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eHealth literacy is an essential skill for pursuing electronic health information, particularly for older people whose health needs increase with age. South Korea is now at the intersection of a rapidly digitalizing society and an increasingly aged population. eHealth literacy enables older people to maximize the effective use of emerging digital technology for their health and quality of life. Understanding the eHealth literacy of Korean older adults is critical to eliminating the gray digital divide and inequity in health information access. This study aims to investigate factors influencing eHealth literacy in older Korean adults and its impact on health outcomes and eHealth use. This was a cross-sectional survey. Community-dwelling older adults 65 years and older in 2 urban cities in South Korea were included. eHealth literacy was measured by the eHealth Literacy Scale. Ordinal logistic regression was used to analyze factors associated with eHealth literacy and multivariate ANOVA for the impact of eHealth literacy on health outcomes and eHealth use. In total, 434 participants were analyzed. A total of 22.3% (97/434) of participants had high eHealth literacy skills. Increasing age, higher monthly income, and time spent on the internet were significantly associated with eHealth literacy (P<.001), and social media users were 3.97 times (adjusted odds ratio 3.97, 95% CI 1.02-15.43; P=.04) more likely to have higher skill. Higher eHealth literacy was associated with better self-perceived health and frequent use of digital technologies for accessing health and care services (P<.001). Disparity in socioeconomic status and engagement on the internet and social media can result in different levels of eHealth literacy skills, which can have consequential impacts on health outcomes and eHealth use. Tailored eHealth interventions, grounded on the social and digital determinants of eHealth literacy, could facilitate eHealth information access among older adults and foster a digitally inclusive healthy aging community.

This paper advances the “eHealth Literacy 3.0” model following Norman and Skinner’s 2006 original eHealth literacy 1.0 model and Norman’s 2011 2.0 update, and introduces a corresponding revision to the eHealth Literacy Scale (eHEALS) measurement instrument (eHEALS-R).

Digitalization is not fully implemented in clinical practice, and several factors have been identified as possible barriers, including the competencies of health care professionals. However, no summary of the available evidence has been provided to date to depict digital health competencies that have been investigated among health care professionals, the tools used in assessing such competencies, and the effective interventions to improve them. This review aims to summarize digital health competencies investigated to date and the tools used to assess them among health care professionals. A systematic review based on the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) checklist was performed. The MEDLINE, Cumulative Index to Nursing and Allied Health Literature, PsycINFO, and Scopus databases were accessed up to September 4, 2021. Studies assessing digital health competencies with quantitative designs, targeting health care professionals, and written in English were included. The methodological quality of included studies was evaluated using the Joanna Briggs Institute tools. A total of 26 studies, published from 1999 to 2021, met the inclusion criteria, and the majority were cross sectional in design, while only 2 were experimental study designs. Most studies were assessed with moderate to low methodological quality; 4 categories and 9 subcategories of investigated digital health competencies have been identified. The most investigated category was \"Self-rated competencies,\" followed by \"Psychological and emotional aspects toward digital technologies,\" \"Use of digital technologies,\" and \"Knowledge about digital technologies.\" In 35% (9/26) of the studies, a previously validated tool was used to measure the competencies assessed, while others developed ad hoc questionnaires. Mainly descriptive studies with issues regarding methodology quality have been produced to date investigating 4 main categories of digital health competencies mostly with nonvalidated tools. Competencies investigated might be considered while designing curricula for undergraduate, postgraduate, and continuing education processes, whereas the methodological lacks detected might be addressed with future research. There is a need to expand research on psychological and emotional elements and the ability to use digital technology to self-learn and teach others. PROSPERO International Prospective Register of Systematic Reviews CRD42021282775; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=282775.

