Influencing factors and mechanisms of health-related quality of life of elderly patients with chronic diseases in rural China: a cross-sectional study

Influencing factors and mechanisms of health-related quality of life of elderly patients with chronic diseases in rural China: a cross-sectional study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Influencing factors and mechanisms of health-related quality of life of elderly patients with chronic diseases in rural China: a cross-sectional study Yujie Chen, Xiaoting Wang, Yi Li, Chi Wang, Hui Wang, Yaodong Zhao, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4665655/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background The study aimed to understand the factors influencing health-related quality of life (HRQOL) and the intricate biological, psychological, and social processes that underlie it in elderly chronic disease patients in rural China. To do this, structural equation model(SEM) was utilized to construct a model based on the Wilson and Cleary model. Methods In this cross-sectional study, 1145 senior individuals with chronic illnesses from three cities in Anhui Province, China were chosen by a multi-stage random sampling procedure. Households were surveyed face-to-face using the following instruments: the five-level version of the European Five Dimensional Health Scale (EQ-5D-5L), Generalized Anxiety Scale (GAD-7), 9-item Patient Health Questionnaire (PHQ-9), Social Support Rating Scale (SSRS), Pittsburgh Sleep Quality Index (PSQI), Chinese Version of the elderly Kihon Checklist (KCL), and a self-designed questionnaire on vision conditions. Results This study identified 13 individual and environmental characteristics associated with HRQOL in rural elderly patients with chronic diseases, including gender, age, education, working status, main economic source, drinking, roughage, labor intensity, siesta, social support, marital status, and dwelling status, as well as the directional pathways of action of these factors affecting HRQOL, which included 26 indirect and 6 direct pathways. Conclusions This study adds to the body of knowledge on HRQOL and advances our comprehension of the potentially intricate biological and psychological processes that influence HRQOL in older individuals with chronic diseases by revealing the influencing factors and directed pathways of action on HRQOL. Providing timely and personalized therapies to address these causes and processes may eventually improve their HRQOL. Health sciences/Diseases Health sciences/Health care Health sciences/Health occupations Health sciences/Risk factors Rural elderly Chronic disease HRQOL Structural equation model Figures Figure 1 Figure 2 Background The Global Health Statistics Report 2017 states that chronic diseases have become a major global health threat, causing more than 41 million deaths, accounting for 71.3% of all deaths worldwide [ 1 ] . China has entered an aging society, and the degree of population aging is still deepening, in 2020, the elderly population aged 60 and above has reached 264 million, of which about 20% suffer from various chronic diseases [ 2 , 3 ] . The incidence of chronic diseases is still rising rapidly, and their long duration and difficulty to cure not only aggravate the disease burden of patients, but also significantly increase the cost of health care, which has a detrimental effect on the older individuals' quality of life, physical health, and mental health [ 4 – 6 ] .Compared with urban residents, rural older folks are vulnerable in chronic disease management due to their lack of health management concepts, lower education level, insufficient awareness of self-care, and lack of resources in material, medical and spiritual aspects [ 7 ] . In the 1990s Schipper et al. introduced the concept of health-related quality of life (HRQOL), which refers to a person's quality of life that is directly related to health, and is a subjective and multidimensional concept that expresses the degree to which a patient or an individual is satisfied with his or her current level of functioning [ 8 – 10 ] .HRQOL can be used to reflect the quality of life of individuals with chronic conditions, and it is an important indicator for assessing successful ageing in the elderly [ 11 , 12 ] . A growing amount of conceptional models are being proposed to clarify the biopsychosocial factors affecting HRQOL. The three frequently cited conceptual models are: the Centre for Health Promotion model, which links quality of life to disease in general; the Contextual Model of HRQOL, which is specifically designed to explain cancer patients' health status; and Wilson and Cleary's model of patient prognosis [ 13 – 15 ] . The Wilson and Cleary model (Fig. 1 ) is a now widely recognised universal model in the academic community that describes the causal relationships and pathways of action between the basic concepts of health-related quality of life. They argue that this model emphasises that the health of an individual or group is the result of a combination of their own and the environment in which they live.The model consists of five main factors: biological and psychological variables, symptom status, functional status, general health perceptions and overall quality of life, while each factor exists on a complex continuum of social, physiological and psychological variables, with individual and environmental characteristics influencing all but the biological and psychological variables [ 15 ] .Although the relationships of variables in the model may be bidirectional and the formulated relationships are transient, the model has been validated in a wide range of chronic diseases, such as female stroke survivors, heart failure patients, chronic obstructive pulmonary disease (COPD) and diabetic patients, and provides a valuable reference for clinicians and researchers [ 16 – 19 ] . As the current research on the application of the Wilson-Cleary health-related quality of life model is mainly limited to specific chronic disease populations, and the sample size is relatively small. In order to enrich the application of this theoretical model in a population of non-specific chronic disease patients, this study constructed a HRQOL model based on the Wilson-Cleary model for elderly chronic disease patients in rural China, and validated it using empirical data, aiming to reveal the directional pathways and related factors affecting HRQOL, in order to establish a thorough knowledge of the potentially complex physiological, psychological, and social processes that affect HRQOL in rural Chinese individuals with chronic conditions, and to offer a scientific foundation for developing effective interventions to improve patients' quality of life. Methods Study population This study conducted a cross-sectional survey from July 2023 to February 2024 using multi-stage random sampling. First, we chose a prefecture-level city from Anhui's northern (Suzhou), central (Hefei), and southern (Anqing) areas. Second, a county was randomly chosen from each of these cities. A township was then picked at random from each county. Finally, villages were randomly selected from the townships, yielding a total of 18 survey locations. The participants, excluding individuals with communication challenges such as speech and hearing impairments, were at least sixty years old. They were questioned in a household survey format, with uniformly trained people administering face-to-face questions. A total of 1,556 participants were chosen, and 1,546 completed questionnaires were received, yielding an overall response rate of 99.4%. The Ethics Committee of Anhui Medical University accepted the study (permission number: 83244655), and before to the interview, all interviewees completed an informed consent form. Assessment variables of the model We used the Wilson-Cleary model (Fig. 1 ) as a theoretical guide, combined with a literature review of related fields, selected variables related to chronically ill patients, and constructed a model of HRQOL in rural elderly chronically ill patients based on expert discussion. Given that the pathway that an individual's biological and physiological variables affect his or her HRQOL by influencing symptoms, functional status, and general health perceptions has been well-documented by existing studies, the present study proposes the hypothesis that the starting point of the HRQOL model for rural elderly patients with chronic diseases starts from symptoms, which in turn, through physical functioning and general health perceptions, ultimately act on the patient's HRQOL, and that individual characteristics, environmental characteristics have a direct role in all four factors [ 19 ] . Then we test the hypotheses by using empirical analyses. HRQOL In this study, HRQOL was assessed using the 5-level European 5-Dimensional Health Scale (EQ-5D-5L) [ 20 ] .The questionnaire is divided into two parts: the Health Status Description System (HSDS) and the Visual Analogue Scale (EQ-VAS). Five health dimensions were included: mobility, taking care of oneself, daily activities, pain/discomfort, and anxiety/depression, and each health dimension was categorised into five levels. Using the TTO method, the health status was converted into numerical values of an arithmetic nature by means of the Health Utility Value Integration System developed by Luo et al [ 21 ] . This set of exponential values produces health utility values ranging from − 0.391 ~ 1.000, with bigger value indicating a better quality of life. The Cronbach's α was 0.720. General Health Perceptions EQ-VAS was used in the study to assess general health perceptions.The VAS is a vertical visual scale with 100 points on which respondents quantitatively assess their overall health. A number of 0 at the bottom signifies "worst imaginable health," while a score of 100 at the top symbolizes "best imaginable health". The score of 80 is considered to be a good self-assessment of health, and an EQ-VAS score of 80 is considered to be a poor self-assessment of health [ 22 , 23 ] . Functional Status To assess functional status we used three variables obtained from the Chinese Version of the elderly Kihon Checklist (KCL), Pittsburgh Sleep Quality Index (PSQI), and a self-designed visual questionnaire.The Khion Checklist was designed by Japan as a comprehensive assessment tool to screen for frailty in the elderly [ 24 ] . The Chinese version of the KCL was designed by Wang ZY et al. It contains 7 dimensions and 25 entries, with simple "yes" or "no" choices, and "0" or "1" answers based on the content of the entries. A score of "0" or "1" is determined by the content of the entry, and a score of 7 is considered debilitating, higher values indicate more severe debilitation. The reliability and validity have been tested [ 25 ] . The Cronbach's α of the KCL was 0.809. The PSQI assesses the sleep quality in the last month, which contains 7 dimensions, each dimension is scored as 0 ~ 3. The Cronbach's α of the PSQI was 0.815. A score of 7 on the PSQI suggests poor sleep quality,higher values indicate more severe poor sleep [ 26 ] . The vision condition questionnaire was self-designed and consisted of six questions, including whether they had vision problems, how blurred their vision was, whether they had double vision, whether they wore glasses, whether they had any eye diseases, and whether they had had any eye surgeries, with the higher the score the worse their vision condition.The Cronbach's α for this questionnaire was 0.725. Symptom Status We used three variables to describe symptoms. These included the presence of self-reported comorbidities and the number of chronic diseases included in the comorbidities, with the presence of two or more chronic diseases at the same time being judged as a comorbidity [ 27 ] ; physical symptom profiles, which included the presence of self-reported dizziness/syncope and the number of times the symptoms occurred in the last week, and the presence of physical discomfort in the last two weeks; and psychological symptoms, including anxiety and depression. A score of 5 indicating the presence of anxiety on the Generalized Anxiety Scale (GAD-7) indicates higher anxiety. Higher values indicate more severe anxiety. The Cronbach's α of the GAD-7 was 0.936. A score of ≥ 5 on the 9-item Patient Health Questionnaire (PHQ-9) indicates depression. Higher values indicate more severe depression [ 28 ] . The Cronbach's α for the PHQ-9 was 0.897. Individual Characteristics Variables used to assess individual characteristics were obtained from a self-administered baseline information questionnaire. General demographic and sociological characteristics, including age, gender, education, work status, main source of income, and BMI; and lifestyle characteristics, including smoking, drinking, roughage, taste preference, siesta, caring for grandchildren, and labour intensity. Environmental Characteristics The three variables reflecting environmental characteristics include marital status, residential status, and social support. The Social Support Rating Scale (SSRS) was used to assess social support. The degree of social support increases with score [ 29 ] . The Cronbach's α of the SSRS was 0.796.The details of the assigned values are shown in Table 1 . Table 1 Variables, indicators, and descriptions of the HRQOL model for rural elderly patients with chronic diseases Latent variables Observed variables indicators descriptions Symptom Status Comorbidities Comorbidities and its number The lower the score, the better Somatic symptoms Dizziness/Syncope and its number The lower the score, the better Discomfort in 2weeks 1 = Yes, and 2 = No Psychological symptoms GAD−7 Ranged 0–21,the lower the score the better PHQ−9 Ranged 0–27,the lower the score the better Functional Status Frailty KCL Ranged 0–14,the lower the score the better Sleep PSQI Ranged 0–27,the lower the score the better Vision