Assessing Awareness, Want, and Adoption of Internet Medical Services Among Chronic Disease Patients in China:A Structural Equation Model and Matrix Analysis

Assessing Awareness, Want, and Adoption of Internet Medical Services Among Chronic Disease Patients in China:A Structural Equation Model and Matrix Analysis | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Assessing Awareness, Want, and Adoption of Internet Medical Services Among Chronic Disease Patients in China:A Structural Equation Model and Matrix Analysis Rui Qiu, Rui Song, Xiaoyi Wu, Jiahui Feng, Yingyue Yang, Xiaoying Lin This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6270951/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 Internet Medical Services (IMS) hold substantial potential to address healthcare challenges arising from demographic shifts, such as aging populations, and the evolving disease spectrum, marked by the rising prevalence of chronic conditions. However, their practical impact has yet to fully meet these expectations. This study seeks to investigate the factors influencing the adoption and utilization of IMS among chronic disease patients, focusing on their effects across specific IMS domains and acceptance processes, to provide a fresh perspective on enhancing chronic disease management. Methods We extended the Technology Acceptance Model (TAM) with the Information-Motivation-Behavioral Skills (IMB) framework, incorporating eHealth literacy, patient activation, and demographics (age, education duration, income level). A cross-sectional survey of 520 chronic disease patients in Jinan, China, was analyzed using Structural Equation Modeling (SEM) and matrix analysis to evaluate adoption patterns and influencing factors. Results Information IMS showed high acceptance with minimal disparities, while Diagnose IMS exhibited low uptake and significant gaps, particularly among older, less-educated, rural, and multimorbid patients. Notably, higher-income patients displayed lower acceptance and utilization across all IMS categories, and patient activation, expected to enhance adoption, unexpectedly hindered IMS use. SEM confirmed Perceived Usefulness and education duration as positive drivers of all adoption stages, with eHealth literacy boosting Adoption, and age exerting a negative effect. Conclusions This trailblazing model elucidates IMS adoption complexities, revealing counterintuitive barriers like income and patient activation. It underscores the need for targeted interventions to enhance eHealth literacy and service quality, providing a robust framework for optimizing IMS deployment and advancing digital health strategies for chronic disease care. Internet Medical Services Chronic Disease Management Technology Acceptance Model eHealth Literacy Patient Activation Matrix Analysis Structural Equation Modeling Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Introduction Since the 21st century, the global population structure has been undergoing unprecedented changes, mainly characterized by the intensifying aging trend and the rising incidence of chronic diseases[ 1 ]. These shifts have become a common challenge faced by governments worldwide, with China's situation being particularly prominent[ 2 ]. As projected by the National Bureau of Statistics, China's population aged 65 and above will approach 210 million by 2025, constituting 15% of the total population -- a threshold marking entry into a deeply aged society[ 3 ]. This demographic shift coincides with a high chronic disease burden, evidenced by the 2023 China Health Statistics Yearbook reporting a 34.29% national prevalence of chronic conditions[ 4 ]. Such changes in population structure and disease spectrum pose dual challenges to the traditional healthcare system: firstly, the management of chronic diseases requires long-term and continuous medical services[ 5 ]; secondly, structural mismatches in healthcare capacity, ​particularly the scarcity of specialized geriatric care and chronic disease management infrastructure, ​compromise service continuity for multimorbid elderly patients[ 6 ]. Internet Medical Services (IMS), an innovative healthcare resource leveraging internet platforms, offers a promising approach to optimizing health resource allocation and fulfilling the growing demand for high-quality medical care[ 7 ]. Through IMS, patients can seamlessly perform tasks such as querying health information, scheduling appointments, interpreting medical reports, and conducting follow-up consultations from home, thereby alleviating congestion in public hospitals[ 8 , 9 ]. Evidence from empirical studies suggests that IMS extends the duration of doctor-patient interactions relative to traditional healthcare delivery, enhancing the equitable distribution of health resources and harmonizing supply with demand[ 10 , 11 ]. With its technical characteristics that transcend spatiotemporal limitations, IMS can theoretically optimize the layout of medical resources and enhance service accessibility. However, the penetration rate of IMS among chronic disease populations is relatively low, presenting a contradictory phenomenon of "high demand and low penetration," which further exacerbates the digital health divide and restricts the achievement of health equity[ 12 ]. Moreover, as artificial intelligence (AI) technologies become increasingly embedded in IMS, this divide is poised to widen further. In addressing this issue, existing research often focuses on the technical architecture of IMS or policy analyses across various countries and regions[ 8 , 13 ]. However, two critical dimensions are relatively overlooked: Firstly, there is a lack of systematic research on the characteristics of technology adoption behaviors among patients with chronic diseases. The traditional Technology Acceptance Model (TAM) finds it difficult to explain this group's unique cognitive and behavioral decision-making processes. Second, prevailing research often adopts a technologically deterministic stance, sidelining the interactive roles of eHealth literacy, patient activation, and sociodemographic factors in shaping adoption patterns[ 14 ]. Such theoretical limitations make it challenging for current strategies to effectively address the formation mechanism of the digital divide related to IMS utilization among chronic disease patients. To bridge these deficiencies, our study introduces a novel chronic disease-focused Extended Technology Acceptance Model (c-TAM). Based on the traditional TAM model, we integrate the core dimensions of the Information-Motivation-Behavioral Skills Model (IMB)[ 15 ], introducing eHealth literacy and patient activation level as key external variables. Additionally, we incorporate sociodemographic factors such as age, income level, and education duration. This improved model breaks through the unidirectional technical cognitive path of the traditional TAM, constructing a three-dimensional analytical framework that includes capability building, behavioral drivers, and social gradients. This enables us to systematically untangle the deep-seated obstacles to IMS adoption among chronic disease patients. By analyzing the coupling relationships among various elements using matrix analysis and structural equation modeling (SEM), this study aims to provide a new perspective for developing differentiated digital health intervention strategies. Simultaneously, it offers empirical support for the implementation of the Innovative Care for Chronic Conditions (ICCC) proposed by the WHO[ 16 ]. Theoretical framework and hypothesis development The Technology Acceptance Model (TAM), introduced by Fred Davis in 1989[ 17 ], provides a robust framework for explaining and predicting user acceptance of information technology systems. Rooted in the Theory of Reasoned Action (TRA)[ 18 ], it highlights perceived usefulness (PU) and perceived ease of use (PEOU) as key factors in technology adoption, capturing the core logic of technology promotion[ 19 ]. PU reflects users' belief that technology enhances work efficiency or quality of life, while PEOU indicates users' perception of a technology's ease of use. Users are more inclined to adopt technologies perceived as beneficial (PU) or easy to use (PEOU)[ 17 ]. Over the decades, TAM has been widely applied in information technology research, particularly in the promotion and use of e-commerce, social media, and IMS[ 20 – 22 ], but at the same time, its limitations in terms of insufficient explanatory power for external variables have become increasingly apparent[ 23 – 25 ]. Developed by American psychologist M.J. Fisher, the Information-Motivation-Behavioral Skills (IMB) model elucidates and forecasts health-related behaviors[ 26 ]. According to the IMB, information, motivation, and behavioral skills are the three core factors influencing health behaviors[ 27 ]. Information refers to the knowledge and cognitive content related to health behaviors acquired by individuals, which aligns with the abilities encompassed by eHealth literacy in terms of obtaining, comprehending, and evaluating health information in a digital environment under the umbrella of IMS[ 28 ]. Motivation, on the other hand, represents the intrinsic and extrinsic forces that drive individuals to engage in healthy behaviors, with intrinsic motivation paralleling the concept of patient activation. This study extends the TAM framework by integrating eHealth literacy and patient activation, drawn from the IMB model, and tailoring them to chronic disease patients. Additionally, it considers user characteristics such as age, income level, and education duration to enhance the explanatory power of IMS utilization among this population. Based on a literature review, the following hypotheses are proposed: Perceived Usefulness (PU) and Perceived Ease of Use (PEOU) PU and PEOU serve as cornerstone variables in technology acceptance, reflecting users' cognitive evaluations of functional value and operational convenience in technological adoption[ 19 ]. In chronic disease management contexts, PU manifests in patients’ evaluations of IMS effectiveness for controlling disease progression, preventing complications, and improving quality of life (e.g., accessing tailored treatment plans via online consultations)[ 29 ]. PEOU encompasses platform interface intuitiveness, operational fluency, and information accessibility[ 30 ]. Within TAM, attitude functions as a critical mediator between these cognitive constructs and usage behavior, embodying users’ trust and emotional orientation toward the technology [ 31 ]. Therefore, we hypothesize: H1a. PU exerts a direct positive influence on chronic disease patients' utilization of IMS; H1b. PU positively affects such use, mediated in part by a positive attitude; H1c. PEOU directly and positively impacts IMS utilization among chronic disease patients utilization of IMS; H1d. PEOU positively affects such use, mediated in part by a positive attitude. eHealth Literacy eHealth literacy, an evolving construct, refers to individuals’ proficiency in engaging with health information exchanges and applying this knowledge to manage health and address challenges[ 32 ]. Patients with superior eHealth literacy can more effectively translate health information into actual health management actions[ 33 ]. For instance, by using the self-assessment module for symptoms in IMS, patients can swiftly screen key health information to aid follow-up visit decisions[ 34 ]. This proficiency not only heightens their PU of IMS[ 35 , 36 ] but may also, through hands-on practice, boost technical proficiency, lower usage barriers, and thus strengthen PEOU[ 37 ]. Furthermore, patients with strong eHealth literacy often develop effective information-filtering strategies [ 38 ]. This enables them to better select IMS items suited to their situation, avoid irrelevant information, and considerably increase their trust and usage intention in these services, fostering a positive attitude. This yields the following hypotheses: H2a. eHealth literacy directly promotes the use of IMS among chronic disease patients; H2b. eHealth literacy enhances IMS utilization by amplifying PU; H2c. eHealth literacy promotes such use by positively impacting PEOU; H2d. eHealth literacy promotes such use by improving their attitude. Patient activation Patient activation reflects patients' knowledge, skills, and confidence in self-managing health conditions, along with their capacity to participate in medical decision-making[ 39 ]. Highly activated patients with disease-specific understanding are more likely to perceive the long-term management benefits provided by IMS[ 40 , 41 ], which is aligned with TAM’s “cognition-driven adoption” mechanism[ 17 ]. They may also reduce perceived technical barriers through active learning (e.g., practicing with video simulation systems) and enhance technical trust via higher self-efficacy [ 42 ]. Notably, these patients are more willing and likely to boost their eHealth literacy through learning and practice, optimizing IMS use[ 43 , 44 ]. Consequently, we posit: H3a. Patient activation directly promotes the utilization of IMS by patients with chronic diseases; H3b. Patient activation facilitates IMS utilization through PU mediation; H3c. Patient activation enhances IMS utilization via PEOU mediation; H3d. Patient activation facilitates such use mediated through attitudes; H3e. Patient activation facilitates such use via eHealth literacy elevation. Age Advanced age may attenuate PEOU through declining adaptability (e.g., difficulties with touchscreen navigation) and reduced health information processing efficiency[ 45 ], the digital divide further compounds this effect by potentially suppressing eHealth literacy[ 46 ]. Elderly patients also tend to rely on traditional face-to-face treatment, question the reliability of virtual services, and thus have a more negative attitude towards accepting IMS[ 47 ]. Additionally, with a decreased sense of control over their health (e.g., "I can only follow the doctor's advice after being ill for a long time"), they lack the motivation to seek new technologies, lowering their patient activation and negatively impacting their acceptance of IMS[ 48 ]. Based on this, the hypotheses include: H4a. Age demonstrates direct inhibitory effects of IMS use by chronic disease patients; H4b. Age restrains such use by reducing PU; H4c. Age restrains such use by weakening PEOU; H4d. Age restrains such use through the mediating effect of negative attitudes; H4e. Age inhibits such use by suppressing eHealth literacy; H4f. Age curtails IMS utilization by reducing patient activation. Income Level Economic advantage enables access to premium services (e.g., remote specialist consultation) that strengthen PU while device accessibility (e.g., 5G smartphones) improves PEOU and eHealth literacy [ 49 ]. Moreover, high-income groups place a higher value on their time and may be more willing to use IMS with lower time costs[ 50 ], such as viewing test reports online. This generates: H5a. Income level directly elevates the application of IMS among patients with chronic diseases; H5b. Income level elevates such application through enhanced PU perception; H5c. Income level elevates such application through improved PEOU; H5d. Income level elevates such application through a favorable attitude; Education Duration Extended education facilitates health technology (e.g., electronic medical record interpretation), thereby enhancing PU and PEOU[ 51 ]. Additionally, greater cognitive reserves may amplify eHealth literacy and patient activation[ 52 ]. We hypothesize: H6a. Education duration directly promotes the application of IMS by patients with chronic diseases; H6b. Education duration positively influences such application through strengthening PU; H6c . Education duration positively influences such application through optimized PEOU; H6d. Education duration positively influences such application through positive attitude mediation; H6e. Education duration positively influences such application through elevating eHealth literacy; H6f. Education duration positively influences such applications by enhancing patient activation. By synthesizing these theoretical perspectives and hypothesized relationships, this study constructs a comprehensive conceptual framework, depicted in Fig. 1 . Method 3.1 Study population This cross-sectional study investigated Internet Medical Services (IMS) by categorizing their functional resources into three domains: Information, Intermedia, and Diagnose. The adoption process among chronic disease patients was segmented into progressive stages—awareness, want, and adoption—to evaluate usage patterns and determinants, with the overarching goal of facilitating IMS dissemination within this group. Considering the prolonged follow-up needs of chronic disease patients, we conducted a random survey among individuals attending chronic disease sentinel hospitals in Jinan, Shandong, China. To ensure data precision, participants were screened using the following inclusion and exclusion criteria: Inclusion criteria: 1) Confirmed diagnosis of prevalent chronic conditions (e.g., cardiovascular and cerebrovascular diseases including hypertension, stroke, and coronary heart disease; chronic respiratory diseases such as chronic obstructive pulmonary disease, bronchial asthma, chronic heart failure, and chronic respiratory failure; diabetes; and metabolic-associated fatty liver disease) by qualified physicians, based on clinical symptoms and diagnostic assessments. 2) Conscious and able to understand the questionnaire content and answer accurately by themselves or with the help of others. 3) Voluntary participation with signed informed consent following a thorough explanation of the study’s objectives and procedures. Exclusion criteria: 1) Patients with other serious illnesses or unstable disease states. 2) Patients with severe cognitive impairments or severe mental illnesses who cannot understand or cooperate with the survey, as well as patients with language barriers. 3) Patients who do not have relevant chronic diseases. 4) Those who refuse to cooperate for personal reasons. A simple random sampling method was utilized, with the minimum sample size calculated using the formula[53]: $$\:n=\frac{{Z}_{1-\alpha\:/2}^{2}\times\:p(1-p)}{{\delta\:}^{2}}$$ Where p represents the percentage of the population; δ represents the allowable sampling error; and α represents the significance level. In this study, the confidence level was set to 95%, the allowable error range was 5%, and the overall percentage was set to 50% based on relevant research. According to the formula, 385 samples can meet the research requirements. 3.2 Questionnaire Design An extensive literature review, encompassing domestic and international sources, guided the development of a standardized Chinese questionnaire, which was pre-tested in a pilot survey. Refinements were implemented based on identified issues, resulting in an optimized instrument. This questionnaire was administered to chronic disease patients in Jinan, Shandong Province, from June to October 2023, collecting data on demographics, IMS awareness, want, adoption, and the Patient Activation Measure (PAM). Specific items are detailed in Tables 1 and 2 (excluding demographics), with Table 1 employing a 5-point Likert scale and Table 2 using dichotomous responses. Table 1 Measurement items of the 5-point Likert scale form construct Construct Items Reference Perceived usefulness (PU) [17] PU1 Internet medical services are useful in daily health management PU2 There are significant advantages of using Internet medical services to better manage your health PU3 Using Internet medical services is beneficial to me PU4 Using Internet medical services is of high value to my healthcare Perceived Ease of Use (PEOU) [17] PE1 Learning how to use Internet medical services was easy for me PE2 My interactions using the Internet to search for health information were clear and easy to understand PE3 Using the Internet is very flexible and easy to use when it comes to accessing health information PE4 Overall, Internet medical services were easy to use Attitude towards Internet medical services [17] At1 Using Internet medical services is a good idea At2 Using Internet medical services is a wise decision and is recommended At3 I like using internet medical services Patient Activation Measure 13 (PAM13) [54] PA1 Overall, I am the person responsible for looking after my health PA2 Taking an active role in my health care is the most important thing that affects my health PA3 I believe I can prevent or reduce problems related to my health PA4 I know what each of my prescribed medicines does PA5 I am confident that I can tell if I need to go to the doctor or if I can handle a health problem on my own PA6 I believe I can tell my doctor about my concerns, even if he or she doesn't ask PA7 I am confident that I can insist on medical treatment that I may need to do at home PA8 I understand my health problems and their causes PA9 I know what treatments are available for my health problems PA10 I have been able to maintain (keep up with) lifestyle changes such as eating right or exercising PA11 I know how to prevent my health problems PA12 I believe I can figure out solutions when new problems with my health arise PA13 I believe I can keep up with lifestyle changes, such as eating right and exercising, even during times of stress The eHealth Literacy Scale (eHEALS) [55] eH1 I know how to go online to find useful health resource information eH2 I know how to use the Internet to answer my health questions eH3 I know what health resource information is available on the Internet eH4 I know where to get useful information about health resources on the Internet eH5 I know how to use the information I get about health resources on the Internet to help myself eH6 I have the skills to evaluate good and bad information about health resources on the Internet eH7 I can differentiate between high-quality and low-quality health resource information on the Internet eH8 I am confident in using web-based information to make health-related decisions Table 2 Measurement items of the dichotomous scale form construct Construct Items Item sources Awareness/Want/Adoption of IMS [56] Aw1/Wa1/Ad1 Collect disease/health information online Aw2/Wa2/Ad2 Collect doctor/hospital information online Aw3/Wa3/Ad3 Online collection of medical evaluation information (patient's evaluation of doctors) Aw4/Wa4/Ad4 Online consultation (graphic consultation) Aw5/Wa5/Ad5 Communicate with patients in groups or forums Aw6/Wa6/Ad6 Appointment booking online Aw7/Wa7/Ad7 Online payment of medical fees Aw8/Wa8/Ad8 View electronic medical records and examination reports online Aw9/Wa9/Ad9 Make an appointment for examination or surgery online Aw10/Wa10/Ad10 Purchase medicines (not healthcare products) online Aw11/Wa11/Ad11 Online chronic disease monitoring and management Aw12/Wa12/Ad12 Make an appointment for hospitalization online 3.3 Date Collection Data collection was completed by 35 volunteers from Shandong University. Before the formal survey at designated chronic disease hospitals, a training session ensured volunteers were well-versed in the questionnaire and survey protocols. During the training, emphasis was placed on the importance of standardized questioning and consistent responding. Additionally, it was confirmed that all surveyors had completed coursework in each surveyor had completed coursework in public health management to guarantee their professional expertise. To maintain the quality of the survey, all completed questionnaires were submitted to another survey team leader for quality inspection and signed confirmation. 