Owing to the development of information and communication technology (ICT), health care service delivery nowadays goes beyond traditional face-to-face interaction. ICT supports health care with electronic communication and system networking capabilities to provide, exchange, and facilitate exchange of health-related information [1]. Mobile health (mHealth) emerged in 2003; Robert Istepanian coined the term to describe the use of emerging mobile communications and network technologies for health care [2]. Compared with web-based health services delivered from desktops and laptops, mHealth services have the advantage of interacting with individuals with greater frequency and flexibility, without being limited by time and place [3]. Mobile technologies, especially smartphone-based apps, can improve the efficiency of health care delivery, ultimately make health care more effective [4-6] and help people to better control their chronic conditions [7,8]. However, despite the numerous benefits of mobile health (mHealth) apps, relatively little is known about whether older adults perceive that these apps confer such benefits. Their perspectives toward the use of mobile apps for health-related purposes have not yet been fully investigated [9].

“Living a healthier independent life” is vital for older adults’ quality of life [10]. Given that the aging population has become a global issue, making mHealth services more acceptable by older adults is of paramount importance. For instance, the World Health Organization has identified a good practice case study in Singapore’s Action Plan for Successful Aging, where a mobile app, Healthy 365, was successfully used [11].

Although there has been a steady increase in the number of studies exploring technology adoption or acceptance among older adults, few have focused on mobile technologies, and even fewer have explored the acceptability of mobile technology use for health-related purposes [12]. Studies on mHealth adoption among older people are far less than those on general technology adoption among older people [13-15]. Because of the importance of, and the increased interest in the field, a scoping review protocol was proposed in 2020 to investigate the willingness, perceived barriers, and motivators in adopting mobile apps for health-related interventions among older adults [9].

Published studies on mHealth adoption [16-20] are mostly based on the technology acceptance model (TAM) [21] and its extended variations (ie, TAM2, TAM3, United Theory of Acceptance and Usage of Technology [UTAUT], and UTAUT2). The Health Belief Model (HBM) and the Protection Motivation Theory (PMT) also prove helpful in understanding mHealth adoption. In research on health behavior, eHealth literacy [22], self-efficacy [23], perceived vulnerability, perceived severity, and health consciousness [24] are listed as influential factors in people’s adoption of health information technologies. Sun et al [25] integrated several models to find that users’ intention to use mHealth services was determined by 5 key factors: performance expectancy, effort expectancy, social influence, facilitating conditions, and threat appraisals.

Deng et al [18] extended the TAM with trust and perceived risks in studying mHealth adoption in China. Alam et al [19] extended UTAUT to include perceived reliability and price value to investigate mHealth adoption in Bangladesh. These studies used quantitative methods (eg, survey questionnaires) and recruited patients from local hospitals. Cajita et al [26] investigated the intention to use mHealth in older adults with heart failure, and associated facilitators and barriers [12], using mixed-methods (ie, large survey + small-scale interview). Minimal qualitative research was conducted with well-old users [27] who are the largest potential beneficiaries of mHealth services.

Previous research has mainly investigated how older adults use technologies before the objectification phase and usability problems after the conversion phase [28], and few have investigated the initial adoption stage, that is, using only elementary features and limited functions of mobile technologies. Grindrod et al [29] evaluated user perceptions of 4 mobile medication management apps with older adults (those aged ≥50 years) through usability testing and found that most participants were frustrated by their initial experiences with the apps.”

This paper fills these gaps by exploring older adults’ initial adoption of mHealth apps, using qualitative questionnaires combined with interviews and user trial workshops to reveal their perceptions and contextualized experiences. The insights help generate design suggestions to make mHealth services more acceptable to older adults.

mHealth services use ICT. They are relevant to technology adoption theories and can be traced back to the theory of reasoned action (TRA) [30]. On the basis of the TRA, Davis et al [31] developed the TAM in which they suggested that perceived usefulness and perceived ease of use are the 2 most important individual beliefs about using information technology. Other researchers have extended the TAM and proposed the TAM2 [21] and the TAM3 [32], decomposing perceived usefulness and perceived ease of use. Ajzen [33] developed the Theory of Planned Behavior (TPB) to extend the TRA and added the new construct of perceived behavioral control. Venkatesh et al proposed the UTAUT [34], combining 8 existing theories, and the UTAUT2 [35] emphasized the consumer use context.

As a kind of health behavior, mHealth adoption is also relevant to theories of health behavior, such as the HBM [36] and PMT [37]. The HBM hypothesizes that health-related behavior depends on the combination of perceived susceptibility, perceived severity, perceived benefits, perceived barriers, cues to action, and self-efficacy. The PMT stems from both threat appraisal (perceived vulnerability and perceived severity) and coping appraisal (response efficacy, self-efficacy, and response cost) processes.

The theoretical framework of this study is based on the TRA, TAM, TPB, UTAUT2, HBM, and PMT (column 2 in Table 1). Eight main constructs (column 1 in Table 1) were extracted by grouping similar factors in these models. These constructs will be further investigated through primary studies in order to gain insights into older adults’ perceptions and the initial adoption of mHealth services.

