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Purchase access Subscribe to the journal. Sign in to download free article PDFs Sign in to access your subscriptions Sign in to your personal account. Sign in to save your search Sign in to your personal account. Bacigalupe [ 49 ] and McAuley [ 12 ] stress that the cultural component in the development of eHealth tools is critical for populations at risk of SHI and, thus, they suggest using targeted strategies tools specifically designed for these populations , rather than universal strategies intended for everyone.
A failure to consider beliefs, values, and habits of populations or individuals targeted can lessen the value of the tool developed for these individuals [ 47 ]. The utilization of photographs representing populations at risk of SHI and a variety of testimonies, the availability of the tool in a number of languages, and focusing on specific needs of this clientele are concrete examples of strategies favoring the consideration of the cultural dimension in eHealth [ 49 , 50 , 63 , 69 ].
The active participation of future users and, in particular, people at risk of SHI, in the development of eHealth tools has the potential to reduce inequalities [ 49 ]. Involving future users with diverse perspectives, circumstances, capacities, and experiences in the design process increases the chances that the tool will ensure significant universal access [ 42 ]. Future users have the skills to evaluate, choose, and use eHealth tools and to gain from the experience [ 42 ].
Nonetheless, the involvement of low income or poorly educated people, various ethnic groups, as well as those with low literacy levels, still requires specific abilities on the part of the designer to encourage their active participation in designing an eHealth tool [ 42 ]. This review of the literature had three objectives: 1 identifying characteristics of people at risk of experiencing a situation of SHI; 2 determining the possibilities for action in the development of eHealth tools that avoid increasing SHI; and 3 modeling the process of using an eHealth tool by people at risk of experiencing a situation of SHI.
For the first objective, we saw that a number of sociodemographic characteristics were brought up in various studies to identify or characterize individuals at risk of SHIs ethnicity, low income, low level of education, age, low literacy level, gender, rurality, incapacities, psychological distress, homelessness, and sexual orientation.
Now, these characteristics should be analyzed with due caution. On one hand, they could contribute to supporting a discourse based on differences, but they also fail to consider the heterogeneity that one finds within a single population group [ 70 ]. Thus, it seems essential to ensure a range of characteristics when recruiting participants for studies on SHIs and eHealth. For the second and third objectives, the results obtained from this review of the literature show that the digital divide, in its primary, secondary, and tertiary forms is the principal cause of the exacerbation of SHI by eHealth and that it affects those people already at risk of SHI [ 42 , 47 , 49 ].
Alternative ways of modeling the link between eHealth and SHI exist. In this model, existing SHI are exacerbated by technologies that require a certain level of literacy, sustained motivation, and digital capacities [ 41 ]. Also, the Structural Influence model identifies the importance of communication in the relationship between social determinants and results linked to health [ 40 , 71 , 72 ]. It suggests that the differences among social groups including ethnic minorities in the utilization of channels of communication result in an exacerbation of SHI [ 72 ].
These are highly interesting models. However, the goal of these models is not the development of eHealth tools, and certain key elements, such as the cultural component and the importance of involving future users, are absent. Thus, descriptive metasynthesis allows us to respond to the second objective. Individuals characteristics linked to SHI will encounter difficulties during the process of using an eHealth tool. First, it is possible that they will be less inclined to seek health-related information or to use an eHealth tool to improve their health [ 41 , 46 , 50 ].
In the case where these people do initiate a process of looking for help, they will need physical access to digital technology a computer, electronic tablet, or mobile phone and sufficient bandwidth [ 66 , 61 , 73 ]. Then, they need to draw upon their capacities to use the technology.
Probably they will lack confidence in their abilities or in the technology and will interrupt the process [ 11 , 12 , 73 ]. However, if they persevere, they will require a level of health literacy sufficient to understand what the eHealth tool is able to offer them and a capacity to integrate and make use of what has been learned [ 40 , 73 , 74 ]. Individuals with sufficient income, a high level of education, and adequate digital health literacy will be better able to complete the process and improve their state of health.
Thus, it is possible that there are gaps between these groups of individuals in the effective utilization of eHealth tools and, therefore, in the improvement of their health, which will contribute to increasing SHI [ 72 ]. Nonetheless, if in the designing or adaptation of the tool, the developers consider the future user as a person at risk of SHI [ 49 ], design or adapt the tool to respond to the needs of such a user at each stage [ 42 ], and integrate the cultural dimension in the process of development [ 49 ], it might be possible to reduce the digital divide present in eHealth Figure 2.
The current increase of technologies in eHealth justifies a reexamination of interventions unlikely to worsen SHI [ 42 , 47 ]. Among other suggestions, it is proposed to target interventions for populations at risk of SHI. Yet, developing an eHealth tool is an undertaking requiring time, energy, and funds. Realistically, developers hope to reach the greatest possible number, and targeted interventions are likely to be rarer.
Little participatory research action has been done despite the promising nature of participation of people at risk of SHI in developing eHealth tools to reduce these inequalities. Can we consider developing eHealth tools with the end goal of a universal strategy, but designed to take into account people at risk of SHI and even to involve them in the process? Could we, in developing the tool, question ourselves and question the people at risk of SHI at each stage of the process of using an eHealth tool Figure 2 and reduce the barriers liable to interrupt the process?
Each stage of the process Figure 2 or conceptualizing category refers to its own field of research. It is difficult, indeed impossible, in the context of this article, to showcase the wealth of knowledge available for each of these concepts. However, the relation between these concepts, more iterative than linear, allows us to envisage a process of coherent codesign, the effect of which might be to reduce SHI. Although research often raises the potential of eHealth to reduce SHI and offers promising solutions for reducing the digital divide, we agree with Chou [ 20 ] that, to date, there are still insufficient empirical studies to prove this definitively, as demonstrated in this review of the literature.