3.4 Data Analysis Statistical analysis was performed using SPSS 27.0.1.0 software[57]. Continuous variables were presented as means and standard deviations (SD), while categorical data were expressed as frequencies and percentages. Differences across groups were assessed using the Mann-Whitney U and Kruskal-Wallis H tests, with supplementary analyses and visualizations conducted in Origin Pro 2025 (version 10.2.0.196). Statistical significance was set at p < 0.05. To investigate digital divide variations within IMS categories, we employed the AWAG segmented matrix method, introduced by Liang T.H.[56, 58]. The method analyzed differences in the digital divide among various categories of IMS through awareness gap, want gap, adoption gap, and utilization gap ratios. The utilization gap ratio was quantified using the following formula: $$\:g\left(x\right)=\frac{\text{min}\left(\text{Pr}\left(A\left(x\right)\right),\text{Pr}\left(W\left(x\right)\right)\right)-\text{Pr}\left(U\left(x\right)\right)}{\text{min}\left(\text{Pr}\left(A\left(x\right)\right),\text{Pr}\left(W\left(x\right)\right)\right)}\times\:100\%$$ where Pr(A(x)) represents awareness rate for eHealth servicex; Pr(W(x)) represents the want rate for eHealth servicex; and Pr(U(x)) represents the adoption rate of eHealth servicex. Next, integrating the consumer purchase decision model[59] and the technology adoption lifecycle theory[60], a matrix is constructed using 50% as the midpoint to divide both the awareness rate and the demand rate into two levels. Consequently, the AWAG matrix is segmented into four categories: the open group, the awareness-defective group, the demand-defective group, and the closed group. Furthermore, these were further subdivided into strong, awareness-defective, demand-defective, and general subcategories using 15% and 85% thresholds (see Fig. 2). In the AWAG matrix, group centers’ coordinates represent awareness and want levels, with circle radii (scaled by a factor of 30 for enhanced visualization) reflecting gap rates. IMS services were classified into Information, Intermedia, and Diagnose subgroups (Table 3) [8, 61]. Table 3 Classification of digital medical service (DMS). Categories Digital medical service (DMS) Information Collect disease/health information online Collect doctor/hospital information online Collect medical reviews (patient comments about doctors) online Communicate with patients in groups or forums Intermedia Make an appointment online Pay medical fees online View electronic medical records and test reports online Book an appointment for an examination or surgery online Make an appointment for hospitalization online Buy medicines (not healthcare products) online Diagnose Consult online (graphical consultation) Monitor and manage a chronic condition online Table 4 The Sociodemographic characteristics of chronic disease patients (N = 520) Variables Number (%) Gender Male 292 (56.15%) Female 228 (43.85%) Age (years) ≤ 49 96 (18.46%) 50–69 311 (59.81%) ≥ 70 113 (21.73%) Marital status Single 16 (3.08%) Married 471 (90.58%) Divorced 3 (0.58%) Widowed 30 (5.77%) Region of residence City 409 (78.65%) Village 111 (21.35%) Years of education ≤ 6 178 (34.23%) 7–12 254 (48.85%) ≥ 13 88 (16.92%) Income level Very poor or underprivileged 233 (44.81%) Average level 220 (42.31%) Above-average or relatively affluent 67 (12.88%) Self-assessment of health status Extremely poor or poor 205 (39.42%) Average level 238 (45.77%) Good or excellent 77 (14.81%) Number of chronic diseases 1 302 (58.08%) 2 149 (28.65%) ≥ 3 69 (13.27%) Table 5 Non-Parametric Test Results by Residence Region of residence City (M, IQR) Village (M, IQR) Z P Information Awareness 0.75(0.50,1.00) 0.75(0.00,1.00) 4.444 < 0.001 Information Want 1.00(0.25,1.00) 0.50(0.00,1.00) 3.033 0.002 Information Adoption 0.25(0.00,0.75) 0.00(0.00,0.50) 3.974 < 0.001 Intermedia Awareness 0.50(0.33,0.83) 0.50(0.00,0.67) 2.706 0.007 Intermedia Want 0.67(0.33,1.00) 0.50(0.00,1.00) 2.657 0.008 Intermedia Adoption 0.33(0.00,0.50) 0.00(0.00,0.33) 4.265 < 0.001 Diagnose Awareness 0.50(0.00,1.00) 0.50(0.00,1.00) 3.907 < 0.001 Diagnose Want 0.50(0.00,1.00) 0.00(0.00,1.00) 2.004 0.045 Diagnose Adoption 0.00(0.00,0.50) 0.00(0.00,0.00) 1.633 0.102 Note: M = Median, IQR = Interquartile Range. Table 6 Non-Parametric Test Results by Education Duration Education Duration ≤ 6 (M, IQR) 7–9 (M, IQR) 10–12 (M, IQR) 13–16 (M, IQR) ≥ 17 (M, IQR) H P Information Awareness 0.50 (0.25,1.00) 0.75 (0.25,1.00) 1.00 (0.75,1.00)a 1.00 (0.75,1.00) a 1.00 (1.00,1.00)a 85.77 <0.001 Information Want 0.50 (0.00,1.00) 0.75 (0.00,1.00) 1.00 (0.50,1.00)a 1.00 (0.94,1.00)a 1.00 (1.00,1.00)a 51.47 <0.001 Information Adoption 0.00 (0.00,0.25) 0.25 (0.00,0.50) 0.50 (0.25,0.75)a 0.75 (0.25,1.00)a 0.75 (0.44,0.75)a 84.10 <0.001 Intermedia Awareness 0.50 (0.00,0.83) 0.50 (0.17,0.67) 0.67 (0.50,0.83)a 0.67 (0.50,0.88)a 0.67 (0.50,1.00) 42.47 <0.001 Intermedia Want 0.50 (0.00,1.00) 0.50 (0.00,1.00) 0.83 (0.50,1.00)a 0.83 (0.63,1.00)a 0.83 (0.67,1.00) 38.61 <0.001 Intermedia Adoption 0.17 (0.00,0.33) 0.17 (0.00,0.33) 0.33 (0.17,0.50)a 0.50 (0.33,0.50)a 0.33 (0.33,0.54)b 83.88 <0.001 Diagnose Awareness 0.50 (0.00,1.00) 0.50 (0.00,1.00) 1.00 (0.50,1.00)a 1.00 (0.50,1.00)a 0.75 (0.50,1.00) 61.21 <0.001 Diagnose Want 0.00 (0.00,1.00) 0.00 (0.00,1.00) 0.50 (0.00,1.00)a 1.00 (0.50,1.00)a 1.00 (0.50,1.00) 41.16 <0.001 Diagnose Adoption 0.00 (0.00,0.00) 0.00 (0.00,0.00) 0.00 (0.00,0.50)a 0.00 (0.00,0.50)a 0.25 (0.00,0.50) 34.02 <0.001 Note: a denotes comparison between subgroups with education duration ≤ 6 and those with 7–9, with Adj. P value < 0.05; b denotes comparison between subgroups with education duration ≤ 6 and others, with Adj. P value < 0.05, M = Median, IQR = Interquartile Range. Table 7 Non-Parametric Test Results by Income Level Income Level underprivileged (M, IQR) generic (M, IQR) affluent (M, IQR) H P Information Awareness 0.75(0.50,1.00) 0.75(0.50,1.00) 0.75(0.25,1.00) 5.968 0.051 Information Want 0.75(0.25,1.00) 1.00(0.25,1.00) 0.75(0.00,1.00) 2.774 0.25 Information Adoption 0.25(0.00,0.75) 0.25(0.00,0.75) 0.00(0.00,0.50)ab 7.299 0.026 Intermedia Awareness 0.67(0.33,0.83) 0.50(0.17,0.83) 0.50(0.17,0.67)a 6.151 0.046 Intermedia Want 0.67(0.17,1.00) 0.83(0.21,1.00) 0.33(0.00,1.00)b 8.217 0.016 Intermedia Adoption 0.17(0.00,0.50) 0.33(0.00,0.50) 0.17(0.00,0.33) 3.48 0.176 Diagnose Awareness 0.50(0.00,1.00) 0.50(0.00,1.00) 0.50(0.00,1.00) 4.683 0.096 Diagnose Want 0.50(0.00,1.00) 0.50(0.00,1.00) 0.50(0.00,1.00) 3.265 0.195 Diagnose Adoption 0.00(0.00,0.50) 0.00(0.00,0.00) 0.00(0.00,0.00)a 9.573 0.008 Note: “a” denotes a comparison with the underprivileged income subgroup, where Adj. P value < 0.05; “b” represents a comparison with the generic income subgroup, where Adj. P value < 0.05, M = Median, IQR = Interquartile Range. Table 8 Descriptive statistics and normality test of each dimension Construct M SD Skewness Kurtosis Total M Total SD Age 60.06 13.214 -0.389 0.551 Education Duration 8.92 4.35 0.121 -0.512 Income Level 2.63 0.873 0.313 0.204 PU1 3.67 0.967 -0.993 1.007 3.518 0.966 PU2 3.47 1.027 -0.682 0.137 PU3 3.52 1.073 -0.735 -0.001 PU4 3.41 1.063 -0.643 -0.031 PE1 2.77 1.262 0.088 -1.17 2.804 1.190 PE2 2.73 1.238 0.099 -1.088 PE3 2.92 1.274 -0.113 -1.183 PE4 2.78 1.22 0.047 -1.056 At1 3.55 0.96 -0.921 0.574 3.360 0.952 At2 3.41 1.01 -0.646 0.048 At3 3.11 1.09 -0.262 -0.619 PA1 3.42 0.709 -1.039 1.317 3.227 0.529 PA2 3.38 0.628 -0.55 0.223 PA3 3.16 0.743 -0.552 -0.135 PA4 2.82 0.93 -0.109 -0.839 PA5 4.24 0.716 -0.509 -0.467 PA6 3.39 0.669 -0.456 -0.286 PA7 3.19 0.73 -0.304 -0.591 PA8 3.09 0.722 -0.353 0.152 PA9 2.94 0.794 -0.032 -0.859 PA10 3.17 0.731 -0.422 -0.052 PA11 3.12 0.715 -0.236 -0.273 PA12 2.94 0.827 -0.107 -0.648 PA13 3.1 0.762 -0.357 -0.123 eH1 2.87 1.328 -0.016 -1.225 2.686 1.166 eH2 2.83 1.3 -0.012 -1.221 eH3 2.72 1.308 0.114 -1.233 eH4 2.65 1.268 0.135 -1.178 eH5 2.86 1.269 -0.067 -1.186 eH6 2.52 1.218 0.33 -0.909 eH7 2.48 1.173 0.272 -0.945 eH8 2.56 1.239 0.248 -1.013 He1 0.750 0.435 -1.146 -0.689 0.579 0.324 He2 0.680 0.469 -0.749 -1.444 He3 0.480 0.500 0.100 -1.998 He4 0.460 0.499 0.178 -1.976 He5 0.810 0.396 -1.550 0.405 He6 0.740 0.439 -1.100 -0.794 He7 0.650 0.477 -0.631 -1.608 He8 0.340 0.476 0.658 -1.574 He9 0.730 0.446 -1.022 -0.960 He10 0.340 0.475 0.667 -1.562 He11 0.640 0.481 -0.569 -1.682 He12 0.340 0.476 0.658 -1.574 Wa1 0.630 0.483 -0.544 -1.711 0.588 0.385 Wa2 0.620 0.487 -0.476 -1.781 Wa3 0.550 0.498 -0.202 -1.967 Wa4 0.480 0.500 0.077 -2.002 Wa5 0.720 0.451 -0.968 -1.067 Wa6 0.690 0.462 -0.836 -1.307 Wa7 0.660 0.476 -0.658 -1.574 Wa8 0.440 0.497 0.225 -1.957 Wa9 0.640 0.481 -0.569 -1.682 Wa10 0.540 0.499 -0.147 -1.986 Wa11 0.510 0.500 -0.031 -2.007 Wa12 0.580 0.493 -0.344 -1.889 Us1 0.430 0.496 0.280 -1.929 0.276 0.249 Us2 0.340 0.475 0.667 -1.562 Us3 0.200 0.400 1.504 0.264 Us4 0.100 0.303 2.639 4.984 Us5 0.480 0.500 0.069 -2.003 Us6 0.520 0.500 -0.069 -2.003 Us7 0.400 0.490 0.418 -1.833 Us8 0.080 0.270 3.135 7.855 Us9 0.400 0.490 0.426 -1.826 Us10 0.080 0.267 3.185 8.173 Us11 0.180 0.388 1.630 0.661 Us12 0.100 0.298 2.711 5.368 Note: M = Median, SD = standard deviations. Table 9 Reflective constructs assessment. Construct/ measure Unstandardized Regression Coefficients Factor loading Cronbach's Alpha Coefficient CR AVE MSV ASV PU1 1 0.881 0.952 0.952 0.833 0.738 0.421 PU2 1.096 0.91 PU3 1.156 0.918 PU4 1.173 0.941 PE1 1 0.945 0.966 0.967 0.879 0.767 0.437 PE2 0.978 0.942 PE3 0.969 0.907 PE4 0.977 0.956 At1 1 0.908 0.925 0.931 0.817 0.738 0.439 At2 1.105 0.954 At3 1.059 0.847 PA1 1 0.56 0.917 0.919 0.469 0.121 0.1 PA2 0.921 0.582 PA3 1.278 0.683 PA4 1.313 0.56 PA5 1.321 0.733 PA6 0.975 0.579 PA7 1.384 0.752 PA8 1.368 0.752 PA9 1.434 0.717 PA10 1.255 0.682 PA11 1.44 0.8 PA12 1.517 0.728 PA13 1.375 0.716 eH1 1 0.934 0.975 0.975 0.828 0.764 0.45 eH2 0.986 0.94 eH3 0.984 0.933 eH4 0.95 0.929 eH5 0.956 0.934 eH6 0.861 0.876 eH7 0.817 0.864 eH8 0.866 0.867 Note: CR = Construct reliability; AVE = Average variance extracted; MSV = Maximum shared squared variance; ASV = Average shared square variance. Table 10 Descriptive statistics and inter-correlations of the constructs. PU PEOU Attitude PAM13 eHEALS PU 0.833 PEOU 0.653 0.879 Attitude 0.859 0.671 0.817 PAM13 0.265 0.327 0.318 0.469 eHEALS 0.669 0.874 0.684 0.347 0.828 The square root of AVE 0.913 0.938 0.904 0.685 0.910 Note: PU = Perceived Usefulness; PEOU = Perceived Ease of Use; PAM13 = Patient Activation Measure 13, e HEALS = The eHealth Literacy Scale. Boldfaced diagonal elements are the AVE value calculated before. Table 11 Hypothesis testing results of the research model. Pathways Coef β# S.E. C.R. P PAM13 <--- Age -0.13 0.002 -2.861 0.004 PAM13 <--- ED 0.15 0.005 3.27 0.001 PAM13 <--- Income Level -0.07 0.027 -1.56 0.119 PEOU <--- Age -0.478 0.003 -12.684 *** PEOU <--- Income Level -0.123 0.047 -3.324 *** PEOU <--- ED 0.102 0.01 2.726 0.006 PEOU <--- PAM13 0.205 0.09 4.906 *** PU <--- ED -0.094 0.008 -2.654 0.008 PU <--- PEOU 0.602 0.04 13.365 *** PU <--- Age -0.079 0.003 -1.944 0.052 PU <--- Income Level 0.028 0.04 0.806 0.42 PU <--- PAM13 0.068 0.074 1.761 0.078 eHEALS <--- ED 0.106 0.006 4.452 *** eHEALS <--- PU 0.188 0.037 5.775 *** eHEALS <--- PEOU 0.723 0.039 18.731 *** eHEALS <--- Age 0.031 0.002 1.135 0.257 eHEALS <--- Income Level -0.014 0.03 -0.601 0.548 eHEALS <--- PAM13 0.049 0.056 1.912 0.056 Attitude <--- PU 0.705 0.035 17.829 *** Attitude <--- eHEALS 0.112 0.044 1.981 0.048 Attitude <--- PAM13 0.063 0.047 2.285 0.022 Attitude <--- Age -0.054 0.002 -1.865 0.062 Attitude <--- ED -0.037 0.005 -1.435 0.151 Attitude <--- PEOU 0.069 0.044 1.205 0.228 Adoption <--- Age -0.264 0.001 -5.891 *** Want <--- Age -0.162 0.001 -3.954 *** Awareness <--- Age -0.182 0.001 -4.4 *** Want <--- Income Level 0.093 0.014 2.672 0.008 Adoption <--- ED 0.181 0.003 4.664 *** Want <--- ED 0.137 0.003 3.774 *** Awareness <--- ED 0.197 0.002 5.297 *** Awareness <--- PU 0.428 0.023 5.226 *** Want <--- PU 0.534 0.029 6.52 *** Adoption <--- PU 0.225 0.025 2.708 0.007 Adoption <--- eHEALS 0.323 0.022 3.867 *** Awareness <--- PAM13 -0.136 0.021 -3.41 *** Want <--- PAM13 -0.127 0.027 -3.225 0.001 Adoption <--- PAM13 -0.087 0.023 -2.162 0.031 Adoption <--- Income Level 0.066 0.012 1.835 0.067 Awareness <--- Income Level 0.02 0.011 0.583 0.56 Awareness <--- PEOU 0.027 0.02 0.336 0.737 Want <--- PEOU 0.009 0.025 0.118 0.906 Adoption <--- PEOU 0.062 0.022 0.753 0.451 Awareness <--- Attitude 0.048 0.026 0.585 0.559 Want <--- Attitude 0.023 0.033 0.28 0.779 Adoption <--- Attitude -0.002 0.029 -0.02 0.984 Awareness <--- eHEALS 0.153 0.019 1.944 0.052 Want <--- eHEALS 0.065 0.024 0.827 0.408 Note: # Standardized Path Coefficients, *** P value < 0.001. This study employed IBM SPSS AMOS software (version 24.0.0)[62] to construct a Structural Equation Model (SEM). The latent variables included PU, PEOU, Attitude, Awareness, Want, Adoption, patient activation, and e-Health literacy. Appropriate items were selected as observed variables. Given the large sample size and the presence of non-normality in some data, which could potentially affect the judgment of model fit, the Maximum Likelihood method was adopted for parameter estimation. Additionally, the Bootstrap method was chosen to adjust the fit indices during Confirmatory Factor Analysis (CFA) and SEM analysis[63]. The criteria for a good fit were set as follows: Chi-square to degrees of freedom ratio (χ²/df) 0.90, Tucker-Lewis Index (TLI) > 0.90, Adjusted Goodness of Fit Index (AGFI) > 0.90, Comparative Fit Index (CFI) > 0.90, and Root Mean Square Error of Approximation (RMSEA) < 0.05[64]. Based on the collected data, the model was revised and re-estimated to verify its fit, ultimately iterating to achieve the best-fit model. 3.5 Ethical Approval This study was approved by the Ethics Committee of the School of Public Health, Shandong University (Approval No. LL20230602). Informed consent was secured from all participants, who received detailed briefings on the study’s purpose, procedures, and privacy protections. Consent forms were signed before data collection, and participants were assured of their right to withdraw at any time without consequences. Data from withdrawn participants were excluded from the analysis. 3.6 Validity and Reliability The scales used in this study exhibited satisfactory validity and reliability. A pilot study was conducted prior to the research to evaluate the appropriateness of the content. After data collection, to ensure data authenticity and validity, all questionnaires were coded and double - entered by two independent professional data entry personnel, and a database was established based on the valid questionnaires. Cronbach's alpha reliability analysis and confirmatory factor analysis were performed during the analysis, and the results indicated good reliability and validity. Results 4.1 Demographic Characteristics and Overview of the Sample A total of 520 valid questionnaires were collected. The average age of the participants was 60.06 (SD=13.21), and the average years of education were 8.92 (SD=4.35). The majority of participants were male (56.15%, N=292), married (90.58%, N=471), and city residents (78.65%, N=409). Further details are presented in Table 4. Beyond sociodemographic traits, we evaluated responses across various 5-point Likert scales (Figure 3A). Mean scores for Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude, Patient Activation, and eHealth Literacy were 3.52 (SD = 0.97), 2.80 (SD = 1.19), 3.36 (SD = 0.95), 3.17 (SD = 0.49), and 2.69 (SD = 1.17), respectively. To examine relationships among factors tied to Awareness, Want, and Adoption of IMS, bivariate analyses were conducted to assess interactions and effect sizes between variables. The results (Figure 3B) showed positive correlations between Awareness of IMS and PU (r = 0.581, p < 0.001), PEOU (r = 0.559, p < 0.001), Attitude (r = 0.554, p < 0.001), Patient Activation (r = 0.142, p < 0.01), and eHealth Literacy (r = 0.560, p < 0.001). Similarly, Want for IMS showed positive associations with PU (r = 0.594, p < 0.001), PEOU (r = 0.488, p < 0.001), Attitude (r = 0.553, p < 0.001), Patient Activation (r = 0.111, p < 0.01), and eHealth Literacy (r = 0.485, p < 0.001). Adoption of IMS was positively linked to PU (r = 0.513, p < 0.001), PEOU (r = 0.624, p < 0.001), Attitude (r = 0.544, p < 0.001), Patient Activation (r = 0.211, p < 0.001), and eHealth Literacy (r = 0.636, p < 0.001). Additionally, sociodemographic factors influenced IMS engagement: advancing age correlated with reduced Awareness, Want, and Adoption, whereas longer education duration was associated with elevated levels of these stages. 4.2 Matrix Analysis and Non-Parametric Test In terms of awareness, want, and adoption of IMS, chronic disease patients exhibit relatively low rates across all three aspects: 57.87% for awareness, 58.77% for want, and 27.56% for adoption. Simultaneously, there is a significant disparity rate of 52.37%, placing them in the open-general group. To gain deeper insights, matrix analyses subdivided IMS into Information, Intermedia, and Diagnose subgroups. Figure 4A indicated that patients displayed the highest Awareness and Want for Information-related IMS, with a markedly smaller disparity rate compared to Intermedia and Diagnose subgroups. This pattern may reflect the widespread practice of using internet search engines for symptom information and self-diagnosis[65, 66]. In contrast, Awareness of Intermedia and Diagnose IMS was notably lower, with Diagnose IMS showing the largest disparity rate, possibly due to limited promotion and patient skepticism[67]. Further matrix analyses and non-parametric tests were performed on subgroups stratified by age, residence, education duration, income level, and number of chronic diseases. The Kolmogorov-Smirnov test yielded p < 0.001 for all groups. For age-based grouping (Figure 4B), results showed that as age increases, the awareness, want, and adoption of information, intermedia, and diagnose all significantly decrease. This is consistent with the Kruskal-Wallis H test results (P < 0.001 for each group and between groups, not shown). For subgroups divided by region of residence of chronic disease patients (Figure 4C and Table 5), matrix analysis and the Mann-Whitney U test were performed. Only in the Diagnose Adoption was there no significant difference between city and village residents. In other aspects, city residents had significantly better utilization than village residents. The analysis according to education duration (Figure 4C and Table 6) demonstrated significant differences (P<0.001) in awareness, want, and adoption of IMS across subgroups. Multiple comparisons within groups, with Bonferroni correction for adjusted significance (Adj.P value)[68], still found statistical differences in several groups. We divided respondents by household per-capita annual income into underprivileged, generic, and affluent groups, and performed matrix analysis (Figure 4E) and non-parametric tests (Table 7). Results showed that in the adoption of informational IMS, there were significant differences between the underprivileged and affluent groups, as well as between the generic and affluent groups (Adj.P value <0.05). Regarding the intermediary IMS, the affluent group exhibited considerably lower adoption rates than the underprivileged and generic groups in certain items. Similar patterns were observed in the adoption of diagnostic IMS. Matrix analysis also revealed that the affluent group's utilization of various services was more skewed towards the lower-left (closer to the close group) compared to other groups. Finally, an analysis was conducted based on the number of chronic diseases patients suffered from. The matrix analysis results (Figure 4F) showed a trend towards the lower left in terms of patients' utilization of various types of IMS as the number of chronic diseases increased. The results of the Kruskal-Wallis H test indicated a remarkable difference among groups with different chronic disease counts only in the Diagnose Want (H=13.86, P < 0.001). Subsequently, the Bonferroni correction method was applied to calculate the adjusted significance for within-group comparisons. A notable difference was observed only between the subgroup with three or more chronic diseases and the subgroup with one chronic disease (Adj. P value = 0.001). This suggests that clinicians should enhance the promotion of IMS among patients with multiple chronic diseases during clinical diagnosis and treatment. 