Vision condition questionnaire Ranged 0–7,the lower the score the better General Health perceptions Self-rated health EQ-VAS Ranged 0−100,the higher the score the better Overall Quality of Life HRQOL EQ−5D−5L Ranged − 0.391–1.000,the higher the score the better Individual Characteristics Sociodemographic characteristics Age 1 = 60–69 years, 2 = 70–79 years, 3= \(\ge\) 80 years Gender 1 = Male, and 2 = Female Education 1 = Illiterate, and 2 = Non-illiterate Working status 1 = Full-time,2 = Part-time,3 = Housework,4 = Free Main economic source 1 = Self-labor, 2 = Child support, 3 = Government subsidy, 4 = Past savings, 5 = Other Lifestyle characteristics Smoking 1 = No,2 = Used to, 3 = Yes Drinking 1 = No,2 = Used to, 3 = Yes Siesta 1 = Yes, and 2 = No Roughage 1 = 0 times/week, 2 = 1–2 times/week, 3 = 3–5 times/week, 4 = Almost everyday Taste preference 1 = Salty, 2 = Medium, 3 = Bland Labour intensity Ranged from 1 = very light to 5 = very heavy Caring for grandchildren 1 = Yes, and 2 = No Environmental Characteristics Marital status 1 = Married, 2 = Widowed, 3 = Single/Divorced Dwelling status 1 = Solitary,2 = Non-solitary Social support SSRS The higher the score the better Data analysis We used two types of software, SPSS 25.0 and SmartPLS 3.0, for statistical description and model construction. Since none of the study's data were normally distributed, the data were described using the median and interquartile spacing, and the maximum, minimum, and constitutive ratios were used to describe the count and rank data. The Kruskal-Wallis H and Mann-Whitney U test were used for comparisons between groups. A statistical significance level of α = 0.05 was used to all two-sided tests used in the statistical analyses. Based on the Wilson-Cleary model, a structural equation model of HRQOL in elderly patients living in rural areas with chronic illnesses was created in this study using the partial least squares structural equation model (PLS-SEM). Cronbach's α, composite reliability (CR) assessed the reliability of the measurement model, VIF assessed indicator validity, average variance extracted (AVE), discriminant validity assessed the validity of the measurement model, SRMR, NFI were used to assess the fit of the structural model, and R 2 , Q 2 assessed the explanatory power of the model [ 30 , 31 ] . The models and path coefficients were tested for significance using the bootstrapping test. The sampling number for the autonomous sample technique was set to 5000, while the maximum number of iterations for path weighting was set at 300 [ 32 ] . Results Descriptives and Variance analysis As seen in Table 2 , of the 1145 respondents, 510 (44.5%) were male and 635 (55.5%) were female, with an average age of 72 (68, 78) years. The average HRQOL score of rural old individuals with chronic conditions was 0.942 (0.824, 1.000), and the majority of the patients (62.6%) had poor self-rated health. Differences in HRQOL among elderly rural Chinese patients with chronic diseases in terms of gender, age, education, work status, main economic source, BMI, smoking, drinking, roughage, taste, labour intensity, hospitalisation in a year, siesta, caring for grandchildren, comorbidities, number of chronic diseases, dizziness/syncope, Frequency of dizziness / syncope, discomfort in 2weeks, anxiety, depression, quality of sleep, frailty, self-rated health, vision score, and social support were statistically significant (p < 0.05). Table 2 Comparison of basic information and HRQOL of elderly rural patients with chronic diseases with different characteristics (n = 1145) Variables n(%) health state value \(\mathbf{Z}/{\varvec{X}}^{2}\) P Gender −3.961 0.000 ** Male 510(44.5%) 0.951(0.862, 1.000) Female 635(55.5%) 0.942(0.813, 1.000) Age(years) 58.274 0.000 ** 60 \(\sim\) 69 360(31.4%) 1.000(0.893, 1.000) 70 \(\sim\) 79 568(49.6%) 0.942(0.814, 0.942) \(\ge\) 80 217(20.0%) 0.882(0.737, 1.000) Marital status 5.544 0.063 Married 844(73.7%) 0.942(0.824, 1.000) Widowed 266(23.2%) 0.942(0.824, 1.000) Single/Divorced 35(3.1%) 1.000(0.841, 1.000) Education −6.857 0.000 ** Illiterate 680(59.4%) 0.934(0.787, 1.000) Non-illiterate 465(40.6%) 1.000(0.841, 1.000) Dwelling status −0.431 0.666 Solitary 233(20.3%) 0.942(0.841, 1.000) Non-solitary 913(79.7%) 0.942(0.813, 1.000) Working status 89.509 0.000 ** Full-time 260(22.7%) 1.000(0.893, 1.000) Part-time 201(17.6%) 0.942(0.862, 1.000) Housework 302(26.4%) 0.942(0.893, 1.000) Free 382(33.4%) 0.862(0.700, 1.000) Main economic source 49.201 0.000 ** Self-labor 410(35.8%) 0.951(0.893, 1.000) Child support 402(35.1%) 0.942(0.824, 1.000) Government subsidy 263(23.0%) 0.893(0.734, 1.000) Past savings 41(3.6%) 1.000(0.909, 1.000) Other 29(2.5%) 0.893(0.747, 1.000) BMI( \(\text{k}\text{g}/{\text{m}}^{2}\) ) 14.184 0.003 ** \(<\) 18.5 77(6.7%) 0.942(0.744, 1.000) 18.5–23.9 523(45.7%) 0.942(0.862, 1.000) 24−27.9 382(33.4%) 0.942(0.830, 1.000) \(\ge\) 28 163(14.2%) 0.893(0.776, 1.000) Smoking 13.755 0.001 ** No 849(74.1%) 0.942(0.813, 1.000) Used to 92(8.0%) 0.942(0.866, 1.000) Yes 204(17.8%) 1.000(0.862, 1.000) Drinking 18.326 0.000 ** No 807(70.5%) 0.942(0.813, 1.000) Used to 90(7.9%) 0.942(0.862, 1.000) Yes 248(21.7%) 1.000(0.887, 1.000) Roughage(times/week) 25.890 0.000 ** 0 477(41.7%) 0.934(0.761, 1.000) 1–2 325(28.4%) 0.942(0.876, 1.000) 3–4 163(14.2%) 1.000(0.893, 1.000) Almost everyday 180(15.7%) 0.942(0.749, 1.000) Taste preference 14.062 0.001 ** Salty 369(32.2%) 0.942(0.813, 1.000) Medium 556(48.6%) 0.942(0.862, 1.000) Bland 220(19.2%) 0.894(0.750, 1.000) Labour intensity 140.303 0.000 ** Very light 465(40.6%) 0.876(0.734, 1.000) Light 364(31.8%) 0.942(0.882, 1.000) Average 234(20.4%) 1.000(0.942, 1.000) Heavy 76(6.6%) 1.000(0.893, 1.000) Very heavy 6(0.5%) 0.931(0.797, 1.000) Hospitalisation in a year −9.729 0.000 ** Yes 416(36.3%) 0.882(0.744, 1.000) No 729(63.7%) 0.951(0.882, 1.000) Siesta −6.241 0.000 ** Yes 781(68.2%) 0.942(0.796, 1.000) No 364(31.8%) 0.980(0.893, 1.000) Caring for grandchildren −3.262 0.001 ** Yes 205(17.9%) 0.942(0.893, 1.000) No 940(82.1%) 0.942(0.813, 1.000) Comorbidities −6.739 0.000 ** Yes 690(60.3%) 0.942(0.785, 1.000) No 455(39.7%) 1.000(0.893, 1.000) Number of chronic diseases 71.738 0.000 ** 1 455(39.7%) 1.000(0.893, 1.000) 2–3 584(51.0%) 0.942(0.813, 1.000) \(\ge\) 4 106(9.3%) 0.820(0.700, 0.942) Dizziness / Syncope −9.863 0.000 ** Yes 767(67.0%) 0.893(0.785, 1.000) No 378(33.0%) 1.000(0.942, 1.000) Frequency of dizziness / syncope (times/week) 196.245 0.000 ** 0 380(33.2%) 1.000(0.942, 1.000) 1–2 311(27.2%) 0.942(0.893, 1.000) 3–4 167(14.6%) 0.942(0.824, 1.000) \(\ge\) 5 287(25.1%) 0.813(0.700, 0.896) Discomfort in 2weeks −8.701 0.000 ** Yes 425(37.1%) 0.893(0.747, 1.000) No 720(62.9%) 0.951(0.882, 1.000) Anxiety 295.629 0.000 ** No( \(<\) 5) 668(58.3%) 1.000(0.934, 1.0000) Light(5–9) 297(25.9%) 0.893(0.813, 1.000) Medium(10–14) 93(8.1%) 0.785(0.681, 0.882) Heavy( \(\ge\) 15) 87(7.6%) 0.727(0.541, 0.824) Depression 355.815 0.000 ** No( \(<\) 5) 607(53.0%) 1.000(0.942, 1.000) Light(5–9) 293(25.6%) 0.894(0.813, 1.000) Medium(10–14) 118(10.3%) 0.813(0.711,0.942) Heavy( \(\ge\) 15) 127(11.1%) 0.727(0.541,0.824) Sleep −6.621 0.000 ** Good( \(\le 7\) ) 534(46.6%) 0.951(0.889,1.000) Bad( \(>7\) ) 611(53.4%) 0.934(0.782,1.000) Frailty −14.877 0.000 ** Yes( \(\ge 7\) ) 807(70.5%) 0.893(0.776,1.000) No( \(<7\) ) 338(29.5%) 1.000(0.942, 1.000) Self-rated health −10.789 0.000 ** Good( \(\ge 80\) ) 428(37.4%) 1.000(0.942, 1.000) Bad( \(<\) 80) 717(62.6%) 0.893(0.767, 1.000) Vision score 73.904 0.000 ** 0 126(11.0%) 1.000(0.942, 1.000) 1–2 374(32.7%) 0.951(0.893, 1.000) 3–4 495(43.2%) 0.942(0.813, 1.000) \(\ge\) 5 150(13.1%) 0.862(0.702, 1.000) Social support 70.708 0.000 ** Low( \(\) 44) 212(18.5%) 1.000(0.942, 1.000) *P \(<\) 0.05, **P \(<\) 0.01 Establishment of structural equations We used SmartPLS 3.0 software to construct the initial structural equation model of HRQOL in rural elderly patients with chronic diseases. The factor loadings of the measurement model should \(>\) 0.70 in the partial least squares structural equation model, indicating that the latent variables may account for \(>\) 50% of the measured variables [ 33 ] . The results of the factor analysis of the initial model indicated that the factor loadings of comorbidity, dizziness/syncope, and physical discomfort within two weeks in symptoms were 0.528, 0.547, and − 0.378, respectively, which were less than 0.70, and the factor loadings of sleep and vision in functional status were 0.656 and 0.645, respectively, which were less than 0.70, and should have been excluded to obtain a higher degree of validity. After removing these variables, the results of the second fitting were as follows: the factor loadings of anxiety and depression in symptoms were 0.957 and 0.960 respectively, which were greater than 0.70. Based on the Bootstrapping test findings, all non-significant pathways were discarded. After removing the insignificant paths, the results of the third fitting were as follows: the factor loadings of anxiety and depression in the symptoms were 0.956 and 0.961, respectively, and the factor loadings were all exceeded 0.7. The VIF were all less than 5, indicating no multicollinearity [ 34 ] . The model had a good convergence efficacy, as evidenced by CR values over 0.70 and AVE values exceeding 0.50 [ 35 ] .All of the AVE values' square roots were higher than the factor's correlation coefficient with any other factor, which met the Fornell-Larcker Criterion, so the measurement model was judged to have good discriminant validity [ 36 ] . The model fitness test was further conducted in this study, and the R 2 values by the PLS Algorithm were all higher above the typical acceptability value of 0.10, indicating that the predictive accuracy of the model was good; the Q² values were all greater than 0, indicating that the research model could effectively predict the correlation between variables [ 31 ] . In addition, SRMR is 0.012, which is less than the accepted standard value of 0.08, and NFI is 0.934, which exceed the standard value of 0.90, both of which indicate that the model fits well [ 37 ] . In summary, this indicates that the model fit is good enough for subsequent analyses, and the results are shown in Table 3 . Table 3 Results of model fitting Cronbach's α CR AVE R 2 Q 2 SRMR NFI HRQOL 1 1 1 0.306 0.299 — — Functional status 1 1 1 0.523 0.510 — — General health perceptions 1 1 1 0.350 0.334 — — Symptom status 0.912 0.958 0.919 0.154 0.139 — — Structural model — — — — — 0.012 0.934 Standard value 0.70 0.70 0.50 0.10 0 0.08 0.90 The Bootstrapping test showed that all paths were significant (p \(<\) 0.05) .The Bootstrapping test revealed that all pathways were significant (p≤0.05).The final model (Fig. 2 ) explained 30.6% of the variance in HRQOL, 35.0% in overall health perceptions, 52.3% in functional status, and 15.4% in symptom status. In summary, there are 6 pathways that directly affect HRQOL, namely: GHP → HRQOL, WS → HRQOL, Siesta → HRQOL, Education → HRQOL, Age → HRQOL, and LI → HRQOL; and 26 pathways that indirectly affect HRQOL, namely: FS → GHP → HRQOL, Sym → FS → GHP → HRQOL, SS → GHP → HRQOL, MS → GHP → HRQOL, and Age → GHP → HRQOL, etc., as shown in Table 4 ; and 13 individual and environmental characteristic variables could directly or indirectly affect HRQOL including: gender, age, education, working status, main economic source, drinking, roughage, labour intensity, caring for grandchildren, siesta, social support, marital status, and dwelling status. Table 4 Direct and indirect pathways affecting HRQOL in older rural patients with chronic diseases Effect Rank pathways β t P 95% confidence Lower Upper Direct 1 GHP→HRQOL 0.395 13.592 0.000 ** 0.338 0.451 2 WS→HRQOL -0.148 5.865 0.000 ** -0.196 -0.097 3 Siesta→HRQOL 0.113 5.130 0.000 ** 0.070 0.154 4 Education→HRQOL 0.101 4.385 0.000 ** 0.055 0.145 5 Age→HRQOL -0.075 2.520 0.012 * -0.134 -0.017 6 LI→HRQOL 0.063 2.532 0.011 * 0.015 0.112 Indirect 1 FS→GHP→HRQOL -0.183 9.123 0.000 ** -0.223 -0.145 2 Sym→FS→GHP→HRQOL -0.082 7.488 0.000 ** -0.105 -0.062 3 SS→GHP→HRQOL 0.081 6.837 0.000 ** 0.058 0.105 4 MS→GHP→HRQOL 0.050 4.350 0.000 ** 0.027 0.073 5 Age→GHP→HRQOL 0.049 4.498 0.000 ** 0.027 0.070 6 LI→GHP→HRQOL 0.036 3.203 0.001 ** 0.015 0.058 7 LI→FS→GHP→HRQOL 0.033 5.425 0.000 ** 0.022 0.045 8 Age→FS→GHP→HRQOL -0.032 6.011 0.000 ** -0.043 -0.022 9 Roughage→GHP→HRQOL 0.028 2.775 0.006 ** 0.009 0.048 10 Drinking→GHP→HRQOL 0.028 2.499 0.012 * 0.006 0.050 11 SS→Sym→FS→GHP→HRQOL 0.026 6.031 0.000 ** 0.018 0.035 12 Gender→GHP→HRQOL 0.025 2.239 0.025 * 0.003 0.047 13 MES→GHP→HRQOL -0.024 2.071 0.038 * -0.048 -0.002 14 SS→FS→GHP→HRQOL 0.022 3.945 0.000 ** 0.012 0.034 15 Siesta→FS→GHP→HRQOL 0.022 4.765 0.000 ** 0.013 0.031 16 CFG→GHP→HRQOL -0.022 2.476 0.013 * -0.039 -0.005 17 WS→FS→GHP→HRQOL -0.020 3.788 0.000 ** -0.031 -0.010 18 Siesta→GHP→HRQOL -0.020 2.003 0.045 * -0.041 -0.001 19 DS→FS→GHP→HRQOL -0.018 4.115 0.000 ** -0.027 -0.010 20 MS→Sym→FS→GHP→HRQOL 0.017 5.219 0.000 ** 0.011 0.025 21 Education→FS→GHP→HRQOL 0.014 3.230 0.001 ** 0.006 0.022 22 Drinking→FS→GHP→HRQOL 0.014 3.433 0.001 ** 0.006 0.022 23 Gender→Sym→FS→GHP→HRQOL -0.013 4.563 0.000 ** -0.019 -0.008 24 LI→Sym→FS→GHP→HRQOL 0.008 2.895 0.004 ** 0.003 0.014 25 MES→Sym→FS→GHP→HRQOL -0.008 2.734 0.006 ** -0.013 -0.002 26 Siesta→Sym→FS→GHP→HRQOL 0.007 2.789 0.005 ** 0.002 0.012 * P \(<\) 0.05, ** P \(<\) 0.01 Discussion This study provides the first in-depth examination of the determinants and directed pathways of HRQOL in rural elderly individuals with chronic conditions based on the Wilson-Cleary model. These characteristics directly or indirectly influence patients' HRQOL through symptoms, functional state, and overall health perceptions in the model, including general demographic characteristics (gender, age, cultural status, work status, and main economic source), lifestyle characteristics (drinking, roughage, labour intensity, caring