The use of digital health care has been increasing over the past decade [1], with a rapid acceleration in 2020 as services transferred to web-based platforms during the COVID-19 pandemic [2]. When digital health care is provided with appropriate infrastructure, training, and engagement, it has the potential to improve population health by using artificial intelligence, big data, and precision medicine [3]. Digital health care is especially useful where there is increasing demand and limited resources [4]. However, the use of digital technologies within health care provision (eg, telehealth, eHealth, and artificial intelligence), termed digital health, can contribute to health inequity due to systematic disparities and social determinants of health needs and the ability to engage with digital platforms [5].

The development of digital technologies and digital skills initiatives for patients is beginning to appear in local, national, and global policy and practice strategies, one of which is the World Health Organization (WHO) global strategy to strengthen the international approach to the implementation of digital health [6]. However, an understanding of the underlying barriers to digital technologies and embedding mitigation strategies at an early stage is needed if policy implementation is to be effective. For example, despite digital skills being on the European Commission’s agenda since 2016 [7], in 2021, the majority (54%) of the European Union’s population (aged 16-74 years) had limited basic digital skills, and 14% had none at all [8], with variations present by age group, education level, employment status, and rurality. The extent of the digital inequalities and the need for digital skills in Europe became even more apparent during the COVID-19 pandemic due to the reliance on digital technologies for social support and economic functions, including access to health services and public health systems [2,4].

Digital health equity can be explored through an individual’s ability to access, use, and engage with digital health technology [9]:

There is some evidence of digital inequities within Europe [2,5,9]. This is generally reported for older individuals, rural communities, and women and is attributed to a lack of knowledge, opportunities, skill, inaccessibility [10], fear of discrimination, and concern for the cessation of face-to-face service [11,12]. However, there is limited evidence of factors contributing to digital health exclusion for subgroups of the population within the 3 components of digital health (access, use, and engagement) and in assessing the interconnected nature of social categorizations, termed intersectionality. Failure to acknowledge and account for all dimensions of equity within digital health, including the role of intersectionality, can lead to widening health inequities.

Understanding the role of inequity in people’s ability to access, use, and engage with digital health is necessary to ensure that digital health does not cause greater health inequities. Therefore, the aim of this scoping review was to map inequities in digital health technology across different equity domains to help inform future developments toward integrating digital technology into health care practices, systems, and policy within the WHO European region.

This scoping review used the methodology outlined by Arksey and O’Malley [13]. It comprises the following stages: (1) identifying the research question; (2) identifying relevant studies; (3) study selection; (4) data extraction and analysis; and (5) collating, summarizing, and reporting the results. The PRISMA-ScR (Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews) checklist was used to guide the reporting of the scoping review [14].

We conceptualized inequalities using the PROGRESS PLUS framework [15]. The framework, first developed by Evans and Brown and further adapted by the Campbell and Cochrane Equity Methods Group [16], indicates the different characteristics in which health inequities may be experienced. The acronym stands for place of residence, race, ethnicity, culture, language, occupation, gender and sex, religion, education, socioeconomic status, social capital, and other characteristics PLUS (eg, disability, age, and sexual orientation). On the basis of the collected data from the included reviews, the PLUS category was subcategorized further into age, disability or complex health needs, and homeless or substance misuse. In addition, for the purpose of this review, religion was combined with race, ethnicity, culture, and language.

The MEDLINE, ASSIA, PsycINFO, and Scopus databases were searched for reviews and meta-analyses published between January 2016 and May 2022 to capture the most recent evidence of evolving technologies and changes in health provision resulting from the COVID-19 pandemic. Included search terms related to “Digital Health” AND “Inequities” AND “Review” (Multimedia Appendix 1). A further hand search of key relevant journals (eg, JMIR and Lancet Digital Health), gray literature databases (eg, the Health Management Information Consortium and the Turning Research into Practice databases), and citation tracking of included literature was performed to identify additional records. Once the data had been extracted and thematically mapped, a further search was undertaken for recent primary research (2018-2022) conducted in WHO Europe countries for equity domains where evidence from reviews could not be extracted. For this step, the search terms to cover “reviews” that were included in the initial search were omitted. Additional inclusion and exclusion criteria remained the same.

Studies were eligible for inclusion if they (1) contained a digital technology that connected an individual to a health care professional or service; (2) documented equity through a lens of access, use, and engagement; (3) contained research from countries within the WHO’s European region or was global in interpretation [17]; (4) reported quantitative results; and (5) had a systematic, scoping, rapid, or mapping review methodology (Table 1).

Screening by title and abstract and subsequent full paper review of relevant literature was carried out by 3 reviewers (TA, DB, and KEW). Before screening and full paper review, we followed a calibration process against the inclusion and exclusion criteria and the feasibility of disaggregating quantitative results into 10 equity domains and 3 components of digital health (access, use, and engagement) that we were interested in reporting. A second, and in some cases, a third reviewer resolved any uncertainty throughout the screening and full paper review process.

