App-Based Digital Health Equity Determinants According to Ecological Models: Scoping Review

Abstract

Technological advances have increased the availability of diverse digital health services. However, digital health benefits are not equally accessible. Recent studies have focused on digital health equity. Researchers are progressively identifying digital determinants of health (DDoH) to address potential health disparities stemming from digital health. This study investigated the determinants of disparities in app-based digital health within the framework of an ecological model. The method proposed by Arksey and O’Malley was adopted in this review. The PubMed, Embase, Scopus, and Google Scholar databases were searched from January 2016 to December 2021. Two reviewers independently screened and selected topics according to the guidelines for the scope of the topic. A consensus was reached to reconcile the differences, and the findings were collated, synthesized, summarized, and reported. This study identified 21 studies pertaining to health equity in app-based digital health. Seven countries were included in this study. Health inequities caused by the adoption of app-based digital health can be reflected in the following three levels according to the ecological model. At the individual level (N = 20), it was influenced by sociodemographic characteristics and digital literacy factors. At the interpersonal level (N = 10), factors such as feedback mechanisms, monitoring, communication modalities, technology-sharing practices, and standardized design were observed. At the community or social level (N = 7), disparities were noted in residential locality, integrated network infrastructure, and Internet accessibility. Finally, digital health policies should consider determinants of digital health inequalities. Ensuring health equity in digital health requires the equitable implementation and measurement of health outcomes through an equity lens. Based on the findings of this study, it is essential to maintain a continued focus on digital health to prevent the further widening of health disparities.

1. Introduction

1.1. Background

Globally, heightened attention toward digital health is evident owing to the aging population, the increasing prevalence of chronic diseases, and the emergence of novel infectious diseases such as COVID-19. Technologies that seamlessly integrate into healthcare are advancing progressively. Recent advancements in information and communication technology (ICT) have facilitated the development of various digital technologies, such as mobile apps, wearable devices, the Internet of Things (IoT), artificial intelligence (AI), big data, blockchain, machine learning, and the metaverse. The World Health Organization [1] defines the integration of digital technologies into healthcare services as digital health.

The utilization of digital health may vary depending on factors such as knowledge and familiarity with technology, disposable income for purchases, and possession of social networks to share information about technology [2]. Disparities in the adoption of these technologies, differences in access capabilities to digital tools, and variations in competency for utilization may also manifest in digital health utilization. These distinctions can potentially exacerbate health inequalities. Initially, efforts to address digital disparities focused on reducing differences in device ownership and accessibility. However, with recent advancements in technology and the prolonged COVID-19 pandemic leading to increased familiarity with remote interactions and the widespread availability of the Internet, our society has shifted its attention not only to digital access but also to differences in digital literacy. Researchers are interested in the outcomes and achievements associated with variations in digital literacy [3].

The WHO has emphasized the importance of understanding the digital determinants of health and making efforts to prevent health inequalities arising from digital advancements as digital health has become widely disseminated [1]. Research on digital health equity has gradually begun focusing on understanding the digital determinants of health (DDoH) from an equity perspective that may arise from digital health akin to the social determinants of health (SDoH).

Crawford and Serhal [4] emphasized the significance of an ecological perspective in approaching digital health by incorporating the health equity measurement framework proposed by Dover and Belon [5]. Building on this, they introduced a Digital Health Equity Framework (DHEF) for applications rooted in an ecological perspective [4]. Kaihlanen et al. [6] conducted an empirical study to identify digital health equity determinants among vulnerable populations in Finland, including older adults, immigrants, mental health service users, heavy healthcare service users, and the unemployed. They applied the DHEF proposed by Crawford and Serhal [4] to examine the digital determinants of health in the context of COVID-19, shedding light on the factors influencing digital health service utilization among vulnerable populations in Finland.

Lawrence [7] investigated how digital determinants affecting health at the individual, community, and structural levels differ from the social determinants of health. The results indicate that different factors influence the accessibility of digital health depending on the ecological level. Similarly, Richardson et al. [8] proposed the DHEF and digital determinants of health based on an ecological theoretical model in their study. Using the COVID-19 pandemic as a case study, Jahnel et al. [9] identified how the digitalization of all aspects of life affects different levels of determinants of health inequalities in the Dahlgren–Whitehead model [10] and provided examples of intervention points.