4.3 Normality test Since Structural Equation Modeling (SEM) assumes normally distributed data, a normality test was conducted prior to CFA and SEM analyses to ensure model reliability (Table 8). Per Kline’s (1998) criteria[69], data approximate normality if skewness and kurtosis absolute values fall within 3 and 8, respectively. All items except Us8 and Us10 met these thresholds. 4.4 Reliability, Validity, and Confirmatory Factor Analysis The CFA model (Figure 5A) was constructed to assess whether the observed variables accurately measured the latent variables. The contribution of each observed variable to the latent variables was determined by calculating the analytical factor loadings. The results indicated that the corrected model fit reached excellent standard ( x 2 =605.83, df = 454, x 2 / df = 1.33, RMSEA = 0.03, NFI = 0.97, CFI = 0.99, IFI = 0.99). Item reliability was assessed through unstandardized regression weights and standardized factor loadings. Most loadings exceeded 0.70[70], except for select PAM13 items with lower values. Cronbach’s Alpha and Construct Reliability (CR) minima were 0.917 and 0.919, respectively, indicating strong internal consistency [71]. Average Variance Extracted (AVE), Maximum Shared Squared Variance (MSV), and Average Shared Square Variance (ASV) were evaluated (Table 9). All AVE values exceeded 0.5 except for PAM13 (0.4–0.5, deemed acceptable[72, 73]), with MSV and ASV below AVE, confirming discriminant validity[74]. Additional validation is detailed in Table 10. In this study, a comprehensive examination of the measurement model for factors related to the usage of IMS was conducted. Based on the above results, it is reasonable to proceed with subsequent structural equation modeling analysis. 4.5 Structural Equation Model To be more flexible in analyzing the potential links between various latent variables and factors related to IMS, SEM was developed based on previously proposed hypotheses. Paths with P values greater than 0.05 were removed from the model based on the output results from AMOS software, indicating that the hypotheses associated with these paths were not supported. Through iteration, the final version of the model was established. The results of the model fit correction were x 2 =2897.30, df = 2389, x 2 / df = 1.21, RMSEA = 0.02, NFI = 0.92, CFI = 0.98, and IFI = 0.98, which reached the excellent level of fit criteria. Further analysis of the significant paths within the model was conducted to evaluate the hypotheses proposed earlier. The results (Table 11) showed that PU had a significant direct positive impact on the three stages of IMS utilization: Awareness (β=0.428, P<0.001), Want (β=0.534, P<0.001), and Adoption (β=0.225, P<0.01). Additionally, PU positively influenced Attitude (β=0.705, P<0.001), supporting H1a and H1b. eHealth Literacy significantly promoted the Adoption stage (β=0.323, P<0.001) and Attitude (β=0.112, P<0.01) towards IMS, verifying H2a and H2b. Patient activation had a notable positive effect on PEOU (β=0.205, P<0.001) and Attitude (β=0.063, P<0.05), supporting H3c and H3d. Age had a significant direct negative impact on the three stages of IMS utilization for chronic disease patients: Awareness (β=-0.182, P<0.001), Want (β=-0.162, P<0.001), and Adoption (β=-0.264, P<0.001). Additionally, age exerted inhibitory effects through the mediating effects of PEOU (β=-0.478, P<0.001) and patient activation (β=-0.13, P<0.01), supporting H4a, H4c, and H4f. Income level significantly promoted Want (β=0.093, P<0.05), supporting H5a. Education duration not only directly drove Awareness (β=0.197, P<0.001), Want (β=0.137, P<0.001), and Adoption (β=0.181, P<0.001) but also significantly promoted PU (β=0.602, P<0.01), PEOU (β=0.102, P<0.01), eHealth Literacy (β=0.106, P<0.001), and patient activation (β=0.15, P<0.01), indicating partial mediating effects and thereby verifying H6a, H6b, H6c, H6e, and H6f. It is worth noting that patient activation demonstrated a significant negative impact on Awareness (β=0.534, p<0.001), Want (β=0.534, p<0.01), and Adoption (β=0.534, p<0.05), which is contrary to H3a. PEOU exhibited an enhancing effect between income level and IMS usage (β=-0.123, P<0.001), representing a special form of H5c. Other significant relationships were not verified. The final structural equation model is presented in Figure 5B. Discussion This study developed an extended Technology Acceptance Model (TAM) tailored for chronic disease patients by integrating core components of the Information-Motivation-Behavioral Skills (IMB) model and addressing the specific needs of this population. The enhanced framework incorporates eHealth literacy as the information processing dimension, patient activation as the motivational driver, and demographic variables such as age, education duration, and income level. This multidimensional approach provides a robust lens to explore variations in technology adoption among chronic disease patients. Our findings reveal that Perceived Usefulness (PU) and eHealth literacy, alongside age and income level, exhibit significant positive correlations with Internet Medical Services (IMS) usage. We categorized IMS into three domains—Information, Intermedia, and Diagnose—and observed that chronic disease patients broadly embraced Information IMS, with minimal disparities across the sample. In contrast, Diagnose IMS showed lower adoption rates and pronounced disparities. Additionally, patients who were older, had shorter education durations, resided in rural areas, or managed multiple chronic conditions displayed reduced engagement across all IMS categories. Interestingly, affluent patients demonstrated lower IMS utilization compared to their underprivileged counterparts; a counterintuitive finding potentially explained by several factors. First, high-income individuals often enjoy greater access to premium offline healthcare resources, such as tertiary hospitals and specialist clinics[75, 76], resulting in lower demand and dependency on IMS. Secondly, the affluent group tends to have higher requirements for service quality[77], while the service quality of IMS may exhibit certain fluctuations, with variations in the professional level and service attitude of doctors on some platforms[78]. Additionally, they may place a strong emphasis on the privacy protection of personal health data and may have concerns about medical security risks such as data breaches during the transmission and storage of personal health data through the internet[79]. These potential issues suggest that the promotion of Internet medical services IMS for chronic disease patients should prioritize the improvement of medical quality[80]. Measures such as strengthening the training and management of internet medical doctors, establishing strict doctor admission and dynamic assessment mechanisms, and improving the quality evaluation and supervision mechanisms of internet medical services are essential. Furthermore, the assistance of AI in the future may enhance the diagnostic accuracy[81], potentially helping to rebuild trust among the high-income population. Moreover, it is important to enhance tiered service design and create high-end customized products tailored to the affluent group. This could include collaborating with top-tier hospitals to establish exclusive online consultation channels for experts and accessing international high-quality resources like the Mayo Clinic. Additionally, upgrading privacy protection technologies is crucial to alleviate patients' concerns about data security[82]. This study also constructed a SEM to quantify the actual influence levels of various factors on the three stages (awareness, want, and adoption) of IMS to test the preset hypotheses. The results indicated that PU and education duration both exerted a direct and positive impact on the awareness, want, and adoption of IMS, substantially promoting the gradual acceptance process of IMS among chronic disease patients. eHealth literacy played a notable direct and positive role in the final adoption stage of IMS, facilitating patients' more efficient use of IMS. However, age and patient activation levels demonstrated direct negative effects on these three stages, somewhat impeding the utilization of IMS by chronic disease patients. Specifically, the effect of patient activation was contrary to our hypothesis. This could be attributed to the fact that chronic disease patients with higher levels of patient activation often exhibit greater self-efficacy[83] in health, leading to increased confidence in self-managing their conditions. Consequently, this might reduce their reliance on technological tools[84], indirectly inhibiting IMS utilization behavior. Furthermore, the SEM pathway untangled that PEOU did not directly and significantly affect IMS usage among chronic disease patients but rather had an indirect positive impact through the chained mediation effect of PU. This might be attributed to the special attributes of medical services, where chronic disease patients tend to focus more on the benefits of technology to their physical health and have a relatively higher tolerance for technology ease of use when selecting new technologies[85]. Based on these findings, it is recommended that IMS development platforms prioritize the enhancement of patients' eHealth literacy. This could include designing dedicated training courses covering operational procedures such as searching for health information, viewing online reports, and online consultations, and placing them in prominent positions on the platform for easy access by patients. Simultaneously, it is suggested to actively promote the PU of IMS applications in chronic disease management through channels such as medical institution websites and social media, sharing successful cases to enhance PU and stimulate their interest and enthusiasm in using it. limitations The present study is not without limitations. Firstly, the sample was primarily drawn from chronic disease-designated hospitals in Jinan, representing a single geographical region. Chronic disease patients visiting these hospitals may possess certain specific characteristics (such as residing nearby), which could potentially influence the findings. Therefore, the results require further corroboration and support from additional related studies. It is also anticipated that scholars from other countries or regions can conduct further analyses using different chronic disease patient datasets, adjusting the model's framework to enhance its adaptability to a wider range of scenarios. Secondly, the current model validation is based on cross-sectional study data. Due to the non-continuity of cross-sectional studies, the analysis results can only reflect current relationships. However, subjects' attitudes towards IMS, patient activation, and eHealth literacy are subject to dynamic changes. The exact causal relationships between various study variables cannot be determined. Future studies could collect long-term follow-up information from chronic disease populations and utilize other types of research data to provide more in-depth supplementation to the proposed model. Thirdly, with the integration of new technologies such as AI into internet healthcare, there may be entirely new characteristics in the use of IMS by chronic disease patients in the future. More analytical scales for AI usage and AI acceptance could be incorporated into subsequent investigations, enabling research to better reflect the development trends of the future internet healthcare market and provide reliable references for more subsequent studies. Abbreviations IMS Internet Medical Services TAM Technology Acceptance Model IMB Information-Motivation-Behavioral Skills PU Perceived Usefulness PEOU Perceived Ease of Use SEM Structural Equation Modeling CFA Confirmatory Factor Analysis PAM Patient Activation Measure eHEALS RMSEA Root Mean Square Error of Approximation NFI Normed Fit Index CFI Comparative Fit Index IFI Incremental Fit Index AVE Average Variance Extracted MSV Maximum Shared Squared Variance ASV Average Shared Square Variance AI Artificial Intelligence Declarations Acknowledgments The authors would like to thank the participants in the study referred to in this paper and each of the investigators for their conscientious and meticulous work. Authors' contributions XYL contributed to the conception, design of the study and the revision of the article. RQ# and RS# contributed to the analysis interpretation and the drafting of the article. XYW# contributed to the conception and design of the study. JHF and YYY contributed to the data collection. All the authors approved the final manuscript. #Contributed equally. Funding This work was jointly supported by grants from the 2024 Shandong Province Postgraduate Education and Teaching Reform Research Project (XYJG2024010). Data Availability The data sets generated and analyzed during this study are available from the corresponding author on reasonable request. Conflicts of Interest None declared. References Padeiro M, Santana P, Grant M. Chapter 1 - Global aging and health determinants in a changing world. In: Oliveira PJ, Malva JO, editors. Aging. Academic Press; 2023. p. 3–30. Su B, Li D, Xie J, Wang Y, Wu X, Li J, et al. Chronic Disease in China: Geographic and Socioeconomic Determinants Among Persons Aged 60 and Older. Journal of the American Medical Directors Association. 2023;24:206-212.e5. STATISTICAL COMMUNIQUÉ OF THE PEOPLE’S REPUBLIC OF CHINA ON THE 2023 NATIONAL ECONOMIC AND SOCIAL DEVELOPMENT. 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Evaluating Structural Equation Models with Unobservable Variables and Measurement Error: A Comment. Journal of Marketing Research. 1981;18:375–81. Zhang Z, Zheng L. Consumer community cognition, brand loyalty, and behaviour intentions within online publishing communities: An empirical study of Epubit in China. Learned Publishing. 2021;34:116–27. Cheung GW, Cooper-Thomas HD, Lau RS, Wang LC. Reporting reliability, convergent and discriminant validity with structural equation modeling: A review and best-practice recommendations. Asia Pac J Manag. 2024;41:745–83. Xing L, Chen Q, Liu Y, He H. Evaluating the accessibility and equity of urban health resources based on multi-source big data in high-density city. Sustainable Cities and Society. 2024;100:105049. ACCESS TO HEALTHCARE AND DISPARITIES IN ACCESS. In: 2021 National Healthcare Quality and Disparities Report [Internet]. Agency for Healthcare Research and Quality (US); 2021. Strandberg C, Wahlberg O, Öhman P. Challenges in serving the mass affluent segment: Bank customer perceptions of service quality. Managing Service Quality. 2012;22. Han T, Wei Q, Wang R, Cai Y, Zhu H, Chen J, et al. Service Quality and Patient Satisfaction of Internet Hospitals in China: Cross-Sectional Evaluation With the Service Quality Questionnaire. J Med Internet Res. 2024;26:e55140. chloetejada. How high-net-worth individuals can mitigate cybersecurity risks to protect their assets. RBC Wealth Management. 2024. https://www.rbcwealthmanagement.com/en-us/insights/how-high-net-worth-individuals-can-mitigate-cybersecurity-risks-to-protect-their-assets. Accessed 19 Mar 2025. Nguyen NX, Tran K, Nguyen TA. Impact of Service Quality on In-Patients’ Satisfaction, Perceived Value, and Customer Loyalty: A Mixed-Methods Study from a Developing Country. Patient Prefer Adherence. 2021;15:2523–38. McGenity C, Clarke EL, Jennings C, Matthews G, Cartlidge C, Freduah-Agyemang H, et al. Artificial intelligence in digital pathology: a systematic review and meta-analysis of diagnostic test accuracy. npj Digit Med. 2024;7:1–19. Wang C, Zhang N, Wang C. Managing privacy in the digital economy. Fundamental Research. 2021;1:543–51. Mirmazhari R, Ghafourifard M, Sheikhalipour Z. Relationship between patient activation and self-efficacy among patients undergoing hemodialysis: a cross-sectional study. Renal Replacement Therapy. 2022;8:40. G A, Ml R, D C, G G, G D, S B. The association between patient activation and healthcare resources utilization: a systematic review and meta-analysis. Public health. 2022;210. Patients’ Acceptance of Smartphone Health Technology for Chronic Disease Management: A Theoretical Model and Empirical Test. JMIR mHealth and uHealth. 2017;5. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6270951","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":431668981,"identity":"4ae51a18-03e6-4dd0-83f6-4ca2be5ac629","order_by":0,"name":"Rui Qiu","email":"","orcid":"","institution":"The Second School of Clinical Medicine, Cheeloo College of Medicine, Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Qiu","suffix":""},{"id":431668982,"identity":"7a59911d-d6de-4809-8f22-f53ff89a600a","order_by":1,"name":"Rui Song","email":"","orcid":"","institution":"The Second School of Clinical Medicine, Cheeloo College of Medicine, Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Rui","middleName":"","lastName":"Song","suffix":""},{"id":431668983,"identity":"75996898-6d20-4632-a751-09156ffaf7d7","order_by":2,"name":"Xiaoyi Wu","email":"","orcid":"","institution":"Department of Neurology, the Second Hospital, Cheeloo College of Medicine, Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Xiaoyi","middleName":"","lastName":"Wu","suffix":""},{"id":431668984,"identity":"08f089cd-328d-445f-a2af-55c8919351f5","order_by":3,"name":"Jiahui Feng","email":"","orcid":"","institution":"Department of Neurology, the Second Hospital, Cheeloo College of Medicine, Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Jiahui","middleName":"","lastName":"Feng","suffix":""},{"id":431668993,"identity":"50c5a4d7-4ce5-46a7-88c9-35fd4f8f8ea4","order_by":4,"name":"Yingyue Yang","email":"","orcid":"","institution":"Department of Geriatrics, the Second Hospital, Cheeloo College of Medicine, Shandong University","correspondingAuthor":false,"prefix":"","firstName":"Yingyue","middleName":"","lastName":"Yang","suffix":""},{"id":431668994,"identity":"8d22c7c2-f9f8-4367-9cf1-5fa06467d767","order_by":5,"name":"Xiaoying Lin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+ElEQVRIiWNgGAWjYDACZhBhwMDAx8zA+ICBDcRLIFILGzMDswFxWmAAqJhNgigtBseZHz7mKbhj18bOe6zyR9lhBn72HAOGnztwa5FsZjM25jF4ltzGzJd2m+fcYQbJnjcGjL1ncGvhZ2Ywk+YxOJzMxsxjdpux7TCDwY0cA2bGNjxeYGb/BtdS+BOoxZ6QFn6gSpAWO5AWBl6QLRIEtEg28xQbzjE4nADUYizNcy6dR+LMs4KDvXi0GJw/vvHBmz+H7fn5zxh+/FFmLcffnrzxwU88WkCAiYeBIbEByuEBEQfwa2BgYPzBwGBPSNEoGAWjYBSMYAAACzVDrn6ewNsAAAAASUVORK5CYII=","orcid":"","institution":"The Second Hospital, Cheeloo College of Medicine, Shandong University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoying","middleName":"","lastName":"Lin","suffix":""}],"badges":[],"createdAt":"2025-03-20 15:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6270951/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6270951/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":79217059,"identity":"acf7d79e-a97d-43cd-b9ad-3a205e87d5a3","added_by":"auto","created_at":"2025-03-25 19:03:01","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":244567,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eProposed conceptual model.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6270951/v1/03a66e95185d4346a1fde9e0.png"},{"id":79217533,"identity":"8d928e1b-5b81-4690-b35b-dea3949d81bd","added_by":"auto","created_at":"2025-03-25 19:11:01","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":628463,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eImproving directions for each region in the awareness, want, and adoption gap ratio (AWAG)segment matrix. \u003c/strong\u003eAb = awareness-bias; G = generic; S = strong; Wb = want-bias\u003c/p\u003e","description":"","filename":"Figure2.ImprovingdirectionsforeachregionintheawarenesswantandadoptiongapratioAWAGsegmentmatrix.png","url":"https://assets-eu.researchsquare.com/files/rs-6270951/v1/ad61cd97c247881d9ca9cb54.png"},{"id":79217764,"identity":"81af45d5-af02-4bce-84bf-213ba94eb72b","added_by":"auto","created_at":"2025-03-25 19:19:01","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":3406317,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eStacked bar chart and correlation heatmap.\u003c/strong\u003e (A) Stacked bar chart of different sections of the questionnaire (N=520); (B) Correlation analysis of factors related to awareness, want, and adoption of Internet medical services.\u003c/p\u003e","description":"","filename":"Figure3.Stackedbarchartandcorrelationheatmap.png","url":"https://assets-eu.researchsquare.com/files/rs-6270951/v1/292bc2237eb01353e0c07e6e.png"},{"id":79217075,"identity":"11079fec-ca97-402b-9139-655cb8efab81","added_by":"auto","created_at":"2025-03-25 19:03:01","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":5297301,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eAWAG matrix analysis of different subgroups of IMS in surveyed patients. \u003c/strong\u003e(A) Overall situation; (B)-(F) Grouped by: (B) age; (C) region of residence; (D) education duration; (E) income level; (F) number of chronic diseases.\u003c/p\u003e","description":"","filename":"Figure4AWAGmatrixanalysisofdifferentsubgroupsofIMSinsurveyedpatients.png","url":"https://assets-eu.researchsquare.com/files/rs-6270951/v1/099ed74c1a9995910405ee7b.png"},{"id":79217083,"identity":"17c1317c-0baa-4487-b0eb-b4bf701e0c6d","added_by":"auto","created_at":"2025-03-25 19:03:02","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":19455032,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eConfirmatory factor analysis (CFA) and final structural equation model (SEM).