To ensure that an eHealth technology fits with its intended users, other stakeholders, and the context within which it will be used, thorough development, implementation, and evaluation processes are necessary. The CeHRes (Centre for eHealth and Wellbeing Research) Roadmap is a framework that can help shape these processes. While it has been successfully used in research and practice, new developments and insights have arisen since the Roadmap’s first publication in 2011, not only within the domain of eHealth but also within the different disciplines in which the Roadmap is grounded. Because of these new developments and insights, a revision of the Roadmap was imperative. This paper aims to present the updated pillars and phases of the CeHRes Roadmap 2.0. The Roadmap was updated based on four types of sources: (1) experiences with its application in research; (2) literature reviews on eHealth development, implementation, and evaluation; (3) discussions with eHealth researchers; and (4) new insights and updates from relevant frameworks and theories. The updated pillars state that eHealth development, implementation, and evaluation (1) are ongoing and intertwined processes; (2) have a holistic approach in which context, people, and technology are intertwined; (3) consist of continuous evaluation cycles; (4) require active stakeholder involvement from the start; and (5) are based on interdisciplinary collaboration. The CeHRes Roadmap 2.0 consists of 5 interrelated phases, of which the first is the contextual inquiry, in which an overview of the involved stakeholders, the current situation, and points of improvement is created. The findings from the contextual inquiry are specified in the value specification, in which the foundation for the to-be-developed eHealth technology is created by formulating values and requirements, preliminarily selecting behavior change techniques and persuasive features, and initiating a business model. In the Design phase, the requirements are translated into several lo-fi and hi-fi prototypes that are iteratively tested with end users and other stakeholders. A version of the technology is rolled out in the Operationalization phase, using the business model and an implementation plan. In the Summative Evaluation phase, the impact, uptake, and working mechanisms are evaluated using a multimethod approach. All phases are interrelated by continuous formative evaluation cycles that ensure coherence between outcomes of phases and alignment with stakeholder needs. While the CeHRes Roadmap 2.0 consists of the same phases as the first version, the objectives and pillars have been updated and adapted, reflecting the increased emphasis on behavior change, implementation, and evaluation as a process. There is a need for more empirical studies that apply and reflect on the CeHRes Roadmap 2.0 to provide points of improvement because just as with any eHealth technology, the Roadmap has to be constantly improved based on the input of its users.

Digital health technology (DHT) plays an increasingly vital role in managing chronic diseases by enabling patients to actively manage their health. These tools have been shown to improve self-management and adherence to medical advice. However, for DHT to be fully effective, patients with chronic conditions must be digitally literate. The eHealth Literacy Scale (eHEALS), an 8‑item tool with scores ranging from 8 to 40, was developed to assess individuals' perceived ability to find, evaluate, and apply digital health information. Assessing patients' digital health literacy (DHL) and understanding the factors influencing it are essential for improving the accessibility and usability of health resources. This study aimed to assess DHL in patients with diabetes mellitus (DM), hypertension, and rheumatoid arthritis (RA) through a systematic review and meta‑analysis using eHEALS. We sought to determine average DHL scores, examine demographic and socioeconomic factors influencing DHL, and explore its impact on disease management to inform future strategies for enhancing DHL and improving chronic disease outcomes. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, we conducted a systematic review across 7 databases (PubMed, SCOPUS, Embase, ERIC, CINAHL, Library Literature and Information Science Index, and Google Scholar) from inception to August 14, 2022, with an updated search in October 2024. Eligible studies included adults (≥18 years) with DM, hypertension, or RA who reported DHL data using eHEALS (8-40) and were original research published in English. Exclusion criteria included studies involving participants younger than 18 years, reviews, meta‑analyses, studies not addressing the target diseases, or non‑English publications. Study quality was evaluated using the Newcastle‑Ottawa Scale (NOS). Eight studies involving 2527 participants were included. The pooled mean eHEALS score was 27.03 (95% CI 25.08-28.98), indicating high overall DHL. Stratified by disease, scores were higher for DM (27.79) and hypertension (28.48) but lower for RA (24.74). Quality assessment indicated a high standard of included studies. Factors influencing DHL included age, education, employment, and perception of the internet as a health resource. Due to the limited number of studies, meta‑regression analysis could not be performed. DHL is critical for individuals with chronic conditions, empowering them to make informed decisions and manage their health effectively. However, the scarcity of studies limits comprehensive analysis of DHL determinants. While the internet offers abundant health information, unequal DHL and health skills remain barriers. More inclusive research is needed to fully understand DHL's impact on health outcomes and mitigate disparities, ensuring equitable access to digital health resources and improving disease management.