Two reviewers (TA, DB, or KEW) conducted data extraction independently. Any dispute was solved by consulting a third reviewer (TA or KEW). The form captured information including (1) author and publication date; (2) participant characteristics; (3) interventions and exposures; (4) included study features; (5) equity outcomes; and (6) solutions, limitations, and evidence gaps.

Scoping reviews do not critically appraise the literature, but where this information was available in the included reviews, it was captured. The results were then thematically mapped using the PROGRESS PLUS framework [15] to examine the role of equity within the key components of digital health: access, use, and engagement, in addition to looking for any evidence of intersectionality across the PROGRESS PLUS equity domains. This ensured that socially stratifying factors were considered throughout the review process [15]. The thematically mapped data were summarized using a heat map for each equity domain, with a potential direction of effect being interpreted, where at least 75% of the included reviews found consistent evidence of equity or inequity.

The database searches yielded 404 unique results, with a further 62 obtained from hand searches of journal websites and gray literature, and 2 from other sources, leading to the inclusion of 22 reviews (Figure 1).

The primary studies included in this scoping review were mainly from the United States (258/520, 49.4%), followed by Europe (177/520, 33.9%), other developed countries (eg, Australia and Canada; 59/520, 11.3%), and a minority from low- and middle-income countries (LMICs; 20/520, 5.4%; Figure 2). The characteristics of the included reviews are presented in Table 2.

Of the 22 reviews, 3 (14%) concluded that digital technology improved access to health care for rural residents [19,20,24], but these reviews did not consider those who may live in rural areas who do not have access to the digital technology required to use a digital health service. Overall, 14% (3/22) of reviews concluded that the use of digital health care was higher in urban areas [18,21,31]. However, 5% (1/22) of reviews that focused on digital health care among patients with chronic disease reported no difference in use by place of residence [32]. A key reason for lower use in the older adult rural population was lower eHealth literacy levels [34]. No evidence of the difference in engagement with digital technologies by place of residence was identified in the included reviews. There is a lack of research exploring differences in place of residence that considers underlying differences in digital infrastructure, health care provisions or models, and population demographics to clearly ascertain the impact of place of residence on digital health.

Access to digital health services among patients from ethnic minority backgrounds with depression was found to be reduced in 5% (1/22) of reviews [38]. Evidence of an effect was observed with the use of digital health technology by ethnicity [4,18,21,22,32,37] and immigration status [31], with 5 reviews [4,18,21,22,37] showing a decreased use by ethnic minorities compared with individuals from a White ethnic background. Although 9% (2/22) of reviews reported no direction [27] or a mixed direction of effect [32], many of the included studies within these reviews showed a greater use among individuals from White ethnic backgrounds.

Mixed evidence was reported for the association between ethnicity and engagement with digital health technology, with 14% (3/22) of reviews showing higher engagement [18,23,32], 5% (1/22) of reviews showing lower engagement by patients from White ethnic backgrounds than ethnic minority backgrounds [33], and 9% (2/22) of reviews finding no conclusive evidence of a difference between ethnic groups [4,27].

Only 5% (1/22) of reviews reported that language may negatively influence access to digital health technology, as patient portals were not offered in the patients’ preferred language [18]. In addition, Litchfield et al [4] and Carini et al [21] noted 1 primary study each reporting lower use of digital health technology among non–English-speaking patients. There is a paucity of research exploring the influence of language barriers on engagement with digital health services. In addition, no evidence was found in the included reviews for culture and religion.

No evidence was identified within the included reviews reporting the impact of occupation on access to digital health care and inconclusive evidence was reported for engagement [23]. Of the 22 reviews, 1 (5%) review concluded that being employed made no significant difference to eHealth use among patients with chronic diseases [32], while Chambers et al [22] found evidence that e-consultation users were more likely to be in employment than nonusers sociodemographic factors, such as gender and sex and age as well as health status, could explain these mixed results [32].

Among the included reviews, there was no evidence of the impact of gender and sex on access to digital health. Of the 22 reviews, 8 (36%) reviews reported on the impact of gender and sex on the use of digital technology, with 4 (18%) reviews indicating consistent evidence of a greater use of digital health technology among women [22,31,35,37]. Of the remaining 18% (4/22) of reviews, 2 observed no difference in use by gender and sex [4,21] and 2 reported mixed evidence [32,39]. There was mixed evidence (2/22, 9%) reported for the association between gender and sex and engagement with digital health, and it was not consistent across all interventions. Men were more likely to accept telemonitoring and electronic self-reported systems than women, but higher satisfaction and engagement were reported in women using eHealth [23,32].

The association between education and access to digital health was not reported in any of the reviews. However, 18% (4/22) of reviews reported a possible association between the use of digital health care and education showing that individuals with higher levels of education were more likely to use digital health services than individuals with a lower education level [18,21,29,35]. Only 5% (1/22) of reviews reported inconclusive evidence [32]. A higher level of education was also reported to lead to greater engagement with digital health services (2/22, 9%), but this may vary according to the type of technology. For instance, Chambers et al [22] noted that, in 1 primary study [40], individuals with a low to medium level of education were more motivated toward indirect consultation (eg, involving communication with a health professional via email) to reduce uncertainty.

