Network analysis of self-management and its associations with knowledge and social support in elderly patients with hypertension: A Cross-Sectional Study

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Most of studies on self-management behavior are based on the scores of questionnaire or the dimension which limits a comprehensive understanding of the full spectrum of hypertension self-management behavior. This study aimed to investigate the network structure of self-management of elderly patients with hypertension, and explore the correlation between self-management behaviour, knowledge level, and social support. Methods The survey was conducted in Tianjin, China, from March to May 2023. Network analysis was employed to examine the network structure of hypertension self-management behavior, and flow network analysis was used to assess the relationships between self-management behavior, knowledge level, and social support. Results A total of 804 patients were enrolled. The three core behaviors of elderly patients with hypertension were: physical exercise, take medication prescribed by doctor, and balanced nutrition. Flow network analysis indicated that take medication as they feel from knowledge had the highest negative correlation with self-management behavior, while support and care of family members from social support had the highest positive correlation with self-management behaviour. Conclusion Regular exercise, adherence to prescribed medication schedules, and balanced nutrition were identified as core components of hypertension self-management. Enhancing patients’ knowledge about proper medication use and promoting support and care from family members will help improve self-management behavior in elderly patients with hypertensive. network analysis hypertension self-management Figures Figure 1 Figure 2 Figure 3 Figure 4 1 Introduction The prevalence of hypertension increases with age and affects nearly 75% of the population aged over 60 years old[ 1 ]. Hypertension is a chronic disease requiring long-term management and care, and self-management behaviour is crucial for controlling blood pressure[ 2 ]. Effective self-management can effectively prevent the progression of hypertension and improve the quality of life of patients[ 3 ]. However, elderly patients with hypertension often experience memory loss, insufficient disease knowledge, and a lack of social support, which can result in low levels of self-management and poor blood pressure control[ 4 – 6 ]. A study of 486 elderly hypertensive patients in a community in China, found that 68.1% of the patients had low levels of self-management behavior[ 7 ]. Patients benefit from the benefits of self-management for a long time. However, there is insufficient evidence pinpointing which specific behaviors are most critical for effective hypertension management in this demographic. The most commonly used self-management assessment tools for hypertension is Hypertension Patients Self-Management Behavior Rating Scale (HPSMBRS) which include these dimensions: drug management, diet management, rest and work management, emotional management, exercise management and disease monitoring. However, no studies have explored which item is the most important and influential in hypertension self-management behavior[ 8 ]. This gap in knowledge underscores the need for targeted research to identify and prioritize the core self-management behaviors that significantly impact blood pressure control in elderly patients with hypertension. Such insights are essential for developing tailored interventions and support strategies to enhance self-management and improve health outcomes in this population. Higher levels of hypertension knowledge are associated with enhanced self-management behaviors in elderly patients[ 9 ]. Besides, previous studies have shown that increased social support for people with high blood pressure can improve their self-management and treatment compliance, and even reduce the risk of hypertension[ 10 , 11 ]. While existing research has explored the relationship between self-management behaviors, hypertension knowledge, and social support, these studies often do not examine these associations at the item level. For instance, it remains unclear which specific aspects of social support most strongly correlate with effective hypertension self-management. Identifying these key elements is crucial, as it would enable healthcare professionals to develop more targeted management strategies, optimize resource allocation, and ultimately improve the self-management behaviors of elderly hypertensive patients, facilitating better blood pressure control. Network analysis offers a robust method for examining complex interrelationships among various factors influencing hypertension self-management[ 12 ]. In this approach, individual items are represented as nodes, with their interconnections depicted as edges, facilitating an intuitive visualization of data-driven relationships[ 13 ]. Network analysis can also identify the most core nodes in the network model, which are the most correlated with other nodes[ 14 ]. A key advantage of network analysis is its ability to identify central nodes—those most strongly connected to others—using centrality indices such as Expected Influence (EI), which quantifies the influence of a node within the network[ 15 , 16 ]. By leveraging network analysis, researchers can construct a detailed map of hypertension self-management behaviors, identifying core behaviors that serve as pivotal points within the network. By identifying the mesh structure of high blood pressure self-management behavior in network analysis, intervening in it can activate more structures within the network, ultimately enhancing the entire network and improving self-management behavior in hypertensive patients[ 17 ]. Therefore, the purpose of this study was as follows: (1) Using network analysis to explore the network structure of hypertension self-management behaviour and identify central items. (2) Using flow networks to visualize the relationship between hypertension self-management behaviour and social support, knowledge level, and explore which item is most associated with self-management behaviour. 2 Methods 2.1 Study Design and Participants This cross-sectional study was performed between March and May 2023. Data were collected from the Outpatients Departments of three tertiary hospitals in Tianjin, China. Patients were eligible if they (1) Met the World Health Organization (WHO) diagnostic criteria for hypertension; (2) Were aged 60 years or older; (3) Had been diagnosed with hypertension for at least 3 months; and (4) Willing to participate in the study. Patients were excluded if they (1) Suffered from severe psychiatric and psychological disorders, and (2) Suffered from severe cardiopulmonary disease. Using PASS software for sample size calculation. According to global burden of disease, we set prevalence of hypertension = 0.33[18], δ (margin of error)= 0.05, Z1-α/2 (confidence level) = 1.96 and α (type I error) = 0.05 and obtained the minimum required sample size of 340. To account for potential invalid questionnaires at a rate of 20%, the sample size was adjusted to 425. In our study, 810 patients initially enrolled in the study, and only six did not complete the survey. Ultimately, 804 patients completed the survey. 2.2 Measurements 2.2.1 Sociodemographic Information Participants were asked to provide their sociodemographic information, including their age, gender, education level, marital status, years of diagnosed hypertension, living arrangement and so on. 2.2.2 Self-Management Behavior Rating Scale (HPSMBRS) Formulated by Zhao, the HPSMBRS was used to evaluate the level of self-management behavior in elderly hypertensive patients[19]. This scale comprises 33 items distributed across six dimensions: medication management, emotional management, work-rest management, diet management, disease monitoring, and exercise management. Each item is rated on a 5-point Likert scale, with responses ranging from “never” (1) to “always” (5). The total score ranges from 33 to 165, with higher scores indicating better self-management behaviors. The scale demonstrates excellent internal consistency, with a Cronbach’s α coefficient of 0.914[19]. 