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Abstract
https://cardio.jmir.org/2022/2/e37360 JMIR Cardio 2022 | vol. 6 | iss. 2 | e37360 | p. 1 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Ramasawmy et al Background: Digital health interventions have become increasingly common across health care, both before and during the COVID-19 pandemic. Health inequalities, particularly with respect to ethnicity, may not be considered in frameworks that address the implementation of digital health interventions. We considered frameworks to include any models, theories, or taxonomies that describe or predict implementation, uptake, and use of digital health interventions. Objective: We aimed to assess how health inequalities are addressed in frameworks relevant to the implementation, uptake, and use of digital health interventions; health and ethnic inequalities; and interventions for cardiometabolic disease. Methods: SCOPUS, PubMed, EMBASE, Google Scholar, and gray literature were searched to identify papers on frameworks relevant to the implementation, uptake, and use of digital health interventions; ethnically or culturally diverse populations and health inequalities; and interventions for cardiometabolic disease. We assessed the extent to which frameworks address health inequalities, specifically ethnic inequalities; explored how they were addressed; and developed recommendations for good practice. Results: Of 58 relevant papers, 22 (38%) included frameworks that referred to health inequalities. Inequalities were conceptualized as society-level, system-level, intervention-level, and individual. Only 5 frameworks considered all levels. Three frameworks considered how digital health interventions might interact with or exacerbate existing health inequalities, and 3 considered the process of health technology implementation, uptake, and use and suggested opportunities to improve equity in digital health. When ethnicity was considered, it was often within the broader concepts of social determinants of health. Only 3 frameworks explicitly addressed ethnicity: one focused on culturally tailoring digital health interventions, and 2 were applied to management of cardiometabolic disease. Conclusions: Existing frameworks evaluate implementation, uptake, and use of digital health interventions, but to consider factors related to ethnicity, it is necessary to look across frameworks. We have developed a visual guide of the key constructs across the 4 potential levels of action for digital health inequalities, which can be used to support future research and inform digital health policies. (JMIR Cardio 2022;6(2):e37360) doi: 10.2196/37360 KEYWORDS eHealth; framework; cardiometabolic; health inequalities; health inequality; health technology; ethnicity; minority; digital health; review; cultural; diverse; diversity; cardiology; metabolism; metabolic include those adapted from other fields [12,13], as well as those Introduction developed specifically for health and health care technology [14]. Despite multiple ways of analyzing health inequalities Individuals of an ethnic minority background constitute at least [15], frameworks have often overlooked the experiences of 14% of the UK population [1] and have an increased risk of ethnic minority populations. Given the excess cardiometabolic type 2 diabetes [2] and cardiovascular disease [3] (together, also burden faced by ethnic minority groups, digital health known as cardiometabolic disease), particularly South Asian interventions designed for cardiometabolic disease are an and Black individuals. Even before, but particularly during, the important area of study. COVID-19 pandemic, digital health interventions became important in the education, prevention, diagnosis, treatment, This scoping review aims to identify existing frameworks, and rehabilitation [4,5] of diseases such as cardiometabolic models, or theories that address (1) implementation, uptake, disease [6,7]. and use of digital health interventions by end users; (2) health interventions in ethnically or culturally diverse populations; or Whether via smartphones, websites, or text messaging, digital (3) interventions for cardiometabolic disease. For identified health interventions need to be culturally competent (ie, able to frameworks, we examine the extent to which they include and meet the needs of users with diverse values, beliefs, and how they address health inequalities, specifically regarding behaviors) to be accessible to all [8,9], but the effectiveness of ethnicity and relevance to ethnic inequalities in cardiometabolic digital health interventions may vary across different groups disease. (by age, clinical need, socioeconomic, or other factors) [7]. Moreover, unequal access to hardware, software, and the Methods internet, as well as variations in digital literacy, create a digital divide through which digital health interventions could Search Strategy and Selection Criteria exacerbate existing socioeconomic, educational, and health We conducted this review in accordance with PRISMA-ScR inequalities [10,11]. Therefore, digital health interventions, (Preferred Reporting Items for Systematic Reviews and similar to other health interventions, require robust evaluation Meta-Analyses for Scoping Reviews) guidelines (Multimedia before and after implementation, by using frameworks