for grandchildren, and siesta), and environmental characteristics (social support, marital status, and dwelling status). This not only validates the underlying assumptions of the Wilson-Cleary model, but also clarifies the intricate directional pathways between symptom status, functional status, general health perceptions, HRQOL, and individual and environmental characteristics in older patients with chronic diseases. Based on this research, we can intervene early to effectively improve patients' HRQOL, ultimately reaching the aim of active ageing. In all direct pathways, work status and age were risk factors for HRQOL in rural elderly chronic disease patients, which is consistent with previous studies [ 38 , 39 ] ; While general health perception, siesta, education, and labour intensity were protective factors. According to research, self-assessed health is significantly associated with mortality risk and quality of life in later life [ 40 , 41 ] . The majority of the elderly chronic disease patients (62.6%) in this study had poor self-assessed health, and we should pay special attention to this segment of the population in health management. However, there is evidence that better self-assessed health does not imply better health status [ 42 ] . Therefore, while measuring the health state of older individuals with chronic diseases, we need include multiple indicators to better reflect their true health status.Furthermore, the education is connected to HRQOL; nevertheless, we can promote the improvement of the health status and quality of life of older people with chronic conditions living in rural areas by raising their health literacy, even if it can be challenging to do so later in life [ 43 – 46 ] . Significantly, in the HRQOL model for older rural patients with chronic diseases, both variables, labor intensity and siesta, can have a direct influence on symptoms, functional state, overall health perceptions, and HRQOL, all of which are statistically associated with the four model factors.After adjusting for a number of variables, Naska et al. discovered that siesta decreased mortality from chronic diseases [ 47 ] . However, several researchers disagreed, contending that siesta raises the chance of developing chronic illnesses as well as the chance of dying from them in older adults [ 48 , 49 ] . While there is no clear consensus among academics on the benefits and drawbacks of siesta, this study's findings offer a possible path for enhancing HRQOL health management for elderly patients with chronic illnesses living in rural areas by introducing early targeted napping interventions.On the other hand, appropriate physical activity can lower the incidence of chronic metabolic and other diseases in the elderly, especially above moderate intensity physical activity has a positive health-promoting effect, the probability of engaging in moderate-to-high levels of physical activity declines with age [ 50 – 52 ] . For this reason, in order to enhance their health and reduce their risk of sickness, elderly people with chronic illnesses must be supported and encouraged to participate in appropriate physical activity. The Wilson-Cleary model's assumptions and validity as a theoretical framework were confirmed by the statistical significance (P \(<\) 0.05) of the indirect paths FS → GHP → HRQOL (β=-0.183) and Sym → FS → GHP → HRQOL (β=-0.082). Second, the relatively large effect values of social support (β = 0.081) and marital status (β = 0.050), two types of environmental characteristics that are indirectly related to HRQOL through general health perceptions, may provide partial evidence for the stress buffer model theory. Social support may function as an intermediary between stressful experiences and subjective evaluations [ 53 , 54 ] . Previous research has shown that social support and involvement are closely correlated to the health of the elderly, and those who regularly participate in leisure, cultural, and spiritual activities in their family and community after retirement had a higher quality of life [ 55 – 57 ] . But in China today, there's a big difference in the social assistance received by older people in rural and urban areas, so it is necessary to focus on the environmental characteristics of rural old people with chronic illnesses and provide them with the necessary material and emotional support in a timely manner to promote their physical and mental health [ 58 ] . The results of the study showed significant associations between individual characteristics including BMI, smoking, and taste preference with HRQOL, which is consistent with prior research [ 59 , 60 ] . However, further structural model study revealed that the three variables had no significant direct relationships with symptom status, functional status, general health perceptions, or HRQOL. This may be due to the fact that in complicated models, these variables may be moderated or masked by other variables, thus causing their direct associations with HRQOL to become insignificant [ 61 , 62 ] .The Wilson and Cleary model stresses the combined influence of all model components on HRQOL,and in this complex model, single factors such as BMI, smoking habits and dietary preferences may play only a relatively minor role.On the contrary, two environmental characteristic variables, marital status and residential status, which were not significant in univariate analyses, became statistically significant in the model, supporting our findings that some latent variables may not be directly related to HRQOL but can indirectly influence HRQOL in elderly rural chronic disease patients via factors such as symptoms, functional status, and overall health perceptions.As a result, we must look further into the likely link and mechanism of action between these variables in order to design more effective methods for managing the health of senior patients with chronic illnesses in the future. Limitations There are certain drawbacks to this study that need be addressed. First, the cross-sectional survey approach utilized in this study limits our capacity to identify causal links, thus proceed with caution when interpreting the data. Second, our study did not look at the effects of clinical variables on the model pathway, and the generic latent variables utilized may not appropriately represent the symptomatic and functional condition of older individuals with chronic conditions.In the future, we will add more relevant factors to better understand the mechanism of action of the HRQOL model in older patients with chronic conditions. Conclusions Based on the Wilson-Cleary model, this study used empirical research to develop a model of HRQOL in rural older individuals with chronic conditions, as well as to investigate the intervention pathways and influencing factors of HRQOL in the population, including 6 direct pathways, 26 indirect pathways, and 13 individual and environmental factors affecting HRQOL. This not only offers a holistic perspective on HRQOL in rural elderly chronic disease patients, but it also provides a scientific foundation for devising tailored therapies. To promote active aging, future interventions should prioritize improving health management services for rural older individuals with chronic conditions by developing individualized programs. Declarations Ethics approval and consent to participate This study was done in compliance with the Declaration of Helsinki and authorized by Anhui Medical University's Research Ethics Committee (permit number: 83244655). All participants were properly informed about the study's goal and methodology. Before conducting the survey, explain the research's goal and methods to all interviewees and confirm that they have given their informed permission. For the illiterate participants, informed consent from the guardian was also acquired. Consent for publication Not applicable. Availability of data and materials The datasets generated during the study are not publicly available due to an ethical restriction but are available from the corresponding author on reasonable request. Conflicts of interest The authors declare that they have no conflict of interest. Funding This study is funded by the Open Project of Anhui Provincial Key Laboratory of Philosophy and Social Sciences on Public Health and Social Governance (PHG202311). The funder has no role in the design of the study, data collection, analysis, and interpretation, as well as in the writing of the manuscript. 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A Unification of Mediator, Confounder, and Collider Effects [J]. Prevention Science, 2021, 3). WEN ZL, HOU JT, ZHANG L. Comparison and application of moderating and mediating effects [J]. Journal of Psychology, 2005, 2). Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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-4665655","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":329654442,"identity":"eb84555c-fb22-4988-aab6-8fccf3ccb28c","order_by":0,"name":"Yujie Chen","email":"","orcid":"","institution":"School of Health Management, Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yujie","middleName":"","lastName":"Chen","suffix":""},{"id":329654443,"identity":"f5a226a6-af24-45db-8db2-0023a3192970","order_by":1,"name":"Xiaoting Wang","email":"","orcid":"","institution":"School of Health Management, Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoting","middleName":"","lastName":"Wang","suffix":""},{"id":329654444,"identity":"06271d5d-34d0-43a8-b94f-41d6c1c884c9","order_by":2,"name":"Yi Li","email":"","orcid":"","institution":"School of Health Management, Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yi","middleName":"","lastName":"Li","suffix":""},{"id":329654445,"identity":"acd52aeb-cf25-4a1e-b1b1-7bb8ec588f65","order_by":3,"name":"Chi Wang","email":"","orcid":"","institution":"School of Health Management, Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Chi","middleName":"","lastName":"Wang","suffix":""},{"id":329654446,"identity":"e7bb80d3-6f1a-487a-9773-21995fa8ede9","order_by":4,"name":"Hui Wang","email":"","orcid":"","institution":"School of Health Management, Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hui","middleName":"","lastName":"Wang","suffix":""},{"id":329654447,"identity":"b7bf017d-2e33-4e12-aee8-7801132b0e68","order_by":5,"name":"Yaodong Zhao","email":"","orcid":"","institution":"School of Health Management, Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yaodong","middleName":"","lastName":"Zhao","suffix":""},{"id":329654448,"identity":"97a62663-b943-477b-a89d-501a231e4a22","order_by":6,"name":"Min Li","email":"","orcid":"","institution":"School of Health Management, Anhui Medical University","correspondingAuthor":false,"prefix":"","firstName":"Min","middleName":"","lastName":"Li","suffix":""},{"id":329654449,"identity":"cdc18d53-36f9-43c1-b474-52dcc122a45e","order_by":7,"name":"Hong Ding","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA60lEQVRIiWNgGAWjYDCCwwwMzCCan5mx+cEHAxs54rVItjcfM5xRkGZMWMsBqBaDM8cSpHk+HE4kqIPvOO/h1wUVd+wabuQYGNsYMCcwsB8+ugGfFsnDfGnWM848S26ckWPwOMeALY+BJy3tBj4tBod5zIx52w4nM0sAbckx4ClmkOAxI04LG1CLtIWBRGIDEVqMHwO12PHwAL3PYGBAWIsk0BZmnjOHEyTYgYHcY5BgzEbIL3znzxh/5qk4bG9/GBiVP/78l+NnP3wMrxYgYJMAEokNcC4B5SDA/AFI2BOhcBSMglEwCkYqAADwqUs51f2ATQAAAABJRU5ErkJggg==","orcid":"","institution":"School of Health Management, Anhui Medical University","correspondingAuthor":true,"prefix":"","firstName":"Hong","middleName":"","lastName":"Ding","suffix":""}],"badges":[],"createdAt":"2024-07-01 06:14:45","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4665655/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4665655/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":61009515,"identity":"48493395-c0c2-4705-8948-3492c27360a7","added_by":"auto","created_at":"2024-07-24 14:18:48","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":16522,"visible":true,"origin":"","legend":"\u003cp\u003eWilson and Cleary Theoretical Model of Health-Related Quality of Life.\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4665655/v1/68e2a2a6c66f2d4c8a376e2c.jpg"},{"id":61009514,"identity":"0e543dd9-a5a2-4987-846b-f61950c77eb5","added_by":"auto","created_at":"2024-07-24 14:18:48","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31797,"visible":true,"origin":"","legend":"\u003cp\u003ePathway model of HRQOL in rural elderly chronic disease patients. Directional effects are indicated by single arrows. * P\u0026lt;0.05 , ** P\u0026lt;0.01. Sym, symptom status; FS, functional status; GHP, general health perceptions; HRQOL, health-related quality of life; SS, social support; DS, dwelling state; MS, marital status; LI, labour intensity; MES, main economic source; WS, working status; CFG, caring for grandchildren.\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-4665655/v1/75c7431b109eb01003e4abc2.jpg"},{"id":79917329,"identity":"1cc2eb50-6204-42af-b45c-4f9918140384","added_by":"auto","created_at":"2025-04-04 12:53:57","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1354248,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4665655/v1/30115217-4350-4033-80b3-5051a997294e.