2.2.3 Hypertension Knowledge-Level Scale (HK-LS) The HK-LS, developed by SultanBaliz Erkoc in 2012, is mainly used to assess the level of hypertension knowledge among patients[20]. Zhang et al. have sinicized it and made it applicable to the Chinese[21]. The scale has 6 dimensions and 22 items. The six dimensions are definition, medication, medication compliance, lifestyle, diet, and complications. Each item offers three response options: “Yes,” “No,” and “Don’t Know,” scored as follows: Yes = 1, No or Don’t Know = 0. The total score ranges from 0 to 22, with higher the score indicates the higher the knowledge level of hypertension. The scale demonstrates satisfactory internal consistency, with a Cronbachs’α coefficient of 0.810[20]. 2.2.4 Social Support Rating Scale (SSRS) Compiled by Xiao[22] in 1986, it includes three dimensions: objective support, subjective support, and utilization of support. Each dimension contains corresponding items: 2, 6 and 7 items, 1, 3–5 and 8–10 items. The scale includes single-choice and multiple-choice items, and the score ranges from 12 to 66. Higher scores indicate that the individual receives, feels, and uses social support to a better degree. The scale is widely used to assess social support in healthy people, chronic diseases, and other research areas. The Cronbach’s α coefficient was 0.920, indicating excellent internal consistency[22]. 2.3 Statistical Analysis Statistical analyses were conducted using SPSS version 26.0 and R software. Descriptive statistics were calculated as mean ± standard deviation for continuous variables and as frequencies or percentages for categorical variables. The independent t-tests, chi-square tests and Mann-Whitney U tests were used to compare sociodemographic variables of self-management behaviour in elderly patients with hypertension. Variables with significant differences in univariate analysis were used as independent variables, linear regression analysis was used, and dummy variables were set when independent variables were categorical variables to examine the independent correlation factors of self-management behaviour in elderly hypertension patients. A statistically significant level was set at P < 0.05 (two-tailed). Network analysis was conducted with the R, utiling the packages bootnet v1.4.3 and qgraph v1.6.9[13, 23]. The network model was estimated with the graphic least absolute shrinkage and selection operator (LASSO) and Extended Bayesian Information Criterion (EBIC) model to ensure a sparse and interpretable network model. In the network, items were represented as nodes, and their correlations were represented as edges. The thickness of the edge represents the strength of the correlation, with thicker edges representing stronger correlations. The color of the edge represents the direction of the correlation, with green representing positive associations and red representing negative associations. To quantify which node shows the highest connectivity in the network, the centrality index-expected influence (EI) was computed. The direct and indirect effects of social support and knowledge level on self-management behavior were plotted using the functional flow in the package diagram[13]. To evaluate the robustness of the estimated network model, the correlation stability coefficient (CS - coefficient) was computed for EI using the package bootnet v1.4.3, the value of which above 0.25 indicates stable results[23]. 3 Results 3.1 Univariate analysis of self-management behaviour Out of 810 invited patients 804 met the inclusion criteria and completed the assessment, with a participation rate of 99.26 %. Among them, 47.60% were male, the average of age was 71.33±7.034, 95.60% were married, 12.80% lived alone, and 73.13% had complications (Table 1). Univariate analysis revealed that higher education level, greater annual family income, and lower average monthly medical expenses were significantly associated with better self-management behavior scores among elderly hypertensive patients. Additionally, both the Hypertension Knowledge-Level Scale (HK-LS) and the Social Support Rating Scale (SSRS) demonstrated significant positive correlations with self-management behavior scores, as detailed in Table 1. 3.2 Multiple linear regression of self-management behaviour Based on the findings of the univariate analysis, the independent variables to be included in the multiple linear regression were SSRS and HK-LS, while the control variables were education level, annual household income, and average monthly medical expenses. The multiple linear regression demonstrated that HK-LS (P＜0.001) and SSRS ( P ＜0.001) can positively influence the self-management behavior of elderly patients with hypertension (Table 2). Table 1 Multiple linear regression of self-management behaviour Variables Unstandardized Coefficients Standardized Coefficients T P 95% CI B Standard Error Beta HK-LS 0.954 0.263 0.124 3.623 ＜0.001 0.437-1.471 SSRS 0.661 0.113 0.207 5.860 ＜0.001 0.440-0.883 Note: Bolded values: P < 0.05; Abbreviations: CI, confidence interval; HK-LS, Hypertension Knowledge-Level Scale; SSRS, Social Support Rating Scale. Table 2 Characteristics of patients with hypertension and univariate analysis of self-management behavior Variables Number (%)/Mean±SD Univariable analysis F/T P Value Gender 1.275 0.203 Male 383 (47.60) Female 421 (52.40) Education level 4.321 0.001 Illiteracy 32 (4.00) Primary school 144 (17.90) Junior high school 326 (40.50) Senior high school 189 (23.50) Vocational college 78 (9.70) Bachelor degree or above 32 (4.40) Marital status 0.606 0.548 Married 796 (95.60) Spinster 35 (4.40) Annual household income 3.899 0.004 ＜10 000yuan 62 (7.70) 10 000-30 000yuan 225 (28.00) 30 000-80 000yuan 265 (33.00) 80 000-200 000yuan 197 (24.50) ＞200 000yuan 55 (6.80) Average monthly medical bills 3.190 0.013 ＜500yuan 362 (45.00) 500-999yuan 267 (33.20) 1000-1499yuan 122 (15.20) 1500-1999yuan 35 (4.40) ≥2000yuan 18 (2.20) Living arrangement 0.901 0.364 Living along 103 (12.80) Living with family 701 (87.20) Complications or not 1.343 0.180 Yes 588 (73.13) No 216 (26.87) Age (years) 71.33±7.034 1.989 0.114 Years of diagnosed hypertension 12.48±9.42 2.440 0.063 HK-LS 35.45±5.338 ＜ 0.001 SSRS 38.47±6.676 ＜ 0.001 Note: Bolded values: P < 0.05; Abbreviations: M, mean; SD, standard deviation; HK-LS, Hypertension Knowledge-Level Scale; SSRS, Social Support Rating Scale. 3.3 Network of self-management behaviour Fig. 1 presents the network structure of self-management behaviors in elderly hypertension patients. The centrality index, specifically the Expected Influence (EI), was computed to identify the most influential behaviors within the network. Item M20, “Do physical exercise 3 to 5 times a week,” exhibited the highest EI value, indicating its pivotal role in the network. This was followed by M2, “Take your blood pressure medication at the time prescribed by your doctor,” and M18, “Pay attention to balanced nutrition.” The centrality difference test confirmed significant differences between these high-EI nodes and others, reinforcing their importance in the network structure. Results of case-dropping bootstrap for centrality index EI showed that primary results had an excellent level of stability (CS-C =0.439 ) (Fig. 2). 3.4 Flow network of social support and hypertension knowledge level In terms of individual relationships of social support, knowledge level with self-management behaviors, Fig. 3 shows that K6. “People with high blood pressure can take medication as they feel” form the HK-LS had the strongest negative association with self-management behaviors and Fig. 4 shows that S5, “Support and care from family members” from the SSRS had the highest negative association with self-management behaviors. 