\u003c/strong\u003e(A) The model diagram of one-dimensional CFA; (B) SEM that retains only significant paths (Values in the graph are normalized regression coefficients)\u003c/p\u003e","description":"","filename":"Figure5ConfirmatoryfactoranalysisCFAandfinalstructuralequationmodelSEM.png","url":"https://assets-eu.researchsquare.com/files/rs-6270951/v1/71fd4c613489034e3bcfecd9.png"},{"id":79218387,"identity":"0e374c54-e431-4ed3-b567-58191dfd84c9","added_by":"auto","created_at":"2025-03-25 19:35:18","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":29166042,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6270951/v1/3117cded-5d03-4650-8097-c4f3193cc5ef.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eAssessing Awareness, Want, and Adoption of Internet Medical Services Among Chronic Disease Patients in China:A Structural Equation Model and Matrix Analysis\u003c/p\u003e","fulltext":[{"header":" Introduction","content":"\u003cp\u003eSince the 21st century, the global population structure has been undergoing unprecedented changes, mainly characterized by the intensifying aging trend and the rising incidence of chronic diseases[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. These shifts have become a common challenge faced by governments worldwide, with China's situation being particularly prominent[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. As projected by the National Bureau of Statistics, China's population aged 65 and above will approach 210\u0026nbsp;million by 2025, constituting 15% of the total population -- a threshold marking entry into a deeply aged society[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. This demographic shift coincides with a high chronic disease burden, evidenced by the 2023 China Health Statistics Yearbook reporting a 34.29% national prevalence of chronic conditions[\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Such changes in population structure and disease spectrum pose dual challenges to the traditional healthcare system: firstly, the management of chronic diseases requires long-term and continuous medical services[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]; secondly, structural mismatches in healthcare capacity, ​particularly the scarcity of specialized geriatric care and chronic disease management infrastructure, ​compromise service continuity for multimorbid elderly patients[\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eInternet Medical Services (IMS), an innovative healthcare resource leveraging internet platforms, offers a promising approach to optimizing health resource allocation and fulfilling the growing demand for high-quality medical care[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Through IMS, patients can seamlessly perform tasks such as querying health information, scheduling appointments, interpreting medical reports, and conducting follow-up consultations from home, thereby alleviating congestion in public hospitals[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. Evidence from empirical studies suggests that IMS extends the duration of doctor-patient interactions relative to traditional healthcare delivery, enhancing the equitable distribution of health resources and harmonizing supply with demand[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. With its technical characteristics that transcend spatiotemporal limitations, IMS can theoretically optimize the layout of medical resources and enhance service accessibility. However, the penetration rate of IMS among chronic disease populations is relatively low, presenting a contradictory phenomenon of \"high demand and low penetration,\" which further exacerbates the digital health divide and restricts the achievement of health equity[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Moreover, as artificial intelligence (AI) technologies become increasingly embedded in IMS, this divide is poised to widen further.\u003c/p\u003e \u003cp\u003eIn addressing this issue, existing research often focuses on the technical architecture of IMS or policy analyses across various countries and regions[\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. However, two critical dimensions are relatively overlooked: Firstly, there is a lack of systematic research on the characteristics of technology adoption behaviors among patients with chronic diseases. The traditional Technology Acceptance Model (TAM) finds it difficult to explain this group's unique cognitive and behavioral decision-making processes. Second, prevailing research often adopts a technologically deterministic stance, sidelining the interactive roles of eHealth literacy, patient activation, and sociodemographic factors in shaping adoption patterns[\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Such theoretical limitations make it challenging for current strategies to effectively address the formation mechanism of the digital divide related to IMS utilization among chronic disease patients.\u003c/p\u003e \u003cp\u003eTo bridge these deficiencies, our study introduces a novel chronic disease-focused Extended Technology Acceptance Model (c-TAM). Based on the traditional TAM model, we integrate the core dimensions of the Information-Motivation-Behavioral Skills Model (IMB)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e], introducing eHealth literacy and patient activation level as key external variables. Additionally, we incorporate sociodemographic factors such as age, income level, and education duration. This improved model breaks through the unidirectional technical cognitive path of the traditional TAM, constructing a three-dimensional analytical framework that includes capability building, behavioral drivers, and social gradients. This enables us to systematically untangle the deep-seated obstacles to IMS adoption among chronic disease patients. By analyzing the coupling relationships among various elements using matrix analysis and structural equation modeling (SEM), this study aims to provide a new perspective for developing differentiated digital health intervention strategies. Simultaneously, it offers empirical support for the implementation of the Innovative Care for Chronic Conditions (ICCC) proposed by the WHO[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003cb\u003eTheoretical framework and hypothesis development\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe Technology Acceptance Model (TAM), introduced by Fred Davis in 1989[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], provides a robust framework for explaining and predicting user acceptance of information technology systems. Rooted in the Theory of Reasoned Action (TRA)[\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e], it highlights perceived usefulness (PU) and perceived ease of use (PEOU) as key factors in technology adoption, capturing the core logic of technology promotion[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. PU reflects users' belief that technology enhances work efficiency or quality of life, while PEOU indicates users' perception of a technology's ease of use. Users are more inclined to adopt technologies perceived as beneficial (PU) or easy to use (PEOU)[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. Over the decades, TAM has been widely applied in information technology research, particularly in the promotion and use of e-commerce, social media, and IMS[\u003cspan additionalcitationids=\"CR21\" citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e], but at the same time, its limitations in terms of insufficient explanatory power for external variables have become increasingly apparent[\u003cspan additionalcitationids=\"CR24\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eDeveloped by American psychologist M.J. Fisher, the Information-Motivation-Behavioral Skills (IMB) model elucidates and forecasts health-related behaviors[\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. According to the IMB, information, motivation, and behavioral skills are the three core factors influencing health behaviors[\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Information refers to the knowledge and cognitive content related to health behaviors acquired by individuals, which aligns with the abilities encompassed by eHealth literacy in terms of obtaining, comprehending, and evaluating health information in a digital environment under the umbrella of IMS[\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. Motivation, on the other hand, represents the intrinsic and extrinsic forces that drive individuals to engage in healthy behaviors, with intrinsic motivation paralleling the concept of patient activation.\u003c/p\u003e \u003cp\u003eThis study extends the TAM framework by integrating eHealth literacy and patient activation, drawn from the IMB model, and tailoring them to chronic disease patients. Additionally, it considers user characteristics such as age, income level, and education duration to enhance the explanatory power of IMS utilization among this population. Based on a literature review, the following hypotheses are proposed:\u003c/p\u003e \u003cp\u003e \u003cb\u003ePerceived Usefulness (PU) and Perceived Ease of Use (PEOU)\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePU and PEOU serve as cornerstone variables in technology acceptance, reflecting users' cognitive evaluations of functional value and operational convenience in technological adoption[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. In chronic disease management contexts, PU manifests in patients\u0026rsquo; evaluations of IMS effectiveness for controlling disease progression, preventing complications, and improving quality of life (e.g., accessing tailored treatment plans via online consultations)[\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e]. PEOU encompasses platform interface intuitiveness, operational fluency, and information accessibility[\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. Within TAM, attitude functions as a critical mediator between these cognitive constructs and usage behavior, embodying users\u0026rsquo; trust and emotional orientation toward the technology [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Therefore, we hypothesize:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH1a.\u003c/b\u003e PU exerts a direct positive influence on chronic disease patients' utilization of IMS;\u003c/p\u003e \u003cp\u003e \u003cb\u003eH1b.\u003c/b\u003e PU positively affects such use, mediated in part by a positive attitude;\u003c/p\u003e \u003cp\u003e \u003cb\u003eH1c.\u003c/b\u003e PEOU directly and positively impacts IMS utilization among chronic disease patients utilization of IMS;\u003c/p\u003e \u003cp\u003e \u003cb\u003eH1d.\u003c/b\u003e PEOU positively affects such use, mediated in part by a positive attitude.\u003c/p\u003e \u003cp\u003e \u003cb\u003eeHealth Literacy\u003c/b\u003e \u003c/p\u003e \u003cp\u003eeHealth literacy, an evolving construct, refers to individuals\u0026rsquo; proficiency in engaging with health information exchanges and applying this knowledge to manage health and address challenges[\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Patients with superior eHealth literacy can more effectively translate health information into actual health management actions[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. For instance, by using the self-assessment module for symptoms in IMS, patients can swiftly screen key health information to aid follow-up visit decisions[\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This proficiency not only heightens their PU of IMS[\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e, \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] but may also, through hands-on practice, boost technical proficiency, lower usage barriers, and thus strengthen PEOU[\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Furthermore, patients with strong eHealth literacy often develop effective information-filtering strategies [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. This enables them to better select IMS items suited to their situation, avoid irrelevant information, and considerably increase their trust and usage intention in these services, fostering a positive attitude. This yields the following hypotheses:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH2a.\u003c/b\u003e eHealth literacy directly promotes the use of IMS among chronic disease patients;\u003c/p\u003e \u003cp\u003e \u003cb\u003eH2b.\u003c/b\u003e eHealth literacy enhances IMS utilization by amplifying PU;\u003c/p\u003e \u003cp\u003e \u003cb\u003eH2c.\u003c/b\u003e eHealth literacy promotes such use by positively impacting PEOU;\u003c/p\u003e \u003cp\u003e \u003cb\u003eH2d.\u003c/b\u003e eHealth literacy promotes such use by improving their attitude.\u003c/p\u003e \u003cp\u003e \u003cb\u003ePatient activation\u003c/b\u003e \u003c/p\u003e \u003cp\u003ePatient activation reflects patients' knowledge, skills, and confidence in self-managing health conditions, along with their capacity to participate in medical decision-making[\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Highly activated patients with disease-specific understanding are more likely to perceive the long-term management benefits provided by IMS[\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e], which is aligned with TAM\u0026rsquo;s \u0026ldquo;cognition-driven adoption\u0026rdquo; mechanism[\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. They may also reduce perceived technical barriers through active learning (e.g., practicing with video simulation systems) and enhance technical trust via higher self-efficacy [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. Notably, these patients are more willing and likely to boost their eHealth literacy through learning and practice, optimizing IMS use[\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. Consequently, we posit:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH3a.\u003c/b\u003e Patient activation directly promotes the utilization of IMS by patients with chronic diseases;\u003c/p\u003e \u003cp\u003e \u003cb\u003eH3b.\u003c/b\u003e Patient activation facilitates IMS utilization through PU mediation;\u003c/p\u003e \u003cp\u003e \u003cb\u003eH3c.\u003c/b\u003e Patient activation enhances IMS utilization via PEOU mediation;\u003c/p\u003e \u003cp\u003e \u003cb\u003eH3d.\u003c/b\u003e Patient activation facilitates such use mediated through attitudes;\u003c/p\u003e \u003cp\u003e \u003cb\u003eH3e.\u003c/b\u003e Patient activation facilitates such use via eHealth literacy elevation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eAge\u003c/b\u003e \u003c/p\u003e \u003cp\u003eAdvanced age may attenuate PEOU through declining adaptability (e.g., difficulties with touchscreen navigation) and reduced health information processing efficiency[\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], the digital divide further compounds this effect by potentially suppressing eHealth literacy[\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e]. Elderly patients also tend to rely on traditional face-to-face treatment, question the reliability of virtual services, and thus have a more negative attitude towards accepting IMS[\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. Additionally, with a decreased sense of control over their health (e.g., \"I can only follow the doctor's advice after being ill for a long time\"), they lack the motivation to seek new technologies, lowering their patient activation and negatively impacting their acceptance of IMS[\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Based on this, the hypotheses include:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH4a.\u003c/b\u003e Age demonstrates direct inhibitory effects of IMS use by chronic disease patients;\u003c/p\u003e \u003cp\u003e \u003cb\u003eH4b.\u003c/b\u003e Age restrains such use by reducing PU;\u003c/p\u003e \u003cp\u003e \u003cb\u003eH4c.\u003c/b\u003e Age restrains such use by weakening PEOU;\u003c/p\u003e \u003cp\u003e \u003cb\u003eH4d.\u003c/b\u003e Age restrains such use through the mediating effect of negative attitudes;\u003c/p\u003e \u003cp\u003e \u003cb\u003eH4e.\u003c/b\u003e Age inhibits such use by suppressing eHealth literacy;\u003c/p\u003e \u003cp\u003e \u003cb\u003eH4f.\u003c/b\u003e Age curtails IMS utilization by reducing patient activation.\u003c/p\u003e \u003cp\u003e \u003cb\u003eIncome Level\u003c/b\u003e \u003c/p\u003e \u003cp\u003eEconomic advantage enables access to premium services (e.g., remote specialist consultation) that strengthen PU while device accessibility (e.g., 5G smartphones) improves PEOU and eHealth literacy [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Moreover, high-income groups place a higher value on their time and may be more willing to use IMS with lower time costs[\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e], such as viewing test reports online. This generates:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH5a.\u003c/b\u003e Income level directly elevates the application of IMS among patients with chronic diseases;\u003c/p\u003e \u003cp\u003e \u003cb\u003eH5b.\u003c/b\u003e Income level elevates such application through enhanced PU perception;\u003c/p\u003e \u003cp\u003e \u003cb\u003eH5c.\u003c/b\u003e Income level elevates such application through improved PEOU;\u003c/p\u003e \u003cp\u003e \u003cb\u003eH5d.\u003c/b\u003e Income level elevates such application through a favorable attitude;\u003c/p\u003e \u003cp\u003e \u003cb\u003eEducation Duration\u003c/b\u003e \u003c/p\u003e \u003cp\u003eExtended education facilitates health technology (e.g., electronic medical record interpretation), thereby enhancing PU and PEOU[\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Additionally, greater cognitive reserves may amplify eHealth literacy and patient activation[\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e]. We hypothesize:\u003c/p\u003e \u003cp\u003e \u003cb\u003eH6a.\u003c/b\u003e Education duration directly promotes the application of IMS by patients with chronic diseases;\u003c/p\u003e \u003cp\u003e \u003cb\u003eH6b.\u003c/b\u003e Education duration positively influences such application through strengthening PU;\u003c/p\u003e \u003cp\u003e \u003cb\u003eH6c\u003c/b\u003e. Education duration positively influences such application through optimized PEOU;\u003c/p\u003e \u003cp\u003e \u003cb\u003eH6d.\u003c/b\u003e Education duration positively influences such application through positive attitude mediation;\u003c/p\u003e \u003cp\u003e \u003cb\u003eH6e.\u003c/b\u003e Education duration positively influences such application through elevating eHealth literacy;\u003c/p\u003e \u003cp\u003e \u003cb\u003eH6f.\u003c/b\u003e Education duration positively influences such applications by enhancing patient activation.\u003c/p\u003e \u003cp\u003eBy synthesizing these theoretical perspectives and hypothesized relationships, this study constructs a comprehensive conceptual framework, depicted in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Method","content":"\u003cdiv id=\"Sec3\"\u003e\n \u003ch2\u003e3.1 Study population\u003c/h2\u003e\n \u003cp\u003eThis cross-sectional study investigated Internet Medical Services (IMS) by categorizing their functional resources into three domains: Information, Intermedia, and Diagnose. The adoption process among chronic disease patients was segmented into progressive stages—awareness, want, and adoption—to evaluate usage patterns and determinants, with the overarching goal of facilitating IMS dissemination within this group.\u003c/p\u003e\n \u003cp\u003eConsidering the prolonged follow-up needs of chronic disease patients, we conducted a random survey among individuals attending chronic disease sentinel hospitals in Jinan, Shandong, China. To ensure data precision, participants were screened using the following inclusion and exclusion criteria:\u003c/p\u003e\n \u003cp\u003eInclusion criteria: 1) Confirmed diagnosis of prevalent chronic conditions (e.g., cardiovascular and cerebrovascular diseases including hypertension, stroke, and coronary heart disease; chronic respiratory diseases such as chronic obstructive pulmonary disease, bronchial asthma, chronic heart failure, and chronic respiratory failure; diabetes; and metabolic-associated fatty liver disease) by qualified physicians, based on clinical symptoms and diagnostic assessments. 2) Conscious and able to understand the questionnaire content and answer accurately by themselves or with the help of others. 3) Voluntary participation with signed informed consent following a thorough explanation of the study’s objectives and procedures.\u003c/p\u003e\n \u003cp\u003eExclusion criteria: 1) Patients with other serious illnesses or unstable disease states. 2) Patients with severe cognitive impairments or severe mental illnesses who cannot understand or cooperate with the survey, as well as patients with language barriers. 3) Patients who do not have relevant chronic diseases. 4) Those who refuse to cooperate for personal reasons.\u003c/p\u003e\n \u003cp\u003eA simple random sampling method was utilized, with the minimum sample size calculated using the formula[53]:\u003c/p\u003e\n \u003cdiv id=\"Equa\"\u003e\n \u003cdiv id=\"FileID_Equa\" name=\"EquationSource\"\u003e$$\\:n=\\frac{{Z}_{1-\\alpha\\:/2}^{2}\\times\\:p(1-p)}{{\\delta\\:}^{2}}$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003eWhere p represents the percentage of the population; δ represents the allowable sampling error; and α represents the significance level.\u003c/p\u003e\n \u003cp\u003eIn this study, the confidence level was set to 95%, the allowable error range was 5%, and the overall percentage was set to 50% based on relevant research. According to the formula, 385 samples can meet the research requirements.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\"\u003e\n \u003ch2\u003e3.2 Questionnaire Design\u003c/h2\u003e\n \u003cp\u003eAn extensive literature review, encompassing domestic and international sources, guided the development of a standardized Chinese questionnaire, which was pre-tested in a pilot survey. Refinements were implemented based on identified issues, resulting in an optimized instrument. This questionnaire was administered to chronic disease patients in Jinan, Shandong Province, from June to October 2023, collecting data on demographics, IMS awareness, want, adoption, and the Patient Activation Measure (PAM). Specific items are detailed in Tables 1 and 2 (excluding demographics), with Table 1 employing a 5-point Likert scale and Table 2 using dichotomous responses.