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Influencing factors and mechanisms of health-related quality of life of elderly patients with chronic diseases in rural China: a cross-sectional study","fulltext":[{"header":"Background","content":"\u003cp\u003eThe Global Health Statistics Report 2017 states that chronic diseases have become a major global health threat, causing more than 41\u0026nbsp;million deaths, accounting for 71.3% of all deaths worldwide\u003csup\u003e[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]\u003c/sup\u003e. China has entered an aging society, and the degree of population aging is still deepening, in 2020, the elderly population aged 60 and above has reached 264\u0026nbsp;million, of which about 20% suffer from various chronic diseases\u003csup\u003e[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]\u003c/sup\u003e. The incidence of chronic diseases is still rising rapidly, and their long duration and difficulty to cure not only aggravate the disease burden of patients, but also significantly increase the cost of health care, which has a detrimental effect on the older individuals' quality of life, physical health, and mental health\u003csup\u003e[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]\u003c/sup\u003e.Compared with urban residents, rural older folks are vulnerable in chronic disease management due to their lack of health management concepts, lower education level, insufficient awareness of self-care, and lack of resources in material, medical and spiritual aspects\u003csup\u003e[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eIn the 1990s Schipper et al. introduced the concept of health-related quality of life (HRQOL), which refers to a person's quality of life that is directly related to health, and is a subjective and multidimensional concept that expresses the degree to which a patient or an individual is satisfied with his or her current level of functioning\u003csup\u003e[\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e]\u003c/sup\u003e.HRQOL can be used to reflect the quality of life of individuals with chronic conditions, and it is an important indicator for assessing successful ageing in the elderly\u003csup\u003e[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/sup\u003e. A growing amount of conceptional models are being proposed to clarify the biopsychosocial factors affecting HRQOL. The three frequently cited conceptual models are: the Centre for Health Promotion model, which links quality of life to disease in general; the Contextual Model of HRQOL, which is specifically designed to explain cancer patients' health status; and Wilson and Cleary's model of patient prognosis\u003csup\u003e[\u003cspan additionalcitationids=\"CR14\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe Wilson and Cleary model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) is a now widely recognised universal model in the academic community that describes the causal relationships and pathways of action between the basic concepts of health-related quality of life. They argue that this model emphasises that the health of an individual or group is the result of a combination of their own and the environment in which they live.The model consists of five main factors: biological and psychological variables, symptom status, functional status, general health perceptions and overall quality of life, while each factor exists on a complex continuum of social, physiological and psychological variables, with individual and environmental characteristics influencing all but the biological and psychological variables\u003csup\u003e[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/sup\u003e.Although the relationships of variables in the model may be bidirectional and the formulated relationships are transient, the model has been validated in a wide range of chronic diseases, such as female stroke survivors, heart failure patients, chronic obstructive pulmonary disease (COPD) and diabetic patients, and provides a valuable reference for clinicians and researchers\u003csup\u003e[\u003cspan additionalcitationids=\"CR17 CR18\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eAs the current research on the application of the Wilson-Cleary health-related quality of life model is mainly limited to specific chronic disease populations, and the sample size is relatively small. In order to enrich the application of this theoretical model in a population of non-specific chronic disease patients, this study constructed a HRQOL model based on the Wilson-Cleary model for elderly chronic disease patients in rural China, and validated it using empirical data, aiming to reveal the directional pathways and related factors affecting HRQOL, in order to establish a thorough knowledge of the potentially complex physiological, psychological, and social processes that affect HRQOL in rural Chinese individuals with chronic conditions, and to offer a scientific foundation for developing effective interventions to improve patients' quality of life.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy population\u003c/p\u003e \u003cp\u003eThis study conducted a cross-sectional survey from July 2023 to February 2024 using multi-stage random sampling. First, we chose a prefecture-level city from Anhui's northern (Suzhou), central (Hefei), and southern (Anqing) areas. Second, a county was randomly chosen from each of these cities. A township was then picked at random from each county. Finally, villages were randomly selected from the townships, yielding a total of 18 survey locations. The participants, excluding individuals with communication challenges such as speech and hearing impairments, were at least sixty years old. They were questioned in a household survey format, with uniformly trained people administering face-to-face questions. A total of 1,556 participants were chosen, and 1,546 completed questionnaires were received, yielding an overall response rate of 99.4%. The Ethics Committee of Anhui Medical University accepted the study (permission number: 83244655), and before to the interview, all interviewees completed an informed consent form.\u003c/p\u003e \u003cp\u003eAssessment variables of the model\u003c/p\u003e \u003cp\u003eWe used the Wilson-Cleary model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) as a theoretical guide, combined with a literature review of related fields, selected variables related to chronically ill patients, and constructed a model of HRQOL in rural elderly chronically ill patients based on expert discussion. Given that the pathway that an individual's biological and physiological variables affect his or her HRQOL by influencing symptoms, functional status, and general health perceptions has been well-documented by existing studies, the present study proposes the hypothesis that the starting point of the HRQOL model for rural elderly patients with chronic diseases starts from symptoms, which in turn, through physical functioning and general health perceptions, ultimately act on the patient's HRQOL, and that individual characteristics, environmental characteristics have a direct role in all four factors\u003csup\u003e[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]\u003c/sup\u003e. Then we test the hypotheses by using empirical analyses.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eHRQOL\u003c/h2\u003e \u003cp\u003eIn this study, HRQOL was assessed using the 5-level European 5-Dimensional Health Scale (EQ-5D-5L)\u003csup\u003e[\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]\u003c/sup\u003e.The questionnaire is divided into two parts: the Health Status Description System (HSDS) and the Visual Analogue Scale (EQ-VAS). Five health dimensions were included: mobility, taking care of oneself, daily activities, pain/discomfort, and anxiety/depression, and each health dimension was categorised into five levels. Using the TTO method, the health status was converted into numerical values of an arithmetic nature by means of the Health Utility Value Integration System developed by Luo et al\u003csup\u003e[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]\u003c/sup\u003e. This set of exponential values produces health utility values ranging from \u0026minus;\u0026thinsp;0.391\u0026thinsp;~\u0026thinsp;1.000, with bigger value indicating a better quality of life. The Cronbach's α was 0.720.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eGeneral Health Perceptions\u003c/h2\u003e \u003cp\u003eEQ-VAS was used in the study to assess general health perceptions.The VAS is a vertical visual scale with 100 points on which respondents quantitatively assess their overall health. A number of 0 at the bottom signifies \"worst imaginable health,\" while a score of 100 at the top symbolizes \"best imaginable health\". The score of 80 is considered to be a good self-assessment of health, and an EQ-VAS score of 80 is considered to be a poor self-assessment of health\u003csup\u003e[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eFunctional Status\u003c/h2\u003e \u003cp\u003eTo assess functional status we used three variables obtained from the Chinese Version of the elderly Kihon Checklist (KCL), Pittsburgh Sleep Quality Index (PSQI), and a self-designed visual questionnaire.The Khion Checklist was designed by Japan as a comprehensive assessment tool to screen for frailty in the elderly\u003csup\u003e[\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]\u003c/sup\u003e. The Chinese version of the KCL was designed by Wang ZY et al. It contains 7 dimensions and 25 entries, with simple \"yes\" or \"no\" choices, and \"0\" or \"1\" answers based on the content of the entries. A score of \"0\" or \"1\" is determined by the content of the entry, and a score of 7 is considered debilitating, higher values indicate more severe debilitation. The reliability and validity have been tested\u003csup\u003e[\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]\u003c/sup\u003e. The Cronbach's α of the KCL was 0.809. The PSQI assesses the sleep quality in the last month, which contains 7 dimensions, each dimension is scored as 0\u0026thinsp;~\u0026thinsp;3. The Cronbach's α of the PSQI was 0.815. A score of 7 on the PSQI suggests poor sleep quality,higher values indicate more severe poor sleep\u003csup\u003e[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/sup\u003e. The vision condition questionnaire was self-designed and consisted of six questions, including whether they had vision problems, how blurred their vision was, whether they had double vision, whether they wore glasses, whether they had any eye diseases, and whether they had had any eye surgeries, with the higher the score the worse their vision condition.The Cronbach's α for this questionnaire was 0.725.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eSymptom Status\u003c/h2\u003e \u003cp\u003eWe used three variables to describe symptoms. These included the presence of self-reported comorbidities and the number of chronic diseases included in the comorbidities, with the presence of two or more chronic diseases at the same time being judged as a comorbidity\u003csup\u003e[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/sup\u003e; physical symptom profiles, which included the presence of self-reported dizziness/syncope and the number of times the symptoms occurred in the last week, and the presence of physical discomfort in the last two weeks; and psychological symptoms, including anxiety and depression. A score of 5 indicating the presence of anxiety on the Generalized Anxiety Scale (GAD-7) indicates higher anxiety. Higher values indicate more severe anxiety. The Cronbach's α of the GAD-7 was 0.936. A score of \u0026ge;\u0026thinsp;5 on the 9-item Patient Health Questionnaire (PHQ-9) indicates depression. Higher values indicate more severe depression\u003csup\u003e[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/sup\u003e. The Cronbach's α for the PHQ-9 was 0.897.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003eIndividual Characteristics\u003c/h2\u003e \u003cp\u003eVariables used to assess individual characteristics were obtained from a self-administered baseline information questionnaire. General demographic and sociological characteristics, including age, gender, education, work status, main source of income, and BMI; and lifestyle characteristics, including smoking, drinking, roughage, taste preference, siesta, caring for grandchildren, and labour intensity.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eEnvironmental Characteristics\u003c/h2\u003e \u003cp\u003eThe three variables reflecting environmental characteristics include marital status, residential status, and social support. The Social Support Rating Scale (SSRS) was used to assess social support. The degree of social support increases with score\u003csup\u003e[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/sup\u003e. The Cronbach's α of the SSRS was 0.796.The details of the assigned values are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\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\u003e Variables, indicators, and descriptions of the HRQOL model for rural elderly patients with chronic diseases\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLatent variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObserved variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eindicators\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003edescriptions\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e\u003cb\u003eSymptom Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eComorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComorbidities and its number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe lower the score, the better\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSomatic symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDizziness/Syncope and its number\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe lower the score, the better\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDiscomfort in 2weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Yes, and 2\u0026thinsp;=\u0026thinsp;No\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePsychological symptoms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGAD\u0026minus;7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRanged 0\u0026ndash;21,the lower the score the better\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePHQ\u0026minus;9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRanged 0\u0026ndash;27,the lower the score the better\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eFunctional