At the individual level, digital determinants of health include digital literacy, digital self-efficacy, technological accessibility, and attitude toward utilization. Digital literacy refers to the proficiency and capability of technology access, encompassing the language, hardware, and software necessary for the successful exploration of digital technologies. Digital self-efficacy denotes proficiency in effectively and easily utilizing digital technologies [8,11]. Interpersonal determinants refer to individual-level factors related to digital health technologies. These include inherent technological bias, interdependence, and patient–tech–clinician relationship. Community-level determinants include local community infrastructure, healthcare infrastructure, local technological norms, and community partnerships. Local community infrastructure considers wireless and broadband Internet access as well as the availability of quality technology and economic feasibility. Access to broadband Internet is considered a crucial digital determinant, enabling important digital health factors such as patient health monitoring and telemedicine [8]. Social-level determinants include technological policies, data and design standards, social norms, ideologies, and algorithm bias [8]. However, the DHEF envisions the development of digital health through a multidimensional approach in which interventions to address health disparities primarily occur at the individual level. However, vulnerable populations are less likely to benefit from interventions focused solely on individual-level mediation because of the greater barriers resulting from limited resources and competing priorities. Therefore, while interventions targeting the interpersonal, community, and social levels have been limited thus far, it is likely that upstream or multidimensional digital health interventions involving digital infrastructure in the future will be more effective for population-wide impacts.

Most previous studies that introduced the DHEF have been summarized through viewpoints or reviews. Therefore, this study aims to investigate how research on health equity in digital health is being conducted. For this purpose, we introduce the methodology framework proposed by Arksey and O’Malley and conduct a scoping review following the research procedures outlined by the Joanna Briggs Institute [12,13].

1.2. Objectives

This study attempts to identify the digital determinants of health through empirical research based on the recently emerging theoretical framework and ecological model of digital health equity. It seeks a direction for improving digital health equity by synthesizing the main results.

2. Materials and Methods

2.1. Overview

This study investigated how health equity is considered in actual app-based digital health research. A scoping review was conducted following the five stages of the framework proposed by Arksey and O’Malley to achieve this [12]: research question formulation, literature search, literature selection, data charting, data synthesis, summary, and reporting of results. Scoping reviews are suitable to explore a wide range of health equity studies in the digital health context. Unlike systematic reviews and meta-analyses based on quantitative evidence, scoping reviews encompass various research methods, including quantitative, qualitative, and mixed methods. Therefore, by expanding the range of data types, scoping reviews can provide additional evidence, including recent trends and reports from different institutions [13]. The review followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses Extension for Scoping Reviews (PRISMA-ScR) checklist to promote consistency and transparency among researchers [14].

2.2. Search Strategy

In this scoping review, we formulated research questions based on the population, concept, and context criteria. The study population comprised the entire population, including app developers, app users, and healthcare service providers. Guided by the ecological model [10], we examined the digital determinants of health that influence health equity, categorized into individual, interpersonal, and community or social levels. Additionally, in terms of context, we focused on smartphone app-based digital health services that are easily accessible and are actively expanding among the public. However, mobile health services offering only simple text services were excluded; as a result, the comprehensive research question was, ‘What is known from existing literature about the digital determinants of health for enhancing equity in app-based digital health?’.

The subject-related search terms were as follows according to PubMed: (app[tiab] OR application*[tiab] OR “health application*”[tiab]) AND (“digital health”[tiab] OR “global digital health”[tiab] OR “artificial intelligence”[tiab] OR “virtual models of care”[tiab] OR “smart Health”[tiab] OR smartphfone*[tiab] OR m-Health[tiab] OR mhealth[tiab] OR “mobile health”[tiab] OR ehealth[tiab] OR e-health[tiab] OR telehealth[tiab] OR telemedicine[mh] OR telemonitoring[tiab] OR teleconsulting[tiab] OR “health informatics”[tiab] OR “medical informatics”[tiab] OR “clinical informatics”[tiab] OR “global health informatics”[tiab] OR telematics[tiab] OR “health information system*”[tiab] OR “digital health technolog*”[tiab] OR ICT[tiab] OR “information and communication technolog*”[tiab] OR information technology[mh]] AND (“health equit*”[tiab] OR “health inequit*”[tiab] OR “health equal*”[tiab] OR “health inequal*”[tiab] OR “health disparit*”[tiab] OR “health depriv*”[tiab] OR health equity[mh] OR social determinants of health[mh] OR Health Status Disparities[mh] OR Right to Health[mh] OR Universal Health Care[mh]).