4 Discussion This study provides valuable insights into the self-management behaviors of elderly patients with hypertension, focusing on the interrelationships between key behaviors, social support, and knowledge level. Network analysis revealed that physical exercise, medication adherence, and balanced nutrition emerged as the most influential self-management behaviors. Notably, the knowledge item “People with high blood pressure can take medication as they feel” showed a strong negative correlation with self-management. Similarly, family support, as captured by the SSRS, was found to have a notable impact on self-management behaviors, suggesting that strengthening family involvement in managing hypertension is crucial. Our network analysis revealed that physical exercise and medication adherence were among the most influential self-management behaviors. According to the 2020 National Hypertension Institute, regular weekly exercise not only helps prevent or delay the onset of high blood pressure but also reduce cardiovascular risk. Moreover, regular exercise is considerd the first line of antihypertensive treatment and enhances the effectiveness of antihypertensive treatment[24]. Additionally, exercise training has been shown to improve vascular structure and function in hypertension individuals, benefiting both large arteries and peripheral circulation[25]. Additionally, medication adherence has long been recognized as a cornerstone in the effective management of hypertension. Non-adherence to antihypertensive medication is a common challenge that often results in poor health outcomes[26]. Given the strong influence of both physical exercise and medication adherence, future research should focus on developing and testing interventions that simultaneously target both behaviors. Such interventions could include personalized approaches that integrate physical activity programs with strategies to improve medication adherence. Another crucial item for the self-management behavior network structure was balanced nutrition. Clinical and population-based studies show that several components of the diet, such as fruits and vegetables, and foods high in saturated fats, trans fats, salt, and sugar, affect blood pressure[27, 28]. Specifically, dietary patterns such as the DASH (Dietary Approaches to Stop Hypertension) diet have been shown to lower systolic and diastolic blood pressure, thereby reducing the risk of cardiovascular events in hypertensive individuals[29]. Despite the established benefits of balanced nutrition, challenges persist in its implementation among elderly populations. Factors such as limited access to healthy foods, physical limitations affecting meal preparation, and a lack of nutritional knowledge can hinder adherence to dietary recommendations[30]. Moreover, cultural preferences and ingrained eating habits may influence dietary choices, necessitating culturally sensitive interventions. To address these barriers, future research should focus on developing and evaluating tailored nutritional interventions that consider the unique needs and preferences of elderly patients. The knowledge item “taking medication as they feel” exhibited the strongest negative correlation with hypertension self-management behaviors in our study. Because elderly patients often share several comorbidities needing drug therapies and might suffer from potential cognitive deficits, it is commonly assumed that poor adherence is more prevalent and more severe in elderly than in younger patients[31]. Interestingly, taking medication on time is also an important item in the self-management behavior network structure. This contrast underscores a critical gap in patient education: while some patients understand the importance of timely medication intake, they may still harbor misconceptions about the necessity of continuous medication use. Such beliefs can undermine the effectiveness of treatment regimens and contribute to suboptimal health outcomes[32, 33]. Future research should focus on developing and testing educational interventions that address these misconceptions. These interventions could aim to enhance patients' understanding of the chronic nature of hypertension and the necessity of consistent medication adherence, regardless of symptom presence. Tailoring these educational strategies to the specific needs and preferences of elderly patients could further improve their effectiveness. Our study found that family support and care had the strongest positive association with hypertension self-management behaviors among elderly patients, which was similay with previous research[34]. Hypertensive patients may fail to take their medication due to the long duration of therapy, the symptomless nature of the condition, adverse drug reactions, complicated drug regimens, a lack of understanding about hypertension management, a lack of motivation and the challenge to their health beliefs[35].Family involvement can enhance medication adherence, encourage lifestyle modifications, and provide emotional support, all of which contribute to better disease management and quality of life[36, 37]. Most importantly, family-centered approaches not only support the patient but also strengthen the family unit's capacity to manage chronic diseases effectively[38]. Given the pivotal role of family support in hypertension self-management, future research should focus on evaluating family-centered interventions. Investigating the effectiveness of programs that educate and empower family members to actively participate in the care of elderly hypertensive patients could provide valuable insights. Limitations This study provides valuable insights into hypertension self-management behaviors among elderly patients; however, several limitations should be considered: First, the study’s cross-sectional nature limits our ability to establish causal relationships between self-management behaviors, knowledge levels, and social support. Second, the findings of this study are based on self-reported data, even though we ask patients to recall very recent events, recall bias cannot be ruled out. Third, the sample was limited to hospitals in Tianjin, China, and generalizing the results to a larger population remains to be considered. 5 Conclusion This study highlighted that regular exercise, adherence to prescribed medication schedules, and balanced nutrition are central to effective self-management. The interventions targeting these key behaviors and addressing misconceptions about medication adherence, while simultaneously enhancing family involvement, could substantially improve hypertension self-management in older adults. Declarations Ethics approval and consent to participate The study protocol was approved by the Research Ethics Committee of the University (TMUhMEC2022021). The study was conducted following the tenets of the Declaration of Helsinki. Oral and written informed consent was provided at the beginning of the study and signed by each participant. Consent for publication Not applicable. Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author on reasonable request. Competing interests The authors declare that there is no conflict of interest. Funding This work was supported by the Humanities and Social Science Foundation of Ministry of Education of China (23YJAZH189) and the National Natural Science Foundation of China (72304206). Authors' contributions HRC and QYL participated in the design of the study, HRC and YJW performed the statistical analysis. MAK, YQW & XYX conceived of the study, and HRC, XNZ, XYZ & NW participated in its coordination and helped to draft the manuscript. All authors read and approved the final manuscript. Acknowledgements We gratefully acknowledge the contribution of the research team who worked hard to collect quality data in a timely manner. We are also grateful to the study participants for their valuable time and assistance. References Ostchega Y, Fryar CD, Nwankwo T, Nguyen DT: Hypertension Prevalence Among Adults Aged 18 and Over: United States, 2017-2018 . NCHS data brief 2020(364):1-8. 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Park M, Giap TT, Lee M, Jeong H, Jeong M, Go Y: Patient- and family-centered care interventions for improving the quality of health care: A review of systematic reviews . International journal of nursing studies 2018, 87 :69-83. 