Status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrailty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKCL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRanged 0\u0026ndash;14,the lower the score the better\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePSQI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRanged 0\u0026ndash;27,the lower the score the better\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVision\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVision condition questionnaire\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRanged 0\u0026ndash;7,the lower the score the better\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGeneral Health perceptions\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSelf-rated health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEQ-VAS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRanged 0\u0026minus;100,the higher the score the better\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOverall Quality of Life\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEQ\u0026minus;5D\u0026minus;5L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRanged \u0026minus;\u0026thinsp;0.391\u0026ndash;1.000,the higher the score the better\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"11\" rowspan=\"12\"\u003e \u003cp\u003e\u003cb\u003eIndividual Characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003eSociodemographic\u003c/p\u003e \u003cp\u003echaracteristics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;60\u0026ndash;69 years, 2\u0026thinsp;=\u0026thinsp;70\u0026ndash;79 years, 3=\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e80 years\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Male, and 2\u0026thinsp;=\u0026thinsp;Female\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Illiterate, and 2\u0026thinsp;=\u0026thinsp;Non-illiterate\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWorking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Full-time,2\u0026thinsp;=\u0026thinsp;Part-time,3\u0026thinsp;=\u0026thinsp;Housework,4\u0026thinsp;=\u0026thinsp;Free\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMain economic source\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Self-labor, 2\u0026thinsp;=\u0026thinsp;Child support, 3\u0026thinsp;=\u0026thinsp;Government subsidy, 4\u0026thinsp;=\u0026thinsp;Past savings, 5\u0026thinsp;=\u0026thinsp;Other\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"6\" rowspan=\"7\"\u003e \u003cp\u003eLifestyle\u003c/p\u003e \u003cp\u003echaracteristics\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\u003e1\u0026thinsp;=\u0026thinsp;No,2\u0026thinsp;=\u0026thinsp;Used to, 3\u0026thinsp;=\u0026thinsp;Yes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;No,2\u0026thinsp;=\u0026thinsp;Used to, 3\u0026thinsp;=\u0026thinsp;Yes\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSiesta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Yes, and 2\u0026thinsp;=\u0026thinsp;No\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRoughage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;0 times/week, 2\u0026thinsp;=\u0026thinsp;1\u0026ndash;2 times/week, 3\u0026thinsp;=\u0026thinsp;3\u0026ndash;5 times/week, 4\u0026thinsp;=\u0026thinsp;Almost everyday\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTaste preference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Salty, 2\u0026thinsp;=\u0026thinsp;Medium, 3\u0026thinsp;=\u0026thinsp;Bland\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLabour intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eRanged from 1\u0026thinsp;=\u0026thinsp;very light to 5\u0026thinsp;=\u0026thinsp;very heavy\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCaring for grandchildren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Yes, and 2\u0026thinsp;=\u0026thinsp;No\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u003cb\u003eEnvironmental Characteristics\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Married, 2\u0026thinsp;=\u0026thinsp;Widowed, 3\u0026thinsp;=\u0026thinsp;Single/Divorced\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDwelling status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u0026thinsp;=\u0026thinsp;Solitary,2\u0026thinsp;=\u0026thinsp;Non-solitary\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSocial support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSSRS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eThe higher the score the better\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eWe used two types of software, SPSS 25.0 and SmartPLS 3.0, for statistical description and model construction. Since none of the study's data were normally distributed, the data were described using the median and interquartile spacing, and the maximum, minimum, and constitutive ratios were used to describe the count and rank data. The Kruskal-Wallis H and Mann-Whitney U test were used for comparisons between groups. A statistical significance level of α\u0026thinsp;=\u0026thinsp;0.05 was used to all two-sided tests used in the statistical analyses. Based on the Wilson-Cleary model, a structural equation model of HRQOL in elderly patients living in rural areas with chronic illnesses was created in this study using the partial least squares structural equation model (PLS-SEM). Cronbach's α, composite reliability (CR) assessed the reliability of the measurement model, VIF assessed indicator validity, average variance extracted (AVE), discriminant validity assessed the validity of the measurement model, SRMR, NFI were used to assess the fit of the structural model, and R\u003csup\u003e2\u003c/sup\u003e, Q\u003csup\u003e2\u003c/sup\u003e assessed the explanatory power of the model\u003csup\u003e[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. The models and path coefficients were tested for significance using the bootstrapping test. The sampling number for the autonomous sample technique was set to 5000, while the maximum number of iterations for path weighting was set at 300\u003csup\u003e[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eDescriptives and Variance analysis\u003c/p\u003e \u003cp\u003eAs seen in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, of the 1145 respondents, 510 (44.5%) were male and 635 (55.5%) were female, with an average age of 72 (68, 78) years. The average HRQOL score of rural old individuals with chronic conditions was 0.942 (0.824, 1.000), and the majority of the patients (62.6%) had poor self-rated health. Differences in HRQOL among elderly rural Chinese patients with chronic diseases in terms of gender, age, education, work status, main economic source, BMI, smoking, drinking, roughage, taste, labour intensity, hospitalisation in a year, siesta, caring for grandchildren, comorbidities, number of chronic diseases, dizziness/syncope, Frequency of dizziness / syncope, discomfort in 2weeks, anxiety, depression, quality of sleep, frailty, self-rated health, vision score, and social support were statistically significant (p\u0026thinsp;\u0026lt;\u0026thinsp;0.05).\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\u003e Comparison of basic information and HRQOL of elderly rural patients with chronic diseases with different characteristics (n\u0026thinsp;=\u0026thinsp;1145)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ehealth state value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\mathbf{Z}/{\\varvec{X}}^{2}\\)\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026minus;3.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e510(44.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.951(0.862, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e635(55.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.813, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAge(years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e58.274\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e60\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sim\\)\u003c/span\u003e\u003c/span\u003e69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e360(31.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000(0.893, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e70\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\sim\\)\u003c/span\u003e\u003c/span\u003e79\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e568(49.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.814, 0.942)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e80\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e217(20.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.882(0.737, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e5.544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e844(73.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.824, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWidowed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e266(23.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.824, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSingle/Divorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35(3.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000(0.841, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eEducation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026minus;6.857\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIlliterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e680(59.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.934(0.787, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-illiterate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e465(40.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000(0.841, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDwelling status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026minus;0.431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.666\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSolitary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e233(20.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.841, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNon-solitary\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e913(79.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.813, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eWorking status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e89.509\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFull-time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e260(22.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000(0.893, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePart-time\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e201(17.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.862, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousework\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e302(26.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.893, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFree\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e382(33.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.862(0.700, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eMain economic source\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e49.201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSelf-labor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e410(35.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.951(0.893, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChild support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e402(35.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.824, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGovernment subsidy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e263(23.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.893(0.734, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePast savings\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e41(3.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000(0.909, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eOther\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e29(2.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.893(0.747, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eBMI(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\text{k}\\text{g}/{\\text{m}}^{2}\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e14.184\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.003\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e18.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77(6.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.744, 1.000)\u003c/p\u003e \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\u003e523(45.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.862, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e24\u0026minus;27.9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e382(33.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.830, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163(14.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.893(0.776, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSmoking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e13.755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \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\u003e849(74.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.813, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsed to\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e92(8.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.866, 1.000)\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\u003e204(17.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000(0.862, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDrinking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e18.326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \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\u003e807(70.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.813, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUsed to\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e90(7.