behaviors are consistently linked to health status. For instance, glycemic control in patients with diabetes is directly associated with improved health outcomes[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], while high BMI and multimorbidity are shown to significantly impair physical functioning in older adults[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Similarly, disrupted sleep rhythms are correlated with poorer self-perceived health[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePsychological factors such as self-efficacy, personality traits, perceived stress, and Attention-Deficit/Hyperactivity Disorder (ADHD) symptoms emerge as key correlates as well. For example, university students with higher stress levels and lower self-efficacy report significantly poorer mental well-being [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Evidence among individuals with psychiatric disorders also suggests that a positive psychological profile may be linked to reduced adverse experiences of physical symptoms and improved perceived health[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSocial determinants, including education, income, urban\u0026ndash;rural residence, and social support, are widely recognized as critical factors. For instance, older adults living with HIV experience worse functional outcomes when faced with social isolation[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e], while access to healthcare services and community resources play an important role in alleviating the disease burden among patients with rheumatoid arthritis[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Longitudinal studies further highlight the cumulative impact of social determinants on well-being throughout the life course[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn recent years, studies have increasingly shifted from examining the isolated effect of the above mentioned factors toward exploring their dynamic and interrelated pathways. Emerging evidence suggests that biological health may statistically mediate the associations between psychological and social factors and health outcomes, and that more complex chain mediation pathways may also exist among these dimensions. For instance, Lingam et al. find that children with chronic illnesses and low socioeconomic status often experience multiple layers of vulnerability, in which unmet physiological needs, persistent psychological distress, and lack of social support interact to exacerbate health risks[\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Similarly, Yoo-Jeong et al. report that social isolation among older adults living with HIV is associated with elevated inflammatory markers, which may be linked to emotional dysregulation and cognitive decline, and ultimately which may be related to compromised overall health[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. All those imply that structural social disadvantages\u0026mdash;such as limited education or low income\u0026mdash;may be associated with higher psychological burden and lower psychological resilience, which are in turn associated with poorer physical health and lower HRQoL. This chain-mediated association pattern highlights the cascading effects among social, psychological, and biological domains, revealing the cumulative and systemic nature of health inequalities[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Accordingly, there is a pressing need to construct integrative models that capture the interactions among biopsychosocial factors, while systematically identifying both mediating and sequential mechanisms, in order to better understand the social determinants of HRQoL\u0026mdash;particularly within culturally distinctive and structurally diverse settings such as China.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e1.3 Limitations in Scope, Context, and Measurement\u003c/h2\u003e \u003cp\u003eAlthough the BPS model has been widely used as a conceptual framework in health-related research, its usage still has several issues in the field. First, existing studies typically focus on specific populations, such as individuals with diabetes[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], rheumatoid arthritis[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], older adults living with HIV[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e], or those with mental disorders[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]; or on specific life stages, such as adolescents[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], university students[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e], or older adults[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. These studies are also predominantly conducted in high-income countries such as Europe and North America[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Therefore, the applicability and validity of the BPS model in culturally distinct contexts, such as China, remain underexplored and require further empirical scrutiny. Next, the majority of studies rely on objective indicators, including physiological functioning, clinical diagnoses, or behavioral changes[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]; while a few address individuals' subjective perceptions of health and overall self-evaluation, such as HRQoL, reflecting individuals' perception on their physical health, perception of pain, functional limitations in daily life, or emotional well-being[\u003cspan additionalcitationids=\"CR28\" citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. Lastly, evidence on the pathways through which the biological, psychological, and social dimensions are associated with health outcomes, especially the exploration into chain-mediated pathways is limited.\u003c/p\u003e \u003c/div\u003e"},{"header":"2 Study Objectives and Hypotheses","content":"\u003cp\u003eDrawing upon the BPS framework, the study aimed to systematically examine how biological, psychological, and social factors are jointly associated with HRQoL in general Chinese population by developing a culturally contextualized structural equation model (SEM). The model seeks to capture both direct associations and to identify underlying mediated association pathways, as well as subgroup-specific variations within the Chinese sociocultural context. The hypotheses are as follows:\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH1\u003c/strong\u003e \u003cp\u003eFavorable biological health indicators\u0026mdash;including an optimal body mass index (BMI), absence of multimorbidity, regular physical activity, non-smoking status, consistent sleep patterns, appropriate age, and non-drinking\u0026mdash;are expected to be directly positively associated with HRQoL.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH2\u003c/strong\u003e \u003cp\u003ePsychological resources, such as higher self-efficacy, lower perceived stress, positive personality traits (such as high conscientiousness, low neuroticism, high extraversion, high agreeableness, and low openness), and fewer symptoms of Attention Deficit Hyperactivity Disorder (ADHD), are expected to be directly positively associated with higher HRQoL.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH3\u003c/strong\u003e \u003cp\u003eSocial structural factors \u0026mdash; including higher education levels, higher household income, urban residency, stronger health literacy, more positive family communication, social support, geographic location, and employment status \u0026mdash; are expected to be directly positively associated with better HRQoL.\u003c/p\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eH4\u003c/b\u003e: Biological and psychological health are expected to statistically mediate the associations between social factors and HRQoL. Specifically, social and psychological factors are expected to be indirectly associated with HRQoL through biological health (H4a: Social \u0026rarr; Biological \u0026rarr; HRQoL; H4b: Psychological \u0026rarr; Biological \u0026rarr; HRQoL); and social factors are also expected to be indirectly associated with HRQoL through psychological health (H4c: Social \u0026rarr; Psychological \u0026rarr; HRQoL).\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eH5\u003c/strong\u003e \u003cp\u003eA chain-mediated association pattern is also anticipated, whereby social disadvantage is associated with higher psychological distress and lower psychological resilience, which are in turn associated with poorer biological health and lower HRQoL (Social \u0026rarr; Psychological \u0026rarr; Biological \u0026rarr; HRQoL).\u003c/p\u003e \u003c/p\u003e \u003cp\u003eSubgroup analyses were further conducted to examine whether the strength and structure of direct and indirect associations differ significantly across different subgroups such as urban and rural residents, different socioeconomic status (SES) subgroups, etc.\u003c/p\u003e"},{"header":"3 Methods","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Survey Design and Data Collection\u003c/h2\u003e \u003cp\u003eThis study utilized data from the 2024 Psychology and Behavior Investigation of Chinese Residents (PBICR) survey, a nationwide cross-sectional health survey conducted annually. It was conducted from June to September, 2024, covering 22 provinces, 5 autonomous regions, and 4 municipalities directly under the central government. A total of 150 cities, 202 districts/counties, 390 townships/towns/streets, and 800 communities were sampled. Both stratified and quota sampling methods were employed to obtain a nationally representative sample, and details of the PBICR-2024 survey design and protocol, including sampling procedures, investigator training, and interview settings, have been described previously (PBICR-2024 Study Protocol, 2025). Trained investigators or investigation teams were assigned to each city to conduct face-to-face interviews.\u003c/p\u003e \u003cp\u003eThe inclusion criteria were as follows: (1) aged\u0026thinsp;\u0026ge;\u0026thinsp;18 years, (2) Chinese nationality, (3) permanent residency in China (with a maximum absence of 1 month), (4) voluntary participation in the study, (5) ability to complete the online questionnaire independently or with assistance from the investigator, and (6) ability to understand the meaning of the survey questions. The exclusion criteria were: (1) mental disorders or psychiatric conditions, (2) cognitive impairment, (3) participation in other similar health surveys, and (4) unwillingness to cooperate.\u003c/p\u003e \u003cp\u003eTo ensure data quality, the survey implemented a rigorous screening process. Initially, 38,793 questionnaires were distributed, and after excluding invalid or non-consenting responses, 38,424 valid questionnaires were obtained, with a response rate of 99.05%. Subsequently, low-quality samples, including those with ambiguous consent, underage participants, non-Chinese residents, and responses completed in under 5 minutes, were excluded, leaving 36,240 valid samples. After logical consistency checks, 35,861 qualified samples were retained, which may be related to a qualification rate of 98.95%. Finally, after quota filtering, 25,047 samples were retained from the qualified responses. The attrition rate at each stage of the data cleaning process remained below 2%, demonstrating the high reliability of the data and strong participant compliance.\u003c/p\u003e \u003cp\u003eThe PBICR-2024 survey encompasses a wide range of aspects, including sociodemographic attributes, health status, family structure, social environment, childhood experiences, behavioral patterns, psychological states, and contemporary social issues, in which several internationally and domestically standardized instruments were employed. The adopted measurement tools in the study are described in the following sections.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Measures\u003c/h2\u003e \u003cp\u003e \u003cb\u003eEQ-5D-5L\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe EQ-5D-5L is a new version of the widely used HRQoL instrument EQ-5D. It assesses individuals\u0026rsquo; HRQoL in terms of five dimensions: \u003cem\u003eMobility, self-care, usual activities, pain/discomfort, and anxiety/depression\u003c/em\u003e. Each dimension has five functioning levels, ranging from \"no problems\" (level 1) to \"extreme problems\" (level 5), yielding 3,125 distinct health states. Each health state can be expressed using a 5-digit number (eg, \u0026ldquo;no problems\u0026rdquo; in any dimension is \u0026ldquo;11111\u0026rdquo; and \u0026ldquo;extreme problems\u0026rdquo; in every dimension is \u0026ldquo;55555\u0026rdquo;) and can be assigned a utility score using a value set derived from a valuation study. In the analysis, the Chinese 5L value set was adopted, ranging from \u0026minus;\u0026thinsp;0.391(the worst health state) to 1.0 (full health). The Visual Analog Scale (VAS) is often used alongside the EQ-5D-5L utility and measures overall health perception on a scale from 0 (worst) to 100 (best), providing a subjective health measure in addition to its health-state descriptive system. The Chinese version of the EQ-5D-5L utility has been culturally adapted and validated in various populations[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eBiological Dimension Measurement\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe biological dimension was assessed using the International Physical Activity Questionnaire \u003cem\u003e(IPAQ-7)\u003c/em\u003e, inquiring participants' physical activity levels over the past 7 days using 7 items. Each item is divided into three main categories: \u003cem\u003evigorous physical activity\u003c/em\u003e (duration: minutes, frequency: days), \u003cem\u003emoderate-intensity physical activity\u003c/em\u003e (duration: minutes, frequency: days), and \u003cem\u003ewalking time of at least 10 minutes\u003c/em\u003e (frequency: days). The duration of each activity is converted into the metabolic equivalent (MET) corresponding to the basal metabolic rate, and the total physical activity score (MET-minutes/week) is calculated. Specifically, the MET for walking is calculated as 3.3 \u0026times; average daily walking time \u0026times; weekly walking days; the MET for moderate-intensity activity is calculated as 4.0 \u0026times; average daily time spent in moderate-intensity activity \u0026times; days engaged in moderate-intensity activity per week; the MET for vigorous activity is calculated as 8.0 \u0026times; average daily time spent in vigorous activity \u0026times; days engaged in vigorous activity per week. Thus, the total basal metabolic time (in minutes) per week is the sum of the walking MET, moderate-intensity activity MET, and vigorous activity MET. According to the classification criteria, \u0026lt;\u0026thinsp;600 MET-min/week is defined as low activity, 600\u0026ndash;3000 MET-min/week as moderate activity, and \u0026ge;\u0026thinsp;3000 MET-min/week as high activity; higher values indicate greater frequency, duration, or intensity of physical activity, which are generally associated with better fitness and health status[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003ePsychological Dimension Measurements\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe psychological dimension was evaluated using the Perceived Stress Scale (PSS-4), General Self-Efficacy Scale (NGSES-SF), Big Five Personality Inventory (BFI-10), and Attention Deficit Hyperactivity Disorder Scale (ASRS).\u003c/p\u003e \u003cp\u003eThe PSS-4 is based on 4 questions to assess individuals' perception of stressful situations in their lives. The questionnaire is divided into 2 dimensions: \u003cem\u003esense of loss of control\u003c/em\u003e (items 1 and 2) and \u003cem\u003etension\u003c/em\u003e (items 3 and 4). The Likert method is used to score items, with a range from 1 to 5 (from \"never\" to \"always\"). The total score is calculated by summing the scores of all 4 items, with a score range from 4 to 20. A higher score indicates that the individual felt more unpredictable, uncontrollable, or overloaded in their life during the past month[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. In this study, the Cronbach α coefficient for the \u003cem\u003ePSS-4\u003c/em\u003e scale was 0.934.