that take Appendix 1). We included papers that presented a new, revised, into account society-level (eg, political context, or adapted framework that could be used to understand either interorganizational networks), system- or organization-level factors in: the adoption and acceptance of digital health; or (eg, organizational capacity and engagement), and individual cardiometabolic interventions; or sociodemographic inequalities (eg, literacy, financial resources) factors. Existing frameworks in health (Multimedia Appendix 2). We considered frameworks https://cardio.jmir.org/2022/2/e37360 JMIR Cardio 2022 | vol. 6 | iss. 2 | e37360 | p. 2 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Ramasawmy et al to be any models, theories, or taxonomies. There are multiple summarized, the remainder of the papers were charted. Citation definitions of implementation and the technology acceptance details, evidence type, framework context, framework focus, lifecycle [16,17]. We focused on 3 stages: implementation and framework beneficiary were charted. Qualitative analysis (putting interventions to use within a setting) [17], uptake was conducted. Data are reported according to PRISMA-ScR (adoption by end users), and use (sustained use and acceptance) [20]. Papers were assessed for the degree to which they [16]. We excluded frameworks aimed at delivery processes, considered factors related to inequalities: this was defined technology development processes, or economic assessments. broadly to include racial, ethnic, or cultural diversity; health Given the extensive literature on frameworks for technology inequalities; digital inequalities; or social determinants of health. adoption, only papers that presented frameworks that have been designed or adapted to health and care settings were included. Results There was no limit on publication date. Scoping Review Information Sources A total of 7830 unique records were identified. A total of 58 SCOPUS, PubMed, EMBASE, and Google Scholar were papers were included (Figure 1; Multimedia Appendix 7), of searched electronically in April 2021 (by MR). Gray literature which 32 papers included adapted or extended existing was identified via OpenGrey [18] and the New York Academy frameworks. A majority included the Technology Acceptance of Medicine Grey Literature Report [19]. Model [21-37] or the Unified Theory of Acceptance and Use of Technology [26,27,38-43]. New frameworks, developed from Search the review and synthesis of existing frameworks or from An initial keyword search (“digital” AND “health” AND empirical research, were proposed by 26 papers [14,15,44-67]. “ethnicity” AND “cardiometabolic” AND “framework”) First author institution was listed in Europe, North America, or demonstrated that there was no existing systematic or scoping Australia for the majority of papers (n=39) review that addressed ethnic digital health inequalities. The 3 [14,23,24,31-33,35,37,39,43,44,46-48,51-55,58-77]; Asia or areas of interest for review were used to define relevant the Middle East (n=13); and South Africa (n=2) [50,57]. The keywords for the search strategy (Multimedia Appendix 3). remaining had first authors with affiliations in more than one country [15,26,27,36,56]. Many papers did not specify the Study Selection geographic location in which the framework was designed for Search result records were imported into Rayyan (Qatar use or testing [14,15,24,27,31,35,44-46,49-55,58-61,68,69, Computing Research Institute) after removing duplicate records. 71,74,75] (n=25); of those that did, the majority (n=14) were Title and abstract screening against inclusion and exclusion developed or tested in Europe, North America, or Australia criteria were conducted by a team (AC, AGM, JP, LP, MB, [37,39,43,47,62-67,70,72,76,77]. MM, MR, PJ, ZTB), with 2 rounds of testing in which any queries were discussed. The guide for interpretation of the The majority of frameworks had digital health interventions or inclusion criteria that was developed via this iterative approach health technology (such as electronic health records, or remote can be found in Multimedia Appendix 4. Additional frameworks monitoring) as the only or key focus (n=39). Fifteen of the identified at the abstract screening stage were searched for and remaining frameworks considered at least two of digital health added to the full-text review (Multimedia Appendix 5). Full interventions, health inequalities and ethnicity, or texts were reviewed (by MR) if abstracts lacked sufficient cardiometabolic disease. The purpose of most frameworks was information. The final selection was made by 2 authors (MR to understand factors related to the adoption, acceptance, and and LP); disagreements in study selection were resolved by use of digital health technology (n=43), with the remaining discussion until consensus was reached, or with a third reviewer frameworks (n=15) considering health inequalities, chronic (ZTB) when it was not reached. disease management, and evaluation of interventions. In the majority of papers, the end user who was likely to benefit from Data Analysis the application of the framework was either a patient or member Data charting was piloted on 10 randomly selected papers and of the public (eg, as targets for interventions for disease refined to ensure consistency across researchers (categories of prevention