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.862, 1.000)\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\u003e248(21.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000(0.887, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eRoughage(times/week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e25.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\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\u003e477(41.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.934(0.761, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e325(28.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.876, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e163(14.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000(0.893, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAlmost everyday\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e180(15.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.749, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eTaste preference\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e14.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSalty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e369(32.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.813, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e556(48.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.862, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e220(19.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.894(0.750, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eLabour intensity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e140.303\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"5\" rowspan=\"6\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery light\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e465(40.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.876(0.734, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e364(31.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.882, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e234(20.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000(0.942, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76(6.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000(0.893, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery heavy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6(0.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.931(0.797, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eHospitalisation in a year\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026minus;9.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\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\u003e416(36.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.882(0.744, 1.000)\u003c/p\u003e \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\u003e729(63.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.951(0.882, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSiesta\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026minus;6.241\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\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\u003e781(68.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.796, 1.000)\u003c/p\u003e \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\u003e364(31.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.980(0.893, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eCaring for grandchildren\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026minus;3.262\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.001\u003csup\u003e**\u003c/sup\u003e\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\u003e205(17.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.893, 1.000)\u003c/p\u003e \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\u003e940(82.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.813, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eComorbidities\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026minus;6.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\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\u003e690(60.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.785, 1.000)\u003c/p\u003e \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\u003e455(39.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000(0.893, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eNumber of chronic diseases\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e71.738\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \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\u003e455(39.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000(0.893, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u0026ndash;3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e584(51.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.813, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e106(9.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.820(0.700, 0.942)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDizziness / Syncope\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026minus;9.863\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\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\u003e767(67.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.893(0.785, 1.000)\u003c/p\u003e \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\u003e378(33.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000(0.942, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eFrequency of dizziness / syncope (times/week)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e196.245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\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\u003e380(33.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000(0.942, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e311(27.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.893, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e167(14.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.824, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e287(25.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.813(0.700, 0.896)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDiscomfort in 2weeks\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026minus;8.701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\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\u003e425(37.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.893(0.747, 1.000)\u003c/p\u003e \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\u003e720(62.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.951(0.882, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eAnxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e295.629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e668(58.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000(0.934, 1.0000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight(5\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e297(25.9%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.893(0.813, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium(10\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e93(8.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.785(0.681, 0.882)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e87(7.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.727(0.541, 0.824)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eDepression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e355.815\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e607(53.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000(0.942, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLight(5\u0026ndash;9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e293(25.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.894(0.813, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium(10\u0026ndash;14)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e118(10.3%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.813(0.711,0.942)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeavy(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e127(11.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.727(0.541,0.824)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSleep\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026minus;6.621\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\le 7\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e534(46.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.951(0.889,1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBad(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026gt;7\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e611(53.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.934(0.782,1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eFrailty\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026minus;14.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge 7\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e807(70.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.893(0.776,1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026lt;7\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e338(29.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000(0.942, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSelf-rated health\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e\u0026minus;10.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge 80\\)\u003c/span\u003e\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e428(37.4%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000(0.942, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBad(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e717(62.6%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.893(0.767, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eVision score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e73.904\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"4\" rowspan=\"5\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\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\u003e126(11.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000(0.942, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u0026ndash;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e374(32.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.951(0.893, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u0026ndash;4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e495(43.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.813, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\ge\\)\u003c/span\u003e\u003c/span\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e150(13.1%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.862(0.702, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"3\" nameend=\"c3\" namest=\"c1\"\u003e \u003cp\u003eSocial support\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e70.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLow(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(2.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.843(0.731, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedium(22\u0026ndash;44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e901(78.7%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.942(0.813, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHigh(\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026gt;\\)\u003c/span\u003e\u003c/span\u003e44)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e212(18.5%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.000(0.942, 1.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"5\" nameend=\"c5\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e*P\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e0.05, **P\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e0.01\u003c/b\u003e\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\u003eEstablishment of structural equations\u003c/p\u003e \u003cp\u003eWe used SmartPLS 3.0 software to construct the initial structural equation model of HRQOL in rural elderly patients with chronic diseases. The factor loadings of the measurement model should \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026gt;\\)\u003c/span\u003e\u003c/span\u003e0.70 in the partial least squares structural equation model, indicating that the latent variables may account for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026gt;\\)\u003c/span\u003e\u003c/span\u003e50% of the measured variables\u003csup\u003e[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/sup\u003e. The results of the factor analysis of the initial model indicated that the factor loadings of comorbidity, dizziness/syncope, and physical discomfort within two weeks in symptoms were 0.528, 0.547, and \u0026minus;\u0026thinsp;0.378, respectively, which were less than 0.70, and the factor loadings of sleep and vision in functional status were 0.656 and 0.645, respectively, which were less than 0.70, and should have been excluded to obtain a higher degree of validity. After removing these variables, the results of the second fitting were as follows: the factor loadings of anxiety and depression in symptoms were 0.957 and 0.960 respectively, which were greater than 0.70. Based on the Bootstrapping test findings, all non-significant pathways were discarded.