E-Health Literacy Among Liver Transplant Recipients: Assessment of Digital Health Competencies and Sociodemographic Correlates

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Methods A cross-sectional study was conducted including 250 adult liver transplant recipients who underwent transplantation between January 2024 and June 2025. The E-Health Literacy Scale (E-HEALS) was used to assess participants' e-health literacy. Sociodemographic characteristics including age, gender, education level, monthly income, marital status, donor type, and post-transplant duration were recorded. Results The mean E-HEALS score was 24.9 ± 8.7 (range: 8–40). Significant associations were found between e-health literacy and age (p < 0.001), education level (p < 0.001), marital status (p = 0.047), and income level (p = 0.003). Younger patients (18–40 years) demonstrated higher e-health literacy compared to older age groups. University graduates showed significantly higher scores than those with lower education levels. No significant associations were found with gender, donor type, or post-transplant duration. Conclusion E-health literacy among liver transplant recipients varies significantly based on sociodemographic factors, particularly age and education. These findings suggest the need for tailored digital health education programs to address disparities and improve health information access for vulnerable patient populations. E-health literacy liver transplantation digital health patient education health information Introduction Liver transplantation represents a definitive treatment for end-stage liver disease, with advancing surgical techniques and immunosuppressive therapies leading to improved survival rates over recent decades ( 1 , 2 ). However, successful long-term outcomes depend not only on surgical excellence but also on patients' ability to navigate complex post-transplant care requirements, including medication adherence, recognition of complications, and lifestyle modifications ( 3 , 4 ). The digital revolution has fundamentally transformed how patients access and utilize health information. E-health, defined as the use of information and communication technologies for health, has become increasingly prevalent in healthcare delivery ( 5 ). Patients now have unprecedented access to online health resources, telemedicine platforms, electronic health records, and mobile health applications ( 6 , 7 ). This digital transformation necessitates a new form of literacy—e-health literacy—which Norman and Skinner define as "the ability to seek, find, understand, and appraise health information from electronic sources and apply the knowledge gained to addressing or solving a health problem" ( 8 ). E-health literacy encompasses multiple dimensions beyond basic computer skills, including traditional literacy, health literacy, information literacy, scientific literacy, media literacy, and computer literacy ( 9 ). For transplant recipients, adequate e-health literacy may facilitate better understanding of their condition, improved medication management, earlier recognition of complications, and enhanced communication with healthcare providers ( 10 , 11 ). Despite the growing importance of digital health competencies, limited research has examined e-health literacy specifically among solid organ transplant recipients. Previous studies have documented that e-health literacy levels vary significantly across different patient populations and are influenced by various sociodemographic factors including age, education, and socioeconomic status ( 12 , 13 , 14 ). Understanding these patterns in transplant populations is crucial for developing targeted interventions. Liver transplant recipients represent a unique population with distinct characteristics. They often require lifelong medical management, frequent healthcare interactions, and continuous monitoring for complications ( 15 , 16 ). The ability to effectively utilize digital health resources could potentially improve self-management capabilities and health outcomes in this population ( 17 ). However, disparities in digital health literacy may inadvertently widen health inequities if certain patient subgroups lack the skills necessary to benefit from technological advances ( 18 , 19 ). The objective of this study was twofold: first, to assess the overall level of e-health literacy among liver transplant recipients at our center; and second, to investigate the relationships between e-health literacy levels and various sociodemographic factors including age, gender, education level, income, marital status, donor type, and time since transplantation. Methods Study Design and Population This study was approved by the Ethics Committee of Inonu University (approval number :2025/7269, date 20 july 2025) As this was not a prospective interventional study, informed consent was not required. All adult patients (≥18 years) who underwent liver transplantation between January 2024 and June 2025 were eligible for inclusion. Exclusion criteria included: patients who declined to participate, those with early postoperative mortality, and illiterate patients who could not complete the assessment instruments. During the study period, 284 adult patients underwent liver transplantation at our center. Of these, 34 patients were excluded based on the exclusion criteria, resulting in a final study population of 250 participants. Data Collection Sociodemographic data collected included age, gender, education level (primary school, high school, university), monthly income (categorized as $2500 USD), marital status (married, single, divorced), donor type (living or cadaveric), and time since transplantation (12 months). Additionally, participants were asked about their perceptions regarding the usefulness of the internet in making health decisions and the importance of access to online health resources. These items were rated on a 5-point Likert scale ranging from 1 (not useful/important at all) to 5 (very useful/important). E-Health Literacy Assessment The E-Health Literacy Scale (E-HEALS), originally developed by Norman and Skinner (8), was used to measure e-health literacy. The Turkish version of the scale, validated by Coşkun and Bebiş (20), was administered to participants. The E-HEALS consists of eight items that assess perceived skills and comfort with using the internet for health-related purposes. Each item is rated on a 5-point Likert scale (1=strongly disagree to 5=strongly agree), yielding a total score ranging from 8 to 40. Higher scores indicate greater e-health literacy. The eight items assess the following competencies: Knowledge of available online health resources Knowledge of where to find helpful health resources Knowledge of how to find helpful health resources Skills to use the internet to answer health questions Ability to use found health information Skills to evaluate online health resources Ability to distinguish high-quality from low-quality resources Confidence in using online information for health decisions Statistical Analysis Descriptive statistics were used to summarize sociodemographic characteristics and E-HEALS scores. Continuous variables were expressed as mean ± standard deviation, while categorical variables were presented as frequencies and percentages. Independent samples t-tests were used to compare E-HEALS scores between two groups, while one-way ANOVA was used for comparisons across three or more groups. Post-hoc pairwise comparisons were conducted using appropriate tests when overall group differences were significant. A p-value of <0.05 was considered statistically significant. Results Participant Characteristics The final study cohort comprised 250 liver transplant recipients. Table 1 presents the sociodemographic characteristics of the study population. The majority of participants were male (63.6%, n=159), aged 41-64 years (60.4%, n=151), and married (80.4%, n=201). Nearly half had completed only primary school education (47.6%, n=119), while 35.6% (n=89) had high school education and 16.8% (n=42) had university education. A substantial proportion (68%, n=170) reported monthly income below $1000. The vast majority received living donor transplants (91.2%, n=238), with only 8.8% (n=22) receiving cadaveric grafts. More than half of participants (58.4%, n=146) were more than 12 months post-transplant. Perceptions of Internet Usefulness and Importance Table 2 summarizes participants' perceptions regarding the