Maintenance of good health and a healthy lifestyle have significant impacts on the lives of university students. However, university students are prone to engage in risky health behaviors, resulting in impaired health status. Electronic health (eHealth) literacy is an important factor in maintaining a healthy lifestyle. However, no studies have assessed the eHealth literacy levels and the associated lifestyle behaviors among university students in Japan. The purposes of this study were to clarify the eHealth literacy level, the participant characteristics associated with eHealth literacy, and the association of eHealth literacy with lifestyle behaviors of students in a Japanese university. A questionnaire-based cross-sectional study of 3183 students at a national university in Japan was conducted. eHealth literacy was quantified using the Japanese version of the eHealth Literacy Scale (eHEALS). The association between participant characteristics (gender, school year, department of study, and living status) and eHEALS score was assessed using t tests. Additionally, the associations of eHealth literacy with lifestyle behaviors (exercise, smoking, alcohol consumption, etc.) were evaluated using logistic regression analyses. The mean eHEALS score was 23.6/40 points. The mean eHEALS score for students in medical departments was 27.0/40 points, which was 2.9 points higher than that of nonmedical students (P<.001). Similarly, the graduate school participants had higher scores than the undergraduate students. The proportion of participants who exercised regularly was higher in the high eHEALS score group than in the low score group, with an adjusted odds ratio of 1.39 (P<.001). The eHealth literacy level of university students in Japan was comparable to that of the general Japanese population. Graduate students, as well as those in medical departments, had higher eHealth literacy. Furthermore, students with higher eHealth literacy had better exercise routines.

Purpose of ReviewThis review summarizes HIV care delivered via telemedicine before and during the COVID-19 pandemic and highlights areas of study to inform optimal usage of telemedicine in HIV clinical practice in the future.Recent FindingsTo address barriers to care created by the COVID-19 pandemic, regulatory agencies and payors waived longstanding restrictions, which enabled rapid expansion of telemedicine across the country. Preliminary data show that providers and persons with HIV (PWH) view telemedicine favorably. Some data suggest telemedicine has facilitated retention in care, but other studies have found increasing numbers of PWH lost to follow-up and worsened virologic suppression rates despite offering video and/or telephone visits.SummaryThe COVID-19 pandemic has exacerbated gaps in the HIV care continuum. To help mitigate the impact, most clinics have adopted new virtual care options and are now evaluating usage, impact, and concerns. Further research into the effects of telemedicine on HIV care and continued work towards universal access are needed.

Digital health (dHealth) technologies, such as telehealth, artificial intelligence (AI), and mobile apps, are increasingly essential in medical practice. However, despite their growing significance, medical curricula often lack structured dHealth training, leaving students underprepared for digitally integrated health care environments. This study investigates the factors influencing medical students' intentions to integrate dHealth technologies into their future practice and examines changes in their perceptions over time. We conducted a 2-phase survey at a large Canadian medical school to assess changes in perceptions before (N=184) and after (N=177) the COVID-19 pandemic. A mixed methods approach combined component-based structural equation modeling and fuzzy-set qualitative comparative analysis. The model was grounded in the technology acceptance model and Triandis' theory of interpersonal behavior, examining constructs such as individual background, facilitating conditions, perceived usefulness, and beliefs about AI. Across both phases, over 85% (306/361) of students agreed that dHealth education should be a mandatory component of medical training. Mean ratings for intention to use dHealth in future practice increased significantly between t0 and t1 for patient communication (3.4 to 4.2, P<.001), monitoring (3.3 to 4.0, P<.001), and diagnosis/treatment (3.6 to 4.2, P<.001). Experience with AI tools increased from 1.3 to 1.5 (P<.001), and telehealth from 1.2 to 1.6 (P<.001), while exposure to hospital IT systems and mobile apps remained unchanged. Results confirmed that perceived usefulness (β=.37 at t0; β=.34 at t1) and beliefs about AI (β=.39 at t0; β=.27 at t1) were strong predictors of intention to integrate dHealth (P<.001). The explanatory power of the structural equation modeling model declined postpandemic (R²=0.53 at t0 vs R²=0.25 at t1), suggesting increasing complexity in influencing factors. Fuzzy-set qualitative comparative analysis revealed multiple configurations leading to high intention, with consistency values exceeding 0.88 and overall solution coverage of 0.74 postpandemic. Core conditions across high-intention profiles included strong beliefs in the role of AI and perceived importance of dHealth education. Conversely, gender appeared as a recurring core condition in non-high-intention configurations, suggesting persistent disparities in dHealth adoption. The study advocates for the integration of formal dHealth training in medical curricula to better prepare future physicians for the demands of an increasingly digital health care landscape. While the COVID-19 pandemic may have contributed to shifting perceptions, other factors, such as recent AI advancements, likely played a role. These findings highlight the urgent need for medical education to adapt to the changing dHealth environment.