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-7226850","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":500807834,"identity":"84ebdc1e-c261-4838-bad8-0ee19e54bc0a","order_by":0,"name":"Hairong Chang","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Hairong","middleName":"","lastName":"Chang","suffix":""},{"id":500807835,"identity":"564d76b1-8dd7-4087-8ecf-65f1a7928bdf","order_by":1,"name":"Qingyun Lv","email":"","orcid":"","institution":"Tianjin Medical 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University","correspondingAuthor":false,"prefix":"","firstName":"Xueying","middleName":"","lastName":"Xu","suffix":""},{"id":500807841,"identity":"3f9665cd-95f8-46ce-b90d-b427819aafa7","order_by":5,"name":"Yuan He","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yuan","middleName":"","lastName":"He","suffix":""},{"id":500807843,"identity":"05f8f609-f710-43b6-99cc-f8c662d56faf","order_by":6,"name":"Jingwen Liu","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jingwen","middleName":"","lastName":"Liu","suffix":""},{"id":500807844,"identity":"62974f19-4432-45d0-a01f-d7fbc27eb195","order_by":7,"name":"Li Fu","email":"","orcid":"","institution":"The Second Hospital of Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Li","middleName":"","lastName":"Fu","suffix":""},{"id":500807845,"identity":"2cfef20a-5254-47bd-aac3-e2514fb4803a","order_by":8,"name":"Xinxing Gao","email":"","orcid":"","institution":"The Second Hospital of Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xinxing","middleName":"","lastName":"Gao","suffix":""},{"id":500807846,"identity":"ac4b37c3-6bba-4678-8dcc-f7b97a5a7d05","order_by":9,"name":"Xia Chen","email":"","orcid":"","institution":"The Fourth Central Hospital Affiliated to Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Chen","suffix":""},{"id":500807847,"identity":"5e1127f5-98b3-4a90-800b-166fa315f2ef","order_by":10,"name":"Xiaonan Zhang","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaonan","middleName":"","lastName":"Zhang","suffix":""},{"id":500807848,"identity":"1c35e434-274b-4ae2-b2aa-e4e9ae6a6ace","order_by":11,"name":"Xiaoying Zang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA7UlEQVRIiWNgGAWjYDACZhBhAOV8MLCxI00L44yCtGQSbeT5cIixgZAqg+PMDx/zFNyxm99+9vBrG4MDzAzsh49uwKdFspnN2JjH4FlyY09emnWOwR0+Bp60tBv4tPAzM5hJ8xgcTmZmyDEzzjF4xswgwWOGVwsbM/s3sBY2/jdmxhYGhxkbCGnhZ+YB22LHI5Fj/JiBGC2SzTzFhnMMDidISLwxY+wxSEtmI+QXg/PHNz548+ewvXx/jvGHH39s7PjZDx/DqwUEmHgYGBIbgP6SAPuOkHIQYPzBwGAPpJk/EKN6FIyCUTAKRh4AAB0DQ9hZS16qAAAAAElFTkSuQmCC","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoying","middleName":"","lastName":"Zang","suffix":""},{"id":500807849,"identity":"af219367-ef98-46ef-81a2-795a820a128d","order_by":12,"name":"Na Wei","email":"","orcid":"","institution":"Tianjin Medical University","correspondingAuthor":false,"prefix":"","firstName":"Na","middleName":"","lastName":"Wei","suffix":""}],"badges":[],"createdAt":"2025-07-27 14:38:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7226850/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7226850/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":89559255,"identity":"953f0015-2715-4763-b93b-e0799ca5f42b","added_by":"auto","created_at":"2025-08-21 10:06:42","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":332113,"visible":true,"origin":"","legend":"\u003cp\u003eNetwork structure (left) and expected influence (right) of self-management behaviors in elderly hypertension patients\u003c/p\u003e\n\u003cp\u003eNote: M1-M33 belong to HPSMBRS. Abbreviations: M1, Take the blood pressure medicine as prescribed by your doctor; M2, Take your blood pressure medication at the time prescribed by your doctor; M3, Take anti-hypertensive drugs according to the doctor’s prescription; M4, Adhere to long-term regular use of anti-hypertensive drugs; M5, If suffering from hypertension, check blood biochemistry, blood sugar, kidney function, funds, electrocardiogram once a year; M6, High or low pressure, I am daily blood pressure measurement times; When high pressure is greater than, low pressure is greater than, I take blood pressure times a day; M7, Consult your doctor when your blood pressure fluctuates; M8, Regular review according to the time required by the hypertension classification; M9, Control the intake of sodium salt, eat less salty food; M10, Eat less high-fat food; M11, Eat less food with high cholesterol; M12, Choose the right amount of high-quality protein foods; M13, Choose the right amount of anti-hypertensive food; M14, Eat less irritating food; M15, Eat more fresh fruits and vegetables; M16, Control your weight; M17, Eat more fiber-rich foods to prevent dry stool; M18, Pay attention to balanced nutrition; M19, Appropriate exercise; M20, Do physical exercise 3 to 5 times a week; M21, Each exercise lasts 30 to 60 minutes; M22, Pay attention to slow down when working in daily life; M23, Adjust the amount of time and content of work (housework) according to blood pressure; M24, Stop and rest when you feel tired; M25, According to the situation of blood pressure, use labor-saving tools to reduce housework; M26, According to the blood pressure situation to do some housework; M27, Try to change their impatient character; M28, High blood pressure dizziness, will calm down to rest; M29, Try to calm down when you are emotional; M30, Control your emotions when you are angry; M31, Persuade yourself to relax when you are worried about something; M32, After suffering from hypertension, try to control emotions and try to maintain a normal heart; M33, Maintain emotional stability, avoid mood swings.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-7226850/v1/1d4f6a16f73c97d9251d6a1e.png"},{"id":89558859,"identity":"730c7dc0-54ed-4865-a9ff-9efb99ba25e4","added_by":"auto","created_at":"2025-08-21 09:58:42","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":10993,"visible":true,"origin":"","legend":"\u003cp\u003eThe stability of centrality index expected influence using casedropping bootstrap\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-7226850/v1/f3f02550bd9b100839c17152.png"},{"id":89558860,"identity":"ddd8ef75-2283-4cd3-ac74-ce9bc9d2f81a","added_by":"auto","created_at":"2025-08-21 09:58:42","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":44496,"visible":true,"origin":"","legend":"\u003cp\u003eFlow network of hypertension knowledge level with self-management behavior\u003c/p\u003e\n\u003cp\u003eNote: K1-K22 belong to HK-LS. Abbreviations: SM, Self-management behavior, K1, An increase in diastolic (low pressure) blood pressure indicates an increase in blood pressure; K2, An increase in diastolic blood pressure (low pressure) or systolic blood pressure (high pressure) indicates an increase in blood pressure; K3, Medication for high blood pressure must be taken daily; K4, People with high blood pressure must take medication only when they feel unwell; K5, Hypertension patients must take medication for life; K6, People with high blood pressure can take medication as they feel; K7, If the drug treatment can control the increase in blood pressure, there is no need to change life; K8, Increased blood pressure is the result of aging, so treatment of high blood pressure is not necessary; K9, If people with high blood pressure change their lifestyle, they do not need treatment; K10, As long as regular medication, hypertensive patients can eat salty food; K11, High blood pressure patients can drink alcoholic beverages; K12, People with high blood pressure can not smoke; K13, People with high blood pressure must often eat fruits and vegetables; K14, For people with high blood pressure, frying is the best way to cook food; K15, For people with high blood pressure, cooking or baking is the best way to cook food; K16, Hypertension patients are best to eat white meat (such as chicken, duck, fish); K17, Hypertension patients are best to eat red meat (such as pork, beef, lamb); K18, If left untreated, high blood pressure can lead to early death; K19, If left untreated, high blood pressure can lead to heart conditions such as heart disease; K20, If left untreated, high blood pressure can lead to stroke; K21, If left untreated, high blood pressure can lead to kidney failure; K22, If left untreated, high blood pressure can lead to visual impairment.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-7226850/v1/cd2ccf66485e69ffbdb5e397.png"},{"id":89559254,"identity":"2a4970db-2f75-4408-bb1a-ab46ec397e65","added_by":"auto","created_at":"2025-08-21 10:06:42","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":30014,"visible":true,"origin":"","legend":"\u003cp\u003eFlow network of social support with self-management behavior\u003c/p\u003e\n\u003cp\u003eNote: S1-S10 belong to SSRS. Abbreviations: SM, Self-management behavior, S1, How many close friends do you have that can get support and help; S2, Who have you been living with for the past year; S3, How do you get along with your neighbors; S4, How do you get along with your colleagues; S5, Support and care from family members; S6, In the past, when you were in an emergency situation, there were some sources of financial support and help to solve practical problems; S7, In the past, when you were in an emergency situation, there were some sources of financial support and help to solve practical problems; S8, The way you talk about your troubles; S9, A way for you to help when you are in trouble; S10, For group organized activities, the frequency of your participation.