usefulness of the internet for health decisions and the importance of accessing online health resources. Nearly half of participants (48.4%) rated the internet as useful or very useful for making health decisions, while 19.6% considered it not useful or not useful at all. Regarding the importance of access to online health resources, 54.8% rated it as important or very important, while 20.8% considered it not important or not important at all. A substantial proportion of participants (30.8% and 23.2%, respectively) remained neutral on these questions. E-Health Literacy Scores The overall mean E-HEALS score for the entire cohort was 24.9±8.7 (range: 8-40), indicating moderate e-health literacy levels. Detailed responses to individual E-HEALS items revealed that participants demonstrated variable confidence across different dimensions of e-health literacy. For items assessing knowledge of available resources and where to find them, 44.4% and 48.4% of participants agreed or strongly agreed, respectively. When asked about confidence in using internet information to make health decisions, 45.6% agreed or strongly agreed, while 31.6% disagreed or strongly disagreed. Association Between E-Health Literacy and Sociodemographic Factors Age: A highly significant association was found between age and e-health literacy (p65 years; 21.4±8.5). Post-hoc analyses revealed significant differences between all age groups: 18-40 vs. 41-64 years (p=0.042), 18-40 vs. >65 years (p65 years (p=0.014)(Table 3). Gender: No significant difference in E-HEALS scores was observed between male (25.2±8.9) and female (24.4±8.5) participants (p=0.48) (Table 3).. Marital Status: Marital status was significantly associated with e-health literacy (p=0.047). Single participants had the highest mean score (28.4±8.0), followed by divorced (25.1±6.4) and married participants (24.4±8.9). Post-hoc analysis revealed a significant difference between married and single participants (p=0.041), but no significant differences between married and divorced (p=0.76) or single and divorced participants (p=0.22) (Table 3).. Education Level: Education level showed a highly significant association with e-health literacy (p<0.001). University graduates demonstrated the highest E-HEALS scores (29.7±5.3), followed by high school graduates (25.6±8.7) and primary school graduates (22.8±8.9). All pairwise comparisons were statistically significant: primary school vs. high school (p=0.019), primary school vs. university (p<0.001), and high school vs. university (p=0.01) (Table 3).. Income Level: Monthly income was significantly associated with e-health literacy (p=0.003). Participants earning $1000-2500 had the highest mean score (27.8±8.3), followed by those earning >$2500 (25.3±10.9) and <$1000 (23.7±8.6). The difference between the lowest and middle-income groups was highly significant (p<0.001), while other pairwise comparisons were not significant (Table 3).. Donor Type: No significant difference was found between recipients of living donor (25.0±8.8) and cadaveric donor (24.8±7.6) transplants (p=0.942) (Table 3).. Post-Transplant Duration: Time since transplantation showed no significant association with e-health literacy (p=0.094). Participants 12 months post-transplant (Table 3). Discussion This study provides important insights into the e-health literacy landscape among liver transplant recipients, revealing moderate overall levels with significant sociodemographic disparities. The mean E-HEALS score of 24.9 ± 8.7 indicates that while many transplant recipients possess basic digital health competencies, substantial room for improvement exists, particularly given the increasing digitalization of healthcare delivery. The strong inverse relationship between age and e-health literacy aligns with previous research across various patient populations ( 21 , 22 ). Younger transplant recipients demonstrated significantly higher e-health literacy, likely reflecting greater familiarity with digital technologies and more frequent internet use for health information seeking ( 23 ). This age-related digital divide poses challenges for healthcare delivery, as older patients—who often have more complex health needs—may be least equipped to benefit from digital health innovations ( 24 ). This finding underscores the need for age-appropriate digital health education interventions tailored to older transplant recipients. Education emerged as another powerful predictor of e-health literacy, with university graduates scoring significantly higher than those with lower educational attainment. This finding is consistent with the broader health literacy literature, which consistently demonstrates education as a primary determinant of health information processing capabilities ( 25 , 26 ). The relationship between formal education and e-health literacy likely reflects multiple factors, including enhanced critical thinking skills, greater health knowledge, and increased confidence in navigating complex information systems ( 27 ). For transplant programs serving predominantly lower-education populations, this finding highlights the importance of providing accessible, simplified digital health resources and offering hands-on training to build digital skills. The significant association between income level and e-health literacy, with middle-income participants demonstrating the highest scores, reflects the complex interplay between economic resources and digital access. Lower-income individuals may face barriers including limited internet access, older technology, and competing priorities that restrict time for health information seeking ( 28 , 29 ). Addressing these socioeconomic disparities in e-health literacy requires multifaceted approaches, including ensuring affordable internet access, providing devices for telehealth access, and offering digital literacy training programs ( 30 ). Interestingly, single participants demonstrated higher e-health literacy than their married counterparts. This finding may reflect several factors: single individuals may be more self-reliant in health information seeking, younger age distribution among single participants, or different patterns of social support and information sharing ( 31 ). However, this finding should be interpreted cautiously given the relatively small number of single participants. The absence of significant associations between e-health literacy and gender, donor type, or post-transplant duration provides additional insights. The lack of gender differences contrasts with some earlier studies showing male advantages in digital literacy, suggesting that gender gaps in technology use may be narrowing in contemporary populations ( 32 ). The lack of association with donor type indicates that e-health literacy is determined more by individual characteristics than by clinical factors. The absence of a relationship with post-transplant duration suggests that e-health literacy levels remain relatively stable over time without active intervention, highlighting the importance of early assessment and education in the post-transplant period. Our findings regarding participants' perceptions of internet usefulness reveal a generally positive but not universal attitude toward online health resources. While approximately half viewed online health resources as useful and important, substantial proportions remained neutral or negative. This heterogeneity in perceptions underscores the need for healthcare providers to assess individual patients' readiness and preferences for digital health tools rather than assuming uniform enthusiasm for technology-based interventions. These findings have several important clinical implications. Transplant programs should conduct systematic assessments of e-health literacy to identify patients who may struggle with digital health tools and require additional support. Digital health education should be incorporated into standard post-transplant care pathways, with content and delivery methods tailored to patients' age, education level, and baseline digital skills. When implementing new digital health initiatives such as patient portals, mobile apps, or telemedicine services, programs should ensure that these tools are accessible to patients with varying levels of e-health literacy, following principles of universal design and health literacy best practices. Particular attention should be directed toward older patients and those with lower educational attainment, who may