The world’s population is aging, and this phenomenon will affect the health care system for older people in the future, and we need to be prepared [1]. During the COVID-19 pandemic, it has been revealed that older people are an especially risky group, and public health authorities have advised them to stay safely at home [2]. This makes it harder for many older people to visit hospitals or health care facilities, and the need for eHealth services to provide health care at home has been increasing. The medical services offered by an internet-based platform have the advantage of increased equality in access to medical services during any type of crisis.

eHealth has been a World Health Organization priority since 2005. It defined eHealth as “a health-related field including medical and health services, health surveillance, health literature, health education, knowledge, and research” and has provided international reports on eHealth readiness [3]. In recent years, eHealth has been increasingly used as a generic term that covers a variety of mobile health (mHealth), telemedicine, and telehealth services, as well as eHealth data management [4]. eHealth is becoming an important solution for people who need to consistently manage their health even at home and receive immediate professional medical services by providing low-cost and high-quality health care [3].

The development of the Internet of Things (IoT) has contributed to advances in the eHealth field. IoT is a technology that allows physical objects, devices, and computers to interact using networks to collect and exchange data [5]. Ambient assisted living in health care facilities using IoT is designed to help older adults’ lives. This allows older adults with chronic diseases to measure blood pressure, glucose, electrocardiogram, and body temperature, which need to be monitored every day, and the data can be immediately shared with medical facilities [6]. Recently, advances in sensors and machine learning have made it possible to better perceive and understand the daily lives of older adults. This could lead to the development of eHealth technologies that monitor daily health conditions, share information with health care facilities, and respond to emergencies. The improvement of these technologies can relieve the social burden of aging and accelerate the transition to personalized digital health care that can meet the needs of individuals seeking independent living [7].

However, older people’s acceptance, adoption, and use of technology have lagged behind that of younger people. In addition, older people may have low eHealth literacy or low ability to access, evaluate, and use health information to make medical decisions [8]. Nonetheless, as their health concerns and health care needs are higher than for any other age group, some studies have confirmed that a growing number of people from this demographic segment are accepting the technology and are willing to use it in the future [9]. The first step toward applying eHealth technology in the future and bringing it into real life is to identify the factors that older adults, as users of eHealth, consider important to embrace the technology [10].

In the process of expanding the use of new technologies, many studies have been conducted to find how users accept specific technologies. The 2 representative models are the Technology Acceptance Model (TAM), which posits that both perceived usefulness and perceived ease of use affect the user’s attitude and behavioral intention [11], and the Unified Theory of Acceptance and Use of Technology (UTAUT), which was designed as a synthesis of 8 major technology acceptance models [12]. There are many studies on eHealth acceptance for older adults that extend, transform, or combine these 2 models to identify acceptance factors [13-16]. However, neither is it easy to find evidence for an appropriate acceptance factor model, according to the conditions of the research nor is it easy to construct an optimized acceptance model.

For use in future studies in this field, this study extracts the acceptance factors from studies on eHealth for older people over the past 11 years through a systematic review. After that, we propose an integrated acceptance framework (IAF) that groups the extracted acceptance factors through thematic analysis. Then, the criteria for evaluating the evidence for each factor incorporated into the IAF are provided. Finally, the proposed IAF is analyzed according to the detailed conditions. This study proceeds in 4 steps, from data extraction for the IAF to analysis for the IAF application (Figure 1). One of the main aspects that differentiate IAF from TAM and UTAUT is the presentation of a wider range of factors and dimensions based on acceptance factors that have been covered in research over the past 11 years. Moreover, although TAM and UTAUT are generic acceptance frameworks, IAF is intended to be a framework specifically tailored for a concrete technology (eHealth) and a particular population (older people). In this way, it is expected to be a more useful tool for highlighting the potential barriers and facilitators when planning a new adoption scenario.