2.3. Inclusion Criteria

To select the literature, our research team established inclusion and exclusion criteria according to the framework proposed by the Joanna Briggs Institute [13]. The inclusion criteria for this study were as follows: literature published between 2016 and 2021 was reviewed. The starting point of 2016 was chosen because it corresponds to the onset of the activation of app-based digital health, coinciding with the announcement of the Fourth Industrial Revolution by the World Economic Forum (WEF) [15]. Additionally, the examination of research materials until 2021 was motivated by the ability to observe digital health approximately three years before and after the COVID-19 pandemic. Therefore, this study examined research spanning six years. Only studies published in English were included in this meta-analysis. The data formats considered encompassed primary research, literature reviews, and grey literature.

2.4. Exclusion Criteria

The exclusion criteria were as follows: generally, scoping reviews covering various types of literature, monographs, poster presentations, oral presentations, dissertations, papers without peer review, and systematic literature reviews were excluded. Additionally, as this study aimed to comprehensively examine the equity of digital health, literature that merely validates the effectiveness of digital health and studies focusing on specific racial, ethnic, or sexual minority groups in the local context were excluded. However, systematic review literature excluded from the search criteria was not presented as part of this study’s findings but was utilized to comprehensively interpret the results in the discussion section (Section 4).

2.5. Study Selection

Following the established inclusion and exclusion criteria and the guidelines of the PRISMA-ScR, two researchers independently evaluated the papers for selection. Initially, 155 papers were retrieved from PubMed, 183 were retrieved from Embase, 95 were retrieved from Scopus, and 192 were retrieved from the Web of Science. In addition, a snowball search of Google Scholar identified one additional paper related to our study. After removing the duplicates, 434 articles were identified. Two reviewers independently assessed the titles and abstracts of 303 papers using Rayyan (www.rayyan.ai, accessed on 31 August 2022) and extracted 23 papers addressing health equity in app-based digital health. Subsequently, the full texts of these 23 papers were read, and 21 papers that met the exclusion criteria were selected for this study (Figure 1).

2.6. Data Extraction and Analysis

The process of extracting relevant data focusing on the research questions in a scoping review is referred to as “data charting”. The data were organized into tables categorizing general information (author, year, country, and research method) and specific information related to the research questions (participants, specific services, and key research findings).

3. Results

3.1. Overview of Studies

This study extracted and reviewed 21 papers published over six years (2016–2021). From 2016 to 2020, there were consistently three or fewer publications annually; however, by 2021, there was a significant increase to ten publications. The number of studies examining equity in app-based digital health and countries in which these studies were conducted has increased over the years. Research on equity in app-based digital health was conducted and published in academic journals in seven countries between 2016 and 2021.

The distribution of research on the equity of app-based digital health across countries revealed that researchers in the United States conducted the highest number of studies with 14 papers (67%). Australia had two papers, whereas Canada, Germany, Portugal, France, and China contributed one paper each. The United States consistently reported one to two studies each year, with occasional reports from other countries. However, in 2021, research significantly increased to the extent that a total of ten papers were published, and research was reported not only in the United States but also in various countries. Grundy [16] was included in a scoping review in 2021. Consequently, the number of papers published from 2016 to 2020 (11 papers) was only slightly lower, by one paper, than those published in 2021. Moreover, the year 2021 witnessed a significant surge in studies adopting a perspective on health equity in the digital health domain, as well as the involvement of various countries beyond the United States (Australia, Canada, Germany, and Portugal).

We compared the studies that utilized digital devices and mobile apps in different years (Figure 2). From 2016 to 2020, five papers applied both apps and the web or Internet simultaneously. In 2021, there was evidence of health equity studies incorporating various digital devices. Influenced by changes in the healthcare landscape due to the COVID-19 pandemic and advancements in new technologies, the predominantly used digital devices include those featuring AI applications such as speech recognition, natural language processing, human–computer interaction (HCI), video conferencing, and wearable devices.

Next, we examined 21 studies on equity in app-based digital health to determine the areas of health services as defined by WHO in health promotion, patient self-care, healthcare utilization, and mental health [17]. Out of the 21 papers in the field of health services, 6 papers addressed two to three areas simultaneously, whereas the remaining 15 papers focused on a single area of research. Health promotion and disease prevention (11 papers, 52.4%) and patient self-care (11 papers, 52.4%) dominated, followed by mental healthcare (4 papers, 19.0%) and healthcare utilization (3 papers, 14.3%). When comparing by year, studies on equity in health promotion and disease prevention were conducted every year, with an increase in patient self-care studies in 2020. Moreover, recent trends indicate a growing interest in equity research on app-based digital health in various areas of health services (Figure 3).