benefit from one-on-one training, peer support programs, or family member involvement in digital health activities. Transplant programs should also advocate for broader societal initiatives to address digital divides, including affordable internet access and community-based digital literacy programs. Several limitations should be acknowledged. First, the cross-sectional design precludes conclusions about causality or changes in e-health literacy over time. Longitudinal studies are needed to understand how e-health literacy evolves during the post-transplant trajectory and whether interventions produce sustained improvements. Second, the E-HEALS relies on self-reported perceptions rather than objective performance measures, potentially introducing social desirability bias or inaccurate self-assessment. Third, the study was conducted at a single center, which may limit generalizability to other populations with different demographic characteristics or healthcare contexts. Fourth, we did not assess actual internet usage patterns, quality of information sources accessed, or health outcomes associated with different e-health literacy levels. Future research should examine the relationship between e-health literacy and clinical outcomes, including medication adherence, complication rates, hospital readmissions, and quality of life. Intervention studies are needed to develop and evaluate educational programs designed to enhance e-health literacy among transplant recipients. Additionally, qualitative research could provide deeper insights into the barriers and facilitators that transplant recipients experience when attempting to use digital health resources. Finally, research should investigate the role of caregivers and family members in supporting patients with low e-health literacy. Conclusion This study demonstrates that liver transplant recipients exhibit moderate e-health literacy with significant variation based on sociodemographic factors, particularly age and education level. As healthcare increasingly incorporates digital technologies, understanding and addressing disparities in e-health literacy becomes crucial for ensuring equitable access to health information and services. Transplant programs should implement systematic assessment and targeted interventions to enhance digital health competencies across all patient subgroups, with particular attention to older patients and those with lower educational attainment. By addressing these disparities, healthcare systems can better position all transplant recipients to benefit from the expanding array of digital health tools and resources. Abbreviations E-HEALS E-Health Literacy Scale E-health Electronic health Declarations Ethics approval and consent to participate This study was approved by the Ethics Committee of Inonu University (Approval No: 2025/7269, Date: 20 July 2025). All participants provided informed consent before inclusion in the study. Consent for publication Not applicable. Availability of data and materials The datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Authors’ contributions Deniz Yavuz Baskiran designed and supervised the study. MHC contributed to data collection and statistical analysis. Sezai Yilmaz provided critical revision of the manuscript. All authors read and approved the final version of the manuscript. Acknowledgements The authors would like to thank the staff of İnönü University Liver Transplantation Institute for their kind support during the data collection process. Funding This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Conflicts of Interest and Source of Funding The authors declare that there is no financial or other conflict of interest related to this article. Author Statement This research has not been sent to any other journal. It has not been published anywhere before. There is no conflict of interest between the authors in the study. No financial support was received from any person or institution for the research. Consent was obtained from the participants to participate in the study. Ethical Considerations Statement This study was conducted in full compliance with the principles of research and publication ethics. All data were collected in accordance with ethical standards, with due regard to confidentiality and voluntary participation. This study was conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards. References Adam R, Karam V, Cailliez V, et al. 2018 Annual Report of the European Liver Transplant Registry (ELTR) - 50-year evolution of liver transplantation. Transpl Int. 2018;31(12):1293-1317. Kwong AJ, Ebel NH, Kim WR, et al. 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J Am Med Inform Assoc. 2014;21(6):1113-1117. Hargittai E, Shafer S. Differences in actual and perceived online skills: the role of gender. Soc Sci Q. 2006;87(2):432-448. Tables Table 1. Sociodemographic and Clinical Characteristics of Study Participants Parameter n % Gender Female 91 36.4 Male 159 63.6 Age 18-40 54 21.6 41-64 151 60.4 >65 45 18 Marital Status Married 201 80.4 Single 34 13.6 Divorced 15 6 Education Primary School 119 47.6 High School 89 35.6 University 42 16.8 Income (USD) 2500 4 1.6 Donor Type Living 238 91.2 Cadaver 22 8.8 Post-transplant Duration (Months) 12 146 58.4 Table 2: Patients’ Perceptions of Internet Usefulness and Importance to Online Health Resources Usefulness of the Internet in Making Health Decisions Option n % 1 = Not useful at all 25 10.0 2 = Not useful 24 9.6 3 = Neutral 77 30.8 4 = Useful 91 36.4 5 = Very useful 30 12.0 Importance of Access to Online Health Resources Option n % 1 = Not important at all 22 8.8 2 = Not important 30 12.0 3 = Neutral 58 23.2 4 = Important 89 35.6 5 = Very important 48 19.2 Table 3: Comparison of E-Health Literacy Scale (E-HEALS) Scores According to Sociodemographic and Clinical Characteristics Parameter Point p Value Gender Female 24.4±8.5 p= 0.48 Male 25.2±8.9 Age 18-40 27.8±7.8 p65 p65 p=0.014 41-64 25±8.8 >65 21.4±8.5 Marital Status Married 24.4±8.9 p=0.047 Married vs Single p=0.041 Married vs Divorced p=0.76 Single vs Divorced p=0.22 Single 28.4±8 Divorced 25.1±6.4 Education Primary School 22.8±8.9 p<0.001 Primary School vs High School p=0.019 Primary School vs University p<0.001 High School vs University p=0.01 High School 25.6±8.7 University 29.7±5.3 Income (USD) <1000 23.7±8.6 p=0.003 <1000 vs 1000-2500 p<0.001 2500p=0.78 1000-2500 vs >2500 p= 0.56 1000-2500 27.8±8.3 >2500 25.3±10.9 Donor Type Living 25±8.8 p= 0.942 Cadaver 24.8±7.6 Post-transplant Duration (Months) 12 24.4±8.7 Additional Declarations No competing interests reported. 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07:51:11","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":990536,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7797079/v1/7971f7c3-d787-4dbd-85f8-3d3ea6e19e61.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"E-Health Literacy Among Liver Transplant Recipients: Assessment of Digital Health Competencies and Sociodemographic Correlates","fulltext":[{"header":"Introduction","content":"\u003cp\u003eLiver transplantation represents a definitive treatment for end-stage liver disease, with advancing surgical techniques and immunosuppressive therapies leading to improved survival rates over recent decades (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). However, successful long-term outcomes depend not only on surgical excellence but also on patients' ability to navigate complex post-transplant care requirements, including medication adherence, recognition of complications, and lifestyle modifications (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe digital revolution has fundamentally transformed how patients access and utilize health information. E-health, defined as the use of information and communication technologies for health, has become increasingly prevalent in healthcare delivery (\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). Patients now have unprecedented access to online health resources, telemedicine platforms, electronic health records, and mobile health applications (\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). This digital transformation necessitates a new form of literacy\u0026mdash;e-health literacy\u0026mdash;which Norman and Skinner define as \"the ability to seek, find, understand, and appraise health information from electronic sources and apply the knowledge gained to addressing or solving a health problem\" (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eE-health literacy encompasses multiple dimensions beyond basic computer skills, including traditional literacy, health literacy, information literacy, scientific literacy, media literacy, and computer literacy (\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e). For transplant recipients, adequate e-health literacy may facilitate better understanding of their condition, improved medication management, earlier recognition of complications, and enhanced communication with healthcare providers (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eDespite the growing importance of digital health competencies, limited research has examined e-health literacy specifically among solid organ transplant recipients. Previous studies have documented that e-health literacy levels vary significantly across different patient populations and are influenced by various sociodemographic factors including age, education, and socioeconomic status (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e). Understanding these patterns in transplant populations is crucial for developing targeted interventions.