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-7226850/v1/2b68d221b6e885963f6f66fc.png"},{"id":96055895,"identity":"f5aa6ca0-6821-4c12-b7fa-956f615df523","added_by":"auto","created_at":"2025-11-17 07:39:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3007060,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7226850/v1/2f87813e-7650-408c-8ac8-aaf9ea6abcb6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Network analysis of self-management and its associations with knowledge and social support in elderly patients with hypertension: A Cross-Sectional Study","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eThe prevalence of hypertension increases with age and affects nearly 75% of the population aged over 60 years old[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Hypertension is a chronic disease requiring long-term management and care, and self-management behaviour is crucial for controlling blood pressure[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Effective self-management can effectively prevent the progression of hypertension and improve the quality of life of patients[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. However, elderly patients with hypertension often experience memory loss, insufficient disease knowledge, and a lack of social support, which can result in low levels of self-management and poor blood pressure control[\u003cspan additionalcitationids=\"CR5\" citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. A study of 486 elderly hypertensive patients in a community in China, found that 68.1% of the patients had low levels of self-management behavior[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e\u003cp\u003ePatients benefit from the benefits of self-management for a long time. However, there is insufficient evidence pinpointing which specific behaviors are most critical for effective hypertension management in this demographic. The most commonly used self-management assessment tools for hypertension is Hypertension Patients Self-Management Behavior Rating Scale (HPSMBRS) which include these dimensions: drug management, diet management, rest and work management, emotional management, exercise management and disease monitoring. However, no studies have explored which item is the most important and influential in hypertension self-management behavior[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This gap in knowledge underscores the need for targeted research to identify and prioritize the core self-management behaviors that significantly impact blood pressure control in elderly patients with hypertension. Such insights are essential for developing tailored interventions and support strategies to enhance self-management and improve health outcomes in this population.\u003c/p\u003e\u003cp\u003eHigher levels of hypertension knowledge are associated with enhanced self-management behaviors in elderly patients[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Besides, previous studies have shown that increased social support for people with high blood pressure can improve their self-management and treatment compliance, and even reduce the risk of hypertension[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. While existing research has explored the relationship between self-management behaviors, hypertension knowledge, and social support, these studies often do not examine these associations at the item level. For instance, it remains unclear which specific aspects of social support most strongly correlate with effective hypertension self-management. Identifying these key elements is crucial, as it would enable healthcare professionals to develop more targeted management strategies, optimize resource allocation, and ultimately improve the self-management behaviors of elderly hypertensive patients, facilitating better blood pressure control.\u003c/p\u003e\u003cp\u003eNetwork analysis offers a robust method for examining complex interrelationships among various factors influencing hypertension self-management[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In this approach, individual items are represented as nodes, with their interconnections depicted as edges, facilitating an intuitive visualization of data-driven relationships[\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Network analysis can also identify the most core nodes in the network model, which are the most correlated with other nodes[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. A key advantage of network analysis is its ability to identify central nodes\u0026mdash;those most strongly connected to others\u0026mdash;using centrality indices such as Expected Influence (EI), which quantifies the influence of a node within the network[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. By leveraging network analysis, researchers can construct a detailed map of hypertension self-management behaviors, identifying core behaviors that serve as pivotal points within the network. By identifying the mesh structure of high blood pressure self-management behavior in network analysis, intervening in it can activate more structures within the network, ultimately enhancing the entire network and improving self-management behavior in hypertensive patients[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e\u003cp\u003eTherefore, the purpose of this study was as follows: (1) Using network analysis to explore the network structure of hypertension self-management behaviour and identify central items. (2) Using flow networks to visualize the relationship between hypertension self-management behaviour and social support, knowledge level, and explore which item is most associated with self-management behaviour.\u003c/p\u003e"},{"header":"2 Methods","content":"\u003cp\u003e\u003cstrong\u003e2.1 Study Design and Participants\u003c/strong\u003e \u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThis cross-sectional study was performed between March and May 2023. Data were collected from the Outpatients Departments of three tertiary hospitals in Tianjin, China. Patients were eligible if they (1) Met the World Health Organization (WHO) diagnostic criteria for hypertension; (2) Were aged 60 years or older; (3) Had been diagnosed with hypertension for at least 3 months; and (4) Willing to participate in the study. Patients were excluded if they (1) Suffered from severe psychiatric and psychological disorders, and (2) Suffered from severe cardiopulmonary disease.\u003c/p\u003e\n\u003cp\u003eUsing PASS software for sample size calculation. According to global burden of disease, we set prevalence of hypertension = 0.33[18], \u0026delta; (margin of error)= 0.05, Z1-\u0026alpha;/2 (confidence level) = 1.96 and \u0026alpha; (type I error) = 0.05 and obtained the minimum required sample size of 340. To account for potential invalid questionnaires at a rate of 20%, the sample size was adjusted to 425. In our study, 810 patients initially enrolled in the study, and only six did not complete the survey. Ultimately, 804 patients completed the survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Measurements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.1 Sociodemographic Information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eParticipants were asked to provide their sociodemographic information, including their age, gender, education level, marital status, years of diagnosed hypertension, living arrangement and so on.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2 Self-Management Behavior Rating Scale (HPSMBRS)\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFormulated by Zhao, the HPSMBRS was used to evaluate the level of self-management behavior in elderly hypertensive patients[19]. This scale comprises 33 items distributed across six dimensions: medication management, emotional management, work-rest management, diet management, disease monitoring, and exercise management. Each item is rated on a 5-point Likert scale, with responses ranging from \u0026ldquo;never\u0026rdquo; (1) to \u0026ldquo;always\u0026rdquo; (5). The total score ranges from 33 to 165, with higher scores indicating better self-management behaviors. The scale demonstrates excellent internal consistency, with a Cronbach\u0026rsquo;s \u0026alpha; coefficient of 0.914[19].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.3 Hypertension Knowledge-Level Scale (HK-LS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe HK-LS, developed by SultanBaliz Erkoc in 2012, is mainly used to assess the level of hypertension knowledge among patients[20]. Zhang et al. have sinicized it and made it applicable to the Chinese[21]. The scale has 6 dimensions and 22 items. The six dimensions are definition, medication, medication compliance, lifestyle, diet, and complications. Each item offers three response options: \u0026ldquo;Yes,\u0026rdquo; \u0026ldquo;No,\u0026rdquo; and \u0026ldquo;Don\u0026rsquo;t Know,\u0026rdquo; scored as follows: Yes = 1, No or Don\u0026rsquo;t Know = 0. The total score ranges from 0 to 22, with higher the score indicates the higher the knowledge level of hypertension. The scale demonstrates satisfactory internal consistency, with a Cronbachs\u0026rsquo;\u0026alpha; coefficient of 0.810[20].