\u003c/p\u003e \u003cp\u003eNGSES-SF is used to assess an individual's self-efficacy. The questionnaire comprises three dimensions: \u003cem\u003ethe level or degree of self-efficacy, its intensity\u003c/em\u003e, and \u003cem\u003eits generality\u003c/em\u003e. A Likert scale is employed to score each item, with a range from 1 to 5 (1\u0026thinsp;=\u0026thinsp;strongly disagree, 5\u0026thinsp;=\u0026thinsp;strongly agree). The total score is calculated by summing the scores of all three items, yielding a score range of 3 to 15 points, with higher scores indicating stronger self-efficacy[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In this study, the Cronbach\u0026rsquo;s α coefficient for the \u003cem\u003eNGSES-SF\u003c/em\u003e scale was 0.928.\u003c/p\u003e \u003cp\u003eThe BFI-10 (Ten-Item Big Five Inventory) is designed to assess individual personality traits across five dimensions: \u003cem\u003eextraversion, agreeableness, conscientiousness, neuroticism\u003c/em\u003e, and \u003cem\u003eopenness\u003c/em\u003e using 10 items. It employs a 5-point Likert scale, ranging from 1 (strongly disagree) to 5 (strongly agree). \u003cem\u003eExtraversion\u003c/em\u003e scores are calculated by summing the scores of items 1R and 6, \u003cem\u003eagreeableness\u003c/em\u003e scores are calculated by summing the scores of items 2 and 7R, \u003cem\u003econscientiousness\u003c/em\u003e scores are derived by summing the scores of items 3R and 8, \u003cem\u003eneuroticism\u003c/em\u003e scores are calculated by summing the scores of items 4R and 9, and \u003cem\u003eopenness\u003c/em\u003e scores are obtained by summing the scores of items 5R and 10 (R indicates reverse scoring). The total score of the questionnaire ranges from 10 to 50, with higher scores indicating stronger expression of the corresponding personality traits[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. In this study, the Cronbach\u0026rsquo;s alpha coefficients for \u003cem\u003eextraversion, agreeableness, conscientiousness, neuroticism\u003c/em\u003e, and \u003cem\u003eopenness\u003c/em\u003e were 0.815, 0.733, 0.752, 0.770, and 0.726, respectively, all exceeding the threshold of 0.7, which meets the reliability standards for personality measurement in psychological assessments.\u003c/p\u003e \u003cp\u003eThe Adult ADHD Self-Report Scale V1.1 (ASRS-V1.1) is a six-item instrument used for screening \u003cem\u003eAttention Deficit Hyperactivity Disorder (ADHD)\u003c/em\u003e in adults. It employs a 5-point Likert scale, with response options ranging from \"never\" to \"very often.\" For all items, the \"never\" response is scored as 0. The maximum score for each item varies: item 3 has a maximum of 6 points, items 1 and 2 have a maximum of 5 points, item 5 has a maximum of 4 points, item 6 has a maximum of 3 points, and item 4 has a maximum of 2 points, which may be related to a total score range from 0 to 25. Higher total scores indicate greater levels of attention deficit or hyperactivity disorder[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. In this study, the Cronbach's α coefficient for the \u003cem\u003eASRS-V1.1\u003c/em\u003e scale was 0.915.\u003c/p\u003e \u003cp\u003e \u003cb\u003eSocial Dimension Measurement\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe social dimension was assessed adopting the Health Literacy Scale (HLS-SF4), Family Communication Scale (FCS-SF), and Social Support Scale (PSSS-SF).\u003c/p\u003e \u003cp\u003eHLS-SF4 is a brief health literacy assessment tool consisting of four Likert items. It encompasses three dimensions: \u003cem\u003ehealthcare, disease prevention\u003c/em\u003e, and \u003cem\u003ehealth promotion\u003c/em\u003e. The scoring range for each item is from 0 to 3 (from very difficult to very easy), with the total score ranging from 0 to 12. Higher scores indicate a higher level of health literacy. Owing to its shorter completion time and broad applicability across various populations, it is convenient to use it in large-scale cross-sectional studies[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. In this study, the Cronbach's α coefficient for the \u003cem\u003eHLS-SF4\u003c/em\u003e scale was 0.899.\u003c/p\u003e \u003cp\u003eThe FCS-SF assesses the quality, openness, and effectiveness of communication among family members based on four items. It covers various aspects of family communication and uses a Likert-type scoring method, with ratings ranging from 1 to 5 (strongly disagree to strongly agree). Its total score is calculated by summing the scores of all four items, with a range from 4 to 20 points. Higher scores indicate a higher level of family communication[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. In this study, the Cronbach's α coefficient for the \u003cem\u003eFCS-SF\u003c/em\u003e scale was 0.948.\u003c/p\u003e \u003cp\u003eThe PSSS-SF assesses individuals' perceived social support based on several items. The questionnaire comprises three main dimensions: \u003cem\u003efamily support\u003c/em\u003e (items 1\u0026ndash;3), \u003cem\u003efriend support\u003c/em\u003e (items 4\u0026ndash;6), and \u003cem\u003eother support\u003c/em\u003e (items 7\u0026ndash;9). Each item is scored using a Likert scale ranging from 1 to 7 (from \"strongly disagree\" to \"strongly agree\"). Higher scores indicate a stronger perception of social support [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. In this study, the Cronbach's α coefficient for the \u003cem\u003ePSSS-SF\u003c/em\u003e scale was 0.903.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Statistical Analysis\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1 Descriptive Statistics\u003c/h2\u003e \u003cp\u003eDescriptive statistics were used to describe participants' biological, psychological, and social characteristics and HRQoL. Specifically, mean, standard deviation, and median were used to describe the distribution of EQ-5D-5L utility and EQ-VAS scores across categorical variables; and the differences in EQ-5D-5L utility and EQ-VAS scores between different subgroups were tested using independent samples t-test or analysis of variance.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2 Model construction\u003c/h2\u003e \u003cp\u003eModel construction began with data preprocessing, in which missing values were imputed using mode imputation. Given that most variables were categorical and the proportion of missing data was low, this approach was considered appropriate for the present analysis[\u003cspan additionalcitationids=\"CR41\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The next step was to calculate the score for each measurement scale according to the respective scoring methods, ensuring suitability for modeling and enhancing model interpretability. Categorical variables such as age, BMI, and number of chronic diseases were also dummy-coded prior to modeling. It should be noted that the formal BPS model did not take gender into account, as it remained unclear whether gender should be classified under the biological dimension or the social dimension[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan additionalcitationids=\"CR44\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn SEM, latent variables were treated as abstract concepts or traits that could not be directly observed but could be indirectly measured through multiple observable indicators. For example, psychological well-being could not be directly measured but could be represented by indicators such as anxiety, depression, and life satisfaction. In the analysis, the biological, psychological, and social dimensions were modeled as latent variables, each represented by relevant observable indicators.\u003c/p\u003e \u003cp\u003eTo examine the model fit of the biological, psychological, and social latent variables, as well as their path relationships and mediated association structures with the dependent variables (i.e., EQ-5D-5L utility values and EQ-VAS scores), the study employed Partial Least Squares Structural Equation Modeling (PLS-SEM), a variant of SEM (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). It is particularly suitable for exploratory and predictive research because it could effectively analyze relationships among variables in cross-sectional data and simultaneously handle ordinal categorical and continuous variables.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAll PLS-SEM analyses were conducted using SmartPLS version 4.0 software.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3 Model Evaluation\u003c/h2\u003e \u003cp\u003eThe BPS model was evaluated based on both the Measurement Model and Structural Model, and was then internally validated according to robustness check using PLSpredict.\u003c/p\u003e \u003cp\u003e \u003cb\u003eMeasurement Model\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe measurement model assessed the relationship between latent variables and their corresponding indicators. The selection of indicators was based on the theoretical framework of biological, psychological, and social constructs to ensure conceptual completeness, semantic clarity, and consistency with the formative measurement approach.\u003c/p\u003e \u003cp\u003eThe study adopted a formative measurement model, focusing on multicollinearity diagnostics and the statistical significance of indicator weights. Collinearity was assessed using the Variance Inflation Factor (VIF), where VIF\u0026thinsp;\u0026ge;\u0026thinsp;5, 3\u0026ndash;5, and \u0026lt;\u0026thinsp;3 indicated the presence of collinearity, the possibility of collinearity, and no collinearity problems, respectively[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. The statistical significance of each indicator\u0026rsquo;s outer weight was tested using bootstrapping. If the t-value of an indicator exceeded 1.96 (corresponding to a 5% confidence interval), or if the p-value was less than 0.05, the indicator was considered statistically significant. If an indicator\u0026rsquo;s weight was not statistically significant but was theoretically justified, the indicator was still retained in the model.\u003c/p\u003e \u003cp\u003e \u003cb\u003eStructural Model\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe structural model was used to examine the relationships among latent variables and their associations with endogenous variables, focusing on path coefficients and their statistical significance, VIF, overall model fit, and R\u0026sup2;. The relationships were categorized into direct, indirect, and total associations. As stated above, the hypothesis tested primarily examined the direct and indirect associations of biological, psychological, and social factors on HRQoL. For direct associations, path coefficients represented the direction and magnitude of the relationships among latent variables; coefficients below 0.1 were considered weak (insufficient to explain variance), between 0.1 and 0.3 moderate, and above 0.3 strong[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. For indirect effects, the analysis emphasized on statistical significance rather than the coefficient magnitude, as indirect effects arose from the combined associations of multiple mediating pathways. All path coefficients were evaluated for statistical significance using bootstrapping, based on their corresponding t-values and p-values, ensuring robustness and reliability of the results.\u003c/p\u003e \u003cp\u003eVIF values were also used to assess potential multicollinearity among exogenous latent variables when predicting an endogenous variable, thereby ensuring the stability of the path coefficients. Model fit was assessed using the Standardized Root Mean Square Residual (SRMR) and the Normed Fit Index (NFI), in which SRMR values below 0.05 and NFI values above 0.90 were generally considered indicative of good model fit. R\u0026sup2; was used to assess the extent to which exogenous variables explain the variance of endogenous latent variables: an R\u0026sup2; value of approximately 0.20 was generally considered an acceptable level of explanatory power[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eRobustness Check using PLSpredict\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe PLSpredict method with tenfold cross-validation, repeated ten iterations, was adopted to evaluate the predictive performance of the PLS-SEM model. First, Q\u0026sup2; was used to assess predictive relevance, in which values greater than zero indicated that the model had predictive ability beyond random chance[\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. To further examine predictive accuracy, the method used a linear regression model (LM) as the benchmark, comparing the prediction errors of PLS-SEM and LM for the endogenous variables EQ-5D-5L and EQ-VAS. Root Mean Squared Error (RMSE) and Mean Absolute Error (MAE) served as measures of prediction errors. According to the guidelines of Shmueli et al. and Hair et al., if at least half of the indicators yielded lower errors than LM, the model was considered to demonstrate moderate predictive power; otherwise, it did not[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.4 Subgroups analysis\u003c/h2\u003e \u003cp\u003eThe sample was stratified based on 10 variables: education (low, middle, high), age (young, middle, old), region (eastern, central, western), sex (male, female), income (\u0026le;\u0026thinsp;4000, \u0026gt; 4000), marriage (single, married, other), residence (urban, rural), smoking (yes, no), drinking (yes, no), chronic disease (yes, no). The model was then independently applied within each subgroup to assess the consistency of path-based associations and differences in model fit. Consistent with those of the structural model, the significance, direction, and strength of each path were determined by the path coefficients and p-values, while SRMR and NFI were used to assess the model fit.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Ethics\u003c/h2\u003e \u003cp\u003eThe study was supported by the National Health Commission Key Laboratory of Health Economics and Policy Research under its Key Scientific Research Project (NHC-HEPR202401). The study protocol was approved by the Ethics Review Committee of Shanghai Jiao Tong University (Approval No. H20240237I) and was registered in the Chinese Clinical Trial Registry (Registration No. ChiCTR2400085016). The cover page of the questionnaire will explain the study\u0026rsquo;s purpose and assure anonymity, confidentiality, and the right to refuse to participate in the study. Informed consent was obtained from all subjects involved in the study.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Results","content":"\u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Descriptive statistics\u003c/h2\u003e \u003cp\u003eThe mean age of study participants was 40.7 years, with females accounting for 50.6%.\u003c/p\u003e \u003cp\u003eRegarding biological variables, approximately four-fifths of the participants (80.1%) were young and middle-aged adults (\u0026lt;\u0026thinsp;55 years). More than half of the participants (59.4%) had a BMI within the normal range. The majority of participants did not consume alcohol (72.2%) or smoke (80.4%), and 83.8% had stable sleep patterns. Approximately one-quarter (24.8%) reported having at least one chronic condition (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Appendix Table A1).\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\u003eParticipant Characteristics and Distribution of EQ-5D-5L Utility and EQ-VAS\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eBiological Variable\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eN (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003eEQ-5D-5L utility\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eEQ-VAS\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP value\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMean (SD)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eMedian\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eP value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \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=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18\u0026ndash;34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10244(40.