\u003c/p\u003e\u003cp\u003eLiver transplant recipients represent a unique population with distinct characteristics. They often require lifelong medical management, frequent healthcare interactions, and continuous monitoring for complications (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). The ability to effectively utilize digital health resources could potentially improve self-management capabilities and health outcomes in this population (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e). However, disparities in digital health literacy may inadvertently widen health inequities if certain patient subgroups lack the skills necessary to benefit from technological advances (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe objective of this study was twofold: first, to assess the overall level of e-health literacy among liver transplant recipients at our center; and second, to investigate the relationships between e-health literacy levels and various sociodemographic factors including age, gender, education level, income, marital status, donor type, and time since transplantation.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003e\u003cstrong\u003eStudy Design and Population\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Inonu University (approval number :2025/7269, date 20 july 2025) \u0026nbsp;As this was not a prospective interventional study, informed consent was not required. All adult patients (≥18 years) who underwent liver transplantation between January 2024 and June 2025 were eligible for inclusion. Exclusion criteria included: patients who declined to participate, those with early postoperative mortality, and illiterate patients who could not complete the assessment instruments.\u003c/p\u003e\n\u003cp\u003eDuring the study period, 284 adult patients underwent liver transplantation at our center. Of these, 34 patients were excluded based on the exclusion criteria, resulting in a final study population of 250 participants.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData Collection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eSociodemographic data collected included age, gender, education level (primary school, high school, university), monthly income (categorized as \u0026lt;$1000, $1000-2500, \u0026gt;$2500 USD), marital status (married, single, divorced), donor type (living or cadaveric), and time since transplantation (\u0026lt;6 months, 6-12 months, \u0026gt;12 months).\u003c/p\u003e\n\u003cp\u003eAdditionally, participants were asked about their perceptions regarding the usefulness of the internet in making health decisions and the importance of access to online health resources. These items were rated on a 5-point Likert scale ranging from 1 (not useful/important at all) to 5 (very useful/important).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE-Health Literacy Assessment\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe E-Health Literacy Scale (E-HEALS), originally developed by Norman and Skinner (8), was used to measure e-health literacy. The Turkish version of the scale, validated by Coşkun and Bebiş (20), was administered to participants. The E-HEALS consists of eight items that assess perceived skills and comfort with using the internet for health-related purposes. Each item is rated on a 5-point Likert scale (1=strongly disagree to 5=strongly agree), yielding a total score ranging from 8 to 40. Higher scores indicate greater e-health literacy.\u003c/p\u003e\n\u003cp\u003eThe eight items assess the following competencies:\u003c/p\u003e\n\u003col start=\"1\" type=\"1\"\u003e\n \u003cli\u003eKnowledge of available online health resources\u003c/li\u003e\n \u003cli\u003eKnowledge of where to find helpful health resources\u003c/li\u003e\n \u003cli\u003eKnowledge of how to find helpful health resources\u003c/li\u003e\n \u003cli\u003eSkills to use the internet to answer health questions\u003c/li\u003e\n \u003cli\u003eAbility to use found health information\u003c/li\u003e\n \u003cli\u003eSkills to evaluate online health resources\u003c/li\u003e\n \u003cli\u003eAbility to distinguish high-quality from low-quality resources\u003c/li\u003e\n \u003cli\u003eConfidence in using online information for health decisions\u003c/li\u003e\n\u003c/ol\u003e\n\u003cp\u003e\u003cstrong\u003eStatistical Analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDescriptive statistics were used to summarize sociodemographic characteristics and E-HEALS scores. Continuous variables were expressed as mean ± standard deviation, while categorical variables were presented as frequencies and percentages. Independent samples t-tests were used to compare E-HEALS scores between two groups, while one-way ANOVA was used for comparisons across three or more groups. Post-hoc pairwise comparisons were conducted using appropriate tests when overall group differences were significant. A p-value of \u0026lt;0.05 was considered statistically significant.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eParticipant Characteristics\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe final study cohort comprised 250 liver transplant recipients. Table 1 presents the sociodemographic characteristics of the study population. The majority of participants were male (63.6%, n=159), aged 41-64 years (60.4%, n=151), and married (80.4%, n=201). Nearly half had completed only primary school education (47.6%, n=119), while 35.6% (n=89) had high school education and 16.8% (n=42) had university education. A substantial proportion (68%, n=170) reported monthly income below $1000. The vast majority received living donor transplants (91.2%, n=238), with only 8.8% (n=22) receiving cadaveric grafts. More than half of participants (58.4%, n=146) were more than 12 months post-transplant.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePerceptions of Internet Usefulness and Importance\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTable 2 summarizes participants' perceptions regarding the usefulness of the internet for health decisions and the importance of accessing online health resources. Nearly half of participants (48.4%) rated the internet as useful or very useful for making health decisions, while 19.6% considered it not useful or not useful at all. Regarding the importance of access to online health resources, 54.8% rated it as important or very important, while 20.8% considered it not important or not important at all. A substantial proportion of participants (30.8% and 23.2%, respectively) remained neutral on these questions.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eE-Health Literacy Scores\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe overall mean E-HEALS score for the entire cohort was 24.9±8.7 (range: 8-40), indicating moderate e-health literacy levels. Detailed responses to individual E-HEALS items revealed that participants demonstrated variable confidence across different dimensions of e-health literacy. For items assessing knowledge of available resources and where to find them, 44.4% and 48.4% of participants agreed or strongly agreed, respectively. When asked about confidence in using internet information to make health decisions, 45.6% agreed or strongly agreed, while 31.6% disagreed or strongly disagreed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAssociation Between E-Health Literacy and Sociodemographic Factors\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAge:\u003c/strong\u003e A highly significant association was found between age and e-health literacy (p\u0026lt;0.001). Younger participants (18-40 years) demonstrated the highest mean E-HEALS score (27.8±7.8), followed by the middle-aged group (41-64 years; 25.0±8.8) and older participants (\u0026gt;65 years; 21.4±8.5). Post-hoc analyses revealed significant differences between all age groups: 18-40 vs. 41-64 years (p=0.042), 18-40 vs. \u0026gt;65 years (p\u0026lt;0.001), and 41-64 vs. \u0026gt;65 years (p=0.014)(Table 3).