or management) (n=33) or a clinician (n=5). Seven information are set out in Multimedia Appendix 6). Data frameworks focused on the intervention or technology itself. charting was repiloted on 10 additional studies and after a final The remaining frameworks had no specific end user or covered review to ensure agreement in information extracted and a combination of benefits. https://cardio.jmir.org/2022/2/e37360 JMIR Cardio 2022 | vol. 6 | iss. 2 | e37360 | p. 3 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Ramasawmy et al Figure 1. Paper selection flowchart. and impact on access to care [32] in low- or middle-income Extent of Inclusion of Health Inequalities in Existing countries into account; these frameworks were assessed as Frameworks having limited applicability to the specific challenges of Over half of the papers that showed no or limited inclusion of multiethnic populations in Western countries. Some frameworks inequalities (26/36) did not address inequalities in either the that focused on understanding patient or public acceptance of body text or the framework themselves. A few papers (n=7) and engagement with digital health interventions considered acknowledged the wider socioeconomic context in the paper or how these may be affected by factors related to health or digital included a high-level reference to social or contextual factors inequalities, for example, tech generation (experience of that might influence uptake and use of health technology, for individuals of different age groups of different technologies), example, by including the factor broad context [44]. Another health literacy, and education [58]; demographic, psychological, group of frameworks took digital access into account within physical, and social factors [59]; or personal lifestyle factors the facilitating conditions construct, based on either the [60] (Table 2). Many papers that looked specifically at ethnic Technology Acceptance Model [28] or the Unified Theory of inequalities in health frameworks included ethnicity in the Acceptance and Use of Technology [41,43]. Many were focused demographic factors element of the framework itself on the factors affecting adoption and use in specific populations, [15,25,59,61,62,74-76] or discussed ethnicity in the such as older adults (n=6), the workforce (n=8), or in Asian or accompanying text [63-65]. Notably, Schillinger [65] discussed low- and middle-income contexts (n=5) (Table 1). the limitations of current research on health literacy and known racial and ethnic health disparities [65]. Only 3 frameworks A few frameworks took the specific challenges of mobile health (Table 2) focused on the mechanisms through which ethnicity (mHealth) readiness [56], adoption [26,57], acceptance [23], impacts health and engagement with interventions [25,66,76]. https://cardio.jmir.org/2022/2/e37360 JMIR Cardio 2022 | vol. 6 | iss. 2 | e37360 | p. 4 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Ramasawmy et al Table 1. Frameworks with no or limited consideration of ethnic and social inequalities in health. Reason for which papers were deemed to have no or limited consideration and the key focus of the framework Papers (n=36) Reference n Does not address health or digital inequalities (population) Older adults or elderly populations [21,31,36,45,68] 5 Health care professionals [27,40,46-48,69] 6 Workplace or workforce [34,42] 2 South Asian and low- and middle-income contexts [21,29,30,33] 4 Other [24,39,49-52,70,71] 8 Review paper [35] 1 Acknowledgment of contextual factors in the paper only Digital cardiovascular prevention [37] 1 Implementation effectiveness [53] 1 High-level factoring of the wider context in the framework figure Engagement with health apps [72] 1 Integration of health interventions into health systems [44] 1 High-level factoring of social factors or access into the framework Digital access considered within the facilitating conditions construct of the Technology Acceptance 3 Model or the Unified Theory of Acceptance and Use of Technology variant Electronic health record adoption [43] Older adults [41] Tested in Pakistan [28] Model includes broadly defined factors such as sociodemographic factors 3 National culture differences in acceptance [73] Telehealth in chronic disease intervention design and evaluation [54] Implementation planning and evaluation [55] https://cardio.jmir.org/2022/2/e37360 JMIR Cardio 2022 | vol. 6 | iss. 2 | e37360 | p. 5 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Ramasawmy et al Table 2. Frameworks that show some or detailed consideration of ethnic and social inequalities in health. Reason for which papers were deemed to show some or detailed consideration and the key focus of the framework Papers (n=22) Reference n Model aimed at global health inequalities or developed in low- or middle-income countries [26,57] 2 mHealth adoption in developing world mHealth readiness, developed in rural Bangladesh [56] 1 mHealth contributions to care access, sub-Saharan Africa [32] 1 mHealth interventions targeted at low-literacy end users in resource-limited settings [23] 1 Includes factors related to health or digital inequalities Acceptance of remote patient management [58] 1 Engagement and recruitment to digital health intervention [59,60] 2 Nonadoption, Abandonment, Scale-up, Spread, and Sustainability framework [14] 