Cardiovascular Digital Health Equity Among Immigrant Populations.

Has traditional medicine had its day? The need to redefine academic medicine

The purpose of this concept analysis is to clarify the meaning of digital health equity beyond a simplistic definition, obtaining a richer meaning that can guide the digital healthcare landscape. With the growing spread of digital health, digital health equity should be at the center of healthcare. Health outcomes for equity-deserving groups may be compromised without a clear understanding of digital health equity. Although the concept of 'health equity' has been analysed before; no concept analysis has been completedfor the concept of 'digital health equity'. Concept analysis using Walker and Avant's method. Articles from PubMed, Scopus and Google Scholar with no limitation on the period of data collection. Walker and Avant's concept analysis method was used to outline attributes, antecedents, consequences, and empirical referents of the concept digital health equity. The main attribute of digital health equity is digital health technology that benefits everyone fairly. The antecedents include: (1) appropriate infrastructure; (2) cognitive abilities including digital literacy; (3) intersectionality of multiple vulnerabilities; (4) presence of the core ethical principles in healthcare; (5) digital accessibility with careful consideration of the social determinants of health; and (6) co-creation of digital health technologies. The main consequences are improved patient health outcomes and elimination of the digital divide. This analysis explored the concept of digital health equity as a means to promote positive health outcomes for equity-deserving groups, highlighting the critical role of nursing practice and research in addressing digital health disparities. This paper can have an impact on nursing practice, education and wider social and economic issues. First, various barriers encountered by patients when utilising digital health technologies can be understood. Second, clinicians can be encouraged to assess digital health equity, improve interventions for equity-deserving groups, and evaluate the effectiveness of digital health interventions to ensure they are equitable. In the context of educational implications, the understanding of digital health equity can be used to facilitate the creation of appropriate education materials for clinicians. Finally, on a wider social and economic scale, understanding digital health equity can aid in the creation of policies to enable equitable digital health technologies. No patient or public contribution because this paper is a concept analysis.

BackgroundThe potential benefits of incorporating digital technologies into health care are well documented. For example, they can improve access for patients living in remote or underresourced locations. However, despite often having the greatest health needs, people who are older or living in more socially deprived areas may be less likely to have access to these technologies and often lack the skills to use them. This puts them at risk of experiencing further health inequities. In addition, we know that digital health inequities associated with older age may be compounded by lower socioeconomic status. Yet, there is limited research on the intersectional barriers and facilitators for engagement with digital health technology by older people who are particularly marginalized.ObjectiveThis study aimed to explore factors influencing engagement with digital health technologies among people at the intersection of being older and socially deprived.MethodsWe conducted semistructured interviews with people who were 70 years or older, living in a socially deprived area, or both. Chronic kidney disease was our clinical context. We thematically analyzed interview transcripts using the Unified Theory of Acceptance and Use of Technology as a theoretical framework.ResultsWe interviewed 26 people. The majority were White British (n=20) and had moderate health and digital literacy levels (n=10 and n=11, respectively). A total of 13 participants were 70 years of age or older and living in a socially deprived area. Across participants, we identified 2 main themes from the interview data. The first showed that some individuals did not use digital health technologies due to a lack of engagement with digital technology in general. The second theme indicated that people felt that digital health technologies were “not for them.” We identified the following key engagement factors, with the first 2 particularly impacting participants who were both older and socially deprived: lack of opportunities in the workplace to become digitally proficient; lack of appropriate support from family and friends; negative perceptions of age-related social norms about technology use; and reduced intrinsic motivation to engage with digital health technology because of a perceived lack of relevant benefits. Participants on the intersection of older age and social deprivation also felt significant anxiety around using digital technology and reported a sense of distrust toward digital health care.ConclusionsWe identified factors that may have a more pronounced negative impact on the health equity of older people living in socially deprived areas compared with their counterparts who only have one of these characteristics. Successful implementation of digital health interventions therefore warrants dedicated strategies for managing the digital health equity impact on this group. Future studies should further develop these strategies and investigate their effectiveness, as well as explore the influence of related characteristics, such as educational attainment and ethnicity.

Digital health technologies and health-care privatisation

Designing digital health applications for climate change mitigation and adaptation.