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGender:\u003c/strong\u003e No significant difference in E-HEALS scores was observed between male (25.2±8.9) and female (24.4±8.5) participants (p=0.48) (Table 3)..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMarital Status:\u003c/strong\u003e Marital status was significantly associated with e-health literacy (p=0.047). Single participants had the highest mean score (28.4±8.0), followed by divorced (25.1±6.4) and married participants (24.4±8.9). Post-hoc analysis revealed a significant difference between married and single participants (p=0.041), but no significant differences between married and divorced (p=0.76) or single and divorced participants (p=0.22) (Table 3)..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEducation Level:\u003c/strong\u003e Education level showed a highly significant association with e-health literacy (p\u0026lt;0.001). University graduates demonstrated the highest E-HEALS scores (29.7±5.3), followed by high school graduates (25.6±8.7) and primary school graduates (22.8±8.9). All pairwise comparisons were statistically significant: primary school vs. high school (p=0.019), primary school vs. university (p\u0026lt;0.001), and high school vs. university (p=0.01) (Table 3)..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eIncome Level:\u003c/strong\u003e Monthly income was significantly associated with e-health literacy (p=0.003). Participants earning $1000-2500 had the highest mean score (27.8±8.3), followed by those earning \u0026gt;$2500 (25.3±10.9) and \u0026lt;$1000 (23.7±8.6). The difference between the lowest and middle-income groups was highly significant (p\u0026lt;0.001), while other pairwise comparisons were not significant (Table 3)..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDonor Type:\u003c/strong\u003e No significant difference was found between recipients of living donor (25.0±8.8) and cadaveric donor (24.8±7.6) transplants (p=0.942) (Table 3)..\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePost-Transplant Duration:\u003c/strong\u003e Time since transplantation showed no significant association with e-health literacy (p=0.094). Participants \u0026lt;6 months post-transplant had a mean score of 27.3±8.9, compared to 24.2±9.3 for those 6-12 months post-transplant and 24.4±8.7 for those \u0026gt;12 months post-transplant (Table 3).\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study provides important insights into the e-health literacy landscape among liver transplant recipients, revealing moderate overall levels with significant sociodemographic disparities. The mean E-HEALS score of 24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7 indicates that while many transplant recipients possess basic digital health competencies, substantial room for improvement exists, particularly given the increasing digitalization of healthcare delivery.\u003c/p\u003e\u003cp\u003eThe strong inverse relationship between age and e-health literacy aligns with previous research across various patient populations (\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). Younger transplant recipients demonstrated significantly higher e-health literacy, likely reflecting greater familiarity with digital technologies and more frequent internet use for health information seeking (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e). This age-related digital divide poses challenges for healthcare delivery, as older patients\u0026mdash;who often have more complex health needs\u0026mdash;may be least equipped to benefit from digital health innovations (\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e). This finding underscores the need for age-appropriate digital health education interventions tailored to older transplant recipients.\u003c/p\u003e\u003cp\u003eEducation emerged as another powerful predictor of e-health literacy, with university graduates scoring significantly higher than those with lower educational attainment. This finding is consistent with the broader health literacy literature, which consistently demonstrates education as a primary determinant of health information processing capabilities (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). The relationship between formal education and e-health literacy likely reflects multiple factors, including enhanced critical thinking skills, greater health knowledge, and increased confidence in navigating complex information systems (\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). For transplant programs serving predominantly lower-education populations, this finding highlights the importance of providing accessible, simplified digital health resources and offering hands-on training to build digital skills.\u003c/p\u003e\u003cp\u003eThe significant association between income level and e-health literacy, with middle-income participants demonstrating the highest scores, reflects the complex interplay between economic resources and digital access. Lower-income individuals may face barriers including limited internet access, older technology, and competing priorities that restrict time for health information seeking (\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Addressing these socioeconomic disparities in e-health literacy requires multifaceted approaches, including ensuring affordable internet access, providing devices for telehealth access, and offering digital literacy training programs (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eInterestingly, single participants demonstrated higher e-health literacy than their married counterparts. This finding may reflect several factors: single individuals may be more self-reliant in health information seeking, younger age distribution among single participants, or different patterns of social support and information sharing (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). However, this finding should be interpreted cautiously given the relatively small number of single participants.\u003c/p\u003e\u003cp\u003eThe absence of significant associations between e-health literacy and gender, donor type, or post-transplant duration provides additional insights. The lack of gender differences contrasts with some earlier studies showing male advantages in digital literacy, suggesting that gender gaps in technology use may be narrowing in contemporary populations (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e). The lack of association with donor type indicates that e-health literacy is determined more by individual characteristics than by clinical factors. The absence of a relationship with post-transplant duration suggests that e-health literacy levels remain relatively stable over time without active intervention, highlighting the importance of early assessment and education in the post-transplant period.\u003c/p\u003e\u003cp\u003eOur findings regarding participants' perceptions of internet usefulness reveal a generally positive but not universal attitude toward online health resources. While approximately half viewed online health resources as useful and important, substantial proportions remained neutral or negative. This heterogeneity in perceptions underscores the need for healthcare providers to assess individual patients' readiness and preferences for digital health tools rather than assuming uniform enthusiasm for technology-based interventions.