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.4 Social Support Rating Scale (SSRS)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCompiled by Xiao[22] in 1986, it includes three dimensions: objective support, subjective support, and utilization of support. Each dimension contains corresponding items: 2, 6 and 7 items, 1, 3\u0026ndash;5 and 8\u0026ndash;10 items. The scale includes single-choice and multiple-choice items, and the score ranges from 12 to 66. Higher scores indicate that the individual receives, feels, and uses social support to a better degree. The scale is widely used to assess social support in healthy people, chronic diseases, and other research areas. The Cronbach\u0026rsquo;s \u0026alpha; coefficient was 0.920, indicating excellent internal consistency[22].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.3 Statistical Analysis\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eStatistical analyses were conducted using SPSS version 26.0 and R software. Descriptive statistics were calculated as mean \u0026plusmn; standard deviation for continuous variables and as frequencies or percentages for categorical variables. The independent t-tests, chi-square tests and Mann-Whitney U tests were used to compare sociodemographic variables of self-management behaviour in elderly patients with hypertension. Variables with significant differences in univariate analysis were used as independent variables, linear regression analysis was used, and dummy variables were set when independent variables were categorical variables to examine the independent correlation factors of self-management behaviour in elderly hypertension patients. A statistically significant level was set at \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05 (two-tailed).\u003c/p\u003e\n\u003cp\u003eNetwork analysis was conducted with the R, utiling the packages bootnet v1.4.3 and qgraph v1.6.9[13, 23]. The network model was estimated with the graphic least absolute shrinkage and selection operator (LASSO) and Extended Bayesian Information Criterion (EBIC) model to ensure a sparse and interpretable network model. In the network, items were represented as nodes, and their correlations were represented as edges. The thickness of the edge represents the strength of the correlation, with thicker edges representing stronger correlations. The color of the edge represents the direction of the correlation, with green representing positive associations and red representing negative associations. To quantify which node shows the highest connectivity in the network, the centrality index-expected influence (EI) was computed. The direct and indirect effects of social support and knowledge level on self-management behavior were plotted using the functional flow in the package diagram[13]. To evaluate the robustness of the estimated network model, the correlation stability coefficient (CS - coefficient) was computed for EI using the package bootnet v1.4.3, the value of which above 0.25 indicates stable results[23].\u003c/p\u003e"},{"header":"3 Results","content":"\u003cp\u003e\u003cstrong\u003e3.1 Univariate analysis of self-management behaviour\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOut of 810 invited patients 804 met the inclusion criteria and completed the assessment, with a participation rate of 99.26 %. Among them, 47.60% were male, the average of age was 71.33\u0026plusmn;7.034, 95.60% were married, 12.80% lived alone, and 73.13% had complications (Table 1).\u003c/p\u003e\n\u003cp\u003eUnivariate analysis revealed that higher education level, greater annual family income, and lower average monthly medical expenses were significantly associated with better self-management behavior scores among elderly hypertensive patients. Additionally, both the Hypertension Knowledge-Level Scale (HK-LS) and the Social Support Rating Scale (SSRS) demonstrated significant positive correlations with self-management behavior scores, as detailed in\u0026nbsp;Table 1.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.2 Multiple linear regression of self-management behaviour\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBased on the findings of the univariate analysis, the independent variables to be included in the multiple linear regression were SSRS and HK-LS, while the control variables were education level, annual household income, and average monthly medical expenses. The multiple linear regression demonstrated that HK-LS (P＜0.001) and SSRS (\u003cem\u003eP\u003c/em\u003e＜0.001) can positively influence the self-management behavior of elderly patients with hypertension (Table 2).\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;1\u0026nbsp;Multiple linear regression of self-management behaviour\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 190px;\"\u003e\n \u003cp\u003eUnstandardized Coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eStandardized Coefficients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 52px;\"\u003e\n \u003cp\u003e\u003cem\u003eT\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 69px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 91px;\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cem\u003eB\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003eStandard Error\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003eBeta\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eHK-LS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e0.263\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.124\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e3.623\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.437-1.471\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003eSSRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 50px;\"\u003e\n \u003cp\u003e0.661\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 140px;\"\u003e\n \u003cp\u003e0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 95px;\"\u003e\n \u003cp\u003e0.207\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 52px;\"\u003e\n \u003cp\u003e5.860\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 69px;\"\u003e\n \u003cp\u003e＜0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 91px;\"\u003e\n \u003cp\u003e0.440-0.883\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Bolded values: P \u0026lt; 0.05; Abbreviations: CI, confidence interval; HK-LS, Hypertension Knowledge-Level Scale; SSRS, Social Support Rating Scale.\u003c/p\u003e\n\u003cp\u003eTable\u0026nbsp;2\u0026nbsp;Characteristics of patients with hypertension and univariate analysis of self-management behavior\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" style=\"width: 207px;\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 165px;\"\u003e\n \u003cp\u003eNumber (%)/Mean\u0026plusmn;SD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 195px;\"\u003e\n \u003cp\u003eUnivariable analysis\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u003cem\u003eF/T\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e Value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1.275\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.203\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eMale\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e383 (47.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e421 (52.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eEducation level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e4.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eIlliteracy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e32 (4.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003ePrimary school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e144 (17.90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eJunior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e326 (40.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eSenior high school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e189 (23.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eVocational college\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e78 (9.