1 Framework aims to address health inequalities or to be used in populations facing health inequalities Health inequalities 3 A Conceptual Framework for Action on the Social Determinants of Health [15] Community Chronic Care Model [77] Conceptual Framework for the Pathways that Connect Social Determinants of Health, Health Literacy [65] and Health Disparities Digital health and access or inequalities 6 eHealth Equity Framework [74] Digital Health Equity Framework [75] The Updated Integrative Model of eHealth Use [63] Modeling the process of using an eHealth tool by people vulnerable to social health inequalities [61] Culture-centered Technology Acceptance Model [25] Pathways of access, use, and benefit from digital health services [64] Cardiometabolic disease and inequalities 4 Conceptual framework for understanding the development and role of financial barriers for patients with [67] cardiovascular-related chronic diseases A Gender-Centered Diabetes Management Education Ecological Framework [76] Diabetes in Ageing and Diverse Populations [66] Workforce Evidence-Based model for diabetes [62] mHealth: mobile health. of theoretical approaches used, for example, adaptation of an How Frameworks Address Health Inequalities existing model of social determinants of health to digital health We identified 13 frameworks that explicitly aimed to understand [74,75], adaptation of existing models such as the Technology or address general health inequalities [15,65,77], health Acceptance Model for interventions or innovation [25,63,77], inequalities in relation to the management of cardiometabolic and the development of novel frameworks through methods disease [62,66,67,76], digital health equity [61,63,64,74,75], or such as grounded theory or thematic analysis [61,62,66,67] recommendations on how to culturally tailor digital health (Table 3). approaches [25] (Table 3). Key factors or constructs in these Some frameworks delineated the interaction between these frameworks [15,25,61-67,74-77] could be mapped to the 4 levels levels to account for how health inequalities occur [15,65,77]. of action in which digital health care is seen to operate—society Such frameworks tended to focus on the top-down processes or population, health care system, intervention, and individual by which societal and system factors filter down to affect health (Figure 2)—and 5 frameworks included factors in all 4 levels, outcomes [15,65,77]. For example, the Community Chronic for example, individual health status and beliefs, support for Care Model [77] was used to demonstrate how community digital health use, social policy and action, and cultural resources and health care provider systems contribute to adaptations of the intervention [25,66,74-76]. The wide scope improved community-wide health outcomes. Schillinger [65] of factors included in these frameworks reflects the diversity brought together research from multiple disciplines, such as https://cardio.jmir.org/2022/2/e37360 JMIR Cardio 2022 | vol. 6 | iss. 2 | e37360 | p. 6 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Ramasawmy et al epidemiology, anthropology, and public health, to describe two Three frameworks targeted the design and implementation of routes through which social determinants of health act on health digital health interventions. In 2 papers [61,64], the use of digital outcomes and health disparities: unequal distribution of health tools by people vulnerable to social inequalities and resources and the health care systems themselves. opportunities to identify and address barriers were discussed. In another paper [25], the extension of the Technology We identified 3 frameworks [63,74,75] that were developed as Acceptance Model, by integrating Community Infrastructure tools to understand and address the potential role of digital Theory, was described and approaches to engage with health interventions in exacerbating existing health inequalities. marginalized populations were tested. The eHealth Equity Framework [74], based on the World Health Organization’s Commission on Social Determinants of Health We found 4 frameworks relevant to cardiometabolic disease. conceptual framework [15], incorporates technology into the Two frameworks looked at socioeconomic factors affecting macro socio-techno-economic-political context with health inequalities: one focused on supporting health care intermediary determinants of health care access and use, such professionals to identify and support at-risk groups [62], and as material circumstances, social capital, and literacy. Similarly, the other considered the role of financial barriers on outcomes the Digital Health Equity Framework [75] integrated digital for patients with cardiovascular-related chronic diseases [67]. determinants of health and digital health equity into known Two frameworks aimed to improve outcomes for diabetes in health equity factors based on previous work [78]. The Updated specific ethnic minority groups: older South Asian adults in the Integrated Model of eHealth Use describes how social United Kingdom [66] and Black men in the United States [76]. determinants of health impact user interactions with health technologies and health outcomes [63]. Figure 2. Guide showing how framework constructs that consider inequalities map onto the 4 levels of action. https://cardio.jmir.org/2022/2/e37360 