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.947(0.126)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78.00(18.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e81.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e35\u0026ndash;54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9830(39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.936(0.149)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76.28(19.10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;55\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4973(19.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.872(0.229)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.951\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e71.49(19.54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e76.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBMI group\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \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=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2394(9.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.901(0.213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74.84(20.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e18.5\u0026ndash;23.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14872(59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.934(0.150)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76.49(18.62)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7780(31.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.925(0.166)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75.52(19.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNumber of chronic diseases\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \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=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18823(75.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.944(0.152)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78.14(18.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e81.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4111(16.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.901(0.164)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.951\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e71.31(18.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e76.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\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\u003e1404(5.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.862(0.174)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.893\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e68.16(18.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e70.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e712(2.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.771(0.250)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.831\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63.04(20.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e63.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \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=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4914(19.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.919(0.162)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74.24(20.44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20133(80.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.930(0.163)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76.47(18.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSleep variability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \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=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHighly stable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8176(32.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.956(0.114)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e79.54(19.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e82.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelatively stable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12812(51.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.939(0.116)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76.40(17.13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRelatively unstable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3242(12.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.858(0.264)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.951\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e69.01(21.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e73.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery unstable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e817(3.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.747(0.383)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.942\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e63.03(26.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e66.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlcohol drinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \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=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6962(27.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.937(0.123)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76.81(18.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18085(72.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.924(0.175)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75.73(19.49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eSocial Variable\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eN (%)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eEQ-5D-5L utility\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e\u003cb\u003eEQ-VAS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eMean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eMedian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003csup\u003e\u003cb\u003e1\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eMean (SD)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eMedian\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cb\u003eP value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducational level\u003c/b\u003e\u003csup\u003e\u003cb\u003e4\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \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=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3159(12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.889(0.196)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.951\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e70.64(19.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e75.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8742(34.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.933(0.151)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e75.69(19.14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigher Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13146(52.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.933(0.160)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77.56(18.82)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e81.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly household income (RMB)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \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=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;2,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3153(12.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.889(0.213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e71.46(22.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e77.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2001\u0026ndash;4000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6823(27.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.926(0.155)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74.78(19.46)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4001\u0026ndash;6000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6805(27.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.935(0.146)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76.41(18.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026gt;6000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8266(33.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.937(0.157)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e78.50(17.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e81.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \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=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18815(75.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.935(0.148)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76.89(18.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6232(24.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.905(0.200)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e73.43(20.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e79.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \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=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10591(42.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.912(0.199)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76.09(19.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8983(35.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.943(0.115)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e77.08(17.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5473(21.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.933(0.147)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74.19(19.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e79.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\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 \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \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=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15766(62.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.939(0.153)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e76.70(19.11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9281(37.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.909(0.177)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.000\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 \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e74.89(19.19)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e80.00\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e1 Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the proportions of categorical variables and the distribution of EQ-5D-5L utility and EQ-VAS scores. The mean and standard deviation of continuous variables are provided in Appendix Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e2 Independent sample t-test was used for 2 sample groups, and analysis of variance (ANOVA) was used for 3 or more sample groups.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e3 Chronic diseases include hypertension, diabetes, hyperlipidemia, coronary heart disease, stroke, respiratory diseases, urinary system diseases, digestive 3 system diseases, osteoporosis, arthritis, tumors, rare diseases, and other conditions.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e4 Primary education and below is low education, junior and senior high school (including vocational) is intermediate, and bachelor's degree and above (including associate degree) is higher education.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn terms of psychological variables, the participants' mean \u003cem\u003ePSS-4\u003c/em\u003e score was at a moderate level (8.07, SD: 3.82), while the mean NGSES-SF score was relatively high (10.97, SD: 2.59). Among BFI-10, \u003cem\u003eextraversion\u003c/em\u003e (3.64, SD: 0.96), \u003cem\u003eagreeableness\u003c/em\u003e (3.76, SD: 0.86), and \u003cem\u003econscientiousness\u003c/em\u003e (3.80, SD: 0.88) had relatively high scores, while \u003cem\u003eneuroticism\u003c/em\u003e (3.56, SD: 0.94) and \u003cem\u003eopenness\u003c/em\u003e (3.43, SD: 0.98) were at moderate levels. The distribution of \u003cem\u003eADHD\u003c/em\u003e score was quite wide (12.06, SD: 5.44), which suggested that some participants might have experienced significant attention-related issues (Appendix Table A1).\u003c/p\u003e \u003cp\u003eWith regard to social variables, more than half of the participants (52.5%) received higher education, and nearly two-thirds (60.2%) reported a monthly income greater than 2,000 RMB. Approximately three-quarters (75.1%) lived in urban areas, and more than half (57.8%) were from the central and western regions. The mean score of \u003cem\u003eHLS-SF4\u003c/em\u003e was 11.18 (SD: 2.782), and the mean score for \u003cem\u003eFCS-SF\u003c/em\u003e was 14.68 (SD: 3.80), both of which were relatively high (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Appendix Table A1).\u003c/p\u003e \u003cp\u003eThe mean (SD) EQ-5D-5L utility and EQ-VAS scores of the sample were 0.928 (0.163) and 76.03 (19.16), respectively. Those with lower EQ-5D-5L utility scores included older adults (aged 55 and above), those with multiple chronic conditions, smokers, individuals with lower education levels and monthly income, rural residents, participants with abnormal BMI, non-drinkers, those from the eastern region, individuals with poor sleep quality, and unemployed participants (all p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The results for EQ-VAS scores were generally consistent, except for the participants from the western region had lower EQ-VAS scores (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05) (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and Appendix Table A1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Evaluation of measurement model\u003c/h2\u003e \u003cp\u003eThe results of measurement model were presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u0026amp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Among all the indicators, \u003cem\u003esleep variability\u003c/em\u003e (EQ-5D-5L utility: 0.743; EQ-VAS: 0.732), \u003cem\u003egeneral self-efficacy\u003c/em\u003e (EQ-5D-5L utility: 0.569; EQ-VAS: 0.559), and \u003cem\u003esocial support\u003c/em\u003e (EQ-5D-5L utility: 0.601; EQ-VAS: 0.593) had the weights exceeding 0.5. The other indicators generally exhibited external weights within the range of 0.1 to 0.3. All indicators had the VIF values less than or close to 3, indicating no collinearity issues. According to p-values and t-values, almost all indicators showed statistical significance, except for \u003cem\u003eConscientiousness\u003c/em\u003e (BFI-10) (t\u0026thinsp;=\u0026thinsp;1.049, p\u0026thinsp;=\u0026thinsp;0.294) and \u003cem\u003eSecondary Education\u003c/em\u003e (t\u0026thinsp;=\u0026thinsp;0.604, p\u0026thinsp;=\u0026thinsp;0.546) in the EQ-5D-5L utility model; and \u003cem\u003eConscientiousness\u003c/em\u003e (BFI-10) (t\u0026thinsp;=\u0026thinsp;0.367, p\u0026thinsp;=\u0026thinsp;0.713) and \u003cem\u003eEastern\u003c/em\u003e (t\u0026thinsp;=\u0026thinsp;0.381, p\u0026thinsp;=\u0026thinsp;0.703) in the EQ-VAS model (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\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\u003eMeasurement model results\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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 \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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstructs\u003c/p\u003e \u003cp\u003eof EQ-5D-5L utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCode\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eIndicators\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConvergent validity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCollinearity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eStatistical significance\u003c/p\u003e \u003cp\u003eof weights\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=\"c4\"\u003e \u003cp\u003eOuter weights\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eT-statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"13\" rowspan=\"14\"\u003e \u003cp\u003eBiological\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB1(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge(18\u0026ndash;34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB1(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge(35\u0026ndash;54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.278\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB1(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge(\u0026gt;\u0026thinsp;54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB2(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBMI(\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB2(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBMI(18.5\u0026ndash;23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.224\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB2(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBMI(\u0026gt;\u0026thinsp;23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.619\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB3(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of chronic diseases(\u0026ge;\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.328\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.526\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB3(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of chronic diseases(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB3(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of chronic diseases(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.186\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB3(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of chronic diseases(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSleep variability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e35.404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhysical activity level( IPAQ-7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.771\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlcohol drinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.099\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003ePhysiological\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerceived stress (PSS-4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e18.855\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGeneral Self-Efficacy(NGSES-SF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e53.