\u003c/p\u003e \u003cp\u003eAfter removing the insignificant paths, the results of the third fitting were as follows: the factor loadings of anxiety and depression in the symptoms were 0.956 and 0.961, respectively, and the factor loadings were all exceeded 0.7. The VIF were all less than 5, indicating no multicollinearity\u003csup\u003e[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/sup\u003e. The model had a good convergence efficacy, as evidenced by CR values over 0.70 and AVE values exceeding 0.50\u003csup\u003e[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/sup\u003e.All of the AVE values' square roots were higher than the factor's correlation coefficient with any other factor, which met the Fornell-Larcker Criterion, so the measurement model was judged to have good discriminant validity\u003csup\u003e[\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/sup\u003e. The model fitness test was further conducted in this study, and the R\u003csup\u003e2\u003c/sup\u003e values by the PLS Algorithm were all higher above the typical acceptability value of 0.10, indicating that the predictive accuracy of the model was good; the Q\u0026sup2; values were all greater than 0, indicating that the research model could effectively predict the correlation between variables\u003csup\u003e[\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/sup\u003e. In addition, SRMR is 0.012, which is less than the accepted standard value of 0.08, and NFI is 0.934, which exceed the standard value of 0.90, both of which indicate that the model fits well\u003csup\u003e[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/sup\u003e. In summary, this indicates that the model fit is good enough for subsequent analyses, and the results are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\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\u003e Results of model fitting\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\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCronbach's α\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eAVE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eR\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eQ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSRMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNFI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFunctional status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.523\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.510\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGeneral health perceptions\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.334\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSymptom status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.912\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.958\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.919\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStructural model\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\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\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.934\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStandard value\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.08\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.90\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\u003eThe Bootstrapping test showed that all paths were significant (p\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e0.05) .The Bootstrapping test revealed that all pathways were significant (p\u0026le;0.05).The final model (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) explained 30.6% of the variance in HRQOL, 35.0% in overall health perceptions, 52.3% in functional status, and 15.4% in symptom status.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn summary, there are 6 pathways that directly affect HRQOL, namely: GHP \u0026rarr; HRQOL, WS \u0026rarr; HRQOL, Siesta \u0026rarr; HRQOL, Education \u0026rarr; HRQOL, Age \u0026rarr; HRQOL, and LI \u0026rarr; HRQOL; and 26 pathways that indirectly affect HRQOL, namely: FS \u0026rarr; GHP \u0026rarr; HRQOL, Sym \u0026rarr; FS \u0026rarr; GHP \u0026rarr; HRQOL, SS \u0026rarr; GHP \u0026rarr; HRQOL, MS \u0026rarr; GHP \u0026rarr; HRQOL, and Age \u0026rarr; GHP \u0026rarr; HRQOL, etc., as shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e; and 13 individual and environmental characteristic variables could directly or indirectly affect HRQOL including: gender, age, education, working status, main economic source, drinking, roughage, labour intensity, caring for grandchildren, siesta, social support, marital status, and dwelling status.\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\u003e Direct and indirect pathways affecting HRQOL in older rural patients with chronic diseases\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eEffect\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRank\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003epathways\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c8\" namest=\"c7\"\u003e \u003cp\u003e95% confidence\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eLower\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eUpper\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGHP\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.338\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.451\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWS\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.148\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.865\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.196\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.097\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSiesta\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.113\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.130\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEducation\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.101\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.385\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.075\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.520\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLI\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.011\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFS\u0026rarr;GHP\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.183\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.223\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.145\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSym\u0026rarr;FS\u0026rarr;GHP\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.062\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u0026rarr;GHP\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.081\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMS\u0026rarr;GHP\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026rarr;GHP\u0026rarr;HRQOL\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\u003e4.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.070\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLI\u0026rarr;GHP\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.036\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.203\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLI\u0026rarr;FS\u0026rarr;GHP\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.045\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAge\u0026rarr;FS\u0026rarr;GHP\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eRoughage\u0026rarr;GHP\u0026rarr;HRQOL\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\u003e2.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDrinking\u0026rarr;GHP\u0026rarr;HRQOL\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\u003e2.499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.050\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u0026rarr;Sym\u0026rarr;FS\u0026rarr;GHP\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e6.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.035\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGender\u0026rarr;GHP\u0026rarr;HRQOL\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\u003e2.239\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.025\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e13\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMES\u0026rarr;GHP\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.038\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e14\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSS\u0026rarr;FS\u0026rarr;GHP\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.945\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSiesta\u0026rarr;FS\u0026rarr;GHP\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.765\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCFG\u0026rarr;GHP\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.013\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.039\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWS\u0026rarr;FS\u0026rarr;GHP\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.788\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSiesta\u0026rarr;GHP\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.045\u003csup\u003e*\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDS\u0026rarr;FS\u0026rarr;GHP\u0026rarr;HRQOL\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\u003e4.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMS\u0026rarr;Sym\u0026rarr;FS\u0026rarr;GHP\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.219\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eEducation\u0026rarr;FS\u0026rarr;GHP\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.230\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDrinking\u0026rarr;FS\u0026rarr;GHP\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGender\u0026rarr;Sym\u0026rarr;FS\u0026rarr;GHP\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.000\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLI\u0026rarr;Sym\u0026rarr;FS\u0026rarr;GHP\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.895\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMES\u0026rarr;Sym\u0026rarr;FS\u0026rarr;GHP\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.008\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.006\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e-0.002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSiesta\u0026rarr;Sym\u0026rarr;FS\u0026rarr;GHP\u0026rarr;HRQOL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.789\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"8\" nameend=\"c8\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003e* P\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e0.05, ** P\u003c/b\u003e\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e\u003cb\u003e0.01\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides the first in-depth examination of the determinants and directed pathways of HRQOL in rural elderly individuals with chronic conditions based on the Wilson-Cleary model. These characteristics directly or indirectly influence patients' HRQOL through symptoms, functional state, and overall health perceptions in the model, including general demographic characteristics (gender, age, cultural status, work status, and main economic source), lifestyle characteristics (drinking, roughage, labour intensity, caring for grandchildren, and siesta), and environmental characteristics (social support, marital status, and dwelling status). This not only validates the underlying assumptions of the Wilson-Cleary model, but also clarifies the intricate directional pathways between symptom status, functional status, general health perceptions, HRQOL, and individual and environmental characteristics in older patients with chronic diseases. Based on this research, we can intervene early to effectively improve patients' HRQOL, ultimately reaching the aim of active ageing.\u003c/p\u003e \u003cp\u003eIn all direct pathways, work status and age were risk factors for HRQOL in rural elderly chronic disease patients, which is consistent with previous studies\u003csup\u003e[\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/sup\u003e; While general health perception, siesta, education, and labour intensity were protective factors. According to research, self-assessed health is significantly associated with mortality risk and quality of life in later life\u003csup\u003e[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/sup\u003e. The majority of the elderly chronic disease patients (62.6%) in this study had poor self-assessed health, and we should pay special attention to this segment of the population in health management. However, there is evidence that better self-assessed health does not imply better health status\u003csup\u003e[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/sup\u003e. Therefore, while measuring the health state of older individuals with chronic diseases, we need include multiple indicators to better reflect their true health status.Furthermore, the education is connected to HRQOL; nevertheless, we can promote the improvement of the health status and quality of life of older people with chronic conditions living in rural areas by raising their health literacy, even if it can be challenging to do so later in life\u003csup\u003e[\u003cspan additionalcitationids=\"CR44 CR45\" citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eSignificantly, in the HRQOL model for older rural patients with chronic diseases, both variables, labor intensity and siesta, can have a direct influence on symptoms, functional state, overall health perceptions, and HRQOL, all of which are statistically associated with the four model factors.After adjusting for a number of variables, Naska et al. discovered that siesta decreased mortality from chronic diseases\u003csup\u003e[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]\u003c/sup\u003e. However, several researchers disagreed, contending that siesta raises the chance of developing chronic illnesses as well as the chance of dying from them in older adults\u003csup\u003e[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e, \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]\u003c/sup\u003e. While there is no clear consensus among academics on the benefits and drawbacks of siesta, this study's findings offer a possible path for enhancing HRQOL health management for elderly patients with chronic illnesses living in rural areas by introducing early targeted napping interventions.On the other hand, appropriate physical activity can lower the incidence of chronic metabolic and other diseases in the elderly, especially above moderate intensity physical activity has a positive health-promoting effect, the probability of engaging in moderate-to-high levels of physical activity declines with age\u003csup\u003e[\u003cspan additionalcitationids=\"CR51\" citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]\u003c/sup\u003e. For this reason, in order to enhance their health and reduce their risk of sickness, elderly people with chronic illnesses must be supported and encouraged to participate in appropriate physical activity.