JMIR Cardio 2022 | vol. 6 | iss. 2 | e37360 | p. 7 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Ramasawmy et al Table 3. Frameworks that consider equity in digital health or cardiometabolic disease intervention. Framework or key focus Reference Purpose Theoretical basis Intended audience Digital health equity (conceptual) eHealth Equity Framework [74] Apply a health equity approach within eHealth World Health Organi- Public health, research, zation Conceptual policy, health technolo- Framework for Action gy development on the Social Determi- nants of Health [15] Digital Health Equity [75] Identify the digital determinants of health and their Health equity measure- Research, health (ser- Framework links to digital health equity ment framework [78] vice) implementation Updated Integrative Model [63] Understand how (digital and health) literacy con- Integrative Model of Health communication, of eHealth Use tributes to health and well-being eHealth Use [79] public health Equitable digital health services Pathways of access, use, and [64] Map key factors influencing digital health service Frameworks of access Research, policy, health benefit from digital health outcomes to health services services, and public services health Equitable digital health intervention design Modeling the process of us- [61] Identify stages of the process of using an eHealth Structural Influence Research, health tech- ing an eHealth tool by peo- tool that can account for reducing barriers for those Model nology development ple vulnerable to social at risk of social health inequalities health inequalities Culture-centered Technolo- [25] Describe factors that account for people's social Technology Accep- Policy, health technolo- gy Acceptance Model and cultural needs when considering technology tance Model [80] gy, or intervention de- acceptance velopment Reducing impact of inequalities in patients with cardiometabolic disease Conceptual framework for [67] Understand the patient experience of financial bar- None specified Research, clinical, poli- understanding the develop- riers and impact on behavior and clinical outcomes cy ment and role of financial (in relation to chronic disease) barriers for patients with cardiovascular-related chronic diseases Workforce Evidence-Based [62] Recognize and manage the complex needs of indi- None specified Clinical, research, model for diabetes vidual patients with chronic disease health education, health service, and workforce planning Diabetes in Ageing and Di- [66] Map how links between cultural competency, co- Realist review ap- Research verse Populations morbidity and stratification, and access can con- proach, underpinned tribute to effective diabetes care for aging and di- by the theme of indi- verse populations vidualized care A Gender-Centered Diabetes [76] Incorporate gender into an understanding of vari- Key focus is theories Research (diabetes edu- Management Education ables that affect diabetes health outcomes of gender cation) Ecological Framework Community Chronic Care [77] Map how community and health care provider Chronic Care Model, Community and health Model systems interact with other influences to improve concepts of communi- care provider organiza- community-wide health outcomes and eliminate ty tions, research, clinical health disparities acceptance, such as enhancing cultural pride or using presenters Ethnic Inequalities in Cardiometabolic Disease from the community to increase trust, are identified. Nine papers recommended solutions to increase the adoption The Community Chronic Care Conceptual Model was used to and acceptance of interventions in ethnically or culturally show how community resources and health care provider diverse populations, with some focusing on cardiometabolic systems can interact with other factors to impact disease. The Workforce Evidence-Based model for diabetes community-wide health outcomes, with examples of direct [62] was developed to meet the need for tailored management action, such as increasing community health professional for a diverse patient population, by guiding health professionals training targeted at reducing amputations in African-American in determining which patients may require additional support. men with diabetes [77]. Other recommendations for action In the culture-centered Technology Acceptance Model [25], a included video-based information for the public [63,77], internet range of individual and intervention attributes that can impact training, and meaningful involvement in patient groups from https://cardio.jmir.org/2022/2/e37360 JMIR Cardio 2022 | vol. 6 | iss. 2 | e37360 | p. 8 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Ramasawmy et al co-design to implementation [63,75]. However, working with colleagues [25] describe the experiences of Ethiopian South Asian people with diabetes in the United Kingdom, immigrants in the health care system in Israel and set out an Wilkinson et al [66] noted the need for further data to understand iterative design process for a health website that took into the effectiveness of cultural adaptations and approaches to account views from community groups and individuals. culturally competent care, such as peer support. Crawford and Culture-centered constructs, such as “elements that enhance Serhal [75] also reiterated the need for additional data