This study can contribute to eHealth research and industry in the following three ways: (1) the development of an IAF, which comprises acceptance factors grouped in dimensions for the analysis of eHealth acceptance in old age that emerge from the analysis of existing evidence; (2) a metric for the assessment of the quality of the evidence found in the systematic review study that allows for the normalization and integration of the evidence on the impact of acceptance factors across different and diverse studies; and (3) stratification analyses of the IAF application according to the participant’s health status and verification time and analysis of the evolution of the factors through the years.

We conducted a 4-step study (Table 1). First, the primary studies were selected through a systematic review, and the acceptance factors and basic data for analysis were extracted from the selected studies. Second, the extracted factors were grouped through thematic analysis, and a framework for acceptance was proposed. Third, the metrics for quality assurance were defined to evaluate the weights of the factors addressed in the study and apply them to the framework. Finally, we applied the resulting framework for different scenarios—first, according to the health, and second, according to the verification time—resulting in particularized IAFs. Moreover, we analyzed the changes in these factors over the years.

This study selected and analyzed the studies according to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. The study scope was defined by the population, intervention, comparison, and outcomes (PICO) model, and accordingly, the search scope and research questions were defined. In the screening stage, 3 reviewers collaborated using the Covidence tool (Veritas Health Innovation Ltd). During the process of extracting and organizing data, a Microsoft Excel spreadsheet was used and shared for collaboration through Microsoft Teams.

The PICO model was applied to define the research scope and search strings as follows:

Search strings were defined as combinations that can retrieve as many related studies as possible, with consideration given to PICO. Our final search string was (ehealth OR telehealth OR mhealth OR uhealth OR health technology OR telemedicine) AND (older OR elderly OR senior) AND (adoption OR acceptance) AND (factors OR barriers OR determinants OR facilitators). The search scope was established as article title, abstract, and keywords. The databases used were Web of Science, Scopus, PubMed, IEEE, and MEDLINE. The review was conducted on conference papers or journal articles published during the 11 years from 2010 to 2020.

The set of studies collected through the search was finally selected using the following criteria and quality evaluation questions.

The exclusion criteria were as follows:

The quality evaluation questions were as follows:

The data to be extracted from each study were defined according to the agendas in each step, as shown in Textbox 1. The data were extracted and organized in step 1, and the extracted data from each agenda were used in each step.

The high number and diversity of factors, together with the variety of research methods, make it difficult to collect existing evidence and reach meaningful conclusions about the factors that really have an impact on the acceptance and adoption of these technologies. To overcome this, we formed an integrated framework for eHealth acceptance factors in older adults. All factors extracted from the selected studies by the systematic review process were defined as either positive or negative. Then, a thematic analysis process was conducted with the goal of identifying, analyzing, and interpreting patterns of meaning (or themes) within the set of original acceptance factors. We grouped them according to commonality in the meaning of the original acceptance factors in a bottom-up fashion. The 3 authors jointly analyzed and classified the acceptance factors through Microsoft Teams, and they reviewed and discussed each article to understand the meaning of the factors used in that article. Concretely, the first author conducted an initial thematic analysis after data extraction. Later, the other 2 authors participated in several consensus meetings. The other authors are senior researchers in two complementary disciplines: the first is a professor of computer science with a degree in psychology and extensive experience in acceptance models, and the second is a professor of biomedical engineering with wide experience in eHealth and older adults using technologies. After extracting and defining factors for 3 weeks, grouping was conducted according to themes for an additional 2 weeks.

To assess the impact of the factors, we evaluated and reflected the quality of evidence in each study beyond the frequency of the factors used in the studies. For a systematic review, there is a grading of recommendations, assessment, development, and evaluation (GRADE) method that evaluates the quality of the evidence for each outcome by applying a set of evaluation criteria [17]. With GRADE, the quality of the evidence is evaluated according to the research method, as well as the risk of bias, inconsistency, indirectness, imprecision, and large magnitude of effect. However, GRADE focuses on the results of the study rather than evaluating the overall quality of the research and is mainly targeted for experiments related to health care. Consequently, we found it difficult to apply GRADE in our review. Thus, we only took the research methods criteria considered in GRADE and other studies [17,18], and we felt it was necessary to add some new criteria that could assess the quality of evidence of the selected studies. One of the outcomes of this research is a metric that defines a set of relevant evaluation criteria.