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtraversion(BFI-10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgreeableness(BFI-10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e19.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConscientiousness(BFI-10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.294\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeuroticism(BFI-10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.988\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOpenness(BFI-10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.993\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eADHD(ASRS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.989\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"14\" rowspan=\"15\"\u003e \u003cp\u003eSocial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimary Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.780\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecondary Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.604\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.546\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigher Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonthly household income(\u0026le;\u0026thinsp;2,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.470\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonthly household income(2001\u0026ndash;4000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.954\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonthly household income (4001\u0026ndash;6000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonthly household income(\u0026gt;6000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealth Literacy(HLS-SF4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e38.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSocial support(PSSS-SF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e54.551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFamily Communication(FCS-SF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidence(urban)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.040\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS7(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS7(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e10.270\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS7(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWestern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eConstructs\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eof EQ-VAS\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eCode\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cb\u003eIndicators\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eConvergent validity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eCollinearity\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e\u003cb\u003eStatistical significance\u003c/b\u003e\u003c/p\u003e \u003cp\u003e\u003cb\u003eof weights\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003eOuter weights\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eVIF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eT-statistics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003eP-value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"13\" rowspan=\"14\"\u003e \u003cp\u003eBiological\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB1(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge(18\u0026ndash;34)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.302\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.267\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.985\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB1(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge(35\u0026ndash;54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.249\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.590\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB1(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge(\u0026gt;\u0026thinsp;54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB2(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBMI(\u0026lt;\u0026thinsp;18.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB2(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBMI(18.5\u0026ndash;23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.966\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.205\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB2(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBMI(\u0026gt;\u0026thinsp;23.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.907\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB3(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of chronic diseases(\u0026ge;\u0026thinsp;3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB3(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of chronic diseases(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.324\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB3(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of chronic diseases(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e14.845\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB3(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber of chronic diseases(0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSleep variability\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.732\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e37.096\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePhysical activity level( IPAQ-7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.976\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAlcohol drinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003ePhysiological\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePerceived stress (PSS-4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e17.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGeneral Self-Efficacy(NGSES-SF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.559\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.773\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e55.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eExtraversion(BFI-10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.502\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAgreeableness(BFI-10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.764\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eConscientiousness(BFI-10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.367\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.713\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNeuroticism(BFI-10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eOpenness(BFI-10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.173\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e12.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eP8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eADHD(ASRS)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"14\" rowspan=\"15\"\u003e \u003cp\u003eSocial\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePrimary Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.754\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSecondary Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e2.133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS1(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHigher Education\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonthly household income(\u0026le;\u0026thinsp;2,000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e5.434\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonthly household income(2001\u0026ndash;4000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.549\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonthly household income (4001\u0026ndash;6000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.695\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS2(4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMonthly household income(\u0026gt;6000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHealth Literacy(HLS-SF4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e40.753\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSocial support(PSSS-SF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.593\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.409\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e57.638\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFamily Communication(FCS-SF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.287\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e28.116\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eResidence(urban)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.332\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS7(1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.226\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.381\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.703\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS7(2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.236\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS7(3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWestern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c7\" namest=\"c4\"\u003e \u003cp\u003e(Reference)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eS8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEmployment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.377\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Evaluation of structural model\u003c/h2\u003e \u003cp\u003eIn the structural model, all path coefficients were positive and statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Appendix Table A2). The coefficients of the direct paths were all above 0.1, indicating significant and practically meaningful relationships among the variables. The indirect paths were also positive and statistically significant, confirming robust mediated association patterns. All these were consistent with the five hypotheses. All VIF values were less than 3, indicating acceptable multicollinearity between the latent variables (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). SRMR values were all less than 0.05, and NFI values were close to 0.9 (EQ-5D-5L utility model: SRMR\u0026thinsp;=\u0026thinsp;0.046, NFI\u0026thinsp;=\u0026thinsp;0.884; EQ-VAS model: SRMR\u0026thinsp;=\u0026thinsp;0.043, NFI\u0026thinsp;=\u0026thinsp;0.890), indicating good model fit (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The R\u0026sup2; values for the EQ-5D-5L utility and EQ-VAS scores were 0.188 and 0.222, indicating reasonable variance explained by the model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\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\u003ePath analysis verification\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=\"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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePath Analysis\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEffect Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePath Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eT Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep Value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHypothesis supported or not\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiological\u0026rarr;EQ-5D-5L utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e33.793\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePsychological\u0026rarr;EQ-5D-5L utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.068\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.574\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial\u0026rarr;EQ-5D-5L utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e11.475\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial\u0026rarr;Biological\u0026rarr;EQ-5D-5L utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePsychological\u0026rarr;Biological\u0026rarr;EQ-5D-5L utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial\u0026rarr;Psychological\u0026rarr;EQ-5D-5L utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e22.458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial\u0026rarr;Psychological\u0026rarr;Biological\u0026rarr;EQ-5D-5L utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChain Indirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.951\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFit Indices\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSaturated Model\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eEstimated Model\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel Fit\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003e0.046\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003e0.884\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHypothesis\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003ePath Analysis\u003c/b\u003e\u003csup\u003e\u003cb\u003e2\u003c/b\u003e\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cb\u003eEffect Type\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003ePath Coefficient\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003eVIF\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003eT Value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cb\u003ep Value\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cb\u003eHypothesis supported or not\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBiological\u0026rarr;EQ-VAS utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.178\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.960\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePsychological\u0026rarr;EQ-VAS utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e26.