Evidence on digital health interventions is rapidly emerging, but how health equity is being addressed remains uncertain. This study presents a conceptual framework that identifies causal pathways linking digital health and health equity, serving as an intervention logic model onto which existing and future evidence can be mapped. As an application of this framework, we conduct an umbrella review to map and narratively summarize published systematic reviews on the effectiveness, acceptability, and feasibility of digital health technology interventions. Systematic reviews were searched in electronic databases from 2005 until March 4, 2025, with a focus on chronic disease. Information on study characteristics, digital health technologies, and health equity considerations was extracted. Methodological quality was assessed using the AMSTAR-2 tool. Thirty-five studies met the eligibility criteria. The most prevalent intervention group was telemedicine, while clinical decision support systems were not the focus of any of the included studies. The systematic reviews varied markedly in quality, with most being equity-focused and addressing more than one dimension of horizontal inequality. Extending the mapping of existing research using the developed framework to other pathways—between digital health, health behaviours, and health outcomes—will help to better inform the design of interventions aimed at addressing the digital divide. Future studies should conduct equity-focused meta-analyses for specific interventions, health outcomes, and decision-making contexts.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12913-025-13696-4.

BackgroundDuring the COVID-19 pandemic, the rapid deployment of digital health technologies (DHTs), exemplified by the Health Code, quickly integrated many older adults, many of whom lacked digital readiness, into the digital society. This accelerated integration revealed a range of challenges faced by older adults in using DHTs. To effectively implement digital epidemic control measures while meeting the daily needs of the elderly, the Chinese government needs to adopt pragmatic policy responses to bridge the digital health divide.ObjectiveThis study investigates the barriers faced by older adults in using the Health Code and other DHTs during the COVID-19 pandemic, analyzes the policy measures implemented by the government to address these barriers, and summarizes China’s practical experiences in promoting digital health equity.MethodsThis study employed a multi-method qualitative research design, sequentially combining individual interviews and document analysis to comprehensively explore the research questions. Guided by the Unified Theory of Acceptance and Use of Technology, semi-structured interviews were conducted with 23 older adults residing in rural areas of Zhejiang Province to explore the factors that hinder their use of the Health Code and other DHTs. Informed by the Digital Health Equity Thematic Framework, framework analysis was applied to 92 policy documents to analyze governmental responses addressing these barriers. Finally, the results of the individual interviews and document analysis were integrated to achieve a comprehensive understanding of the barriers faced by older adults and the corresponding policy measures addressing them.ResultsThe interviews identified three major themes influencing older adults’ use of DHTs: Preparedness, Receptiveness, and Willingness, along with nine sub-themes. In response to these barriers, the government introduced a series of policy measures targeting five domains: Individual, Community/Social, Systems, Policy, and DHTs. By integrating the results from both phases of the study, we identified three user types based on technology-related barriers— non-users, conditional users, and independent users— and summarized three categories of policy tools: alternative-based tools, nudge-based tools, and boost-based tools.ConclusionsChina’s experience offers two key insights for advancing global digital health equity: first, assessing disparities in access, use, and benefits is essential to develop tailored, differentiated policy measures for diverse user groups; second, establishing a multi-stakeholder governance system, facilitated by the “3 C” framework (Create, Co-design, Collaborate), can promote collaboration and co-creation of inclusive digital health solutions.Supplementary InformationThe online version contains supplementary material available at 10.1186/s12939-025-02720-y.

To paraphrase a classic, evaluating digital technologies in health is a bit like eating spinach - no one is against it in principle because it is good for you. However, no one would do it unless being asked to. In recent years, the sheer number of digital health technologies that potentially fulfil public health purposes has increased tremendously. The basis for evaluating such tools for public health purposes however has not met this pace, and in particular frameworks for the systematic development and evaluation of digital technologies in public health are rare. Existing frameworks for digital technologies focus on clinical aspects of digital health applications (e.g., NICE Evidence standards framework for digital health technologies), thus lacking both a population and prevention focus. Generic frameworks such as the Health Technology Assessment (HTA) methodology do not contain items specific to digital technologies and public health purposes. Here, we describe the process of developing a framework specific for the development and evaluation of digital public health technologies based on the core HTA model. We conduct a scoping review of frameworks for the development and the evaluation of technologies in public health and digital health, following PRISMA-SCR guidelines. The identified frameworks are then mapped onto the core HTA model to develop additional items specific for the development and the evaluation of digital technologies in public health. These additional items can be used to integrate the development and evaluation of digital technologies for public health purposes within the wider HTA context, making this process both transferable and scalable.