collection cultural pride” and “addresses people’s sociocultural and around health inequalities to implement and evaluate digital personal needs” emerged from this research [25]. These health through an equitable lens. constructs represent motivations to use the website beyond health information, for example, pride in traditional, cultural, Discussion and language identity, and benefits such as improving intergenerational communication [25]. Culturally tailored Principal Findings designs have been found to be important in digital health interventions for ethnic minority and other underserved We identified 58 frameworks relevant to digital health adoption populations [83]. that address health inequalities and cardiometabolic interventions. Several frameworks were found to consider health Two frameworks were specifically designed in the context of inequalities in digital health interventions and inequalities in ethnic differences in diabetes care and outcomes. Knowledge cardiometabolic disease, but none covered all 3 areas of interest. gained from these can be applied to other chronic health Less than half (n=22) addressed health inequalities in detail; conditions and to the design and implementation of digital health the remainder did not address health or digital inequalities at services. Wilkinson and colleagues [66] did not identify any all or included only a high-level factor in the body text of the studies that focused on older people from a South Asian paper or as a framework construct (such as “differentiated by background in a review of literature on diabetes care. Their national culture” [73] or “wider social and health system” [54]). theoretical framework draws relationships between key concepts We identified 3 models for understanding the digital emerging from the literature: cultural stratification and determinants of health equity [74,75] and 3 frameworks that comorbidities, cultural competency, and access [66]. The describe factors related to implementation, uptake, and use of Gender-Centered Diabetes Management Education Ecological health technologies [25,61,64]. Framework takes a more detailed approach to address disparities in diabetes outcomes for Black men in the United States by Where health inequalities were considered, they were broadly placing diabetes management education into a broad context related to social theory, and more specifically, the social that includes demographic characteristics, gender roles, and determinants of health, which is described as “the causes of the family situation. While developed in one particular group, these causes” [81] of health inequality. For example, in the papers constructs are applicable to understanding health management [15,75] describing the Digital Health Equity Framework and in other ethnic minority groups; for example, specific barriers the Commission on Social Determinants of Health Conceptual to exercise have been identified in South Asian women with Framework, it is highlighted that the health system itself acts diabetes and cardiovascular disease, including family as a social determinant of health. In the paper [74] that presented obligations, fears about women going out alone, lack of the eHealth Equity Framework, it is argued that technology single-sex exercise facilities [84], and perceptions of taking should be integrated into models of health, in much the same time to exercise as being “selfish” and taking women away from way that the role of social structures is integrated in models of their “daily work [85].” health and well-being outcomes. Comparison With Prior Work In the majority of frameworks, ethnicity was considered under this broad banner of social determinants of health, rather than It is necessary to consider health disparities in research on health as a separate construct [15,25,59,61,62,74-76]. While this technology, particularly in understanding the role of technology approach is a useful starting point when considering the factors in exacerbating or addressing inequalities, and in the design and related to implementation, uptake, and use, a more detailed evaluation of interventions [86]. Approaches including defining approach is necessary when considering complex social, common terms and proposing standardized language and educational, and cultural factors relevant in ethnic minority measurement tools [16], mapping concepts of engagement with groups for the design, implementation, and evaluation of digital digital behavior change interventions [59], and describing health interventions. For example, a recent report highlighted commonly used frameworks in clinicians’ adoption of mHealth the specific experiences of people from an ethnic minority [27] have been used to review frameworks for the uptake and background using the National Health Service (NHS) in use of digital health interventions. Recently, reviews on England, including lack of trust, fear of discrimination, equitable approaches to research [87] and use [88] of health experiences of culturally insensitive behavior, communication portals have examined digital health equity at the intervention barriers, and racism [82]. There is also evidence of worse level. Researchers have also responded to the need for equitable outcomes for ethnic minority populations with specific digital approaches to virtual care provision (eg, access to phone or