An overview of this study is shown in Figure 1. Study 1 investigated older adults’ perception of mHealth devices through questionnaires and interviews based on a literature review. Study 2 observed how older adults initially use mHealth apps to identify the barriers and experiences they have in mHealth adoption.

The research received ethical approval from the Queen Mary University of London (QMERC2016/31). The insights from these 2 studies help generate design suggestions to make mHealth services more acceptable to older adults.

Study 1 was conducted between January and February 2017 in London. The study comprised a 15-minute questionnaire and a follow-up interview (approximately 30-45 minutes). Conducting face-to-face interviews following questionnaires can not only help to obtain more detailed information from the participants but also help rectify any misunderstanding of the answers. The mHealth service discussed here mainly focused on health-related services that can be accessed by smartphones and tablets, for example, websites and mobile apps.

Existing studies have proved that age plays a moderating role in mHealth adoption [41-43] and factors have different impacts on mHealth adoption intention among different age groups [23]. In Britain, old age can be any age after 50 years and this definition has been adopted in many human computer interaction studies and initiatives such as age-friendly cities. In this study, we recruited well-old users [27] aged between 50 and 70 years in East London. People with serious disease or impairments and aged ≥70 years were excluded; this was to ensure independent participation in the study (requiring traveling and basic understanding of digital technology).

We targeted 30 samples, as suggested by Corder and Foreman [44]. We included all 32 older adults who contacted us, but 2 of them failed to complete the whole process, so the valid responses were 30. Convenience sampling was used; it is cost-effective and has been widely accepted in information system research [45]. Participants were recruited from the Age UK, Hackney Mobile Centre, and the Queen Mary University of London. The questionnaires and interviews were completed in the classrooms of Age UK East London, Hackney Mobile Centre, or the Senior Common Room of Queen Mary University of London, depending on the time and venue availability. The details of study 1 are shown in Table 2.

To understand older adults’ perceived usefulness of mHealth apps, the participants were asked to rate the usefulness of different features of mHealth apps and to give reasons for low scores. An mHealth app typically offers more than one function; in other words, an mHealth app has multiple features. To understand main features offered by typical mHealth apps, the authors (JP and HD) searched for the term health in both the App Store (iOS system) and Google Play Store (Android system) in December 2016, downloaded the top 50 health-related apps in each system, and analyzed the features of each app. For example, Apple Health has features for fitness and exercises and for emergency (providing vital medical information of you in an emergency). As a result, 13 features were extracted from the existing health-related apps, and they were evaluated by the older adults participating in study 1.

To understand older adults’ perceived behavioral control of using mHealth services (eg, mobile apps), the participants were asked to rate how different age-related changes might stop them from using an app, for example, visual impairment, hearing loss, decline in memory, decline in the ability to understand written and spoken languages, decline in the ability to focus attention, and decline in movement control [46]. Generation gap was also added, as we found from our previous pilot study that older adults had difficulties in understanding new terms generated by the younger generation. For example, they were confused by the menu or navigation of a digital interface.

This study took place as a workshop in March 2017 at the Hackney Mobile Centre in East London, where a Wi-Fi connection was available. mHealth apps were introduced to older adults, and they were helped to start using these apps. At the same time, how they initially used mHealth apps was observed to identify the barriers and experiences that older adults have in mHealth adoption. mHealth apps were selected from the App Store and Google Play Store. After reviewing over 100 mHealth apps, we identified 4 categories beneficial to older adults’ health, namely web-based diagnosis, step tracker, calories calculator or food diary, and health monitor.

As Google Fit (Android system only) and Apple Health (iOS only) are embedded in most smartphones, they were also included in the trial. An additional 4 pairs of apps, free and available in both Android and iOS systems, were chosen for each category (Table 3).

The 30 participants completed both the questionnaire and the follow-up interview. The sample characteristics of study 1 are shown in Table 4. The participants were asked to rate their own perceived health condition from 1 to 5 points (1 for poor and 5 for excellent). The average score of all the participants was 3.7 points (SD 1.15 points; minimum=1 point, maximum=5 points); 66% (20/30) of them had a positive perception (scores 4-5) of their own health.

The workshop (study 2) was conducted in March 2017, a month after the completion of study 1. In total, 12 participants (5/12, 42% males and 7/12, 58% females), aged between 50 and 70 years (minimum 52, maximum 66; mean 56.8, SD 4.5) participated in the workshop. Table 8 shows the sample characteristics of study 2.

This paper has uncovered older adults’ perceptions and initial adoption of mHealth services using qualitative data collected from questionnaires, interviews, and workshops in East London.

Study 1 found that the lack of obvious advantage, low reliability, scary information, and risk of privacy leakage will decrease the perceived usefulness of mHealth services; the design of app interface will directly affect the perceived ease of use; aging factors, especially the generation gap, will make mHealth difficult for older adults to use.