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMeasurement items of the 5-point Likert scale form construct\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConstruct\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eReference\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived usefulness\u003c/p\u003e\n \u003cp\u003e(PU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[17]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInternet medical services are useful in daily health management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThere are significant advantages of using Internet medical services to better manage your health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUsing Internet medical services is beneficial to me\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUsing Internet medical services is of high value to my healthcare\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePerceived Ease of\u003c/p\u003e\n \u003cp\u003eUse (PEOU)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[17]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePE1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLearning how to use Internet medical services was easy for me\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMy interactions using the Internet to search for health information were clear and easy to understand\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePE3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUsing the Internet is very flexible and easy to use when it comes to accessing health information\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePE4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverall, Internet medical services were easy to use\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitude towards\u003c/p\u003e\n \u003cp\u003eInternet medical\u003c/p\u003e\n \u003cp\u003eservices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[17]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAt1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUsing Internet medical services is a good idea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAt2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUsing Internet medical services is a wise decision and is recommended\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAt3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI like using internet medical services\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePatient Activation\u003c/p\u003e\n \u003cp\u003eMeasure 13\u003c/p\u003e\n \u003cp\u003e(PAM13)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[54]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOverall, I am the person responsible for looking after my health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTaking an active role in my health care is the most important thing that affects my health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI believe I can prevent or reduce problems related to my health\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI know what each of my prescribed medicines does\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI am confident that I can tell if I need to go to the doctor or if I can handle a health problem on my own\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI believe I can tell my doctor about my concerns, even if he or she doesn't ask\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI am confident that I can insist on medical treatment that I may need to do at home\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI understand my health problems and their causes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI know what treatments are available for my health problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI have been able to maintain (keep up with) lifestyle changes such as eating right or exercising\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI know how to prevent my health problems\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI believe I can figure out solutions when new problems with my health arise\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI believe I can keep up with lifestyle changes, such as eating right and exercising, even during times of stress\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe eHealth Literacy Scale (eHEALS)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[55]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI know how to go online to find useful health resource information\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI know how to use the Internet to answer my health questions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI know what health resource information is available on the Internet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI know where to get useful information about health resources on the Internet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI know how to use the information I get about health resources on the Internet to help myself\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI have the skills to evaluate good and bad information about health resources on the Internet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI can differentiate between high-quality and low-quality health resource information on the Internet\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eI am confident in using web-based information to make health-related decisions\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab2\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eMeasurement items of the dichotomous scale form construct\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConstruct\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItems\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eItem sources\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAwareness/Want/Adoption of IMS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e[56]\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAw1/Wa1/Ad1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollect disease/health information online\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAw2/Wa2/Ad2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollect doctor/hospital information online\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAw3/Wa3/Ad3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOnline collection of medical evaluation information\u003c/p\u003e\n \u003cp\u003e(patient's evaluation of doctors)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAw4/Wa4/Ad4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOnline consultation (graphic consultation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAw5/Wa5/Ad5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunicate with patients in groups or forums\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAw6/Wa6/Ad6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAppointment booking online\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAw7/Wa7/Ad7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOnline payment of medical fees\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAw8/Wa8/Ad8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eView electronic medical records and\u003c/p\u003e\n \u003cp\u003eexamination reports online\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAw9/Wa9/Ad9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMake an appointment for examination or surgery online\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAw10/Wa10/Ad10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePurchase medicines (not healthcare products) online\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAw11/Wa11/Ad11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOnline chronic disease monitoring and management\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAw12/Wa12/Ad12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMake an appointment for hospitalization online\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003e3.3 Date Collection\u003c/h2\u003e\n \u003cp\u003eData collection was completed by 35 volunteers from Shandong University. Before the formal survey at designated chronic disease hospitals, a training session ensured volunteers were well-versed in the questionnaire and survey protocols. During the training, emphasis was placed on the importance of standardized questioning and consistent responding. Additionally, it was confirmed that all surveyors had completed coursework in each surveyor had completed coursework in public health management to guarantee their professional expertise. To maintain the quality of the survey, all completed questionnaires were submitted to another survey team leader for quality inspection and signed confirmation.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\"\u003e\n \u003ch2\u003e3.4 Data Analysis\u003c/h2\u003e\n \u003cp\u003eStatistical analysis was performed using SPSS 27.0.1.0 software[57]. Continuous variables were presented as means and standard deviations (SD), while categorical data were expressed as frequencies and percentages. Differences across groups were assessed using the Mann-Whitney U and Kruskal-Wallis H tests, with supplementary analyses and visualizations conducted in Origin Pro 2025 (version 10.2.0.196). Statistical significance was set at p \u0026lt; 0.05.\u003c/p\u003e\n \u003cp\u003eTo investigate digital divide variations within IMS categories, we employed the AWAG segmented matrix method, introduced by Liang T.H.[56, 58]. The method analyzed differences in the digital divide among various categories of IMS through awareness gap, want gap, adoption gap, and utilization gap ratios. The utilization gap ratio was quantified using the following formula:\u003c/p\u003e\n \u003cdiv id=\"Equb\"\u003e\n \u003cdiv id=\"FileID_Equb\" name=\"EquationSource\"\u003e$$\\:g\\left(x\\right)=\\frac{\\text{min}\\left(\\text{Pr}\\left(A\\left(x\\right)\\right),\\text{Pr}\\left(W\\left(x\\right)\\right)\\right)-\\text{Pr}\\left(U\\left(x\\right)\\right)}{\\text{min}\\left(\\text{Pr}\\left(A\\left(x\\right)\\right),\\text{Pr}\\left(W\\left(x\\right)\\right)\\right)}\\times\\:100\\%$$\u003c/div\u003e\n \u003c/div\u003e\n \u003cp\u003ewhere Pr(A(x)) represents awareness rate for eHealth servicex; Pr(W(x)) represents the want rate for eHealth servicex; and Pr(U(x)) represents the adoption rate of eHealth servicex.\u003c/p\u003e\n \u003cp\u003eNext, integrating the consumer purchase decision model[59] and the technology adoption lifecycle theory[60], a matrix is constructed using 50% as the midpoint to divide both the awareness rate and the demand rate into two levels. Consequently, the AWAG matrix is segmented into four categories: the open group, the awareness-defective group, the demand-defective group, and the closed group. Furthermore, these were further subdivided into strong, awareness-defective, demand-defective, and general subcategories using 15% and 85% thresholds (see Fig. 2).\u003c/p\u003e\n \u003cp\u003eIn the AWAG matrix, group centers’ coordinates represent awareness and want levels, with circle radii (scaled by a factor of 30 for enhanced visualization) reflecting gap rates. IMS services were classified into Information, Intermedia, and Diagnose subgroups (Table 3) [8, 61].\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 3\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eClassification of digital medical service (DMS).\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"2\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCategories\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDigital medical service (DMS)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"4\"\u003e\n \u003cp\u003eInformation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollect disease/health information online\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollect doctor/hospital information online\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCollect medical reviews (patient comments about doctors) online\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCommunicate with patients in groups or forums\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"6\"\u003e\n \u003cp\u003eIntermedia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMake an appointment online\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePay medical fees online\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eView electronic medical records and test reports online\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBook an appointment for an examination or surgery online\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMake an appointment for hospitalization online\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBuy medicines (not healthcare products) online\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eDiagnose\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eConsult online (graphical consultation)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMonitor and manage a chronic condition online\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab4\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 4\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cstrong\u003eThe Sociodemographic characteristics of chronic disease patients (N = 520)\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"3\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eNumber (%)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e292 (56.15%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e228 (43.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge (years)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e≤ 49\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e96 (18.46%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e50–69\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e311 (59.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e≥ 70\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e113 (21.73%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarital status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e16 (3.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e471 (90.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3 (0.58%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWidowed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e30 (5.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRegion of residence\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e409 (78.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVillage\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e111 (21.35%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYears of education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e≤ 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e178 (34.23%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7–12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e254 (48.85%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e≥ 13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e88 (16.92%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eVery poor or underprivileged\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e233 (44.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e220 (42.31%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbove-average or relatively affluent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e67 (12.88%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSelf-assessment of health status\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtremely poor or poor\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e205 (39.42%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAverage level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e238 (45.77%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGood or excellent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e77 (14.81%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNumber of chronic diseases\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e302 (58.08%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e149 (28.65%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e≥ 3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e69 (13.27%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab5\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 5\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eNon-Parametric Test Results by Residence\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eRegion of residence\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCity (M, IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVillage (M, IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eZ\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformation Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75(0.50,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75(0.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.444\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformation Want\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00(0.25,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50(0.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformation Adoption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25(0.00,0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00(0.00,0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.974\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntermedia Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50(0.33,0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50(0.00,0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.706\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntermedia Want\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67(0.33,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50(0.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.657\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntermedia Adoption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33(0.00,0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00(0.00,0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnose Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50(0.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50(0.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.907\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnose Want\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50(0.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00(0.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.045\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnose Adoption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00(0.00,0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00(0.00,0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.633\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"5\"\u003eNote: M = Median, IQR = Interquartile Range.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab6\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 6\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Parametric Test Results by Education Duration\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003cp\u003eDuration\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e≤ 6\u003c/p\u003e\n \u003cp\u003e(M, IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e7–9\u003c/p\u003e\n \u003cp\u003e(M, IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e10–12\u003c/p\u003e\n \u003cp\u003e(M, IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e13–16\u003c/p\u003e\n \u003cp\u003e(M, IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e≥ 17\u003c/p\u003e\n \u003cp\u003e(M, IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformation Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003cp\u003e(0.25,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003cp\u003e(0.25,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(0.75,1.00)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(0.75,1.00)\u003c/p\u003e\n \u003cp\u003ea\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(1.00,1.00)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e85.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformation Want\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003cp\u003e(0.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003cp\u003e(0.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(0.50,1.00)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(0.94,1.00)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(1.00,1.00)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e51.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformation Adoption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003cp\u003e(0.00,0.25)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003cp\u003e(0.00,0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003cp\u003e(0.25,0.75)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003cp\u003e(0.25,1.00)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003cp\u003e(0.44,0.75)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e84.10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntermedia Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003cp\u003e(0.00,0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003cp\u003e(0.17,0.67)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003cp\u003e(0.50,0.83)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003cp\u003e(0.50,0.88)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003cp\u003e(0.50,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e42.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntermedia\u003c/p\u003e\n \u003cp\u003eWant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003cp\u003e(0.