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eBachelor degree or above\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e32 (4.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.606\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e796 (95.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eSpinster\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e35 (4.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eAnnual household income\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e3.899\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.004\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003e＜10 000yuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e62 (7.70)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003e10 000-30 000yuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e225 (28.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003e30 000-80 000yuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e265 (33.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003e80 000-200 000yuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e197 (24.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003e＞200 000yuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e55 (6.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eAverage monthly medical bills\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e3.190\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.013\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003e＜500yuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e362 (45.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003e500-999yuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e267 (33.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003e1000-1499yuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e122 (15.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003e1500-1999yuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e35 (4.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003e\u0026ge;2000yuan\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e18 (2.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eLiving arrangement\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e0.901\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.364\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eLiving along\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e103 (12.80)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eLiving with family\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e701 (87.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eComplications or not\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1.343\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e588 (73.13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e216 (26.87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e71.33\u0026plusmn;7.034\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e1.989\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eYears of diagnosed hypertension\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e12.48\u0026plusmn;9.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e2.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eHK-LS\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e35.45\u0026plusmn;5.338\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 207px;\"\u003e\n \u003cp\u003eSSRS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 165px;\"\u003e\n \u003cp\u003e38.47\u0026plusmn;6.676\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 102px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 93px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e＜\u003c/strong\u003e\u003cstrong\u003e0.001\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote: Bolded values: \u003cem\u003eP\u003c/em\u003e \u0026lt; 0.05; Abbreviations: M, mean; SD, standard deviation; HK-LS, Hypertension Knowledge-Level Scale; SSRS, Social Support Rating Scale.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.3 Network of self-management behaviour\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFig. 1 presents the network structure of self-management behaviors in elderly hypertension patients. The centrality index, specifically the Expected Influence (EI), was computed to identify the most influential behaviors within the network. Item M20, \u0026ldquo;Do physical exercise 3 to 5 times a week,\u0026rdquo; exhibited the highest EI value, indicating its pivotal role in the network. This was followed by M2, \u0026ldquo;Take your blood pressure medication at the time prescribed by your doctor,\u0026rdquo; and M18, \u0026ldquo;Pay attention to balanced nutrition.\u0026rdquo; The centrality difference test confirmed significant differences between these high-EI nodes and others, reinforcing their importance in the network structure. Results of case-dropping bootstrap for centrality index EI showed that primary results had an excellent level of stability (CS-C =0.439 ) (Fig. 2).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e3.4 Flow network of social support and hypertension knowledge level\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn terms of individual relationships of social support, knowledge level with self-management behaviors, Fig. 3 shows that K6. \u0026ldquo;People with high blood pressure can take medication as they feel\u0026rdquo; form the HK-LS had the strongest negative association with self-management behaviors and Fig. 4 shows that S5, \u0026ldquo;Support and care from family members\u0026rdquo; from the SSRS had the highest negative association with self-management behaviors.\u0026nbsp;\u003c/p\u003e"},{"header":"4 Discussion","content":"\u003cp\u003eThis study provides valuable insights into the self-management behaviors of elderly patients with hypertension, focusing on the interrelationships between key behaviors, social support, and knowledge level. Network analysis revealed that physical exercise, medication adherence, and balanced nutrition emerged as the most influential self-management behaviors. Notably, the knowledge item \u0026ldquo;People with high blood pressure can take medication as they feel\u0026rdquo; showed a strong negative correlation with self-management. Similarly, family support, as captured by the SSRS, was found to have a notable impact on self-management behaviors, suggesting that strengthening family involvement in managing hypertension is crucial.\u003c/p\u003e\n\u003cp\u003eOur network analysis revealed that physical exercise and medication adherence were among the most influential self-management behaviors. According to the 2020 National Hypertension Institute, regular weekly exercise not only helps prevent or delay the onset of high blood pressure but also reduce cardiovascular risk. Moreover, regular exercise is considerd the first line of antihypertensive treatment and enhances the effectiveness of antihypertensive treatment[24]. Additionally, exercise training has been shown to improve vascular structure and function in hypertension individuals, benefiting both large arteries and peripheral circulation[25]. Additionally, medication adherence has long been recognized as a cornerstone in the effective management of hypertension. Non-adherence to antihypertensive medication is a common challenge that often results in poor health outcomes[26]. Given the strong influence of both physical exercise and medication adherence, future research should focus on developing and testing interventions that simultaneously target both behaviors. Such interventions could include personalized approaches that integrate physical activity programs with strategies to improve medication adherence.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eAnother crucial item for the self-management behavior network structure was balanced nutrition. Clinical and population-based studies show that several components of the diet, such as fruits and vegetables, and foods high in saturated fats, trans fats, salt, and sugar, affect blood pressure[27, 28]. Specifically, dietary patterns such as the DASH (Dietary Approaches to Stop Hypertension) diet have been shown to lower systolic and diastolic blood pressure, thereby reducing the risk of cardiovascular events in hypertensive individuals[29]. Despite the established benefits of balanced nutrition, challenges persist in its implementation among elderly populations. Factors such as limited access to healthy foods, physical limitations affecting meal preparation, and a lack of nutritional knowledge can hinder adherence to dietary recommendations[30]. Moreover, cultural preferences and ingrained eating habits may influence dietary choices, necessitating culturally sensitive interventions. To address these barriers, future research should focus on developing and evaluating tailored nutritional interventions that consider the unique needs and preferences of elderly patients.