There are dynamic interactions between (digital) technologies and society. Digital technologies have a(re-)structuring effect on social relationships and social innovations in avariety of ways. Because of these characteristics, technological innovations affect our individual lifestyles and living environments. In particular, the development and implementation of interventions with digital (health) technologies is attracting increasing national and international attention (e.g. telematics GP consultations and app-supported patient education programs).Digital health technologies enable new forms of interaction and knowledge-based reproduction in the field of health. The integration of potential users in the development process of digital health technologies and interventions requires the discussion of new research approaches. The interests, needs, and requirements of users may influence the nonuse of digital health technologies. It is above all the successful implementation, involving potential users, that can have an influence on acceptance and integrative use in the later course of care. The discourse on the participatory development and implementation of interventions with digital health technologies in the field of digital public health presents itself as acomplex process characterized by various theoretical approaches and methodological procedures and requiring representation, evaluation, and classification.

Digital health technologies have the potential to improve healthcare access, utilization, and experience for patients; at the same time, their development and use can reinforce, exacerbate, and even create health disparities. Applying a health equity lens to digital health innovations can help inform the equitable design and development of digital health tools. Specifically, areas of health equity impact that can be targeted in the development of a digital health technology include: the tool itself, including its design, technical development, integration into the healthcare environment, and evaluation; the technology’s relationship to various end-users, including individuals, tech proprietors and developers, and the larger healthcare system; and its impact on identified health and social determinant outcomes. Targeting one or more of these areas can help support the design, development, and deployment of digital health tools that actively work to reduce health disparities and promote health equity for socially disadvantaged patient populations. More research is needed to understand the full effect of digital health technology on health disparities, and to develop best practices for equity-centered digital health implementation and evaluation.

• Policy can render health equity in(visible) in the implementation of digital care • Concerns about privacy and data security supersede health equity concerns in policies • Digital equity should be considered a social determinant of health • Technological determinism in policy is subtle but has consequences for inclusion The digitalisation of care, whilst beneficial for some, also risks exacerbating health inequities if existing health (and social) disparities are not considered. Literature has indicated the broad, systemic causes of digital health inequities could be addressed through policy. This article aims to explore how health inequities are rendered (in)visible in and by digital care policies. We inductively analysed sixteen Dutch health policy documents focusing on digital care. Employing a constructivist grounded theory approach, we analysed documents to determine how health equity is addressed in relation to digital care. Although Dutch health policies do consider health inequities, it is not always shown in policies as a concept related to digital care. Health policies portray digital care as progressive and innovative, being able to shape healthcare in several positive ways. The risks of digital care are attended to less, with focus being placed mostly on privacy and data-security rather than also paying attention to digital health inequities. Policies either ignore digital health equity entirely or present digital health equity in ways that risk overlooking how digital care may subtly aggravate health inequities. This creates a blind spot in which technological deterministic narratives can be disguised. Current policies could unintentionally perpetuate exclusion by not highlighting the role of digital health inequities as a part of the health equity landscape. Policy needs to allow for digital health inequities to be better recognised, allowing digital care to drive, rather than limit, the possibilities for a more equitable future. Digital care is increasing in popularity, but risks excluding a significant number of people who usually already experience health inequities. Although Dutch health policy does consider health inequities, it is not shown in policies as a concept related to digital care. As a result, health equity risks being forgotten in the development of digital care. Policies portray digital care as being able to shape healthcare in a number of positive ways but do not address the risks it may pose in widening health inequities. Instead, issues like ensuring privacy receive more attention. By being overly optimistic about technology without being cautious about its other social consequences, achieving aims such as affordable and accessible care could be negatively impacted. Policy needs to allow for digital health inequities to be better recognised, allowing digital care to drive, rather than limit, the possibilities for a more equitable future.