\u003c/p\u003e\u003cp\u003eThese findings have several important clinical implications. Transplant programs should conduct systematic assessments of e-health literacy to identify patients who may struggle with digital health tools and require additional support. Digital health education should be incorporated into standard post-transplant care pathways, with content and delivery methods tailored to patients' age, education level, and baseline digital skills. When implementing new digital health initiatives such as patient portals, mobile apps, or telemedicine services, programs should ensure that these tools are accessible to patients with varying levels of e-health literacy, following principles of universal design and health literacy best practices. Particular attention should be directed toward older patients and those with lower educational attainment, who may benefit from one-on-one training, peer support programs, or family member involvement in digital health activities. Transplant programs should also advocate for broader societal initiatives to address digital divides, including affordable internet access and community-based digital literacy programs.\u003c/p\u003e\u003cp\u003eSeveral limitations should be acknowledged. First, the cross-sectional design precludes conclusions about causality or changes in e-health literacy over time. Longitudinal studies are needed to understand how e-health literacy evolves during the post-transplant trajectory and whether interventions produce sustained improvements. Second, the E-HEALS relies on self-reported perceptions rather than objective performance measures, potentially introducing social desirability bias or inaccurate self-assessment. Third, the study was conducted at a single center, which may limit generalizability to other populations with different demographic characteristics or healthcare contexts. Fourth, we did not assess actual internet usage patterns, quality of information sources accessed, or health outcomes associated with different e-health literacy levels.\u003c/p\u003e\u003cp\u003eFuture research should examine the relationship between e-health literacy and clinical outcomes, including medication adherence, complication rates, hospital readmissions, and quality of life. Intervention studies are needed to develop and evaluate educational programs designed to enhance e-health literacy among transplant recipients. Additionally, qualitative research could provide deeper insights into the barriers and facilitators that transplant recipients experience when attempting to use digital health resources. Finally, research should investigate the role of caregivers and family members in supporting patients with low e-health literacy.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study demonstrates that liver transplant recipients exhibit moderate e-health literacy with significant variation based on sociodemographic factors, particularly age and education level. As healthcare increasingly incorporates digital technologies, understanding and addressing disparities in e-health literacy becomes crucial for ensuring equitable access to health information and services. Transplant programs should implement systematic assessment and targeted interventions to enhance digital health competencies across all patient subgroups, with particular attention to older patients and those with lower educational attainment. By addressing these disparities, healthcare systems can better position all transplant recipients to benefit from the expanding array of digital health tools and resources.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eE-HEALS\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eE-Health Literacy Scale\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003cdiv class=\"DefinitionListEntry\"\u003e\u003cdiv class=\"Term\"\u003eE-health\u003c/div\u003e\u003cdiv class=\"Description\"\u003e\u003cp\u003eElectronic health\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was approved by the Ethics Committee of Inonu University (Approval No: 2025/7269, Date: 20 July 2025). All participants provided informed consent before inclusion in the study.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDeniz Yavuz Baskiran \u0026nbsp;designed and supervised the study. MHC contributed to data collection and statistical analysis. Sezai Yilmaz \u0026nbsp;provided critical revision of the manuscript. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the staff of İnönü University Liver Transplantation Institute for their kind support during the data collection process.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003cbr\u003e\u0026nbsp;This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest and Source of Funding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that there is no financial or other conflict of interest related to this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cul\u003e\n \u003cli\u003eThis research has not been sent to any other journal.\u0026nbsp;It has not been published anywhere before.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eThere is no conflict of interest between the authors in the study.\u003c/li\u003e\n \u003cli\u003eNo financial support was received from any person or institution for the research.\u0026nbsp;\u003c/li\u003e\n \u003cli\u003eConsent was obtained from the participants to participate in the study.\u003c/li\u003e\n\u003c/ul\u003e\n\u003cp\u003e\u003cstrong\u003eEthical Considerations Statement\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in full compliance with the principles of research and publication ethics. All data were collected in accordance with ethical standards, with due regard to confidentiality and voluntary participation. This study was conducted in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n \u003cli\u003eAdam R, Karam V, Cailliez V, et al. 2018 Annual Report of the European Liver Transplant Registry (ELTR) - 50-year evolution of liver transplantation. Transpl Int. 2018;31(12):1293-1317.\u003c/li\u003e\n \u003cli\u003eKwong AJ, Ebel NH, Kim WR, et al. OPTN/SRTR 2020 Annual Data Report: Liver. Am J Transplant. 2022;22(S2):204-309.\u003c/li\u003e\n \u003cli\u003eDew MA, DiMartini AF, De Vito Dabbs A, et al. Rates and risk factors for nonadherence to the medical regimen after adult solid organ transplantation. Transplantation. 2007;83(7):858-873.\u003c/li\u003e\n \u003cli\u003eGermani G, Lazzaro S, Gnoato F, et al. Nonadherent behaviors after solid organ transplantation. Transplantation. 2011;91(9):930-935.\u003c/li\u003e\n \u003cli\u003eEysenbach G. What is e-health? J Med Internet Res. 2001;3(2):E20.\u003c/li\u003e\n \u003cli\u003eKruse CS, Krowski N, Rodriguez B, et al. Telehealth and patient satisfaction: a systematic review and narrative analysis. BMJ Open. 2017;7(8):e016242.\u003c/li\u003e\n \u003cli\u003eShaw T, McGregor D, Brunner M, et al. What is eHealth (6)? Development of a Conceptual Model for eHealth: Qualitative Study with Key Informants. J Med Internet Res. 2017;19(10):e324.\u003c/li\u003e\n \u003cli\u003eNorman CD, Skinner HA. eHEALS: The eHealth Literacy Scale. J Med Internet Res. 2006;8(4):e27.\u003c/li\u003e\n \u003cli\u003eGilstad H. Toward a comprehensive model of eHealth literacy. Proceedings of the 2nd European Workshop on Practical Aspects of Health Informatics. 2014:63-72.\u003c/li\u003e\n \u003cli\u003eMeppelink CS, van Weert JC, Haven CJ, Smit EG. The effectiveness of health animations in audiences with different health literacy levels: an experimental study. J Med Internet Res. 2015;17(1):e11.\u003c/li\u003e\n \u003cli\u003eStellefson M, Chaney B, Barry AE, et al. Web 2.0 chronic disease self-management for older adults: a systematic review. J Med Internet Res. 2013;15(2):e35.\u003c/li\u003e\n \u003cli\u003eNeter E, Brainin E. eHealth literacy: extending the digital divide to the realm of health information. J Med Internet Res. 2012;14(1):e19.\u003c/li\u003e\n \u003cli\u003ePaige SR, Krieger JL, Stellefson M, Alber JM. eHealth literacy in chronic disease patients: An item response theory analysis of the eHealth literacy scale (eHEALS). Patient Educ Couns. 2017;100(2):320-326.\u003c/li\u003e\n \u003cli\u003eRichtering SS, Hyun K, Neubeck L, et al. eHealth literacy: predictors in a population with moderate-to-high cardiovascular risk. JMIR Hum Factors. 2017;4(1):e4.\u003c/li\u003e\n \u003cli\u003eLucey MR, Terrault N, Ojo L, et al. Long-term management of the successful adult liver transplant: 2012 practice guideline by the American Association for the Study of Liver Diseases and the American Society of Transplantation. Liver Transpl. 2013;19(1):3-26.\u003c/li\u003e\n \u003cli\u003eEuropean Association for the Study of the Liver. EASL Clinical Practice Guidelines: Liver transplantation. J Hepatol. 2016;64(2):433-485.