3.2. Digital Determinants of Health According to Ecological Models

We applied the ecological model developed by Dahlgren and Whitehead [10] to examine equity-related studies at the individual, interpersonal, community, and societal levels. Among the 21 papers, digital determinants were addressed in 37 papers, including instances of duplication. Research on equity in app-based digital health has begun investigating various approaches in recent times. When examining the levels of the ecological model, there were 20 studies at the individual level, ten at the interpersonal level, and seven at the community and societal levels (Table 1).

At the individual level, factors such as sociodemographic characteristics and digital literacy each resulted in one paper published in 2016 [18] and 2018 [19], respectively. The number of papers significantly increased in subsequent years, with three papers each in 2017 [20,21,22] and 2020 [23,24,25] and a notable increase to ten papers in 2021 [16,18,26,27,28,29,30,31,32,33]. At the interpersonal level, factors related to feedback, monitoring, communication, technology sharing, and design standardization were addressed in a total of ten papers. Although only two papers were published in 2019 [34,35] and one in 2020 [24], the number was expected to increase to six by 2021 [16,26,27,28,29,31,33]. At the community and societal levels, factors such as regional differences, integrated network infrastructure, and Internet accessibility were explored in seven studies. One study was conducted in 2017 [22], one study was conducted in 2019 [34], two studies were conducted in 2020 [23,24], and three studies were conducted in 2021 [16,26,36]. While 2021 had the highest number of studies, the increase was not as pronounced as the growth at the individual or interpersonal levels.

Table 1. Overview of included studies.

Author	Year	Country	Service	Tool	Methodology	Sample (Sample Size) *	Digital Determinants of Health
							Individual Level	Interpersonal Level	Community and Societal Level
Aromatario et al. [34]	2019	France	Health promotion and disease prevention	App	Qualitative research	Consumers, patients, health professionals, app developers, etc. (N = 32)	Literacy	Communication between stakeholders	Internet accessibility and culture
Balcombe et al. [26]	2021	Australia	Mental health care and healthcare utilization	App and HCI	Literature review	N/A	Disease	Communication, technology sharing, and co-design	Internet accessibility, integrated network, and expanded connectivity between apps
Bhuiyan et al. [36]	2021	USA	Health promotion, disease prevention, and mental health care	App	Quantitative research	Meditation app subscribers 18 years and older (N = 8392)	Income		Residence
Businelle et al. [18]	2016	USA	Health promotion and disease prevention	App	Quantitative research	Hospital smoking cessation clinic users (N = 59)	Income and race
Camacho-Rivera et al. [23]	2020	USA	Patient self-care	App and web	Quantitative research	Adults with chronic disease (N = 10,760)	Socioeconomic status and social security		Residence
Cheng et al. [24]	2020	Australia	Health promotion and disease prevention, patient self-care, and mental health care	App and web	Mixed research	Patients ( N = 530) and telephone interview (N = 5)	Literacy and socioeconomic status	Communication between stakeholders	Integrated network
Grundy [16]	2022*	Canada	Health promotion and disease prevention	App	Literature review	N/A	Disease, literacy, socioeconomic status, and subjective perception	Technology sharing and co-design and universal design	Internet accessibility
Hannemann et al. [27]	2021	Germany	Healthcare utilization, mental health	App and video conference	Quantitative research	Patients who made reservations for treatment during the COVID-19 period (N = 1570)	Disease, literacy, and socioeconomic status	Communication and service standardization
Hernandez-Ramos et al. [28]	2021	USA	patient self-care, and mental health care	App	Quantitative research	Pre- and post-coronavirus treatment patients (N = 43)	Literacy and socioeconomic status	Technical support to secure platform accessibility
Laing et al. [19]	2018	USA	Health promotion and disease prevention, patient self- care, and healthcare utilization	App	Quantitative research	Health center low-income patients over 18 years of age (N = 164)	Socioeconomic status
Leziak et al. [29]	2021	USA	Patient self-care	App	Qualitative research	Pregnant women, low-income people, and people with diabetes (N = 45)	Disease and income	Customized feedback
Luo and White Means [33]	2021	USA	Patient self-care	App	Qualitative research	Individuals who are aged 18 and above and diagnosed with diabetes who own smartphones (N = 15)	Disease and income	Customized feedback
Medairos et al. [20]	2017	USA	Health promotion and disease prevention	App, web, and SNS	Quantitative research	Low-income community (N = 291)	Income
Miller Jr et al. [21]	2017	USA	Patient self-care	App	Quantitative research	Individuals who are aged 50–74 who are scheduled for colorectal cancer screening (N = 450)	Literacy and socioeconomic status
Neves et al. [30]	2021	Portugal	Health promotion, disease prevention, and patient self-care	App	Quantitative research	Individuals who are aged 13 and older diagnosed with asthma (N = 526)	Disease, literacy, and socioeconomic status
Potdar et al. [25]	2020	USA	Patient self-care	App	Quantitative research	Individuals who are aged 18 and above diagnosed with cancer (N = 141)	Socioeconomic status, health knowledge, and disease
Quintiliani et al. [35]	2019	USA	Patient self-care	App	Qualitative research	Breast cancer survivors (N = 13)		Customized feedback and monitoring
Qureshi et al. [37]	2019	USA	Health promotion and disease prevention	App and web	Quantitative research	Countries (N = 154)	Education and life expectancy
Schmaderer et al. [31]	2021	USA	Patient self-care	App	Qualitative research	Patients participating in mobile health interventions (N = 10)	Disease and social security	Communication
Ye and Ma [32]	2021	USA	Health promotion and disease prevention	App and wearable	Quantitative research	General adults (N = 11,411)	Socioeconomic status
Hong et al. [22]	2017	China	Health promotion and disease prevention	App and web	Quantitative research	Individuals who are aged 45 and above (N = 18,215)	Socioeconomic status, smartphone ownership, and disease		Residential area