Digital health implementations and investments continue to expand. As the reliance on digital health increases, it is imperative to implement technologies with inclusive and accessible approaches. A conceptual model can be used to guide equity-focused digital health implementations to improve suitability and uptake in diverse populations. The objective of this study is expand an implementation model with recommendations on the equitable implementation of new digital health technologies. The Digital Health Equity-Focused Implementation Research (DH-EquIR) conceptual model was developed based on a rigorous review of digital health implementation and health equity literature. The Equity-Focused Implementation Research for Health Programs (EquIR) model was used as a starting point and merged with digital equity and digital health implementation models. Existing theoretical frameworks and models were appraised as well as individual equity-sensitive implementation studies. Patient and program-related concepts related to digital equity, digital health implementation, and assessment of social/digital determinants of health were included. Sixty-two articles were analyzed to inform the adaption of the EquIR model for digital health. These articles included digital health equity models and frameworks, digital health implementation models and frameworks, research articles, guidelines, and concept analyses. Concepts were organized into EquIR conceptual groupings, including population health status, planning the program, designing the program, implementing the program, and equity-focused implementation outcomes. The adapted DH-EquIR conceptual model diagram was created as well as detailed tables displaying related equity concepts, evidence gaps in source articles, and analysis of existing equity-related models and tools. The DH-EquIR model serves to guide digital health developers and implementation specialists to promote the inclusion of health-equity planning in every phase of implementation. In addition, it can assist researchers and product developers to avoid repeating the mistakes that have led to inequities in the implementation of digital health across populations.

Digital health technologies can potentially reduce health disparities in cancer care. However, the benefits of digital health technology depend partly on users' digital health literacy, that is, "capabilities and resources required for individuals to use and benefit from digital health resources," which combines health and digital literacy. We examined issues for digital health technology implementation in cancer care regarding digital health literacy, via stakeholder consultation. Consumers, health care professionals, researchers, developers, nongovernment and government/policy stakeholders (N=51) participated in focus groups/interviews discussing barriers, enablers, needs and opportunities for digital health implementation in cancer care. Researchers applied framework analysis to identify themes of digital health literacy in the context of disparity and inclusion. Limited digital and traditional health literacy were identified as barriers to digital technology engagement, with a range of difficulties identified for older, younger and socio-economically or geographically disadvantaged groups. Digital health technology was a potential enabler of health care access and literacy, affording opportunities to increase reach and engagement. Education combined with targeted design and implementation were identified means of addressing health and digital literacy to effectively implement digital health in cancer care. Implementing digital health in cancer care must address the variability of digital health literacy in recipients, including groups living with disadvantage and older and younger people, in order to be effective. SO WHAT?: If cancer outcome disparity is to be reduced via digital health technologies, they must be implemented strategically to address digital health literacy needs. Health policy should reflect this approach.