The impact of the acceptance factors was analyzed by calculating the evidence quality score of each study according to this metric and deriving from it the weight of the acceptance factors studied.

In this way, the proposed IAF was enhanced by reflecting the impact of acceptance factors.

We analyzed the IAF that resulted from the research to better understand the relative importance of the factors according to different conditions. To do this, a stratification analysis [19] was applied that allowed the classification and analysis of the factors according to the conditions. First, the factors that depend on the health condition of the participant were analyzed; then, the analysis was repeated according to the verification time (preadoption and postadoption). In addition, the IAF was analyzed by year to examine the evolution of factors over the years.

In step 1, we conducted a systematic review to select qualified studies and extract the data as the basis for the IAF. The basic data from the research were extricated, and those became the base material for the formation of the various comparative groups and the assessment of the evidence quality for the selected studies. A previous integrative review on the adoption of telehealth in older age [60] extracted facilitators and barriers through the Whittemore and Knafl [61] 5-stage methodology. However, to be applied to future research, a more comprehensive perspective and a detailed guide were needed. Thus, our study not only conducted data extraction and analysis from the selected studies but also presented an integrated framework, the IAF, that can be applied to future works.

In step 2, the factors and dimensions were defined. Existing studies have proposed various frames for analyzing the acceptance factors of technologies. For instance, Schulz et al [62] presented three aspects for understanding aging and technology as follows: (1) user characteristics, (2) technology functions, and (3) social factors. Our study presents five dimensions to analyze the acceptance factors for eHealth: personal, user–technology relational, technological, service-related, and environmental. Compared with existing research, our framework considers not only the main dimensions (user, technology, and environment) but also the issues that emerge as a result of the interaction between the main dimensions. As can be seen in the results of the analysis (step 3; Figure 6), the acceptance factors in the user–technology relational dimension were most significantly addressed by the reference studies, and this dimension is estimated to be a category that should be treated as important in future studies (step 4; Figures 11 and 12). Similarly, the service-related dimension reflects the fact that eHealth technologies are not isolated but integrated into health services that will also be transformed as a result of the adoption of the technology. This dimension makes visible the relevant interaction between the technology and the environment and the impact of this interaction on the user. The proposed 23 acceptance factors and 5 dimensions emerged from the existing evidence by thematic analysis in step 2, forming a framework. This IAF contributes to the state of the art a finer-grained analysis tool that integrates the diversity of elements considered in previous research and provides a common vocabulary.

In step 3, a new metric for evaluating the quality of the existing evidence was presented to expand the IAF with information about the impact of the different factors. Various methods for evaluating the quality of evidence in systematic reviews for health care have been studied [17,18]. A high score obtained through this evaluation method means that there are many confirmations that the actual effect is similar to the effect estimated by the study, whereas a low score means that the actual effect may be significantly different from the estimated effect [17]. In the systematic review on health information technology adoption [18], a ranking of 5 steps was applied to evaluate the quality of evidence, considering the study design and research method.

Although the research method and design are of paramount importance for judging the quality of evidence, additional criteria were needed that were more aligned with our analysis objective. Thus, three primary criteria were suggested by this study: the reliability of the research method, the degree of understanding of the target technology, and the year of the study. These 3 indicators contain detailed evaluation criteria that can be applied to other systematic studies as well. It is a reproducible and transparent framework for evaluating the certainty of the evidence, which minimizes author bias. Through this evaluation, the impact of acceptance factors was organized and analyzed. We believe that this is a more meaningful analysis than the assessment of the impact of the acceptance factors by just the frequency with which they were considered in previous studies. Once the impact of the acceptance factors has been incorporated into the IAF, it becomes evident that not all 23 factors are equally important for the acceptance of eHealth technologies by older users. A classical Pareto analysis was performed to select the set of highest priority factors (those that together account for 80% of the total impact). This is valuable information when facing a concrete situation seeking the adoption of a specific eHealth technology. This would help decision-makers to focus on available resources on the most influential factors.