* The online publication year is 2021., Abbreviation: N/A, not applicable.

Table 1. Overview of included studies.

Author	Year	Country	Service	Tool	Methodology	Sample (Sample Size) *	Digital Determinants of Health
							Individual Level	Interpersonal Level	Community and Societal Level
Aromatario et al. [34]	2019	France	Health promotion and disease prevention	App	Qualitative research	Consumers, patients, health professionals, app developers, etc. (N = 32)	Literacy	Communication between stakeholders	Internet accessibility and culture
Balcombe et al. [26]	2021	Australia	Mental health care and healthcare utilization	App and HCI	Literature review	N/A	Disease	Communication, technology sharing, and co-design	Internet accessibility, integrated network, and expanded connectivity between apps
Bhuiyan et al. [36]	2021	USA	Health promotion, disease prevention, and mental health care	App	Quantitative research	Meditation app subscribers 18 years and older (N = 8392)	Income		Residence
Businelle et al. [18]	2016	USA	Health promotion and disease prevention	App	Quantitative research	Hospital smoking cessation clinic users (N = 59)	Income and race
Camacho-Rivera et al. [23]	2020	USA	Patient self-care	App and web	Quantitative research	Adults with chronic disease (N = 10,760)	Socioeconomic status and social security		Residence
Cheng et al. [24]	2020	Australia	Health promotion and disease prevention, patient self-care, and mental health care	App and web	Mixed research	Patients ( N = 530) and telephone interview (N = 5)	Literacy and socioeconomic status	Communication between stakeholders	Integrated network
Grundy [16]	2022*	Canada	Health promotion and disease prevention	App	Literature review	N/A	Disease, literacy, socioeconomic status, and subjective perception	Technology sharing and co-design and universal design	Internet accessibility
Hannemann et al. [27]	2021	Germany	Healthcare utilization, mental health	App and video conference	Quantitative research	Patients who made reservations for treatment during the COVID-19 period (N = 1570)	Disease, literacy, and socioeconomic status	Communication and service standardization
Hernandez-Ramos et al. [28]	2021	USA	patient self-care, and mental health care	App	Quantitative research	Pre- and post-coronavirus treatment patients (N = 43)	Literacy and socioeconomic status	Technical support to secure platform accessibility
Laing et al. [19]	2018	USA	Health promotion and disease prevention, patient self- care, and healthcare utilization	App	Quantitative research	Health center low-income patients over 18 years of age (N = 164)	Socioeconomic status
Leziak et al. [29]	2021	USA	Patient self-care	App	Qualitative research	Pregnant women, low-income people, and people with diabetes (N = 45)	Disease and income	Customized feedback
Luo and White Means [33]	2021	USA	Patient self-care	App	Qualitative research	Individuals who are aged 18 and above and diagnosed with diabetes who own smartphones (N = 15)	Disease and income	Customized feedback
Medairos et al. [20]	2017	USA	Health promotion and disease prevention	App, web, and SNS	Quantitative research	Low-income community (N = 291)	Income
Miller Jr et al. [21]	2017	USA	Patient self-care	App	Quantitative research	Individuals who are aged 50–74 who are scheduled for colorectal cancer screening (N = 450)	Literacy and socioeconomic status
Neves et al. [30]	2021	Portugal	Health promotion, disease prevention, and patient self-care	App	Quantitative research	Individuals who are aged 13 and older diagnosed with asthma (N = 526)	Disease, literacy, and socioeconomic status
Potdar et al. [25]	2020	USA	Patient self-care	App	Quantitative research	Individuals who are aged 18 and above diagnosed with cancer (N = 141)	Socioeconomic status, health knowledge, and disease
Quintiliani et al. [35]	2019	USA	Patient self-care	App	Qualitative research	Breast cancer survivors (N = 13)		Customized feedback and monitoring
Qureshi et al. [37]	2019	USA	Health promotion and disease prevention	App and web	Quantitative research	Countries (N = 154)	Education and life expectancy
Schmaderer et al. [31]	2021	USA	Patient self-care	App	Qualitative research	Patients participating in mobile health interventions (N = 10)	Disease and social security	Communication
Ye and Ma [32]	2021	USA	Health promotion and disease prevention	App and wearable	Quantitative research	General adults (N = 11,411)	Socioeconomic status
Hong et al. [22]	2017	China	Health promotion and disease prevention	App and web	Quantitative research	Individuals who are aged 45 and above (N = 18,215)	Socioeconomic status, smartphone ownership, and disease		Residential area