Study 2 identified the barriers that older adults face during their initial adoption of mHealth apps (Figure 3). Access to technology, the way of interaction, the risk of money loss, heavy workload to use an mHealth app, and the different lifestyles of older adults have a great influence on older adults’ adoption of mHealth services.

On the basis of the results of the 2 studies, the implications for the design are summarized in Textbox 1. These suggestions can help design practitioners develop more acceptable mHealth services for a wider population.

The implications fall into 4 categories, that is, technical improvement, free trial, information clarification, and participatory design. The specific suggestions are presented in Textbox 1.

Taking traditional or other existing health care services into consideration and offering (added) advantages.

Compared with prior studies, the value of this study lies in its 3 contributions. As for theoretical contribution, this study systematically analyzed several main determinants from theoretical models, such as the theory of reasoned action, technology acceptance model, Theory of Planned Behavior, UTAUT2, Health Belief Model, and Protection Motivation Theory, and investigates them through primary research. Some factors were redefined or decomposed according to our results.

Perceived usefulness has been used to predict mHealth adoption [16-18,20]. In this paper, perceived relative advantage is found to be a better substitute for explaining older adults’ initial adoption of mHealth services. This is in line with the related advantage in the diffusion of innovation theory [50]. mHealth services should not only be good but also have more relative advantage than traditional health care services. Older adults’ perceived usefulness of mHealth services is associated with lifestyle compatibility and information quality. An mHealth service is perceived to be useful only when it is compatible with the lifestyle of older adults. This is in accordance with the compatibility factor in the diffusion of innovation theory, which indicates how consistent the innovation is with the values, experiences, and needs of potential adopters [50]. Information should not only be easy to understand but should also avoid frightening older people.

Perceived behavioral control in this paper is investigated through the access to technology and age-related ability decline. Access to technology affects older adults’ initial adoption of mHealth apps. Many older adults do not often upgrade their mobile devices or internet services, and their out-of-date facilities constrain them from downloading new apps (Figure 3). Age-related ability decline influences older people’s adoption of mobile apps. This is consistent with the findings from previous studies [42,43]. Our study also suggests that generation gap creates understanding barriers (Table 8), which has not been addressed by published studies.

Ease of use is thought to be perceived firstly from the interface of an App, such as the text and icons (study 1). However, heavy workloads for registration and inputting information often put older people off before they start (study 2).

Perceived reliability is positively correlated with the intention to use mHealth services [19]. However, it seems that the accuracy of the information is less important in the workshop than what people said in the questionnaire and interview sessions. A novel and easy interaction (eg, using a phone camera to measure blood pressure) can motivate people to start.

mHealth apps generate new security and privacy concerns [51]. Evidence has shown that perceived risk, including performance risk, legal concern, and privacy risk, may significantly decrease older people’s intention to use mHealth apps [52]. In our study, the risk of using mHealth apps perceived by the participants was mostly due to privacy leakage (study 1) and unexpected money loss (study 2).

In the methodological contribution, the hands-on trial (study 2) illustrates concerns and frustrations when older people bodily experienced mHealth apps and provides deeper insights into key issues of initial adoption. The entire study is digitized (access to internet, smartphones, tablets, downloading apps, and initial trial), and goes beyond common technology use among older adults in general.

In the practical contribution, we not only investigated perceptions and barriers but also proposed suggestions to design potential barriers. The design implications and specific suggestions are based on the findings of our studies (shown in Textbox 1) to support the better design of mHealth services. Our suggestions share some commonality with [53] which proposed to face cultural resistance and concerns, improve engagement of users in design (see the participatory design suggestion in Textbox 1), and build or increase users’ trust (see free trial and information clarification in Textbox 1). Our more detailed suggestions will help designers tackle these barriers more effectively.

This study has several limitations. This research was conducted in East London, and the sampling was not representative of the United Kingdom older population or older adults in general. Participants from different countries and regions could have various perceptions and face different barriers to mHealth services. Gender balance could also have an impact on the results. We tried to balance the participants’ gender, but in reality, study 1 had more male participants (17/30, 57%) and study 2 had more female participants (7/12, 58%). Our participants were relatively well educated, and around 60% were younger well-old users (aged 50-60 years). This is because of our recruitment methods and criteria. However, they may be early adopters of mHealth services in the future. The workshop participants had limited experience of using mHealth services, which is common among older populations (and given the sampling, the situation of a general older population may be worse). We focused on the initial adoption of mHealth, regardless of the users’ prior experience. It is useful to observe 5 users’ withdrawing from the trial because of the various barriers encountered during the process. Seven users still provide good insights into major usability problems [54,55], and we have been able to learn from both successful and failed user-testing.

In our study, eHealth literacy, hedonic motivation, price value, and social influence have not been fully investigated. Future research should address these issues in detail. For future work, more participants with experience using mHealth apps will be recruited to find the motivations in addition to the barriers. Our research was conducted before the COVID-19 pandemic, and health service systems have been largely challenged by the pandemic; significantly more people have experienced remote or web-based health consultation since 2020, which might motivate older adults to accept mHealth if barriers are addressed and trustworthiness is ensured.