In step 4, the diversity of adoption situations considered in the previous research was addressed. The goal was to propose a way in which IAF can be adapted to reflect existing evidence in specific adoption situations and investigate the extent to which IAF is a robust analysis framework. The differences in the IAF according to the health status of participants and the verification time at which acceptance factors were identified (before or after adoption) were compared through stratification analysis. We concluded that the highest priority acceptance factors vary according to the target’s health status. In addition, the data from this analysis illustrate the key acceptance factors in the IAF according to the health status of the users. Further diversity in user profiles could not be analyzed in the existing evidence. It would be interesting to explore in future research the way in which the weights of factors and dimensions differ for the same product or service based on the profile of the users.

The differences in the IAF according to the verification time were examined as well. In a previous study on the acceptance of electronic technology for older users [63], the acceptance factors of the preimplementation and postimplementation stages were compared and analyzed. Some of the factors were considered regardless of the stage; however, the acceptance factors that differed for each stage were also identified. In the same manner, in this study, we confirmed that there is a difference between the acceptance factors found relevant in the preadoption and postadoption phases of eHealth. This result confirms that it is necessary to apply the differentiated acceptance factors to different verification times.

We were also concerned about the validity of aggregating evidence about acceptance factors coming from studies performed in different years. Recently, eHealth technology has been further developed, and investment per country has increased significantly [3]. In addition, as the use rates of smartphones and the internet in older adults were different 10 years ago than they are in the present, we assumed that there would be a difference in acceptance factors with the passage of each year. As a result of comparing the acceptance factors by year in our last analysis, the continuous increase of the acceptance factors in the user–technology relational and the service-related dimensions was confirmed, except in 2018, a year in which there were few selected studies. These results support the inclusion of both dimensions in the IAF. In particular, the factors of experience with technology and attitude toward technology in the user–technology relational dimension have recently been dealt with as important, and the support for use factor in the service-related dimension is also a factor that has become more important in recent years. This result implies that one of the things a service provider needs to care about is ensuring that appropriate technical training is provided so that older users can use this equipment as efficiently as possible [62]. Although there is a belief that older people are not interested in using technology, many studies reveal that the facts prove otherwise and, more importantly, that there is a barrier to use because of a lack of adequate training and technical support [9]. When support, human factors, and stable technology are well combined, the barriers can be overcome. The results of this analysis identify the acceptance factors that have been becoming more important and suggest trends that can guide to set the direction for future studies.

To ensure the validity of this study, three threats were considered: (1) selection bias in systematic reviews, (2) threats to the extraction of acceptance factors, and (3) limitations in assessing the evidence quality.

Plans for and investments in eHealth are expanding worldwide. This is considered a good solution for covering places where traditional health care services do not reach [3]. The application and development of eHealth are growing more important because of the continuous increase in the older population and the availability of solutions. The continuous monitoring of older adults can reduce sudden accidents and help respond immediately to emergencies [64]. Despite their needs, the use of eHealth technology is a new challenge for older people. To pervade in daily life, not only must the technical, service-related, and environmental infrastructures of the eHealth service be prepared but also the personal factors and user–technology relational factors be considered. This is the key to understanding and addressing the needs and characteristics of users more clearly. This study systematically reviewed the research that evaluated the acceptance factors for older people in the eHealth service field over the last 11 years. In addition, this study proposed an IAF through thematic analysis and the assessment of the impact of these factors. In addition, the eHealth acceptance factors were compared and analyzed according to the participants’ health conditions, verification time, and year. We expect that the IAF will become a tool that can be used to predict the main barriers to be overcome and facilitators to be leveraged. These data will form a good research material base for the application of eHealth to older users in the future.