* The online publication year is 2021., Abbreviation: N/A, not applicable.

3.3. Strategies for Mitigating Inequities in App-Based Digital Health

Next, we examined the proposals made in the scoping review to mitigate digital health inequalities and enhance equity (see

Table 2. Strategies to improve health inequality in app-based digital health.

Level	Strategies	Detailed Strategies	List of References Examined in the Scoping Review
Individual level	Digital health literacy education	Enhancing digital health literacy by considering individual factors such as gender, age, and health conditions	[16,19,20,24,30]
		Improvement of digital device proficiency (experience, efficacy, and confidence)	[19]
		Promoting attitude changes toward app usage	[23]
Interpersonal level	Values pursuit for health in app design	Understanding health equity	[16,34]
		Understanding the health impact of apps	[16]
		Application of a DHEF	[16,34]
		Consideration of excluding technological bias	[19]
	Communication, collaboration, and sharing in app design	Co-design	[16,24,26]
		Interdisciplinary collaboration	[27]
		Mutual information provision and sharing	[16]
		Communication and encouragement between patients and healthcare service providers	[19]
	User consideration in app design and requirements in app usage	Understanding app users’ preferences and usage patterns	[20]
		Specialized content based on participant characteristics	[19,23,24,29,33]
	App design configuration	Standardization	[16]
		Simplicity	[33,35]
		Ease of use	[32,35]
	Requirements in app usage	Digital technology proficiency of healthcare service providers	[16,26]
		Technical support for app usage provided by service providers or nearby individuals	[28,33]
		Timeliness of app services	[16,18]
Community and social level	Regulation	Strengthening of cybersecurity against personal information	[16,26,33]
		Support for health app curation	[16]
	Evaluation	Monitoring digital health equity	[16,34]
	Enhanced accessibility	Infrastructure development for integration with healthcare systems	[19]
		Support for the availability of digital devices such as smartphones	[22]
		Adoption of community preferred technological norms (cultural accessibility)	[26,34]
		Expansion of connections to other apps	[26]

). In most studies, strategies to alleviate health inequalities in digital health have been discussed at a conceptual level rather than as an in-depth exploration.

First, at the individual level, the proposed mitigation strategies primarily focused on digital health literacy education in contrast to the diverse range of digital determinants identified. Education is known to be effective not only in knowledge transfer but also in improving awareness and attitudes. Despite identifying individual-level health inequality factors, addressing these factors requires interventions not only at the individual level but also at the interpersonal and community levels. Strengthening digital health governance has emerged as a key mechanism for reducing disparities in app-based digital health utilization, with six studies proposing education to enhance digital health literacy [16,19,20,23,24,30]. Among these, five studies proposed an overall improvement in digital health literacy [16,19,20,24,30], whereas one suggested tailored education based on disease characteristics [30], gender, and age [19]. These studies emphasize the need to consider the unique characteristics of digital health literacy and conduct a detailed investigation and reflection on health demands and management based on gender, especially for diseases that require consideration of health needs and management according to gender.