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003cp\u003e(0.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003cp\u003e(0.50,1.00)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003cp\u003e(0.63,1.00)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.83\u003c/p\u003e\n \u003cp\u003e(0.67,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e38.61\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntermedia Adoption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003cp\u003e(0.00,0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17\u003c/p\u003e\n \u003cp\u003e(0.00,0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003cp\u003e(0.17,0.50)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003cp\u003e(0.33,0.50)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003cp\u003e(0.33,0.54)b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83.88\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnose Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003cp\u003e(0.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003cp\u003e(0.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(0.50,1.00)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(0.50,1.00)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003cp\u003e(0.50,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e61.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnose Want\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003cp\u003e(0.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003cp\u003e(0.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50\u003c/p\u003e\n \u003cp\u003e(0.00,1.00)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(0.50,1.00)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003cp\u003e(0.50,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e41.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnose Adoption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003cp\u003e(0.00,0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003cp\u003e(0.00,0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003cp\u003e(0.00,0.50)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00\u003c/p\u003e\n \u003cp\u003e(0.00,0.50)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003cp\u003e(0.00,0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e34.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt;0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eNote: a denotes comparison between subgroups with education duration ≤ 6 and those with 7–9, with Adj. P value \u0026lt; 0.05; b denotes comparison between subgroups with education duration ≤ 6 and others, with Adj. P value \u0026lt; 0.05, M = Median, IQR = Interquartile Range.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab7\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 7\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cstrong\u003eNon-Parametric Test Results by Income Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eIncome Level\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eunderprivileged\u003c/p\u003e\n \u003cp\u003e(M, IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003egeneric\u003c/p\u003e\n \u003cp\u003e(M, IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eaffluent\u003c/p\u003e\n \u003cp\u003e(M, IQR)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eH\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformation Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75(0.50,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75(0.50,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75(0.25,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.051\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformation Want\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.75(0.25,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.00(0.25,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.75(0.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInformation Adoption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25(0.00,0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25(0.00,0.75)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00(0.00,0.50)ab\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.299\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntermedia Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67(0.33,0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50(0.17,0.83)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50(0.17,0.67)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntermedia Want\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67(0.17,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.83(0.21,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.33(0.00,1.00)b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.217\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIntermedia Adoption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.17(0.00,0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33(0.00,0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.17(0.00,0.33)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnose Awareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50(0.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50(0.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50(0.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.683\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnose Want\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50(0.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.50(0.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.50(0.00,1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.195\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiagnose Adoption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00(0.00,0.50)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.00(0.00,0.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.00(0.00,0.00)a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9.573\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNote: “a” denotes a comparison with the underprivileged income subgroup, where Adj. P value \u0026lt; 0.05; “b” represents a comparison with the generic income subgroup, where Adj. P value \u0026lt; 0.05, M = Median, IQR = Interquartile Range.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab8\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 8\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDescriptive statistics and normality test of each dimension\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConstruct\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eM\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSD\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSkewness\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eKurtosis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal M\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal SD\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e60.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.214\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.389\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.551\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eEducation Duration\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.35\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.512\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.63\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.873\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.204\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.993\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.007\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e3.518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.47\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.073\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.735\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.643\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePE1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.088\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e2.804\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e1.190\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.099\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.088\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePE3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.92\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.274\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.113\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.183\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePE4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAt1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e3.360\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAt2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.646\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAt3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.262\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.619\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.709\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.317\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"13\"\u003e\n \u003cp\u003e3.227\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"13\"\u003e\n \u003cp\u003e0.529\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.38\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.628\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.55\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.223\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.743\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.552\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.93\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.109\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.839\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.509\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.467\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.456\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.286\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.19\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.73\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.304\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.591\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.722\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.353\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.152\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.032\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.17\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.422\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.715\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.236\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.273\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.94\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.107\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.648\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.762\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.357\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.328\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.016\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"8\"\u003e\n \u003cp\u003e2.686\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"8\"\u003e\n \u003cp\u003e1.166\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.221\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.308\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.114\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.233\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.268\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.178\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.86\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.269\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.186\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.218\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.33\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.909\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.272\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.239\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.248\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.013\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHe1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.750\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.689\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"11\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"11\"\u003e\n \u003cp\u003e0.324\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHe2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.680\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.749\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.444\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHe3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.998\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHe4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.460\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.178\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.976\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHe5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.810\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.396\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.405\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHe6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.740\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.794\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHe7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.650\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.477\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.631\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.608\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHe8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHe9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.730\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.446\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.960\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHe10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHe11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.682\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHe12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.574\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWa1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.483\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.544\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"12\"\u003e\n \u003cp\u003e0.588\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"12\"\u003e\n \u003cp\u003e0.385\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWa2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.620\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.487\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.781\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWa3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.550\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.498\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.202\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.967\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWa4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.077\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWa5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.720\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.968\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWa6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.690\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.462\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.836\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.307\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWa7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.660\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.476\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.658\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.574\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWa8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.440\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.497\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.957\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWa9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.640\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.481\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.569\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.682\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWa10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.540\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.499\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.147\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.986\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWa11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.510\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWa12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.580\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.493\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.344\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.889\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUs1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.430\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.496\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.280\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.929\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"11\"\u003e\n \u003cp\u003e0.276\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"11\"\u003e\n \u003cp\u003e0.249\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUs2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.340\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.475\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.667\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.562\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUs3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.200\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.504\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUs4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.303\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.639\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.984\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUs5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.480\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUs6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.520\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUs7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.418\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.833\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUs8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.270\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7.855\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUs9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.400\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.490\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.426\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.826\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUs10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.080\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.267\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.185\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e8.173\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUs11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.180\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.388\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.630\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.661\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eUs12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.100\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.298\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.711\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNote: M = Median, SD = standard deviations.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab9\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 9\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003e\u003cstrong\u003eReflective constructs assessment.