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe knowledge item \u0026ldquo;taking medication as they feel\u0026rdquo; exhibited the strongest negative correlation with hypertension self-management behaviors in our study. Because elderly patients often share several comorbidities needing drug therapies and might suffer from potential cognitive deficits, it is commonly assumed that poor adherence is more prevalent and more severe in elderly than in younger patients[31]. Interestingly, taking medication on time is also an important item in the self-management behavior network structure. This contrast underscores a critical gap in patient education: while some patients understand the importance of timely medication intake, they may still harbor misconceptions about the necessity of continuous medication use. Such beliefs can undermine the effectiveness of treatment regimens and contribute to suboptimal health outcomes[32, 33]. Future research should focus on developing and testing educational interventions that address these misconceptions. These interventions could aim to enhance patients\u0026apos; understanding of the chronic nature of hypertension and the necessity of consistent medication adherence, regardless of symptom presence. Tailoring these educational strategies to the specific needs and preferences of elderly patients could further improve their effectiveness.\u003c/p\u003e\n\u003cp\u003eOur study found that family support and care had the strongest positive association with hypertension self-management behaviors among elderly patients, which was similay with previous research[34]. Hypertensive patients may fail to take their medication due to the long duration of therapy, the symptomless nature of the condition, adverse drug reactions, complicated drug regimens, a lack of understanding about hypertension management, a lack of motivation and the challenge to their health beliefs[35].Family involvement can enhance medication adherence, encourage lifestyle modifications, and provide emotional support, all of which contribute to better disease management and quality of life[36, 37]. Most importantly, family-centered approaches not only support the patient but also strengthen the family unit\u0026apos;s capacity to manage chronic diseases effectively[38]. Given the pivotal role of family support in hypertension self-management, future research should focus on evaluating family-centered interventions. Investigating the effectiveness of programs that educate and empower family members to actively participate in the care of elderly hypertensive patients could provide valuable insights.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study provides valuable insights into hypertension self-management behaviors among elderly patients; however, several limitations should be considered: First, the study\u0026rsquo;s cross-sectional nature limits our ability to establish causal relationships between self-management behaviors, knowledge levels, and social support. Second, the findings of this study are based on self-reported data, even though we ask patients to recall very recent events, recall bias cannot be ruled out. Third, the sample was limited to hospitals in Tianjin, China, and generalizing the results to a larger population remains to be considered.\u003c/p\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis study highlighted that regular exercise, adherence to prescribed medication schedules, and balanced nutrition are central to effective self-management. The interventions targeting these key behaviors and addressing misconceptions about medication adherence, while simultaneously enhancing family involvement, could substantially improve hypertension self-management in older adults.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Research Ethics Committee of the University (TMUhMEC2022021). The study was conducted following the tenets of the Declaration of Helsinki. Oral and written informed consent was provided at the beginning of the study and signed by each participant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the Humanities and Social Science Foundation of Ministry of Education of China (23YJAZH189) and the National Natural Science Foundation of China (72304206).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eHRC and QYL participated in the design of the study, HRC and YJW performed the statistical analysis. MAK, YQW \u0026amp; XYX conceived of the study, and HRC, XNZ, XYZ \u0026amp; NW participated in its coordination and helped to draft the manuscript. All authors read and approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe gratefully acknowledge the contribution of the research team who worked hard to collect quality data in a timely manner. We are also grateful to the study participants for their valuable time and assistance.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eOstchega Y, Fryar CD, Nwankwo T, Nguyen DT: \u003cstrong\u003eHypertension Prevalence Among Adults Aged 18 and Over: United States, 2017-2018\u003c/strong\u003e. \u003cem\u003eNCHS data brief \u003c/em\u003e2020(364):1-8.\u003c/li\u003e\n\u003cli\u003eZhang J, Guo L, Mao J, Qi X, Chen L, Huang H, Sun Y, Yang X: \u003cstrong\u003eThe effects of nursing of Roy adaptation model on the elderly hypertensive: a randomised control study\u003c/strong\u003e. \u003cem\u003eAnnals of palliative medicine \u003c/em\u003e2021, \u003cstrong\u003e10\u003c/strong\u003e(12):12149-12158.\u003c/li\u003e\n\u003cli\u003eAbdalla M, Bolen SD, Brettler J, Egan BM, Ferdinand KC, Ford CD, Lackland DT, Wall HK, Shimbo D: \u003cstrong\u003eImplementation Strategies to Improve Blood Pressure Control in the United States: A Scientific Statement From 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Most of studies on self-management behavior are based on the scores of questionnaire or the dimension which limits a comprehensive understanding of the full spectrum of hypertension self-management behavior. This study aimed to investigate the network structure of self-management of elderly patients with hypertension, and explore the correlation between self-management behaviour, knowledge level, and social support.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eThe survey was conducted in Tianjin, China, from March to May 2023. Network analysis was employed to examine the network structure of hypertension self-management behavior, and flow network analysis was used to assess the relationships between self-management behavior, knowledge level, and social support.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eA total of 804 patients were enrolled. The three core behaviors of elderly patients with hypertension were: physical exercise, take medication prescribed by doctor, and balanced nutrition. Flow network analysis indicated that take medication as they feel from knowledge had the highest negative correlation with self-management behavior, while support and care of family members from social support had the highest positive correlation with self-management behaviour.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eRegular exercise, adherence to prescribed medication schedules, and balanced nutrition were identified as core components of hypertension self-management. Enhancing patients\u0026rsquo; knowledge about proper medication use and promoting support and care from family members will help improve self-management behavior in elderly patients with hypertensive.\u003c/p\u003e","manuscriptTitle":"Network analysis of self-management and its associations with knowledge and social support in elderly patients with hypertension: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-21 09:58:37","doi":"10.21203/rs.3.rs-7226850/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":"619ec0a2-22ca-424e-8211-573482720514","owner":[],"postedDate":"August 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-11-17T07:39:32+00:00","versionOfRecord":[],"versionCreatedAt":"2025-08-21 09:58:37","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7226850","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7226850","identity":"rs-7226850","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}