health approaches, for example, differences in referrals to video consultations) highlighted during the COVID-19 pandemic urgency and emergency care services by the NHS Direct [89,90], including adaptation of the Nonadoption, Abandonment, telephone service [82]. We found only 3 frameworks that Scale-up, Spread, and Sustainability framework [14] to include explicitly considered these factors [25,66,76]. In producing the digital inclusion as a concept that contributes to the patient culture-centered Technology Acceptance Model, Guttman and domain [90]. https://cardio.jmir.org/2022/2/e37360 JMIR Cardio 2022 | vol. 6 | iss. 2 | e37360 | p. 9 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Ramasawmy et al As digital health approaches become embedded in national Future Directions health strategies, there is also a need for the application of Beyond the scope of the review, other papers were identified frameworks to ensure equitable digital health implementation during the screening process, which could have some relevance in ethnically and culturally diverse populations. The NHS is for the process of design and implementation of digital health promoting digital services and tools in England [91], including interventions, for example, the RESET (relevance, evidence for cardiometabolic disease, such as a digital pilot of the NHS base, stages of intervention, ethnicity and trends) tool to adapt Diabetes Prevention Programme [92] and a cardiology digital health promotions to meet the needs of ethnic minority groups playbook that promotes digital tools to support patients remotely [99] and a framework for coproduction of digital services for [93]. Furthermore, the adoption of digital health interventions marginalized people living with complex and chronic conditions was actively encouraged to mitigate the risk of face-to-face [100]. A number of papers have put forward design and interaction during the pandemic [94], and going forward, digital assessment tools for equity in digital health [61,64,101-103]. health interventions are seen as adoption of innovation to A review of tools for inclusivity and cultural sensitivity, provide cost-effective outcomes in health [95]. However, digital coproduction approaches, and equitable design processes could exclusion has the potential to exacerbate health inequalities, identify practical steps that could be taken by developers to both directly (reduced access to services and resources) and promote equity in digital health. indirectly (access to wider determinants of health, such as Future research should assess how the frameworks identified housing or occupation opportunities) [96]. The frameworks in this scoping review can be used and applied to different ethnic identified in this scoping review and the guide to the key minority groups and in the management of other health constructs they contain (Figure 2) can be used as tools to identify conditions. The complex intersections of factors associated with the individual, technological, and contextual factors that health and other inequalities should also be considered. For influence the direct routes between digital and health example, in England, some ethnic groups are more likely to live inequalities. in deprived areas [104], and deprivation is associated with Strengths and Limitations increased mortality across all ethnic groups, including White We aimed to explore the breadth of potential frameworks that ethnicity [105]. Application of appropriate frameworks for were applicable to understanding inequalities in digital health engagement, implementation, and evaluation can improve the uptake and use. The configurative approach to a scoping review reach of measures to address broader health inequalities and generates or explores theories, rather than aggregating data to target all underserved groups. test theories [97]. Taking an iterative approach also allows Conclusions inclusion and exclusion criteria to be refined through the course Health inequalities continue to be a major focus in health policy of the review [98]. In this case, with an unknown literature base and research globally. A number of frameworks have been put regarding digital health inequalities, we were able to further forward to address social determinants of health [15] or to refine inclusion criteria during the full-text review to exclude improve inequalities in particular major chronic health a number of papers that focused on statistically testing minor conditions, such as cardiometabolic diseases [106]. As digital variations of the Technology Acceptance Model. However, health approaches are encouraged and become more scoping reviews do not usually undertake formal quality commonplace, we should use our existing theoretical appraisal [98]; therefore, synthesizing the results was difficult understanding of the interaction between digital health because of the range of frameworks identified. In a review of approaches and health inequalities to improve equitable Technology Acceptance Model adaptations alone, a high degree distribution of benefits, including to ethnic minority populations. of study heterogeneity was identified [12]. Additionally, there We have produced a visual guide (Figure 2) to shape action was a lack of standardization of terms, with the terms when considering preventable or manageable chronic disease