\u003c/li\u003e\n \u003cli\u003eGordon EJ, Prohaska TR, Gallant MP, et al. Longitudinal analysis of physical activity, fluid intake, and graft function among kidney transplant recipients. Transpl Int. 2009;22(10):990-998.\u003c/li\u003e\n \u003cli\u003eKontos E, Blake KD, Chou WY, Prestin A. Predictors of eHealth usage: insights on the digital divide from the Health Information National Trends Survey 2012. J Med Internet Res. 2014;16(7):e172.\u003c/li\u003e\n \u003cli\u003eSarkar U, Karter AJ, Liu JY, et al. Social disparities in internet patient portal use in diabetes: evidence that the digital divide extends beyond access. J Am Med Inform Assoc. 2011;18(3):318-321.\u003c/li\u003e\n \u003cli\u003eCoşkun S, Bebiş H. The Reliability and Validity of the Turkish Version of the E-Health Literacy Scale. J Anatolia Nurs Health Sci. 2015;18(1):42-50.\u003c/li\u003e\n \u003cli\u003eChoi NG, DiNitto DM. The digital divide among low-income homebound older adults: Internet use patterns, eHealth literacy, and attitudes toward computer/Internet use. J Med Internet Res. 2013;15(5):e93.\u003c/li\u003e\n \u003cli\u003eTennant B, Stellefson M, Dodd V, et al. eHealth literacy and Web 2.0 health information seeking behaviors among baby boomers and older adults. J Med Internet Res. 2015;17(3):e70.\u003c/li\u003e\n \u003cli\u003eHsu W, Chiang C, Yang S. The effect of individual factors on health behaviors among college students: the mediating effects of eHealth literacy. J Med Internet Res. 2014;16(12):e287.\u003c/li\u003e\n \u003cli\u003eKim H, Xie B. Health literacy in the eHealth era: a systematic review of the literature. Patient Educ Couns. 2017;100(6):1073-1082.\u003c/li\u003e\n \u003cli\u003eBerkman ND, Sheridan SL, Donahue KE, et al. Low health literacy and health outcomes: an updated systematic review. Ann Intern Med. 2011;155(2):97-107.\u003c/li\u003e\n \u003cli\u003eS\u0026oslash;rensen K, Van den Broucke S, Fullam J, et al. Health literacy and public health: a systematic review and integration of definitions and models. BMC Public Health. 2012;12:80.\u003c/li\u003e\n \u003cli\u003evan der Vaart R, van Deursen AJ, Drossaert CH, et al. Does the eHealth Literacy Scale (eHEALS) measure what it intends to measure? Validation of a Dutch version of the eHEALS in two adult populations. J Med Internet Res. 2011;13(4):e86.\u003c/li\u003e\n \u003cli\u003eHargittai E. Digital na(t)ives? Variation in internet skills and uses among members of the \u0026quot;net generation\u0026quot;. Sociol Inq. 2010;80(1):92-113.\u003c/li\u003e\n \u003cli\u003eRobinson L, Cotten SR, Ono H, et al. Digital inequalities and why they matter. Inf Commun Soc. 2015;18(5):569-582.\u003c/li\u003e\n \u003cli\u003eLatulippe K, Hamel C, Giroux D. Social Health Inequalities and eHealth: A Literature Review With Qualitative Synthesis of Theoretical and Empirical Studies. J Med Internet Res. 2017;19(4):e136.\u003c/li\u003e\n \u003cli\u003eBhandari N, Shi Y, Jung K. Seeking health information online: does limited healthcare access matter? J Am Med Inform Assoc. 2014;21(6):1113-1117.\u003c/li\u003e\n \u003cli\u003eHargittai E, Shafer S. Differences in actual and perceived online skills: the role of gender. Soc Sci Q. 2006;87(2):432-448.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003e\u003cstrong\u003eTable 1. Sociodemographic and Clinical Characteristics of Study Participants\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 240px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;n\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e%\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e36.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e159\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e63.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e18-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e21.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e41-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e60.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026gt;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e18\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eMarital\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eStatus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e201\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e80.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e15\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003ePrimary School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e119\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e47.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eHigh School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e35.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eUniversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e16.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eIncome (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026lt;1000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e170\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e68\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e1000-2500\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e76\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e30.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026gt;2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e1.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eDonor\u003c/p\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eLiving\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e238\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e91.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003eCadaver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003ePost-transplant\u003c/p\u003e\n \u003cp\u003eDuration (Months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026lt; 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e20.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e6-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e21.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 132px;\"\u003e\n \u003cp\u003e\u0026gt;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 85px;\"\u003e\n \u003cp\u003e146\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 71px;\"\u003e\n \u003cp\u003e58.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003ch2\u003eTable 2: Patients\u0026rsquo; Perceptions of Internet Usefulness and Importance to Online Health Resources\u003c/h2\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 576px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUsefulness of the Internet in Making Health Decisions\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eOption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e1 = Not useful at all\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e10.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e2 = Not useful\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e9.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e3 = Neutral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e30.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e4 = Useful\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e36.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e5 = Very useful\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"3\" valign=\"top\" style=\"width: 576px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eImportance of Access to Online Health Resources\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003eOption\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003en\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e%\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e1 = Not important at all\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e2 = Not important\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e12.0\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e3 = Neutral\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e58\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e23.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e4 = Important\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e89\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e35.