\u003c/strong\u003e\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"8\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eConstruct/\u003c/p\u003e\n \u003cp\u003emeasure\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eUnstandardized Regression Coefficients\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eFactor loading\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCronbach's Alpha Coefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCR\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAVE\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMSV\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eASV\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.881\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e0.952\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e0.833\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e0.421\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.156\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.918\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.173\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.941\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePE1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.945\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e0.966\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e0.967\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e0.879\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e0.767\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"4\"\u003e\n \u003cp\u003e0.437\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePE2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.978\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePE3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.969\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.907\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePE4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.977\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAt1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.908\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.925\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.931\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.738\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"3\"\u003e\n \u003cp\u003e0.439\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAt2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.105\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.954\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAt3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.059\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.847\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"13\"\u003e\n \u003cp\u003e0.917\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"13\"\u003e\n \u003cp\u003e0.919\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"13\"\u003e\n \u003cp\u003e0.469\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"13\"\u003e\n \u003cp\u003e0.121\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"13\"\u003e\n \u003cp\u003e0.1\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.921\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.582\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.278\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.683\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.313\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.321\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.733\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.579\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.384\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.368\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.752\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.434\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.717\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA10\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.255\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.682\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.517\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.728\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePA13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.375\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.716\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"8\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"8\"\u003e\n \u003cp\u003e0.975\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"8\"\u003e\n \u003cp\u003e0.828\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"8\"\u003e\n \u003cp\u003e0.764\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\" rowspan=\"8\"\u003e\n \u003cp\u003e0.45\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.986\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.94\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH3\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.933\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.929\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.956\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.934\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.876\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH7\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.817\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.864\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeH8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.866\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.867\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"8\"\u003eNote: CR = Construct reliability; AVE = Average variance extracted; MSV = Maximum shared squared variance; ASV = Average shared square variance.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab10\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 10\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eDescriptive statistics and inter-correlations of the constructs.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"6\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePU\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePEOU\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAttitude\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePAM13\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eeHEALS\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.833\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePEOU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.653\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.879\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.859\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.671\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.817\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAM13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.265\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.327\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.318\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.469\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeHEALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.669\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.874\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.347\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.828\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eThe square root of AVE\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.913\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.938\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.904\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.685\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.910\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eNote: PU = Perceived Usefulness; PEOU = Perceived Ease of Use; PAM13 = Patient Activation\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"6\"\u003eMeasure 13, e HEALS = The eHealth Literacy Scale. Boldfaced diagonal elements are the AVE value calculated before.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab11\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 11\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eHypothesis testing results of the research model.\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"7\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003ePathways\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoef β#\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eS.E.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eC.R.\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eP\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAM13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.861\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAM13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAM13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.56\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.119\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePEOU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.478\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-12.684\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePEOU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.123\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.324\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePEOU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.726\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePEOU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAM13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.09\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.906\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.654\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePEOU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.602\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e13.365\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.079\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.028\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.806\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAM13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.068\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.761\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.078\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeHEALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeHEALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.188\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.775\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeHEALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePEOU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.723\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.039\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18.731\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeHEALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.135\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.257\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeHEALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.601\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.548\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeHEALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAM13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.049\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.912\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.056\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.705\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.035\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e17.829\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeHEALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.112\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.981\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAM13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.063\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.047\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.285\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.865\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.037\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.005\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-1.435\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.151\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePEOU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.069\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.044\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.205\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.228\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdoption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.264\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-5.891\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.954\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAwareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.182\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-4.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.093\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.014\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.672\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdoption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4.664\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.137\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.774\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAwareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eED\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.197\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.297\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAwareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.428\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5.226\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.534\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e6.52\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdoption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2.708\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.007\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdoption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeHEALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.323\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3.867\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAwareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAM13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.136\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.021\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAM13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-3.225\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdoption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePAM13\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-2.162\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.031\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdoption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.066\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.835\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.067\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAwareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIncome Level\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.583\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAwareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePEOU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.027\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.737\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePEOU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.118\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.906\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdoption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePEOU\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.062\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.022\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.753\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.451\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAwareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.048\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.585\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.559\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.023\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.779\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAdoption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAttitude\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.029\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.984\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAwareness\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeHEALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.019\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1.944\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.052\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWant\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt;---\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eeHEALS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.024\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.827\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.408\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003ctfoot\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\"\u003eNote: # Standardized Path Coefficients, *** P value \u0026lt; 0.001.\u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tfoot\u003e\n \u003c/table\u003e\n \u003c/div\u003e\n \u003cp\u003eThis study employed IBM SPSS AMOS software (version 24.0.0)[62] to construct a Structural Equation Model (SEM). The latent variables included PU, PEOU, Attitude, Awareness, Want, Adoption, patient activation, and e-Health literacy. Appropriate items were selected as observed variables. Given the large sample size and the presence of non-normality in some data, which could potentially affect the judgment of model fit, the Maximum Likelihood method was adopted for parameter estimation. Additionally, the Bootstrap method was chosen to adjust the fit indices during Confirmatory Factor Analysis (CFA) and SEM analysis[63]. The criteria for a good fit were set as follows: Chi-square to degrees of freedom ratio (χ²/df) \u0026lt; 3, Normed Fit Index (NFI) \u0026gt; 0.90, Tucker-Lewis Index (TLI) \u0026gt; 0.90, Adjusted Goodness of Fit Index (AGFI) \u0026gt; 0.90, Comparative Fit Index (CFI) \u0026gt; 0.90, and Root Mean Square Error of Approximation (RMSEA) \u0026lt; 0.05[64]. Based on the collected data, the model was revised and re-estimated to verify its fit, ultimately iterating to achieve the best-fit model.\u003c/p\u003e\n \u003ch2\u003e3.5 Ethical Approval\u003c/h2\u003e\n \u003cp\u003eThis study was approved by the Ethics Committee of the School of Public Health, Shandong University (Approval No. LL20230602). Informed consent was secured from all participants, who received detailed briefings on the study’s purpose, procedures, and privacy protections. Consent forms were signed before data collection, and participants were assured of their right to withdraw at any time without consequences. Data from withdrawn participants were excluded from the analysis.\u003c/p\u003e\n \u003ch2\u003e3.6 Validity and Reliability\u003c/h2\u003e\n \u003cp\u003eThe scales used in this study exhibited satisfactory validity and reliability. A pilot study was conducted prior to the research to evaluate the appropriateness of the content. \u0026nbsp;After data collection, to ensure data authenticity and validity, all questionnaires were coded and double - entered by two independent professional data entry personnel, and a database was established based on the valid questionnaires. Cronbach's alpha reliability analysis and confirmatory factor analysis were performed during the analysis, and the results indicated good reliability and validity.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Results","content":"\u003ch2\u003e4.1 Demographic Characteristics and Overview of the Sample\u003c/h2\u003e\n\u003cp\u003eA total of 520 valid questionnaires were collected. The average age of the participants was 60.06 (SD=13.21), and the average years of education were 8.92 (SD=4.35). The majority of participants were male (56.15%, N=292), married (90.58%, N=471), and city residents (78.65%, N=409). Further details are presented in Table 4.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Beyond sociodemographic traits, we evaluated responses across various 5-point Likert scales (Figure 3A). Mean scores for Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Attitude, Patient Activation, and eHealth Literacy were 3.52 (SD = 0.97), 2.80 (SD = 1.19), 3.36 (SD = 0.95), 3.17 (SD = 0.49), and 2.69 (SD = 1.17), respectively.\u003c/p\u003e\n\u003cp\u003eTo examine relationships among factors tied to Awareness, Want, and Adoption of IMS, bivariate analyses were conducted to assess interactions and effect sizes between variables. The results (Figure 3B) showed positive correlations between Awareness of IMS and PU (r = 0.581, p \u0026lt; 0.001), PEOU (r = 0.559, p \u0026lt; 0.001), Attitude (r = 0.554, p \u0026lt; 0.001), Patient Activation (r = 0.142, p \u0026lt; 0.01), and eHealth Literacy (r = 0.560, p \u0026lt; 0.001). Similarly, Want for IMS showed positive associations with PU (r = 0.594, p \u0026lt; 0.001), PEOU (r = 0.488, p \u0026lt; 0.001), Attitude (r = 0.553, p \u0026lt; 0.001), Patient Activation (r = 0.111, p \u0026lt; 0.01), and eHealth Literacy (r = 0.485, p \u0026lt; 0.001). Adoption of IMS was positively linked to PU (r = 0.513, p \u0026lt; 0.001), PEOU (r = 0.624, p \u0026lt; 0.001), Attitude (r = 0.544, p \u0026lt; 0.001), Patient Activation (r = 0.211, p \u0026lt; 0.001), and eHealth Literacy (r = 0.636, p \u0026lt; 0.001). Additionally, sociodemographic factors influenced IMS engagement: advancing age correlated with reduced Awareness, Want, and Adoption, whereas longer education duration was associated with elevated levels of these stages.\u003c/p\u003e\n\u003ch2\u003e4.2 Matrix Analysis and Non-Parametric Test\u003c/h2\u003e\n\u003cp\u003eIn terms of awareness, want, and adoption of IMS, chronic disease patients exhibit relatively low rates across all three aspects: 57.87% for awareness, 58.77% for want, and 27.56% for adoption. Simultaneously, there is a significant disparity rate of 52.37%, placing them in the open-general group.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;To gain deeper insights, matrix analyses subdivided IMS into Information, Intermedia, and Diagnose subgroups. Figure 4A indicated that patients displayed the highest Awareness and Want for Information-related IMS, with a markedly smaller disparity rate compared to Intermedia and Diagnose subgroups. This pattern may reflect the widespread practice of using internet search engines for symptom information and self-diagnosis[65, 66]. In contrast, Awareness of Intermedia and Diagnose IMS was notably lower, with Diagnose IMS showing the largest disparity rate, possibly due to limited promotion and patient skepticism[67].\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Further matrix analyses and non-parametric tests were performed on subgroups stratified by age, residence, education duration, income level, and number of chronic diseases. The Kolmogorov-Smirnov test yielded p \u0026lt; 0.001 for all groups.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;For age-based grouping (Figure 4B), results showed that as age increases, the awareness, want, and adoption of information, intermedia, and diagnose all significantly decrease. This is consistent with the Kruskal-Wallis H test results (P \u0026lt; 0.001 for each group and between groups, not shown).