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial\u0026rarr;EQ-VAS utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e20.198\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4a\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial\u0026rarr;Biological\u0026rarr;EQ-VAS utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e8.023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4b\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePsychological\u0026rarr;Biological\u0026rarr;EQ-VAS utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.414\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH4c\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial\u0026rarr;Psychological\u0026rarr;EQ-VAS utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.171\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e25.641\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eH5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial\u0026rarr;Psychological\u0026rarr;Biological\u0026rarr;EQ-VAS utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eChain Indirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e9.368\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eFit Indices\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e\u003cb\u003eSaturated Model\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003e\u003cb\u003eEstimated Model\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eModel Fit\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c4\" namest=\"c2\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c8\" namest=\"c5\"\u003e \u003cp\u003e0.890\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e1 Model 1 takes EQ-5D-5L utility as the dependent variable.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003e2 Model 2 takes EQ-VAS as the dependent variable.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Robustness Check using PLSpredict\u003c/h2\u003e \u003cp\u003eThe predictive performance of the model was assessed using Q\u0026sup2;, RMSE, and MAE (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The Q\u0026sup2; values for EQ-5D-5L utility and EQ-VAS were 0.098 and 0.158, respectively. Based on the 10-fold cross-validation, the predictive errors of PLS-SEM were identical to those of the linear model (RMSE: 0.15; MAE: 0.09) for the EQ-5D-5L utility model, indicating moderate predictive performance. For the EQ-VAS model, the predictive errors of PLS-SEM were similar with those of the LM (RMSE: 17.58 vs. 17.53; MAE: 13.59 vs. 13.57). All these indicated that the BPS model demonstrated acceptable predictive performance (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\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\u003eResults of the Robustness Check using PLSpredict\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=\"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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eConstructs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eQ\u003csup\u003e2\u003c/sup\u003e Predict\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c6\" namest=\"c3\"\u003e \u003cp\u003ePLSPredict\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePLS-SEM RMSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePLS-SEM MAE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLM RMSE\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLM MAE\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eResult of Predictive Relevance (Q2 Predict) and K-fold Cross-Validation (ten fold, ten iterations)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEQ-5D-5L utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.09\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEQ-VAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.158\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e13.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003e1 The full form of LM is Linear Model.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Subgroup analysis\u003c/h2\u003e \u003cp\u003eThe results of subgroup analysis according to PLS-SEM path-based associations were presented in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e. For direct associations, most path coefficients were greater than 0.1 in the EQ-5D-5L utility model. Meanwhile, the path from Social to EQ-5D-5L utility showed coefficients below 0.1 among the following subgroups: \u003cem\u003elow education level\u003c/em\u003e, \u003cem\u003eyounger and older age groups\u003c/em\u003e, \u003cem\u003ecentral and western regions, female, income\u0026thinsp;\u0026le;\u0026thinsp;4000, single, urban residents, smokers, drinkers\u003c/em\u003e, and \u003cem\u003eindividuals with chronic diseases\u003c/em\u003e. In the EQ-VAS model, all direct associations coefficients were greater than 0.1.With regard to statistical significance, certain direct paths did not reach significance. In the EQ-5D-5L utility model, those included Social \u0026rarr; EQ-5D-5L utility (\u003cem\u003elow education level\u003c/em\u003e and \u003cem\u003esmokers\u003c/em\u003e ) and Biological \u0026rarr; EQ-5D-5L utility (\u003cem\u003eyounger age group, female\u003c/em\u003e, and \u003cem\u003edrinkers\u003c/em\u003e); in the EQ-VAS model, the insignificance of path Biological \u0026rarr; EQ-VAS utility was observed among the subgroups with \u003cem\u003elow education level\u003c/em\u003e and those from the \u003cem\u003ecentral region\u003c/em\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\u003eModel of PLS-SEM path analysis diagram in different groups\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"26\"\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 \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=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c12\" colnum=\"12\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c13\" colnum=\"13\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c14\" colnum=\"14\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c15\" colnum=\"15\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c16\" colnum=\"16\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c17\" colnum=\"17\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c18\" colnum=\"18\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c19\" colnum=\"19\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c20\" colnum=\"20\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c21\" colnum=\"21\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c22\" colnum=\"22\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c23\" colnum=\"23\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c24\" colnum=\"24\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c25\" colnum=\"25\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c26\" colnum=\"26\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEQ-5D Path Analysis\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eEducation\u003csup\u003e1\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"7\" nameend=\"c14\" namest=\"c8\"\u003e \u003cp\u003eAge\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"5\" nameend=\"c19\" namest=\"c15\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c23\" namest=\"c20\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c26\" namest=\"c24\"\u003e \u003cp\u003eincome\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eYoung\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c15\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003eWestern\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4000\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c26\"\u003e \u003cp\u003e\u0026gt;4000\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiological\u0026rarr;EQ-5D-5L utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.354***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.241***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.246***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e-0.194**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.240***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.261***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.267***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e-0.319***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e-0.315***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.274***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.266*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.289***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e0.243***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological\u0026rarr;EQ-5D-5L utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.277***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.194***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.179***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.168***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.191***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.353***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.239***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e0.145***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.174***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.181***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.206***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.192***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e0.197***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial\u0026rarr;EQ-5D-5L utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.105***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.136***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.086***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.113***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.087***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.135***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e0.052***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.038***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.114***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.093***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.084***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e0.118***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial\u0026rarr;Biological\u0026rarr;EQ-5D-5L utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.034***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.012***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.018***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.003*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.006*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.021***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.035***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e0.107***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.063***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.040***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.049***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.041***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e0.033***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological\u0026rarr;Biological\u0026rarr;EQ-5D-5L utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.069***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.053***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.051***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.047***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.063***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.074***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.057***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e-0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.022**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.041***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.034***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.044***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e0.037***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial\u0026rarr;Psychological\u0026rarr;EQ-5D-5L utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.182***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.136***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.132***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.123***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.136***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.248***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.177***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e0.096***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.121***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.131***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.146***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.138***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e0.140***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial\u0026rarr;Psychological\u0026rarr;Biological\u0026rarr;EQ-5D-5L utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.045***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.037***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.038***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.035***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.045***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.052***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.042***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.015**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.030***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.024***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.032***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e0.027***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eVAS Path Analysis\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c7\" namest=\"c2\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"7\" nameend=\"c14\" namest=\"c8\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c19\" namest=\"c15\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c23\" namest=\"c20\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c26\" namest=\"c24\"\u003e \u003cp\u003eincome\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003eYoung\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003eOld\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003eWestern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;4000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e\u0026gt;4000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiological\u0026rarr;EQ-VAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.229*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.152***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.157***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.148***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.153***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.180***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.184***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e0.183*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.190***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.183***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.179***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.183***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e0.174***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological\u0026rarr;EQ-VAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.278***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.231***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.264***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.243***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.264***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.299***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.253***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e0.238***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.192***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.236***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.239***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.254***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e0.231***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial\u0026rarr;EQ-VAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.168***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.171***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.159***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.144***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.173***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.182***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.189***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e0.138***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.210***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.181***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.176***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.161***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e0.179***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial\u0026rarr;Biological\u0026rarr;EQ-VAS utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.015**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.005**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.010***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.004*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.007***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.017***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.027***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e0.033***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.039***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.024***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.031***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.021***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e0.021***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological\u0026rarr;Biological\u0026rarr;EQ-VAS utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.050***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.038***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.039***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.037***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.040***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.047***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.039***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e0.022***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.015**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.033***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.026***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.034***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e0.032***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial\u0026rarr;Psychological\u0026rarr;EQ-VAS utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.187***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.164***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.195***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.177***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.190***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.217***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.188***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e0.165***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.134***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.172***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.170***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.184***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e0.165***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSocial\u0026rarr;Psychological\u0026rarr;Biological\u0026rarr;EQ-VAS utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e0.034***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e0.027***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e0.028***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c10\" namest=\"c8\"\u003e \u003cp\u003e0.027***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c12\" namest=\"c11\"\u003e \u003cp\u003e0.029***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c14\" namest=\"c13\"\u003e \u003cp\u003e0.034***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c15\"\u003e \u003cp\u003e0.029***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c17\" namest=\"c16\"\u003e \u003cp\u003e0.015***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c19\" namest=\"c18\"\u003e \u003cp\u003e0.010**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c21\" namest=\"c20\"\u003e \u003cp\u003e0.024***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c23\" namest=\"c22\"\u003e \u003cp\u003e0.019***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c25\" namest=\"c24\"\u003e \u003cp\u003e0.025***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c26\"\u003e \u003cp\u003e0.023***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eEQ-5D Path Analysis\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003eMarriage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c13\" namest=\"c9\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c18\" namest=\"c14\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c22\" namest=\"c19\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c26\" namest=\"c23\"\u003e \u003cp\u003eChronic disease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eOther\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBiological\u0026rarr;EQ-5D-5L utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.254***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.275***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e0.253***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.243***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.296***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e0.385***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e0.248***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e-0.285*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e0.268***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e0.163***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e0.210***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePsychological\u0026rarr;EQ-5D-5L utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.195***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.188***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e0.300***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.199***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.185***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e0.156***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e0.199***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e0.171***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e0.204***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e0.272***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e0.185***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSocial\u0026rarr;EQ-5D-5L utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.088***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.123***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e0.136***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.098***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.115***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e0.025*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e0.125***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e0.055***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e0.115***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e0.067***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e0.123***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSocial\u0026rarr;Biological\u0026rarr;EQ-5D-5L utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.039***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.053***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e0.020*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.031***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.044***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e0.069***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e0.040***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e0.029***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e0.049***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e0.011***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePsychological\u0026rarr;Biological\u0026rarr;EQ-5D-5L utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.037***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.031***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e0.075***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.035***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.058***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e0.046***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e0.034***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e0.047***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e0.035***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e0.040***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e0.046***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSocial\u0026rarr;Psychological\u0026rarr;EQ-5D-5L utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.140***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.135***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e0.203***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.141***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.137***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e0.111***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e0.142***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e0.116***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e0.148***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e0.181***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e0.136***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSocial\u0026rarr;Psychological\u0026rarr;Biological\u0026rarr;EQ-5D-5L utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.026***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.022***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e0.051***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.025***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.043***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e0.033***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e0.024***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e0.032***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e0.026***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e0.027***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e0.034***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eVAS Path Analysis\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c8\" namest=\"c3\"\u003e \u003cp\u003eMarriage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c13\" namest=\"c9\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c18\" namest=\"c14\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c22\" namest=\"c19\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"4\" nameend=\"c26\" namest=\"c23\"\u003e \u003cp\u003eChronic disease\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eSingle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBiological\u0026rarr;EQ-VAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.167***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.192***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e0.200***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.179***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.167***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e0.226***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e0.162***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e0.197***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e0.170***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e0.081***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e0.117***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePsychological\u0026rarr;EQ-VAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.236***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.238***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e0.257***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.236***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.251***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e0.249***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e0.234***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e0.218***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e0.247***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e0.289***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e0.258***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSocial\u0026rarr;EQ-VAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.179***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.174***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e0.256***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.169***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.191***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e0.135***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e0.192***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e0.144***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e0.191***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e0.154***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e0.168***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSocial\u0026rarr;Biological\u0026rarr;EQ-VAS utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.024***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.033***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e0.017*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.018***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.030***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e0.027***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e0.026***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e0.014***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e0.030***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e-0.002*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e0.006***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003ePsychological\u0026rarr;Biological\u0026rarr;EQ-VAS utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.028***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.027***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e0.057***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.033***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.029***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e0.043***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e0.025***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e0.042***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e0.024***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e0.022***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e0.028***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSocial\u0026rarr;Psychological\u0026rarr;EQ-VAS utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.170***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.172***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e0.179***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.168***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.188***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e0.180***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e0.169***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e0.147***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e0.181***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e0.195***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e0.190***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eSocial\u0026rarr;Psychological\u0026rarr;Biological\u0026rarr;EQ-VAS utility\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003e0.020***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e0.019***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c9\" namest=\"c7\"\u003e \u003cp\u003e0.039***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003e0.023***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c13\" namest=\"c12\"\u003e \u003cp\u003e0.022***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c16\" namest=\"c14\"\u003e \u003cp\u003e0.031***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c18\" namest=\"c17\"\u003e \u003cp\u003e0.018***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c20\" namest=\"c19\"\u003e \u003cp\u003e0.029***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c22\" namest=\"c21\"\u003e \u003cp\u003e0.018***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c24\" namest=\"c23\"\u003e \u003cp\u003e0.015***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c26\" namest=\"c25\"\u003e \u003cp\u003e0.021***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"26\"\u003e1 Primary education and below is low education, junior and senior high school (including vocational) is intermediate, and bachelor's degree and above (including associate degree) is higher education.