Digital health technology provides opportunities to leverage artificial intelligence and other digital applications to promote cardiovascular health. Digital health technologies include artificial intelligence (such as machine learning [ML], neural networks),1 analytic systems, mobile apps, wearables, email, text messaging, and telemedicine.2 In this article, we review the role of digital technology in cardiovascular health and a selection of recent studies to evaluate the evidence of its effectiveness. Artificial intelligence is broadly defined as the capability of computer systems to perform tasks similar to humans.3 Examples include vision, speech, pattern recognition, and decision making. Machine learning is the ability of the computer program to learn from experience. This typically occurs from analysis of large sets of data processed through human-derived algorithms to enhance, predict, and explain outcomes.4 An example of the use of ML in clinical care is cardiovascular disease (CVD) prediction and electrocardiographic interpretation. Neural networks, named after the human nervous system, are nonlinear statistic models that control where signals are sent. Neural networks can be used for decision making such as cardiovascular diagnosis confirmation. Digital Technology Use in Cardiovascular Risk Assessment Several studies have demonstrated improved CVD risk factor identification using ML compared with traditional risk assessment tools. Researchers developed an ML risk calculator and compared it with the American College of Cardiology/American Heart Association CVD risk calculator in 6459 participants from the Multi-Ethnic Study of Atherosclerosis.5 Study participants were free of CVD at baseline and followed for 13 years. Results revealed that the American College of Cardiology/American Heart Association risk calculator was less precise: statin therapy was recommended to 46% of the sample, with 23.8% of CVD events occurring in those not recommended a statin. In comparison, the ML risk calculator recommended a statin to 11% of the sample, with 14.4% of CVD events occurring in those not recommended a statin.5 Similarly in 3 cohorts from Australia, 4 ML models were developed and compared with the 2008 Framingham model. The ML models provided 2.7% to 5.2% better predictions across all 3 cohorts.6 Taken together, the authors of these studies suggest ML provides promise in providing more precise estimates of CVD risk. Digital Health Interventions for Cardiovascular Disease Prevention Digital health interventions have the potential to provide a personalized approach to promote cardiovascular health. Behavior change theory is a key component of digital interventions and includes theoretical frameworks such as supportive accountability,7 self-efficacy theory,8 social cognitive theory, and the health belief model.9 Precision healthcare has been promoted for decades. Many of the challenges in operationalizing precision healthcare are healthcare accessibility, scheduling, care continuity, and inadequate knowledge exchange between provides and patients.10 Thus, promotion of healthy lifestyles and lifestyle risk factor reduction remain inadequately addressed in patients with CVD.11 To achieve sustainable change, individual-level personalized strategies may be leveraged through digital health interventions. Evidence of the effectiveness of digital health interventions has varied but is promising overall. Text messaging has been successfully used to provide information regarding healthy diet and physical activity recommendations, monitoring, and individual feedback. Text messaging has resulted in improvements in diet and activity in many (TextMe,12 Mobile MyPlate,13 MyQuest,14 Text-To-Move15), but not all studies.16 Smartphone/mobile apps have been designed to improve dietary and physical activity behavior. Examples include apps that track dietary patterns and activity through user input of text or visual images.17,18 Users can set their own goals and receive feedback on progress toward goals. Reviews of smartphone apps have had variable results with many demonstrating short-term improvement. Villinger et al19 conducted a systematic review and meta-analysis of the effectiveness of mobile app interventions on nutrition behaviors (41 studies, 27 randomized controlled trials [RCTs]). Findings revealed significantly improved nutrition behaviors and nutrition-related outcomes (P = .004 and P = .043, respectively). A second systematic review of 27, primarily RCTs, found significant between-group improvements in 19 of the 27 studies.20 A meta-analysis of 6 RCTs in adults using a smartphone app as the primary component of the intervention revealed a trend for more steps per day in the intervention compared with the control groups, with programs lasting less than 3 months more effective than longer programs.21 Taken together, text messaging and smartphone/mobile apps have the potential to improve lifestyle behaviors associated with cardiovascular health. The addition of strategies to increase sustainability of the effects needs to be assessed. Digital Health Interventions: Primary and Secondary Prevention Widmer et al2 conducted a meta-analysis of 51 RCTs and cohort studies using digital health interventions for the prevention of CVD events and risk factor modification. Subgroup analyses of primary prevention studies (2 studies) did not provide evidence of a statistically significant reduction in CVD outcomes. However, evaluation of individual risk factors in primary prevention studies found a significant reduction in weight (11 studies; −3.35 lb), systolic blood pressure (23 studies; mean difference, −2.12 mm Hg), total cholesterol (13 studies; mean difference, −5.19 mg/dL), low-density lipoprotein cholesterol (8 studies; mean difference, −4.96 mg/dL), and glucose (6 studies; mean difference, −1.38 mg/dL).2 A subgroup analysis of secondary prevention studies demonstrated a significant impact of digital interventions on CVD outcomes (relative risk, 0.60; a 40% relative risk reduction), improvement in body mass index (6 studies; mean difference, −0.31 kg/m2) but no improvement in weight, systolic blood pressure, total cholesterol, low-density lipoprotein cholesterol, and glucose. Taken together, this meta-analysis suggested that digital interventions were beneficial not only in lowering CVD events in higher-risk patients but also in lowering risk factors in primary prevention approaches.2 In a second meta-analysis conducted by Akinosun et al,11 researchers analyzed 25 RCTs in patients with traditional CVD risk factors who received a digital intervention versus usual care.11 Findings revealed benefits in total cholesterol (mean difference, −0.29), high-density lipoprotein cholesterol (mean difference, −0.09), low-density lipoprotein (mean difference, 0.18), physical activity (mean difference 0.23), physical inactivity (relative risk, 0.54), and diet (relative risk, 0.79). There was no significant improvement in body mass index, systolic and diastolic blood pressure, hemoglobin A1C, alcohol intake, smoking, and medication adherence. Authors concluded that digital interventions were more effective at improving healthy behaviors than reducing unhealthy behaviors. In patients who experienced a myocardial infarction, a digital health intervention providing medication reminders, vital sign and activity tracking, education, and outpatient care coordination resulted in a 52% lower 30-day readmission rate compared with usual care.22 Sociodemographic characteristics (age, sex, and race) did not influence use of the digital intervention, highlighting a potential role for digital interventions in the promotion of equity in social determinants of health.23 Digital Health Interventions in Cardiac Rehabilitation Cardiac rehabilitation is an essential component of secondary prevention of CVD.24 Some patients face barriers in participation in cardiac rehabilitation due to physical accessibility, time, and travel.25 Digital health interventions have the potential to bridge these barriers and increase participation. Digital delivery of cardiac rehabilitation therapy with real-time personalized support has several advantages.26 In a systematic review of 31 studies in which authors examined digital health interventions for cardiac rehabilitation, the results revealed that cardiac rehabilitation program adherence was greater in patients using digital interventions than traditional methods alone. Secondary benefits were found in self-efficacy, weight management, diet, and quality of life. Taken together, digital cardiac rehabilitation was feasible and effective whether used alone or in combination with traditional cardiac rehabilitation.26 Conclusion Digital health technology is an evolving field with tremendous potential to improve cardiovascular health. Cardiovascular disease remains the major cause of death in the United States. The age-adjusted mortality rate has increased in the last decade. More people died from CVD causes in 2020 (nearly 900 000 deaths) than any year since 2003.27 Opportunities to reduce CVD and CVD risk have not been fully leveraged, and digital technology interventions have the potential to meet this need. Digital health technology also has the potential to provide equitable and personalized care. Device data, electronic medical record data, and social determinants of health data provide an opportunity to combine and identify longitudinal trends and risk factors before CVD begins. In the future, large data sets can be created that can be analyzed using ML to identify patterns and structures within and among the data to provide a more robust risk assessment to promote CVD prevention.