Lower income was reported to reduce access to digital health care in 1 included review [32]. However, the association between the use of digital health technology and socioeconomic status was reported in 23% (5/22) of reviews, 4 of which [4,18,35,37] reported higher adoption in higher income than lower income groups and 1 review [31] reported mixed results. Mixed results were also observed with engagement in digital technologies in the included reviews (2/22, 9%) [4,32].

No evidence on social capital was obtained from the included reviews. Two primary studies [41,42] were identified from an additional search of the literature, with 1 showing better access to televisions among patients with dementia with the presence of a caregiver during the Italian COVID-19 pandemic lockdown in Milan [41]. However, Paccoud et al [42] found no association between social capital (whether an individual knows someone who uses digital technology) and access to or engagement with personal health records but found an association with the use of digital health care.

Evidence of a possible effect were evident for access with race, ethnicity, culture, language, and religion and disability or complex needs and for use with place of residence, race, ethnicity, culture, language and religion, education, socioeconomic status, and age (Figure 3). The association between the remaining categories within PROGRESS PLUS with access, use, and engagement had unclear or mixed evidence of a possible effect.

This comprehensive scoping review has highlighted inequities among rural communities, ethnic minorities, lower-educated individuals, those with lower socioeconomic status, older adults, and individuals with disabilities or complex needs, with digital technologies across the 3 dimensions of digital health (access, use, and engagement). Owing to the digitalization of health services during the COVID-19 pandemic and their continued use, there remain key policy implications at both an organizational and governmental level to provide equal access to health care via digital platforms for all individuals within society. Given that digital health care has the potential to lower costs, enable prioritization of care, increase adherence to medicine and treatment, and increase self-care [44], there are widespread benefits if barriers to access, use, and engagement can be equitably addressed through policy and practice. In the subsequent sections, we present key inequities found across the 3 dimensions of digital health (access, use, and engagement) and elaborate on key areas for future development in this area. Considering the limitations of this scoping review, our findings and the identified key areas for future development can be used to inform the development and integration of digital health technologies into everyday health care, in an equitable way.

We conducted a comprehensive scoping review of equity in digital health technology, collating evidence across 3 key components of digital health (access, use, and engagement) and 10 domains of equity, as defined by the PROGRESS PLUS framework. However, there are some limitations that need to be considered.

Despite there being evidence in the literature for a digital divide in singular domains, such as age, occupation, and sex and gender [5,9,45,46], these divides were mainly reported without considering the interconnectedness of different social classifications (eg, women from ethnic minorities live at the intersection of multiple social, economic, and cultural disadvantages that contribute to being digitally excluded). Examining the intersectionality between access and equity is crucial to protecting against widening inequalities in digital health systems. Furthermore, there was a high level of heterogeneity in the included study populations, which could explain why there was no clear evidence identified within the included reviews for age, occupation, gender and sex, and education. There was also considerable heterogeneity in how types of digital technologies were described (eg, inconsistent and evolving definitions of eHealth modalities, such as patient portals).

No evidence could be obtained from other reviews on social capital and religion. These elements of PROGRESS PLUS are more likely to be explored in qualitative research [27]. More research examining ethno-religious groups would increase our understanding of how diverse factors such as age, religion, and gender interact and contribute to a digital divide [27,41,42]. This review focuses on providing evidence for the WHO European region, but many of the studies documenting race, ethnicity, and language come from US studies, which potentially limits it generalizability to the European context. Furthermore, the evidence within Europe is dominated by Western European countries and the situational context may be different in countries that are classified as LMIC.

Finally, only quantitative studies were investigated, and future work is required to capture evidence for qualitative studies to provide a greater understanding of the current facilitators and barriers to digital health care. Nonetheless, a validation check of the results of this review against those of qualitative reviews revealed similar findings. Methodological weakness observed by those reviews that undertook a critical appraisal (12/22, 55%) should be addressed in any future work, these include low participation rate, small sample size, unblinded participants, lack of control for confounding, and biased samples that only included individuals with access to digital technologies. Another important bias that needs to be considered is the underrepresentation of non-English speakers and ethnic minorities in current literature [47]. Efforts to include these population groups are needed to develop culturally informed digital health technologies to address their needs.

Within the context of the WHO European region, this scoping review has highlighted present inequities and evidence gaps across multiple domains with digital health care technologies. Rural communities, ethnic minorities, lower-educated individuals, those with lower socioeconomic status, older adults, and individuals with disabilities or complex needs face digital health inequality, and active approaches should be taken to reduce this gap. These active approaches could include the development and adoption of a common framework to support policies and procedures within the WHO European region for current and new co-designed digital solutions, in addition to mapping inequities, investigating intersectionality, and taking into consideration regional differences within the development stage of any initiative. These recommendations could be achieved through further robust research and evaluation where there is currently mixed evidence and the sharing of good practices to create sustainable solutions to reduce digital health inequalities.