Background eHealth Literacy (eHL) is a set of competencies and skills encompassing the knowledge, comfort and perceived ability to identify, evaluate and apply electronic health information to health problems. Given its role in the appropriate use of health technologies, ensuring equitable access to health information and improving patient outcomes, this study aims to systematically retrieve, qualitatively and quantitative pool and critically appraise available experimental evidence on the effectiveness of eHL interventions across different population groups. Methods Following the PRISMA guidelines, we conducted a systematic review in PubMed/Medline, Scopus, Web of Science, Embase, Cochrane Library and ClinicalTrials.gov, including original experimental studies quantifying the effectiveness of interventions aimed at increasing eHL, as assessed by the eHealth Literacy Scale (eHEALS) or other validated scales. We performed a random-effects model meta-analysis comparing changes in eHL levels before and after the interventions, and between the intervention and control groups. Heterogeneity was assessed using I 2 statistics. Results Out of the 504 studies retrieved, 15 studies conducted between 2011 and 2023 met the inclusion criteria. Target populations of eHL interventions included adults in 7 studies, older people in 5 and young people in 4. The meta-analysis included 10 studies that used the eHEALS. Participants showed a mean increase in eHEALS scores of 5.81 points (95% CI = 3.36–8.26, N  = 1025) following the eHL interventions compared to the pre-intervention period. In the analysis between the intervention and control groups, we found a statistically significant difference in eHL improvement in favour of the intervention group, with mean eHEALS scores 3.62 points (95% CI = 1.63–5.60, N  = 1258) higher in the intervention group than in the control groups. Subgroup analyses by intervention type, stratified by Collaborative Learning (CL) or Individualistic Learning (IL) showed significant increases in eHealth Literacy in the pre-post intervention analysis (CL: UMD = 5.19, CI = 0.01–10.38, N  = 402; IL: UMD = 6.05; CI = 3.14–8.97, N  = 623) and in the intervention vs. control analysis in the IL group (DMD = 4.98; CI = 1.77–8.12, N  = 540). Conclusions Our findings support the effectiveness of tailored interventions in significantly enhancing eHL, providing key insights for evidence-based intervention design targeted to different population groups.

eHealth literacy has increasingly emerged as a critical determinant of health, highlighting the importance of identifying its influencing factors; however, these factors remain unclear. Numerous studies have explored this concept across various populations, presenting an opportunity for a systematic review and synthesis of the existing evidence to better understand eHealth literacy and its key determinants. This study aimed to provide a systematic review of factors influencing eHealth literacy and to examine their impact across different populations. We conducted a comprehensive search of papers from PubMed, CNKI, Embase, Web of Science, Cochrane Library, CINAHL, and MEDLINE databases from inception to April 11, 2023. We included all those studies that reported the eHealth literacy status measured with the eHealth Literacy Scale (eHEALS). Methodological validity was assessed with the standardized Joanna Briggs Institute (JBI) critical appraisal tool prepared for cross-sectional studies. Meta-analytic techniques were used to calculate the pooled standardized β coefficient with 95% CIs, while heterogeneity was assessed using I2, the Q test, and τ2. Meta-regressions were used to explore the effect of potential moderators, including participants' characteristics, internet use measured by time or frequency, and country development status. Predictors of eHealth literacy were integrated according to the Literacy and Health Conceptual Framework and the Technology Acceptance Model (TAM). In total, 17 studies met the inclusion criteria for the meta-analysis. Key factors influencing higher eHealth literacy were identified and classified into 3 themes: (1) actions (internet usage: β=0.14, 95% CI 0.102-0.182, I2=80.4%), (2) determinants (age: β=-0.042, 95% CI -0.071 to -0.020, I2=80.3%; ethnicity: β=-2.613, 95% CI -4.114 to -1.112, I2=80.2%; income: β=0.206, 95% CI 0.059-0.354, I2=64.6%; employment status: β=-1.629, 95% CI -2.323 to -0.953, I2=99.7%; education: β=0.154, 95% CI 0.101-0.208, I2=58.2%; perceived usefulness: β=0.832, 95% CI 0.131-1.522, I2=68.3%; and self-efficacy: β=0.239, 95% CI 0.129-0.349, I2=0.0%), and (3) health status factor (disease: β=-0.177, 95% CI -0.298 to -0.055, I2=26.9%). This systematic review, guided by the Literacy and Health Conceptual Framework model, identified key factors influencing eHealth literacy across 3 dimensions: actions (internet usage), determinants (age, ethnicity, income, employment status, education, perceived usefulness, and self-efficacy), and health status (disease). These findings provide valuable guidance for designing interventions to enhance eHealth literacy. PROSPERO CRD42022383384; https://www.crd.york.ac.uk/PROSPERO/view/CRD42022383384.