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;For subgroups divided by region of residence of chronic disease patients (Figure 4C and Table 5), matrix analysis and the Mann-Whitney U test were performed. Only in the Diagnose Adoption was there no significant difference between city and village residents. In other aspects, city residents had significantly better utilization than village residents.\u003c/p\u003e\n\u003cp\u003eThe analysis according to education duration (Figure 4C and Table 6) demonstrated significant differences (P\u0026lt;0.001) in awareness, want, and adoption of IMS across subgroups. Multiple comparisons within groups, with Bonferroni correction for adjusted significance (Adj.P value)[68], still found statistical differences in several groups.\u003c/p\u003e\n\u003cp\u003eWe divided respondents by household per-capita annual income into underprivileged, generic, and affluent groups, and performed matrix analysis (Figure 4E) and non-parametric tests (Table 7). Results showed that in the adoption of informational IMS, there were significant differences between the underprivileged and affluent groups, as well as between the generic and affluent groups (Adj.P value \u0026lt;0.05). Regarding the intermediary IMS, the affluent group exhibited considerably lower adoption rates than the underprivileged and generic groups in certain items. Similar patterns were observed in the adoption of diagnostic IMS. Matrix analysis also revealed that the affluent group\u0026apos;s utilization of various services was more skewed towards the lower-left (closer to the close group) compared to other groups.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Finally, an analysis was conducted based on the number of chronic diseases patients suffered from. The matrix analysis results (Figure 4F) showed a trend towards the lower left in terms of patients\u0026apos; utilization of various types of IMS as the number of chronic diseases increased. The results of the Kruskal-Wallis H test indicated a remarkable difference among groups with different chronic disease counts only in the Diagnose Want (H=13.86, P \u0026lt; 0.001). Subsequently, the Bonferroni correction method was applied to calculate the adjusted significance for within-group comparisons. A notable difference was observed only between the subgroup with three or more chronic diseases and the subgroup with one chronic disease (Adj. P value = 0.001). This suggests that clinicians should enhance the promotion of IMS among patients with multiple chronic diseases during clinical diagnosis and treatment.\u003c/p\u003e\u003ch2\u003e4.3 Normality test\u003c/h2\u003e\n\u003cp\u003eSince Structural Equation Modeling (SEM) assumes normally distributed data, a normality test was conducted prior to CFA and SEM analyses to ensure model reliability (Table 8). Per Kline\u0026rsquo;s (1998) criteria[69], data approximate normality if skewness and kurtosis absolute values fall within 3 and 8, respectively. All items except Us8 and Us10 met these thresholds.\u003c/p\u003e\n\u003ch2\u003e4.4 Reliability, Validity, and Confirmatory Factor Analysis\u003c/h2\u003e\n\u003cp\u003eThe CFA model (Figure 5A) was constructed to assess whether the observed variables accurately measured the latent variables. The contribution of each observed variable to the latent variables was determined by calculating the analytical factor loadings. The results indicated that the corrected model fit reached excellent standard (\u003cem\u003ex\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e =605.83, \u0026nbsp;\u003cem\u003edf\u003c/em\u003e = 454, \u0026nbsp;\u003cem\u003ex\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e/\u003cem\u003edf\u003c/em\u003e = 1.33, RMSEA = 0.03, NFI = 0.97, CFI = 0.99, IFI = 0.99).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Item reliability was assessed through unstandardized regression weights and standardized factor loadings. Most loadings exceeded 0.70[70], except for select PAM13 items with lower values. Cronbach\u0026rsquo;s Alpha and Construct Reliability (CR) minima were 0.917 and 0.919, respectively, indicating strong internal consistency [71]. Average Variance Extracted (AVE), Maximum Shared Squared Variance (MSV), and Average Shared Square Variance (ASV) were evaluated (Table 9). All AVE values exceeded 0.5 except for PAM13 (0.4\u0026ndash;0.5, deemed acceptable[72, 73]), with MSV and ASV below AVE, confirming discriminant validity[74]. Additional validation is detailed in Table 10.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;In this study, a comprehensive examination of the measurement model for factors related to the usage of IMS was conducted. Based on the above results, it is reasonable to proceed with subsequent structural equation modeling analysis.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;4.5 Structural Equation Model\u003c/p\u003e\n\u003cp\u003eTo be more flexible in analyzing the potential links between various latent variables and factors related to IMS, SEM was developed based on previously proposed hypotheses. Paths with P values greater than 0.05 were removed from the model based on the output results from AMOS software, indicating that the hypotheses associated with these paths were not supported. Through iteration, the final version of the model was established. The results of the model fit correction were \u003cem\u003ex\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e =2897.30, \u0026nbsp;\u003cem\u003edf\u003c/em\u003e = 2389, \u0026nbsp;\u003cem\u003ex\u003c/em\u003e\u003csup\u003e2\u003c/sup\u003e/\u003cem\u003edf\u003c/em\u003e = 1.21, RMSEA = 0.02, NFI = 0.92, CFI = 0.98, and IFI = 0.98, which reached the excellent level of fit criteria.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;Further analysis of the significant paths within the model was conducted to evaluate the hypotheses proposed earlier. The results (Table 11) showed that PU had a significant direct positive impact on the three stages of IMS utilization: Awareness (\u0026beta;=0.428, P\u0026lt;0.001), Want (\u0026beta;=0.534, P\u0026lt;0.001), and Adoption (\u0026beta;=0.225, P\u0026lt;0.01). Additionally, PU positively influenced Attitude (\u0026beta;=0.705, P\u0026lt;0.001), supporting H1a and H1b. eHealth Literacy significantly promoted the Adoption stage (\u0026beta;=0.323, P\u0026lt;0.001) and Attitude (\u0026beta;=0.112, P\u0026lt;0.01) towards IMS, verifying H2a and H2b. Patient activation had a notable positive effect on PEOU (\u0026beta;=0.205, P\u0026lt;0.001) and Attitude (\u0026beta;=0.063, P\u0026lt;0.05), supporting H3c and H3d. Age had a significant direct negative impact on the three stages of IMS utilization for chronic disease patients: Awareness (\u0026beta;=-0.182, P\u0026lt;0.001), Want (\u0026beta;=-0.162, P\u0026lt;0.001), and Adoption (\u0026beta;=-0.264, P\u0026lt;0.001). Additionally, age exerted inhibitory effects through the mediating effects of PEOU (\u0026beta;=-0.478, P\u0026lt;0.001) and patient activation (\u0026beta;=-0.13, P\u0026lt;0.01), supporting H4a, H4c, and H4f. Income level significantly promoted Want (\u0026beta;=0.093, P\u0026lt;0.05), supporting H5a. Education duration not only directly drove Awareness (\u0026beta;=0.197, P\u0026lt;0.001), Want (\u0026beta;=0.137, P\u0026lt;0.001), and Adoption (\u0026beta;=0.181, P\u0026lt;0.001) but also significantly promoted PU (\u0026beta;=0.602, P\u0026lt;0.01), PEOU (\u0026beta;=0.102, P\u0026lt;0.01), eHealth Literacy (\u0026beta;=0.106, P\u0026lt;0.001), and patient activation (\u0026beta;=0.15, P\u0026lt;0.01), indicating partial mediating effects and thereby verifying H6a, H6b, H6c, H6e, and H6f.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIt is worth noting that patient activation demonstrated a significant negative impact on Awareness (\u0026beta;=0.534, p\u0026lt;0.001), Want (\u0026beta;=0.534, p\u0026lt;0.01), and Adoption (\u0026beta;=0.534, p\u0026lt;0.05), which is contrary to H3a. PEOU exhibited an enhancing effect between income level and IMS usage (\u0026beta;=-0.123, P\u0026lt;0.001), representing a special form of H5c. Other significant relationships were not verified. The final structural equation model is presented in Figure 5B.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study developed an extended Technology Acceptance Model (TAM) tailored for chronic disease patients by integrating core components of the Information-Motivation-Behavioral Skills (IMB) model and addressing the specific needs of this population. The enhanced framework incorporates eHealth literacy as the information processing dimension, patient activation as the motivational driver, and demographic variables such as age, education duration, and income level. This multidimensional approach provides a robust lens to explore variations in technology adoption among chronic disease patients.\u003c/p\u003e\n\u003cp\u003eOur findings reveal that Perceived Usefulness (PU) and eHealth literacy, alongside age and income level, exhibit significant positive correlations with Internet Medical Services (IMS) usage. We categorized IMS into three domains—Information, Intermedia, and Diagnose—and observed that chronic disease patients broadly embraced Information IMS, with minimal disparities across the sample. In contrast, Diagnose IMS showed lower adoption rates and pronounced disparities. Additionally, patients who were older, had shorter education durations, resided in rural areas, or managed multiple chronic conditions displayed reduced engagement across all IMS categories.\u003c/p\u003e\n\u003cp\u003eInterestingly, affluent patients demonstrated lower IMS utilization compared to their underprivileged counterparts; a counterintuitive finding potentially explained by several factors. First, high-income individuals often enjoy greater access to premium offline healthcare resources, such as tertiary hospitals and specialist clinics[75, 76], resulting in lower demand and dependency on IMS. Secondly, the affluent group tends to have higher requirements for service quality[77], while the service quality of IMS may exhibit certain fluctuations, with variations in the professional level and service attitude of doctors on some platforms[78]. Additionally, they may place a strong emphasis on the privacy protection of personal health data and may have concerns about medical security risks such as data breaches during the transmission and storage of personal health data through the internet[79]. These potential issues suggest that the promotion of Internet medical services IMS for chronic disease patients should prioritize the improvement of medical quality[80]. Measures such as strengthening the training and management of internet medical doctors, establishing strict doctor admission and dynamic assessment mechanisms, and improving the quality evaluation and supervision mechanisms of internet medical services are essential. Furthermore, the assistance of AI in the future may enhance the diagnostic accuracy[81], potentially helping to rebuild trust among the high-income population. Moreover, it is important to enhance tiered service design and create high-end customized products tailored to the affluent group. This could include collaborating with top-tier hospitals to establish exclusive online consultation channels for experts and accessing international high-quality resources like the Mayo Clinic. Additionally, upgrading privacy protection technologies is crucial to alleviate patients' concerns about data security[82].\u003c/p\u003e\n\u003cp\u003eThis study also constructed a SEM to quantify the actual influence levels of various factors on the three stages (awareness, want, and adoption) of IMS to test the preset hypotheses. The results indicated that PU and education duration both exerted a direct and positive impact on the awareness, want, and adoption of IMS, substantially promoting the gradual acceptance process of IMS among chronic disease patients. eHealth literacy played a notable direct and positive role in the final adoption stage of IMS, facilitating patients' more efficient use of IMS. However, age and patient activation levels demonstrated direct negative effects on these three stages, somewhat impeding the utilization of IMS by chronic disease patients. Specifically, the effect of patient activation was contrary to our hypothesis. This could be attributed to the fact that chronic disease patients with higher levels of patient activation often exhibit greater self-efficacy[83] in health, leading to increased confidence in self-managing their conditions. Consequently, this might reduce their reliance on technological tools[84], indirectly inhibiting IMS utilization behavior. Furthermore, the SEM pathway untangled that PEOU did not directly and significantly affect IMS usage among chronic disease patients but rather had an indirect positive impact through the chained mediation effect of PU. This might be attributed to the special attributes of medical services, where chronic disease patients tend to focus more on the benefits of technology to their physical health and have a relatively higher tolerance for technology ease of use when selecting new technologies[85]. Based on these findings, it is recommended that IMS development platforms prioritize the enhancement of patients' eHealth literacy. This could include designing dedicated training courses covering operational procedures such as searching for health information, viewing online reports, and online consultations, and placing them in prominent positions on the platform for easy access by patients. Simultaneously, it is suggested to actively promote the PU of IMS applications in chronic disease management through channels such as medical institution websites and social media, sharing successful cases to enhance PU and stimulate their interest and enthusiasm in using it.\u003c/p\u003e\n\u003ch2\u003elimitations\u003c/h2\u003e\n\u003cp\u003eThe present study is not without limitations. Firstly, the sample was primarily drawn from chronic disease-designated hospitals in Jinan, representing a single geographical region. Chronic disease patients visiting these hospitals may possess certain specific characteristics (such as residing nearby), which could potentially influence the findings. Therefore, the results require further corroboration and support from additional related studies. It is also anticipated that scholars from other countries or regions can conduct further analyses using different chronic disease patient datasets, adjusting the model's framework to enhance its adaptability to a wider range of scenarios.\u003c/p\u003e\n\u003cp\u003eSecondly, the current model validation is based on cross-sectional study data. Due to the non-continuity of cross-sectional studies, the analysis results can only reflect current relationships. However, subjects' attitudes towards IMS, patient activation, and eHealth literacy are subject to dynamic changes. The exact causal relationships between various study variables cannot be determined. Future studies could collect long-term follow-up information from chronic disease populations and utilize other types of research data to provide more in-depth supplementation to the proposed model.\u003c/p\u003e\n\u003cp\u003eThirdly, with the integration of new technologies such as AI into internet healthcare, there may be entirely new characteristics in the use of IMS by chronic disease patients in the future. More analytical scales for AI usage and AI acceptance could be incorporated into subsequent investigations, enabling research to better reflect the development trends of the future internet healthcare market and provide reliable references for more subsequent studies.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eIMS Internet Medical Services\u003c/p\u003e\n\u003cp\u003eTAM Technology Acceptance Model\u003c/p\u003e\n\u003cp\u003eIMB Information-Motivation-Behavioral Skills\u003c/p\u003e\n\u003cp\u003ePU Perceived Usefulness\u003c/p\u003e\n\u003cp\u003ePEOU Perceived Ease of Use\u003c/p\u003e\n\u003cp\u003eSEM Structural Equation Modeling\u003c/p\u003e\n\u003cp\u003eCFA Confirmatory Factor Analysis\u003c/p\u003e\n\u003cp\u003ePAM Patient Activation Measure\u003c/p\u003e\n\u003cp\u003eeHEALS \u003c/p\u003e\n\u003cp\u003eRMSEA Root Mean Square Error of Approximation\u003c/p\u003e\n\u003cp\u003eNFI Normed Fit Index\u003c/p\u003e\n\u003cp\u003eCFI Comparative Fit Index\u003c/p\u003e\n\u003cp\u003eIFI Incremental Fit Index\u003c/p\u003e\n\u003cp\u003eAVE Average Variance Extracted\u003c/p\u003e\n\u003cp\u003eMSV Maximum Shared Squared Variance\u003c/p\u003e\n\u003cp\u003eASV Average Shared Square Variance\u003c/p\u003e\n\u003cp\u003eAI Artificial Intelligence\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the participants in the study referred to in this paper and each of the investigators for their conscientious and meticulous work.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXYL contributed to the conception, design of the study and the revision of the article. RQ# and RS# contributed to the analysis interpretation and the drafting of the article. XYW# contributed to the conception and design of the study. JHF and YYY contributed to the data collection. All the authors approved the final manuscript.\u003c/p\u003e\n\u003cp\u003e#Contributed equally.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was jointly supported by grants from the 2024 Shandong Province Postgraduate Education and Teaching Reform Research Project (XYJG2024010).\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eData Availability\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data sets generated and analyzed during this study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cstrong\u003eConflicts of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNone declared. \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp; \u0026nbsp;\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003ePadeiro M, Santana P, Grant M. Chapter 1 - Global aging and health determinants in a changing world. 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Relationship between patient activation and self-efficacy among patients undergoing hemodialysis: a cross-sectional study. Renal Replacement Therapy. 2022;8:40.\u003c/li\u003e\n\u003cli\u003eG A, Ml R, D C, G G, G D, S B. The association between patient activation and healthcare resources utilization: a systematic review and meta-analysis. Public health. 2022;210.\u003c/li\u003e\n\u003cli\u003ePatients\u0026rsquo; Acceptance of Smartphone Health Technology for Chronic Disease Management: A Theoretical Model and Empirical Test. JMIR mHealth and uHealth. 2017;5.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Internet Medical Services, Chronic Disease Management, Technology Acceptance Model, eHealth Literacy, Patient Activation, Matrix Analysis, Structural Equation Modeling","lastPublishedDoi":"10.21203/rs.3.rs-6270951/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6270951/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eInternet Medical Services (IMS) hold substantial potential to address healthcare challenges arising from demographic shifts, such as aging populations, and the evolving disease spectrum, marked by the rising prevalence of chronic conditions. However, their practical impact has yet to fully meet these expectations. This study seeks to investigate the factors influencing the adoption and utilization of IMS among chronic disease patients, focusing on their effects across specific IMS domains and acceptance processes, to provide a fresh perspective on enhancing chronic disease management.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe extended the Technology Acceptance Model (TAM) with the Information-Motivation-Behavioral Skills (IMB) framework, incorporating eHealth literacy, patient activation, and demographics (age, education duration, income level). A cross-sectional survey of 520 chronic disease patients in Jinan, China, was analyzed using Structural Equation Modeling (SEM) and matrix analysis to evaluate adoption patterns and influencing factors.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eInformation IMS showed high acceptance with minimal disparities, while Diagnose IMS exhibited low uptake and significant gaps, particularly among older, less-educated, rural, and multimorbid patients. Notably, higher-income patients displayed lower acceptance and utilization across all IMS categories, and patient activation, expected to enhance adoption, unexpectedly hindered IMS use. SEM confirmed Perceived Usefulness and education duration as positive drivers of all adoption stages, with eHealth literacy boosting Adoption, and age exerting a negative effect.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis trailblazing model elucidates IMS adoption complexities, revealing counterintuitive barriers like income and patient activation. It underscores the need for targeted interventions to enhance eHealth literacy and service quality, providing a robust framework for optimizing IMS deployment and advancing digital health strategies for chronic disease care.\u003c/p\u003e","manuscriptTitle":"Assessing Awareness, Want, and Adoption of Internet Medical Services Among Chronic Disease Patients in China:A Structural Equation Model and Matrix Analysis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-03-25 19:02:56","doi":"10.21203/rs.3.rs-6270951/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":"8e4b3ad5-be3e-4155-8928-93de71d4d9c1","owner":[],"postedDate":"March 25th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-03-25T19:02:59+00:00","versionOfRecord":[],"versionCreatedAt":"2025-03-25 19:02:56","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6270951","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6270951","identity":"rs-6270951","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}