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"26\"\u003e2 18\u0026ndash;34 years old are considered young adults, 35\u0026ndash;54 years old are middle-aged, and 55 years and above are elderly.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"26\"\u003e3 * indicates p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.5, ** indicates p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.1, *** indicates p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05. The table shows the path coefficients(β) and their significance.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"26\"\u003e4 Others include divorce or widowhood.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFor indirect associations, several subgroup paths did not reach statistical significance. In the EQ-5D-5L utility model, those included Social \u0026rarr; Biological \u0026rarr; EQ-5D-5L utility (\u003cem\u003eyounger age group, middle age group, other marital status\u003c/em\u003e, and \u003cem\u003eindividuals with chronic diseases\u003c/em\u003e), Psychological \u0026rarr; Biological \u0026rarr; EQ-5D-5L utility (\u003cem\u003ecentral and western regions\u003c/em\u003e), and Social \u0026rarr; Psychological \u0026rarr; Biological \u0026rarr; EQ-5D-5L utility (\u003cem\u003ecentral and western regions\u003c/em\u003e). In the EQ-VAS model, they were Social \u0026rarr; Biological \u0026rarr; EQ-VAS utility (\u003cem\u003elow education level, middle education level, younger age group, other marital status\u003c/em\u003e, and \u003cem\u003eindividuals with chronic diseases\u003c/em\u003e), as well as Social \u0026rarr; Psychological \u0026rarr; Biological \u0026rarr; EQ-VAS utility (\u003cem\u003ewestern region\u003c/em\u003e).\u003c/p\u003e \u003cp\u003eAcross all the subgroups, SRMR values were below 0.05, and NFI values were all above or close to 0.9 (see Appendix Table A2), indicating that the BPS model fit well across all subgroups.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Discussion","content":"\u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Research Findings and Innovations\u003c/h2\u003e \u003cp\u003eThe study, grounded in the BPS model, investigated the associations of biological, psychological, and social factors with the HRQoL of Chinese residents. We found that all the three kinds of factors were directly and positively associated with HRQoL. Moreover, psychological and social factors also served as statistical mediators of the associations between biological, psychological, and social factors and HRQoL. The study also revealed a chain-mediated association pattern, in which social disadvantage was associated with higher psychological burden, which in turn was associated with biological health status, ultimately being associated with lower HRQoL. In terms of subgroup analysis, the BPS model demonstrated acceptable predictive performance and performed well across different subgroups; while certain path-based associations\u0026mdash;particularly the associations between social factors and HRQoL\u0026mdash;were weaker or non-significant among disadvantaged groups such as individuals with lower education, lower income, or chronic diseases.\u003c/p\u003e \u003cp\u003eCompared to prior studies, our study had three advantages. First, it used the largest sample size among existing BPS model studies, and was the first investigation in the general Chinese population. By exploring the BPS model within the context of China\u0026rsquo;s background, this study uncovered the pathway-consistent association patterns through which biological, psychological, and social factors were associated with HRQoL in the population. Second, the analyses demonstrated the model\u0026rsquo;s robustness across different subgroups and examined variations in the association patterns related to HRQoL among these groups. These findings provide important insights for developing more targeted public health interventions and policies tailored to the specific needs of different subpopulations. Third, it revealed complex interconnections and mediated association structures among biological, psychological, and social factors, providing new empirical evidence. These findings deepen the understanding of the intricate interrelationships within the BPS framework and promote the integration of multidisciplinary approaches in future health research.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Model Selection\u003c/h2\u003e \u003cp\u003eThis study explored the complex pathways associated with the HRQoL of Chinese residents. In line with this objective, the BPS model fit well with the research needs because of its strong theoretical foundation, which aligned closely with the multidimensional concept of HRQoL. Meanwhile, several theoretical frameworks have also been adopted in HRQoL research, among which the Ferrans model is one of the most representative. Although it has been widely applied to explain the factors associated with HRQoL among specific populations, including the general population, it primarily emphasizes relatively unidirectional relationships and thus may not fully capture the complex interrelationships among biological, psychological, and social dimensions. Empirically, it has often been used to verify predefined structural relationships, with limited attention to the context-dependent and culturally embedded associations across different social settings[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. Furthermore, although the model has been widely applied, empirical findings regarding its hypothesized pathways have been mixed, with some relationships showing limited or non-significant associations across studies. For example, Duangchan and Matthews reviewed 31 empirical studies and reported that certain links, such as the pathway from environmental characteristics to biological function, were not supported in any study[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEarly studies applying the BPS model mostly relied on linear regression methods, as they typically involved a limited number of variables and relatively simple analytical structures[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In contrast, the present study incorporated latent variable modeling, numerous observed indicators, and complex path-based association structures, for which traditional regression-based approaches were inadequate. Accordingly, Structural Equation Modeling (SEM), which moves beyond simple linear regression by allowing the simultaneous estimation of multiple relationships among latent constructs, was considered more appropriate. Among different SEM approaches, variance-based Partial Least Squares SEM (PLS-SEM) offered several advantages over covariance-based SEM (CB-SEM): it is robust to non-normal data distributions, accommodates both categorical and continuous variables, and supports prediction-oriented model assessment through the PLSpredict procedure for out-of-sample validation [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec24\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Measurement Model Analysis\u003c/h2\u003e \u003cp\u003eThe analyses showed that sleep variability, general self-efficacy, and social support had strong explanatory relevance according to external weights. Sleep variability has been shown to be associated with circadian rhythm stability and the regulation of the hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal (HPA) axis, thereby being linked to immune function and stress responses and being associated with individuals\u0026rsquo; health from both physiological and psychological perspectives. In light of recent empirical evidence on sleep regularity and health outcomes[\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], our results were consistent with the growing emphasis on sleep regularity in East Asian societies and provided empirical support for the relevance of this health concern. Self-efficacy was strongly associated with individuals\u0026rsquo; perceived control over stressors and their environment, and was related to stronger coping and self-regulatory capacities, lower psychological distress and helplessness, and more favorable patterns of health behaviors and mental well-being. Scholz et al., based on cross-cultural samples from Germany, Poland, and South Africa, also found that general self-efficacy significantly predicted psychological health and life satisfaction, serving as an important personal resource for fostering positive mental states[\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Social support not only provided emotional comfort and practical assistance but also has been shown to be associated with buffering the negative impacts of stressful events at the psychological level, thereby being linked to greater psychological resilience and social integration. In the Chinese context, which emphasizes collectivism and interpersonal connectedness, the positive associations between social support and health and well-being appeared particularly pronounced. Similarly, Zimet et al., using a sample of US university students, validated the structural validity of the Multidimensional Scale of Perceived Social Support (MSPSS) and demonstrated that perceived social support from family, friends, and significant others was significantly associated with better social adaptation and well-being[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlthough most indicators passed the significance test, conscientiousness (one of the Big Five personality traits) and secondary education in the EQ-5D-5L utility model, as well as the Eastern region in the EQ-VAS model, did not reach statistical significance. In the Chinese context, conscientiousness tended to emphasize responsibility toward family members, close friends, and organizations, rather than personal health management. Consequently, its association with individual health self-regulation might have been less direct or less prominent[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. Meanwhile, the non-significance of secondary education and the Eastern region may reflect structural or contextual factors. For instance, individuals with secondary education exhibited considerable socioeconomic diversity\u0026mdash;ranging from urban blue-collar workers to upwardly mobile young adults\u0026mdash;which may be related to heterogeneous health outcomes. Regarding the Eastern region, although its overall economic development level was relatively high, residents there tended to have better access to healthcare and health resources, which may have reduced health disparities and led to more homogeneous health outcomes. This concentration effect could have weakened the statistical significance of the regional variable[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAdditionally, we observed a positive association between alcohol consumption and the biological dimension, which contrasts with certain research findings. GBD 2020 Alcohol Collaborators found that higher levels of alcohol consumption were significantly associated with increased risks of all-cause, cardiovascular, cancer, and digestive disease mortality, suggesting potential adverse biological consequences of alcohol use[\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]. Meanwhile, some studies have shown that moderate alcohol consumption may be associated with improved cardiovascular health and potential benefits for brain health [\u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e, \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Therefore, future research is warranted to further refine the classification and measurement of alcohol consumption levels to better understand its heterogeneous associations with health outcomes.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec25\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Structural Model Analysis\u003c/h2\u003e \u003cp\u003eOur study identified multiple path-based association structures among biological, psychological, and social factors in relation to HRQoL. First, the validation of the Biological \u0026rarr; HRQoL, Psychological \u0026rarr; HRQoL, and Social \u0026rarr; HRQoL pathways suggested that these factors were directly and significantly associated with HRQoL. These findings were consistent with prior empirical evidence. For example, a study on multiple sclerosis in Germany emphasized that neuropathic pain and disease-related physiological status were closely associated with HRQoL[\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, the Social \u0026rarr; Biological \u0026rarr; HRQoL pathway indicated that social factors were indirectly associated with HRQoL through their associations with biological health. This pattern is consistent with evidence from a study on older adults living with HIV in the United States, which reported that social conditions were related to biological health indicators such as immune functioning, and subsequently associated with HRQoL[\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Similarly, the Psychological \u0026rarr; Biological \u0026rarr; HRQoL pathway highlighted that psychological health was associated with biological functioning, particularly through the association between depressive symptoms and inflammatory markers, which were in turn linked to HRQoL, as shown in studies on rheumatoid arthritis[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e]. Finally, the Social \u0026rarr; Psychological \u0026rarr; HRQoL pathway suggested that social factors were associated with psychological health, which was subsequently related to HRQoL, a pattern consistent with evidence from a UK study on child health that emphasized the important role of social support in mental health and HRQoL [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eMoreover, compared with previous studies, the analysis revealed a more complex chain-mediated association pattern\u0026mdash;\u0026ldquo;social \u0026rarr; psychological \u0026rarr; physiological \u0026rarr; HRQoL\u0026rdquo;. This pathway suggested that social disadvantage was associated with higher psychological distress and lower psychological resilience, which were subsequently associated with poorer physiological health and, in turn, lower HRQoL. Limited social resources\u0026mdash;such as insufficient social support, weak family communication, or socioeconomic deprivation\u0026mdash;may be associated with higher perceived stress and negative emotions, which are commonly linked to adverse psychological states such as anxiety, depression, or helplessness. These psychological states have been widely documented to be associated with the activation of physiological stress systems, particularly the hypothalamic\u0026ndash;pituitary\u0026ndash;adrenal (HPA) axis, which may be related to elevated cortisol levels, immune dysregulation, and metabolic imbalance. Over time, this cumulative psychophysiological burden may be associated with reduced physical health and functional capacity, ultimately corresponding to lower HRQoL. Notably, among the path analysis results, the association between social factors and psychological factors had the largest coefficient (EQ-5D-5L utility score: β\u0026thinsp;=\u0026thinsp;0.715; EQ-VAS score: β\u0026thinsp;=\u0026thinsp;0.721), indicating that this relationship was relatively stronger compared with other pathways. This pattern may reflect the sociocultural context of China, where dense social support networks and family-oriented social structures are closely linked to psychological well-being, potentially amplifying the strength of the association between social factors and psychological health.