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e5 = Very important\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e48\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 192px;\"\u003e\n \u003cp\u003e19.2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\n\u003cp\u003e\u003cstrong\u003eTable 3: \u0026nbsp; Comparison of E-Health Literacy Scale (E-HEALS) Scores According to Sociodemographic and Clinical Characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003ctable border=\"1\" cellspacing=\"0\" cellpadding=\"0\" width=\"612\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"2\" valign=\"top\" style=\"width: 222px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eParameter\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePoint\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep Value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eGender\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e24.4\u0026plusmn;8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003ep= 0.48\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e25.2\u0026plusmn;8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eAge\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e18-40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e27.8\u0026plusmn;7.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e18-40 vs 41-64 p=0.042\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e18-40 vs \u0026gt;65 p\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e41-64 vs \u0026gt;65 p=0.014\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e41-64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e25\u0026plusmn;8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026gt;65\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e21.4\u0026plusmn;8.5\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eMarital\u0026nbsp;\u003c/p\u003e\n \u003cp\u003eStatus\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eMarried\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e24.4\u0026plusmn;8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep=0.047\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eMarried vs Single p=0.041\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003eMarried vs Divorced p=0.76\u003c/p\u003e\n \u003cp\u003eSingle vs Divorced p=0.22\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eSingle\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e28.4\u0026plusmn;8\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eDivorced\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e25.1\u0026plusmn;6.4\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eEducation\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003ePrimary School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e22.8\u0026plusmn;8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary School vs High School p=0.019\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003ePrimary School vs University p\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eHigh School vs University p=0.01\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eHigh School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e25.6\u0026plusmn;8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eUniversity\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e29.7\u0026plusmn;5.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eIncome (USD)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026lt;1000\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e23.7\u0026plusmn;8.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep=0.003\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026lt;1000 vs 1000-2500 p\u0026lt;0.001\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u0026lt;1000 vs \u0026gt;2500p=0.78\u003c/p\u003e\n \u003cp\u003e1000-2500 vs \u0026gt;2500 p= 0.56\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e1000-2500\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e27.8\u0026plusmn;8.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026gt;2500\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e25.3\u0026plusmn;10.9\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003eDonor\u003c/p\u003e\n \u003cp\u003eType\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eLiving\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e25\u0026plusmn;8.8\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003ep= 0.942\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003eCadaver\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e24.8\u0026plusmn;7.6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 108px;\"\u003e\n \u003cp\u003ePost-transplant\u003c/p\u003e\n \u003cp\u003eDuration (Months)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026lt; 6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e27.3\u0026plusmn;8.9\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"3\" valign=\"top\" style=\"width: 270px;\"\u003e\n \u003cp\u003ep=0.094\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e6-12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e24.2\u0026plusmn;9.3\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"top\" style=\"width: 114px;\"\u003e\n \u003cp\u003e\u0026gt;12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 120px;\"\u003e\n \u003cp\u003e24.4\u0026plusmn;8.7\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"E-health literacy, liver transplantation, digital health, patient education, health information","lastPublishedDoi":"10.21203/rs.3.rs-7797079/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7797079/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eObjective\u003c/h2\u003e\u003cp\u003eThis study aimed to assess e-health literacy levels among liver transplant recipients and investigate the relationship between e-health literacy and sociodemographic factors.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e\u003cp\u003eA cross-sectional study was conducted including 250 adult liver transplant recipients who underwent transplantation between January 2024 and June 2025. The E-Health Literacy Scale (E-HEALS) was used to assess participants' e-health literacy. Sociodemographic characteristics including age, gender, education level, monthly income, marital status, donor type, and post-transplant duration were recorded.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e\u003cp\u003eThe mean E-HEALS score was 24.9\u0026thinsp;\u0026plusmn;\u0026thinsp;8.7 (range: 8\u0026ndash;40). Significant associations were found between e-health literacy and age (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), education level (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001), marital status (p\u0026thinsp;=\u0026thinsp;0.047), and income level (p\u0026thinsp;=\u0026thinsp;0.003). Younger patients (18\u0026ndash;40 years) demonstrated higher e-health literacy compared to older age groups. University graduates showed significantly higher scores than those with lower education levels. No significant associations were found with gender, donor type, or post-transplant duration.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e\u003cp\u003eE-health literacy among liver transplant recipients varies significantly based on sociodemographic factors, particularly age and education. These findings suggest the need for tailored digital health education programs to address disparities and improve health information access for vulnerable patient populations.\u003c/p\u003e","manuscriptTitle":"E-Health Literacy Among Liver Transplant Recipients: Assessment of Digital Health Competencies and Sociodemographic Correlates","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-11-19 05:14:50","doi":"10.21203/rs.3.rs-7797079/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewersInvited","content":"","date":"2025-11-10T06:17:17+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-05T06:15:56+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-10-14T18:09:23+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-10-14T11:28:36+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Health Services Research","date":"2025-10-14T11:25:26+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-health-services-research","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"bhsr","sideBox":"Learn more about [BMC Health Services Research](http://bmchealthservres.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/BHSR/default.aspx","title":"BMC Health Services Research","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"632104a2-2bc2-4a1b-8c43-f2b21212ae64","owner":[],"postedDate":"November 19th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-11-19T05:14:50+00:00","versionOfRecord":[],"versionCreatedAt":"2025-11-19 05:14:50","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7797079","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7797079","identity":"rs-7797079","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}