Moreover, it was acknowledged that older people, owing to their lower confidence in using digital devices and reduced accessibility compared to other age groups, require consideration of their characteristics [19]. Notably, digital health literacy not only enhances the ability to understand and utilize health information but also involves proficiency in using digital devices to instill a sense of digital efficacy and confidence in individuals [38]. Therefore, education that is tailored to the unique features of digital health must be developed. Digital literacy education influences attitudes toward individual app usage [34], bringing about positive changes in perceived usefulness and perceived ease of use, ultimately fostering a positive shift in attitudes toward app usage.

Second, at the interpersonal level, 14 studies differentiated between the app design and app usage stages, providing more detailed strategies for mitigating inequality than at the other levels. During the app design stage, the considerations included interdisciplinary understanding, communication, collaboration, and sharing methods, each of which could be further examined for detailed insights.

In the app design stage, health-related values were pursued. It is essential for ‘app developers’ to have a proper understanding of the values of health equity [16,34]. Some opinions suggest that developers should prioritize understanding the impact of health apps on health [16]. Furthermore, it is necessary to apply a DHEF to app development [16,34] and exclude the possibility of technological bias during this process [19]. When examining communication, collaboration, and sharing strategies in the app design stage, proposals for collaborative design involving app developers, users, and healthcare service providers have been suggested [16,24,26]. Collaboration beyond health-related fields is deemed necessary [27], along with proposals for information exchange and sharing among stakeholders [13] and communication between patients and healthcare providers [16]. In studies applying a collaborative design to improve health equity, sharing permissions between developers and users could reduce health inequality and empower vulnerable communities by considering the digital health literacy levels of various stakeholders [19]. The consideration of user perspectives is also crucial. Understanding the preferences and usage patterns of individuals who use apps should precede the design [20]. In addition, achieving the objectives of an app requires a differentiated approach based on user characteristics [19,23,24,29,33]. The configuration of an app is considered to enhance sustainable use through standardization [16], technological simplicity [33,35], and ease of use [32,35]. In the app usage stage, it has been argued that not only app developers and users but also healthcare professionals need to be proficient in digital technology to integrate digital technology effectively into healthcare [16,26]. For example, to assist in the treatment of patients with schizophrenia, healthcare professionals need to understand digital technologies such as sensors, relapse detection algorithms, and VR immersive therapy and be proficient in their usage. To sustain app participation, technical support for app utilization by service providers or caregivers [28,33] and the timeliness of app services [18,19] are necessary.

In other words, patients of lower socioeconomic status may be interested in using digital platforms to manage their health but have limited access to and utilization of digital platforms. They may require additional support to access digital health services. In particular, the elderly population put forth diligent efforts to acquire skills in using devices; nevertheless, their confidence in using these devices remains low. This study revealed the effectiveness of providing tailored technological support based on digital literacy levels [16].

Third, at the community and societal levels, discussions centered on the establishment of government-level regulatory measures and emphasized aspects such as evaluation and enhanced accessibility. Regarding regulatory measures, discussions centered on strengthening cybersecurity against personal information leaks during app development and usage [16,26,33]. Additionally, there was a discussion on the necessity of support for health app curation, where the government could conduct pre-validation, ensuring safety, efficacy, and other criteria to develop a support system that allows consumers to safely explore the app market before commercialization [16]. Indicator development and regular monitoring are necessary for digital health equity [16,34]. To enhance accessibility, the following measures are required: recognizing that vulnerable populations face a lack of infrastructure for connectivity between patients and healthcare service providers, despite the potential benefits of health management through app-based digital health; establishing infrastructure for connecting healthcare systems has been suggested [19]. For vulnerable populations who may find it difficult to own digital devices such as smartphones owing to personal factors, government support, including device assistance, should be considered [22]. In addition, an approach based on community preferences for technological norms suggests the use of chatbot messages or text and voice messages [26,34]. Proposals have also been made to enhance the use and utility of the app by integrating it with other apps [26].