acceptance, adoption, and acceptability being used in the community that shows ethnic inequalities in outcomes, interchangeably. We took an inclusive approach when such as cardiometabolic disease. considering the use of such terminology [12,16]. Acknowledgments This study was funded by the National Institute for Health Research (NIHR) (NIHR200937). The funding source made no contribution to the design of the study; the collection, analysis, and interpretation of data; the writing of the report; or the decision to submit the manuscript for publication. PG is supported by the NIHR Applied Research Collaborations West Midland. The views expressed in this publication are those of the authors and not necessarily those of the NIHR or the UK Department of Health and Social Care. KK is supported by the NIHR Applied Research Collaboration East Midlands and the NIHR Leicester Biomedical Research Centre. We would also like to thank Ayath Ullah for his contribution during the course of development of this review. Authors' Contributions The review concept was designed by MR, LP, A Banerjee, EM, and A Blandford. Literature searches were conducted by MR. Screening was led by MR and conducted by LP, ZTB, AC, M Murali, PJ, MB, JP, and AG-M. Data charting was carried out by MR, LP, and ZTB, and further analysis was done by MR. Figures were designed by MR, and LP wrote the original draft, with https://cardio.jmir.org/2022/2/e37360 JMIR Cardio 2022 | vol. 6 | iss. 2 | e37360 | p. 10 (page number not for citation purposes) XSL FO RenderX JMIR CARDIO Ramasawmy et al review and edits from A Banerjee, A Blandford, FS, and HWWP. Additional review was carried out by KK, WH, PG, MS, KP, HS, NB, AU, SM, M Mistry, VP, SNA, and AA for the DISC Study consortium. Conflicts of Interest KK is director of the University of Leicester Centre for Ethnic Health Research and trustee of the South Asian Health Foundation. HWWP receives consultancy fees, through his employer, from Ipsos MORI and has PhD students who work at and have fees paid by AstraZeneca and BetterPoints. A Banerjee has received research grants from National Institute for Health and Care Research (NIHR), British Medical Association, UK Research and Innovation, European Union, and Astra Zeneca. Multimedia Appendix 1 PRISMA-ScR checklist. [DOCX File , 55 KB-Multimedia Appendix 1] Multimedia Appendix 2 Inclusion and exclusion criteria for literature searches. [DOCX File , 50 KB-Multimedia Appendix 2] Multimedia Appendix 3 Search strategy as used for SCOPUS. [DOCX File , 55 KB-Multimedia Appendix 3] Multimedia Appendix 4 Inclusion and exclusion guide for title and abstract screening. [DOCX File , 53 KB-Multimedia Appendix 4] Multimedia Appendix 5 Additional frameworks identified through abstract screening. [DOCX File , 51 KB-Multimedia Appendix 5] Multimedia Appendix 6 Data-charting form. [DOCX File , 50 KB-Multimedia Appendix 6] Multimedia Appendix 7 Summary of papers included in the data charting. [DOCX File , 73 KB-Multimedia Appendix 7] References 1. UK Ethnicity facts and figures. 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[doi: 10.1097/00005082-200611000-00007] [Medline: 17293734] Abbreviations mHealth: mobile health NHS: National Health Service NIHR: National Institute for Health Research PRISMA-ScR: Preferred Reporting Items for Systematic Reviews and Meta-Analyses for Scoping Reviews RESET: relevance, evidence base, stages of intervention, ethnicity and trends Edited by T Leung; submitted 17.02.22; peer-reviewed by T Greenhalgh, L Husain; comments to author 28.03.22; revised version received 17.04.22; accepted 18.04.22; published 11.08.22 Please cite as: Ramasawmy M, Poole L, Thorlu-Bangura Z, Chauhan A, Murali M, Jagpal P, Bijral M, Prashar J, G-Medhin A, Murray E, Stevenson F, Blandford A, Potts HWW, Khunti K, Hanif W, Gill P, Sajid M, Patel K, Sood H, Bhala N, Modha S, Mistry M, Patel V, Ali SN, Ala A, Banerjee A Frameworks for Implementation, Uptake, and Use of Cardiometabolic Disease–Related Digital Health Interventions in Ethnic Minority Populations: Scoping Review JMIR Cardio 2022;6(2):e37360 URL: https://cardio.jmir.org/2022/2/e37360 doi: 10.2196/37360 PMID: ©Mel Ramasawmy, Lydia Poole, Zareen Thorlu-Bangura, Aneesha Chauhan, Mayur Murali, Parbir Jagpal, Mehar Bijral, Jai Prashar, Abigail G-Medhin, Elizabeth Murray, Fiona Stevenson, Ann Blandford, Henry W W Potts, Kamlesh Khunti, Wasim Hanif, Paramjit Gill, Madiha Sajid, Kiran Patel, Harpreet Sood, Neeraj Bhala, Shivali Modha, Manoj Mistry, Vinod Patel, Sarah N Ali, Aftab Ala, Amitava Banerjee. Originally published in JMIR Cardio (https://cardio.jmir.org), 11.08.2022. This is an open-access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Cardio, is properly cited. The complete bibliographic information, a link to the original publication on https://cardio.jmir.org, as well as this copyright and license information must be included. https://cardio.jmir.org/2022/2/e37360 JMIR Cardio 2022 | vol. 6 | iss. 2 | e37360 | p. 16 (page number not for citation purposes) XSL FO RenderX
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JMIR Cardio – JMIR Publications
Published: Aug 11, 2022
Keywords: eHealth; framework; cardiometabolic; health inequalities; health inequality; health technology; ethnicity; minority; digital health; review; cultural; diverse; diversity; cardiology; metabolism; metabolic