Digital health technologies can potentially increase the efficiency and quality of pediatric palliative care (PPC), yet their use in home-based PPC remains limited. Limited digital health care literacy and inadequate training can reduce confidence and foster negative attitudes, whereas positive experiences and basic digital health care literacy may encourage adoption. This study aims to explore the use of digital health technologies by Norwegian health care personnel in home-based PPC and examine the association between their digital health care literacy and their attitudes toward digital health. A cross-sectional study was conducted from September 2023 to May 2024, with an online survey targeting health care personnel involved in home-based PPC through primary or specialist health care services. Data were collected using selected items from the Norwegian Healthcare Personnel Survey on eHealth 2022, the Digital Health Care Literacy Scale (DHLS), and the Information Technology Attitude Scales for Health (ITASH), alongside demographic characteristics. Higher DHLS scores indicate greater digital health care literacy, while higher ITASH scores reflect more positive attitudes toward digital health technologies. Pearson correlation, ANOVA, and multiple linear regression analyses were conducted to comprehensively explore the relationships and associations among the variables. Health care personnel (n=148) from diverse health care services responded to the survey. Half of the respondents (72/144, 50%) had experience with real-time video consultation, while phone calls were the primary communication method (138/145, 95.2%). Additionally, 55.6% (79/142) of the respondents had limited or minimal access to electronic health records from other health care services. Health care personnel perceived digital health technologies for remote PPC as a supplement (126/135, 93.3%) rather than a replacement for in-person care. Mean digital health care literacy was 18.29 (SD 3.8) on a scale from 0 to 23. On a scale from 1 to 4, the highest recorded scores pertained to attitudes toward digital health technologies in supporting care (mean 3.17, SD 0.39) and the perceived need for training (mean 3.16, SD 0.43). A statistically significant association was found between the respondents' level of digital health care literacy and their attitudes toward digital health technologies in supporting care (β=0.030, 95% CI 0.014-0.047; P<.001). This study examined the use of digital health technologies by Norwegian health care personnel in home-based PPC, their digital health care literacy, and attitudes toward digital health. Despite positive attitudes and high digital health care literacy, use of digital health technologies was limited, suggesting that inadequate digital health solutions may hinder effective implementation. Addressing these barriers is crucial to enhancing the implementation of digital health in home-based PPC. Future research should focus on integrating digital health technologies into existing infrastructure and workflows while exploring their impact on personalized care to ensure high-quality home-based PPC.