4. Discussion

The convergence of digital technology with healthcare is anticipated to operate positively in extending healthy life expectancy. However, it remains uncertain as to whether rapidly advancing digital health will distribute benefits equally across all demographic groups or, conversely, contribute to the emergence of health disparities. Despite the recognition of the imperative to reduce entry barriers to digital health, there is a notable deficiency in research that employs theoretical frameworks to explore the issues of digital health equity. In response to the recent increase in research emphasizing the need to address health disparities resulting from digital inequality, there is growing recognition of the importance of digital health equity [4,6,8,38].

This study examined the DHEF by reviewing existing research exploring the digital determinants of health that must be considered to pursue a healthier life without perpetuating pre-existing health disparities within app-based digital health based on an ecological model. Additionally, this study comprehensively examined how app-based digital health can reduce health inequalities by focusing on 21 articles selected from a thematic literature review (Figure 4).

At the individual level, similar patterns were observed for various social determinants, including biological factors, socioeconomic status, disease, social security, employment, subjective health perception, and health knowledge levels. Digital literacy is the most frequently mentioned factor. Digital literacy encompasses not only the ability to utilize health information and digital devices but also the technical, cognitive, and socio-emotional skills required for individuals to fulfill their roles in the digital environment [3,11]. It also includes experiences with digital technology, digital efficacy, digital self-confidence, and digital usage patterns and habits.

Furthermore, judgments about whether digital health will be potentially beneficial or lead to losses are incorporated into beliefs about digital health, attitudes toward digital health, and the availability of devices such as smartphones. At the individual level, addressing digital health inequality involves proposing tailored education to enhance overall digital literacy.

Second, at the interpersonal level, the positive function of social networks in promoting users’ health behaviors is facilitated by a high level of interdependence through digital technology [8]. Utilizing digital health tools for appropriate feedback and monitoring is crucial, with balanced communication between patients and healthcare professionals being an essential factor in the patient–doctor relationship. App developers must understand health equity values and the impact of digital health [16,34]. Collaboration between users, healthcare professionals, and app developers is crucial for ensuring fairness in app design [18,27]. From the user’s perspective, user-friendly applications should be developed [28], and healthcare professionals should have a good understanding and proficiency in handling digital technology to enhance its utility [16,26]. In addition, continuous technical support from service providers, family members, and friends is necessary to secure access to digital platforms. Concurrently, conscious management is required to prevent technological biases arising from structurally ingrained discrimination or biased data collection during digital health development.

Third, at the community and societal levels, variations in health outcomes may arise based on the extent of local community infrastructure, broadband Internet access, healthcare infrastructure, and accessibility of high-quality technology at affordable costs. Additionally, differences in the approach and utilization of digital health can operate differently depending on the technological norms and cultural preferences within the local community. Establishing connections between widely used apps in the local community and digital health tools can enhance the sustainability of health management and medical usage resulting from digital health.

However, according to a study by Kaihlanen et al. [6], a significant barrier to using digital platforms was identified as the participants’ concerns about information exposure. Therefore, governmental measures are required, such as strengthening security against the leakage of sensitive information and personal data and instituting the curation of apps to minimize harm to users. Government-level support, including the provision of tangible resources such as digital devices, can also ensure that individuals who genuinely need digital health intervention services are not marginalized in the realm of digital health.

Furthermore, the WHO has recommended strengthening digital health governance at the national level in its digital health strategy [1]; Europe is also encouraging the adoption of digital public health (DPH) to achieve public health goals such as promoting health equity and realizing universal health coverage [39]. Therefore, establishing and strengthening digital health governance should be emphasized to meet the goal of universal health coverage.

5. Conclusions

To ensure equity in digital health, it is imperative to implement it fairly and to measure health outcomes through the lens of equity. Based on the findings of this study, the following policy directions are proposed to enhance equity in digital health: strengthening accessibility to digital health, enhancing digital literacy capabilities, establishing a stable local community infrastructure for digital health and considering preferred norms, implementing measures for digital health developers and users, building and reinforcing digital health governance, integrating digital health with existing healthcare systems, establishing institutional safeguards for the use of digital health, developing indicators for digital health equity, and establishing a monitoring system. These policy directions aim to systematically address various aspects of digital health to ensure fairness and equity in implementation and to impact health outcomes.

To promote public health, it is essential to consider how digital health will unfold in the future. Therefore, there is a need to advocate for ongoing attention to prevent the deepening of health disparities in digital health policies.