Indeed, only three studies examined document the development of an eHealth tool with individuals in a situation of SHI. Although we have attempted to provide a rigorous review of the literature, including a metasynthesis, this review has its limitations. First, in concentrating on a research strategy supported by databases, the gray literature and nontraditional sites of knowledge transfer eg, the Web were not included. In addition, we have only used two databases.
A limited search can generate a set of studies that are not representative, incomplete results, inadequate selection, and reduced generalization [ 23 ]. We have prioritized this choice to ensure greater transparency and reproducibility for this review of the literature.
To avoid biases in the publications, it is recommended not to exclude articles on the basis of year of publication or language. Now, considering that the Internet, social networks, and new technologies have considerably modified the eHealth environment, it was judged sufficient to focus on articles published in the last decade.
Furthermore, for reasons of feasibility, the translation of articles was not possible, and free translation software still leaves much to be desired. In accordance with the suggestion of the EPPI group, it was decided to look for articles in all languages initially but, for greater transparency, to exclude articles that are not in languages in which we are fluent [ 23 ].
Another limitation of this study is the presence of only a single analyst, which could trigger selection bias. To counter this aspect, often linked to student reality, two supervisors provided support for the writing of this text, and a biostatistician examined the articles from a quantitative perspective. Finally, since the analysis was not based on the quality of studies, the results must be interpreted as possibilities, rather than generalizable facts based on solid data.
The rigor of this review stems from the fact that it is systematic undertaken according to a fixed plan or system or method and that it is explicit and justified [ 23 ]. The synthesis of knowledge allowed for 1 a modeling of the process of using an eHealth tool, 2 identifying the actions in eHealth that do not help to reduce SHI, but 3 determining the possibilities for action in the development of tools of eHealth that avoid increasing SHI as well.
The massive expansion of technologies in eHealth justifies the study of interventions less likely to exacerbate SHI through the usage of eHealth, and few current empirical studies reveal concrete and effective solutions. Furthermore, very few studies involve future users at risk of SHI. Research is still necessary for eHealth to fulfill its promise to reduce SHI. Conflicts of Interest: None declared. National Center for Biotechnology Information , U. J Med Internet Res. Published online Apr Karine Latulippe , MSc, Ph.
Author information Article notes Copyright and License information Disclaimer. Corresponding author. Corresponding Author: Karine Latulippe ac. This article has been cited by other articles in PMC. Multimedia Appendix 2. Abstract Background eHealth is developing rapidly and brings with it a promise to reduce social health inequalities SHIs.
Objectives The general objective of this review was to set out how to ensure that eHealth contributes to reducing SHIs rather than exacerbating them. Results Of the 73 articles retained, 10 were theoretical, 7 were from reviews, and 56 were based on empirical studies. Conclusions eHealth has the potential to widen the gulf between those at risk of SHI and the rest of the population.
Keywords: Internet, social media, telemedecine, healthcare disparities. Introduction Background A number of studies have demonstrated that eHealth is effective in preventing and treating illness for the entire population [ 1 - 6 ].
They identified 5 principles to consider when developing an inclusive eHealth tool: Use a design based on experimentation with the tool allowing us to identify the nature of possible errors and the strategies to employ. Minimize the potential of having harmful information inadvertently available.
Evaluate the tool with representative users. Criteria of Inclusion and Exclusion In order to respond adequately to the research questions, criteria of inclusion and exclusion were established. Evaluation of the Quality of Articles To evaluate the quality of quantitative studies, the Quality Assessment Tool for Quantitative Studies [ 26 ] was used.
Analysis The analysis was completed in two stages. Results Articles Selected A total of articles were identified by the databases. Open in a separate window. Figure 1. Description of Included Studies Of the 73 articles retained, 10 were theoretical, 7 were reviews of the literature previously referenced, and 56 were empirical studies. Digital Divide and Social Health Inequalities Unequal access to the Internet, the primary digital divide, has an effect on the utilization of eHealth [ 37 ].
Table 1 Characteristics of people at risk of experiencing a situation of social inequality in health. Promising Strategies for Development of the eHealth Tool to Reduce Social Health Inequalities Ensuring Universal Access to the eHealth Tool To guarantee universal access and reduce the digital divide, it is important to clearly understand the systemic barriers, which potential users may confront [ 42 ].
Creating eHealth Tools That Respect the Cultural Characteristics of Future Users Bacigalupe [ 49 ] and McAuley [ 12 ] stress that the cultural component in the development of eHealth tools is critical for populations at risk of SHI and, thus, they suggest using targeted strategies tools specifically designed for these populations , rather than universal strategies intended for everyone.
Inviting the Participation of People at Risk of SHI in Developing eHealth Technologies The active participation of future users and, in particular, people at risk of SHI, in the development of eHealth tools has the potential to reduce inequalities [ 49 ]. Discussion Principal Findings This review of the literature had three objectives: 1 identifying characteristics of people at risk of experiencing a situation of SHI; 2 determining the possibilities for action in the development of eHealth tools that avoid increasing SHI; and 3 modeling the process of using an eHealth tool by people at risk of experiencing a situation of SHI.
Figure 2. Conclusions The synthesis of knowledge allowed for 1 a modeling of the process of using an eHealth tool, 2 identifying the actions in eHealth that do not help to reduce SHI, but 3 determining the possibilities for action in the development of tools of eHealth that avoid increasing SHI as well. Multimedia Appendix 1 Included articles. Click here to view. Multimedia Appendix 2 Quality of included articles. Footnotes Conflicts of Interest: None declared.
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