\u003c/p\u003e \u003cp\u003eThe Wilson-Cleary model's assumptions and validity as a theoretical framework were confirmed by the statistical significance (P\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\u0026lt;\\)\u003c/span\u003e\u003c/span\u003e0.05) of the indirect paths FS \u0026rarr; GHP \u0026rarr; HRQOL (β=-0.183) and Sym \u0026rarr; FS \u0026rarr; GHP \u0026rarr; HRQOL (β=-0.082). Second, the relatively large effect values of social support (β\u0026thinsp;=\u0026thinsp;0.081) and marital status (β\u0026thinsp;=\u0026thinsp;0.050), two types of environmental characteristics that are indirectly related to HRQOL through general health perceptions, may provide partial evidence for the stress buffer model theory. Social support may function as an intermediary between stressful experiences and subjective evaluations\u003csup\u003e[\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]\u003c/sup\u003e. Previous research has shown that social support and involvement are closely correlated to the health of the elderly, and those who regularly participate in leisure, cultural, and spiritual activities in their family and community after retirement had a higher quality of life\u003csup\u003e[\u003cspan additionalcitationids=\"CR56\" citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]\u003c/sup\u003e. But in China today, there's a big difference in the social assistance received by older people in rural and urban areas, so it is necessary to focus on the environmental characteristics of rural old people with chronic illnesses and provide them with the necessary material and emotional support in a timely manner to promote their physical and mental health\u003csup\u003e[\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]\u003c/sup\u003e.\u003c/p\u003e \u003cp\u003eThe results of the study showed significant associations between individual characteristics including BMI, smoking, and taste preference with HRQOL, which is consistent with prior research\u003csup\u003e[\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e, \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]\u003c/sup\u003e. However, further structural model study revealed that the three variables had no significant direct relationships with symptom status, functional status, general health perceptions, or HRQOL. This may be due to the fact that in complicated models, these variables may be moderated or masked by other variables, thus causing their direct associations with HRQOL to become insignificant\u003csup\u003e[\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e, \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e]\u003c/sup\u003e.The Wilson and Cleary model stresses the combined influence of all model components on HRQOL,and in this complex model, single factors such as BMI, smoking habits and dietary preferences may play only a relatively minor role.On the contrary, two environmental characteristic variables, marital status and residential status, which were not significant in univariate analyses, became statistically significant in the model, supporting our findings that some latent variables may not be directly related to HRQOL but can indirectly influence HRQOL in elderly rural chronic disease patients via factors such as symptoms, functional status, and overall health perceptions.As a result, we must look further into the likely link and mechanism of action between these variables in order to design more effective methods for managing the health of senior patients with chronic illnesses in the future.\u003c/p\u003e \u003cp\u003eLimitations\u003c/p\u003e \u003cp\u003eThere are certain drawbacks to this study that need be addressed. First, the cross-sectional survey approach utilized in this study limits our capacity to identify causal links, thus proceed with caution when interpreting the data. Second, our study did not look at the effects of clinical variables on the model pathway, and the generic latent variables utilized may not appropriately represent the symptomatic and functional condition of older individuals with chronic conditions.In the future, we will add more relevant factors to better understand the mechanism of action of the HRQOL model in older patients with chronic conditions.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eBased on the Wilson-Cleary model, this study used empirical research to develop a model of HRQOL in rural older individuals with chronic conditions, as well as to investigate the intervention pathways and influencing factors of HRQOL in the population, including 6 direct pathways, 26 indirect pathways, and 13 individual and environmental factors affecting HRQOL. This not only offers a holistic perspective on HRQOL in rural elderly chronic disease patients, but it also provides a scientific foundation for devising tailored therapies. To promote active aging, future interventions should prioritize improving health management services for rural older individuals with chronic conditions by developing individualized programs.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was done in compliance with the Declaration of Helsinki and authorized by Anhui Medical University's Research Ethics Committee (permit number: 83244655). All participants were properly informed about the study's goal and methodology. Before conducting the survey, explain the research's goal and methods to all interviewees and confirm that they have given their informed permission. For the illiterate participants, informed consent from the guardian was also acquired.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated during the study are not publicly available due to an ethical restriction but are available from the corresponding author on reasonable request.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of interest\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study is funded by the Open Project of Anhui Provincial Key Laboratory of Philosophy and Social Sciences on Public Health and Social Governance (PHG202311). The funder has no role in the design of the study, data collection, analysis, and interpretation, as well as in the writing of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor’s contributions\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eYJC wrote the article and analysed the data; YJC, XTW and HD revised the article and contributed to the study design; YL, CW, HW ,YDZ and ML collected the data. All authors approved the final version.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to appreciate the involvement of the participants who joined this study. \u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003e\u0026nbsp; School of Health Management, Anhui Medical University, Anhui, China.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eORGANIZATION W H. 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Industrial Management \u0026amp; Data Systems, 2017.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHAIR J F, HULT G T M, RINGLE C M, et al. Primer on Partial Least Squares Structural Equation Modeling [M]. A Primer on Partial Least Squares Structural Equation Modeling, 2014.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHAIR J F, RINGLE C M, SARSTEDT M. PLS-SEM: indeed a silver bullet [J]. The Journal of Marketing Theory and Practice, 2011, 19(2): 139\u0026ndash;51.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eZOMER T, NEELY A, MARTINEZ V. Digital transforming capability and performance: a microfoundational perspective [J]. 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American Journal of Preventive Medicine, 2009, 37(6-supp-S1): S201-S8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBANKS J, NAZROO J, STEPTOE A. The Dynamics of ageing: Evidence from the English Longitudinal Study of Ageing 2002\u0026ndash;2016 (Wave 8) [J]. 2016.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNASKA A, OIKONOMOU E, TRICHOPOULOU A, et al. Siesta in Healthy Adults and Coronary Mortality in the General Population [J]. Archives of Internal Medicine, 2007, 167(3): 296\u0026ndash;301.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBURSZTYN M, STESSMAN J. The Siesta and Mortality: Twelve Years of Prospective Observations in 70-Year-Olds [J]. Sleep, 2005.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLAM K B, JIANG C Q, THOMAS G N, et al. Napping is associated with increased risk of type 2 diabetes: the Guangzhou Biobank Cohort Study [J]. Sleep, 2010, 3): 33.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eANDREATO L V, ESTEVES J V, COIMBRA D R, et al. The influence of high-intensity interval training on anthropometric variables of adults with overweight or obesity: a systematic review and network meta-analysis [J]. Obesity reviews: an official journal of the International Association for the Study of Obesity, 2019, 20(1):142\u0026ndash;55.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eASTORINO T A, SCHUBERT M M, PALUMBO E, et al. Effect of Two Doses of Interval Training on Maximal Fat Oxidation in Sedentary Women [J]. Medicine and science in sports and exercise, 2013, 45(10): 1878\u0026ndash;86.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBARNES, MATT, CHESHIRE, et al. The dynamics of ageing: Evidence from the English Longitudinal Study of Ageing 2002-10 (Wave 5) [J]. 2012.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCOHEN C I, TERESI J, HOLMES D. 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The relationship between nutritional status, functional capacity, and health-related quality of life in older adults with type 2 diabetes: A pilot explanatory study [J]. The journal of nutrition, health \u0026amp; aging, 2013, 17(4): 315\u0026ndash;21.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMACKINNON D P, LAMP S J. A Unification of Mediator, Confounder, and Collider Effects [J]. Prevention Science, 2021, 3).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWEN ZL, HOU JT, ZHANG L. Comparison and application of moderating and mediating effects [J]. Journal of Psychology, 2005, 2).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Rural elderly, Chronic disease, HRQOL, Structural equation model","lastPublishedDoi":"10.21203/rs.3.rs-4665655/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4665655/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eThe study aimed to understand the factors influencing health-related quality of life (HRQOL) and the intricate biological, psychological, and social processes that underlie it in elderly chronic disease patients in rural China. To do this, structural equation model(SEM) was utilized to construct a model based on the Wilson and Cleary model.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eIn this cross-sectional study, 1145 senior individuals with chronic illnesses from three cities in Anhui Province, China were chosen by a multi-stage random sampling procedure. Households were surveyed face-to-face using the following instruments: the five-level version of the European Five Dimensional Health Scale (EQ-5D-5L), Generalized Anxiety Scale (GAD-7), 9-item Patient Health Questionnaire (PHQ-9), Social Support Rating Scale (SSRS), Pittsburgh Sleep Quality Index (PSQI), Chinese Version of the elderly Kihon Checklist (KCL), and a self-designed questionnaire on vision conditions.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThis study identified 13 individual and environmental characteristics associated with HRQOL in rural elderly patients with chronic diseases, including gender, age, education, working status, main economic source, drinking, roughage, labor intensity, siesta, social support, marital status, and dwelling status, as well as the directional pathways of action of these factors affecting HRQOL, which included 26 indirect and 6 direct pathways.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study adds to the body of knowledge on HRQOL and advances our comprehension of the potentially intricate biological and psychological processes that influence HRQOL in older individuals with chronic diseases by revealing the influencing factors and directed pathways of action on HRQOL. Providing timely and personalized therapies to address these causes and processes may eventually improve their HRQOL.\u003c/p\u003e","manuscriptTitle":"Influencing factors and mechanisms of health-related quality of life of elderly patients with chronic diseases in rural China: a cross-sectional study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-07-24 14:18:42","doi":"10.21203/rs.3.rs-4665655/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a5da88cd-ff39-4c99-b8f0-789f5ef9e9b3","owner":[],"postedDate":"July 24th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":34896212,"name":"Health sciences/Diseases"},{"id":34896213,"name":"Health sciences/Health care"},{"id":34896214,"name":"Health sciences/Health occupations"},{"id":34896215,"name":"Health sciences/Risk factors"}],"tags":[],"updatedAt":"2025-04-04T12:53:46+00:00","versionOfRecord":[],"versionCreatedAt":"2024-07-24 14:18:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4665655","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4665655","identity":"rs-4665655","version":["v1"]},"buildId":"_2-kVJe1T_tPrBINL-cwx","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}