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec26\" class=\"Section2\"\u003e \u003ch2\u003e5.5 PLS-Predict Validation Analysis\u003c/h2\u003e \u003cp\u003eWe observed that the prediction errors of PLS-SEM were comparable to those of the linear model (LM) for several indicators. Such findings are not uncommon in the existing literature [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. In fact, the methodological guidelines of PLSpredict emphasize that it is expected that LM may show lower prediction errors under certain conditions [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e]. Nevertheless, this does not imply that linear regression is inherently superior, as relying on a single predictive indicator is insufficient for a comprehensive assessment of predictive adequacy[\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe predictive performance of the LM benchmark was comparable to that of PLS-SEM, possibly because of the relatively low level of multicollinearity in the data. As a result, the strength of PLS-SEM in handling multicollinearity may not have been fully reflected in this specific application. Nevertheless, PLS-SEM\u0026rsquo;s ability to accommodate both continuous and categorical variables and to estimate complex path-based association structures offers advantages beyond traditional linear regression. Thus, PLS-SEM remains an appropriate and robust analytical approach for the present study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec27\" class=\"Section2\"\u003e \u003ch2\u003e5.6 Subgroup Analysis\u003c/h2\u003e \u003cp\u003eSocial factors showed weaker associations with HRQoL among the low-education group but exhibited stronger associations among the medium- and high-education groups. This pattern was consistent with studies from Europe and the US, suggesting that education is associated with higher health literacy and greater efficiency in translating social and institutional resources into health outcomes [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e, \u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. The indirect psychological and biological pathways were more pronounced among highly educated individuals, further indicating that education may function as an important amplifier of health-related resources. In China, education not only reflects individual capacity differences but also determines access to medical and digital resources within the urban\u0026ndash;rural dual structure [\u003cspan citationid=\"CR72\" class=\"CitationRef\"\u003e72\u003c/span\u003e], suggesting that structural inequality is closely associated with how social factors relate to health outcomes.\u003c/p\u003e \u003cp\u003eAge differences followed a similar pattern. Among young adults (18\u0026ndash;34 years), the psychological \u0026rarr; biological and social \u0026rarr; psychological \u0026rarr; biological pathways were not statistically significant or were weak, whereas they were significantly positive among middle-aged and older adults. This finding was consistent with evidence on \u0026ldquo;youth subhealth\u0026rdquo; [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. In the Chinese context, the pressures of involution and excessively long working hours may be associated with a weaker translation of psychological resilience into physical health. By contrast, middle-aged and older adults, who generally exhibit greater emotional regulation and social stability, showed stronger coordination among biopsychosocial dimensions.\u003c/p\u003e \u003cp\u003eRegional and urban\u0026ndash;rural differences revealed clear patterns consistent with structural inequality. In western China, the social \u0026rarr; biological and psychological \u0026rarr; biological pathways were substantially weaker, which aligned with existing evidence on regional poverty and health inequality [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This suggests that traditional kinship networks in western China may not sufficiently compensate for the shortage of medical and technological infrastructure[\u003cspan citationid=\"CR73\" class=\"CitationRef\"\u003e73\u003c/span\u003e]. Urban residents were more likely to exhibit stronger associations between social resources and health-related outcomes, whereas rural residents appeared to benefit less, potentially due to weakened social networks and limited institutional coverage. These findings are consistent with international evidence showing that the health-related associations of urban social capital are generally stronger. In the Chinese context, the urban\u0026ndash;rural dual structure\u0026mdash;through differences in institutional coverage and the spatial concentration of resources\u0026mdash;may further weaken the linkage between rural social capital and formal support systems, thereby amplifying urban\u0026ndash;rural disparities in the health-related returns of social resources.\u003c/p\u003e \u003cp\u003eIn terms of gender differences, the biological \u0026rarr; HRQoL pathway was significantly associated with HRQoL among men but weaker or non-significant among women, a pattern consistent with the \u0026ldquo;gender health paradox\u0026rdquo; [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Women\u0026rsquo;s dual roles in employment and caregiving may be associated with higher psychological stress and lower subjective health perception, even when biological conditions are similar. This pattern appears particularly pronounced in China, suggesting that societal expectations regarding gender roles may be linked to poorer mental health among women, thereby contributing to disparities in health perception.\u003c/p\u003e \u003cp\u003eAmong individuals with chronic diseases, psychological factors showed stronger associations with biological health, whereas the associations between social factors and biological health were weaker. This pattern was consistent with findings from studies conducted in Europe and North America[\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR74\" class=\"CitationRef\"\u003e74\u003c/span\u003e]. In the Chinese context, insufficient family caregiving capacity and limited community-based services may lead patients to rely more heavily on psychological self-regulation, forming a pattern of \u0026ldquo;individualized compensation\u0026rdquo; within an imperfect social support system (see Appendix Table A3).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec28\" class=\"Section2\"\u003e \u003ch2\u003e5.7 Policy Implications\u003c/h2\u003e \u003cp\u003eThe identified key factors and chain-mediated patterns could have several policy implications. First, social resources such as health literacy and social support should be addressed as upstream determinants of psychological health. Strengthening community-based support and health literacy programs may therefore improve HRQoL by enhancing psychological resilience and reducing perceived stress. Second, given the identified psychological-to-biological pathway, stress management and sleep health interventions could be integrated into primary healthcare services to improve biological health indicators and, in turn, HRQoL. Third, the weaker social\u0026ndash;HRQoL associations observed among socioeconomically disadvantaged and chronically ill groups indicate that interventions tailored to these groups may be necessary to reduce inequalities in HRQoL. This approach may help ensure that health improvements are more evenly distributed across different population groups.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec29\" class=\"Section2\"\u003e \u003ch2\u003e5.8 Limitations\u003c/h2\u003e \u003cp\u003eThe main limitation lies in the use of a cross-sectional design, which precludes the examination of temporal dynamics and causal relationships among variables. Future research may adopt panel or longitudinal designs, allowing for a more accurate assessment of changes and directional associations over time. Furthermore, although the study systematically examined subgroup differences, it did not further test potential interaction or moderation effects among variables. Future research could employ appropriate methods such as multi-group structural equation modeling or hierarchical regression analyses to explore these interactive and moderating association patterns.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec30\" class=\"Section2\"\u003e \u003ch2\u003e5.9 Conclusion\u003c/h2\u003e \u003cp\u003eThis study, based on the BPS model, developed and validated a framework for examining the associations of biological, psychological, and social factors with the HRQoL of Chinese residents. According to the framework, key factors (i.e., sleep variability, self-efficacy, and social support) associated with HRQoL were identified, and the direct and indirect chain-mediated association structures linking these factors to HRQoL were demonstrated. In addition, variations in these association patterns across different subgroups were also identified.\u003c/p\u003e \u003c/div\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 Ethics Review Committee of Shanghai Jiao Tong University (Approval No. H20240237I). According to the ethics approval document (page 1), the committee formally approved the study and permitted its implementation, with follow-up monitoring during the study period.\u003c/p\u003e\n\u003cp\u003eAll procedures were conducted in accordance with relevant guidelines and regulations. Informed consent was obtained from all participants prior to data collection.\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\u003eClinical trial number:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data used in this study were derived from the Psychological and Behavioral Investigation of Chinese Residents (PBICR) and are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study has been sponsored by the National Natural Science Foundation of China (Project No.:72274037) awarded to Dr. Pei Wang.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eXiaoting Zhang\u003c/strong\u003e: Conceptualization, Methodology, Formal analysis, Writing – original draft, Writing – review \u0026amp; editing. \u003cstrong\u003eYibo Wu\u003c/strong\u003e: Data curation, Investigation, Writing – review \u0026amp; editing. \u003cstrong\u003eJunhao Li\u003c/strong\u003e: Formal analysis consultation and investigation. \u003cstrong\u003eYuang Dun\u003c/strong\u003e: Investigation. \u003cstrong\u003eLiyang Zhang\u003c/strong\u003e: Investigation. \u003cstrong\u003eYuan Chen\u003c/strong\u003e: Conceptualization, Methodology. \u003cstrong\u003ePei Wang\u003c/strong\u003e: Conceptualization, Methodology, Supervision, Funding acquisition, Validation, Writing – review \u0026amp; editing. \u003cstrong\u003eYuanyuan Gu\u003c/strong\u003e: Conceptualization, Writing – review \u0026amp; editing.\u003c/p\u003e\n\u003cp\u003eAll authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors sincerely thank all participants and investigators involved in the survey.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eWorld Health Organization. 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J Psychosom Res. 2011;71:79\u0026ndash;85. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jpsychores.2011.01.008\u003c/span\u003e\u003cspan address=\"10.1016/j.jpsychores.2011.01.008\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"health-and-quality-of-life-outcomes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hqlo","sideBox":"Learn more about [Health and Quality of Life Outcomes](http://hqlo.biomedcentral.com)","snPcode":"12955","submissionUrl":"https://submission.nature.com/new-submission/12955/3","title":"Health and Quality of Life Outcomes","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Biopsychosocial Model, Health-Related Quality of Life (HRQoL), PLS-SEM, PLSpredict, Chinese Residents","lastPublishedDoi":"10.21203/rs.3.rs-9507474/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9507474/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e \u003cp\u003eTo examine how biological, psychological, and social factors are linked to health-related quality of life (HRQoL) among Chinese residents according to the biopsychosocial (BPS) framework.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were drawn from the 2024 Psychology and Behavior Investigation of Chinese Residents (PBICR), including 25,047 adults from 31 provincial-level regions in China. HRQoL was measured using the EQ-5D-5L. Based on the BPS framework, biological, psychological, and social factors assessed by standardized instruments were specified as formative latent constructs. The BPS model was estimated using Partial Least Squares Structural Equation Modeling (PLS-SEM). Model evaluation consisted of three stages: assessment of the measurement model, evaluation of the structural model, and internal model validation. These analyses focused respectively on indicator relevance and collinearity, structural relationships and model fit, and predictive performance based on PLSpredict with 10-fold cross-validation. Subgroup analyses were conducted to examine heterogeneity in associations across sociodemographic groups defined by education, age, region, sex, income, marital status, residence, smoking status, alcohol consumption, and chronic disease status.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe mean (SD) EQ-5D-5L utility and EQ-VAS scores of the sample were 0.928 (0.163) and 76.03 (19.16), respectively. Measurement model results showed that nearly all indicator weights were statistically significant with no serious collinearity, indicating the validity of the latent constructs. Among the indicators, sleep stability, general self-efficacy, and social support were the most influential factors. Structural model results indicated that all three types of factors were positively and directly linked to HRQoL. Social factors also showed significant indirect linkages through psychological and biological pathways, supporting patterns consistent with the hypothesized chain-mediated pathway. The BPS model also demonstrated acceptable predictive performance according to PLSpredict results. Subgroup analyses revealed that the strength of associations varied across different characteristics such as education level, age, region, and socioeconomic status.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe BPS model was estimated and validated, revealing the multidimensional determinants of HRQoL and their interrelated patterns consistent with chain-mediated pathways. Variations in these patterns across different subgroups were also identified.\u003c/p\u003e","manuscriptTitle":"Biopsychosocial factors associated with health-related quality of life in the general Chinese population: Evidence from a nationwide health survey","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-05-08 18:19:48","doi":"10.21203/rs.3.rs-9507474/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"64006114439914815908631788578311643161","date":"2026-05-06T08:07:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-04-30T13:05:09+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-04-25T10:00:36+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-04-25T09:59:55+00:00","index":"","fulltext":""},{"type":"submitted","content":"Health and Quality of Life Outcomes","date":"2026-04-23T13:21:12+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"health-and-quality-of-life-outcomes","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"hqlo","sideBox":"Learn more about [Health and Quality of Life Outcomes](http://hqlo.biomedcentral.com)","snPcode":"12955","submissionUrl":"https://submission.nature.com/new-submission/12955/3","title":"Health and Quality of Life Outcomes","twitterHandle":"@BioMedCentral","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"136970a4-b26c-4785-bd51-af92d206872b","owner":[],"postedDate":"May 8th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"64006114439914815908631788578311643161","date":"2026-05-06T08:07:11+00:00","index":16,"fulltext":""},{"type":"reviewersInvited","content":"10","date":"2026-04-30T13:05:09+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T18:19:48+00:00","versionOfRecord":[],"versionCreatedAt":"2026-05-08 18:19:48","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9507474","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9507474","identity":"rs-9507474","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}