This section consists of two subsections. In the first section, I will explain mHealth, starting with definition, continuing to a brief overview of the current situation of mHealth in view of application area and trend. Then I will describe research gaps and challenges in mHealth. This is followed by introduction of research agenda proposals by researchers.
2.3.1 Definition
As written in Introduction chapter, mHealth is defined as “the use of mobile computing and communication technologies in health care and public health” [8]. The Global Observatory for eHealth at WHO defines “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices” [104]. This definition indicates communication technologies are not necessarily used in mHealth but the point is that the device is mobile so that it is portable and has a feature of “always on a user”. mHealth is considered to be a part of eHealth [104,105].
Gemert-Pijnen et al. found that eHealth was differently defined in their review of eHealth frameworks [105]. In their proposal of a holistic framework, “CeHRes roadmap”, they define eHealth as “all kinds of information and communication technology used for supporting health care and promoting a sense of well-being” [105]. Thus, in the following sub-sections, I will describe relevant research works in eHealth as well as mHealth to cover common topics and challenges.
2.3.2 Application area and trend
A survey by WHO revealed that mHealth is emerging through experimentation rather than strategic implementation in many WHO member countries [104]. In their categorization, mHealth includes mere use of mobile phone line between citizens and health care providers, which in fact is frequently reported. On the other hand, mHealth taking advantage of mobile computing, such as patient monitoring or decision support systems, is much less frequently reported and very few countries reported that such initiatives were established. Possible reasons for low implementation of monitoring are lack of sensors at reasonable costs [104]
and very limited compatibility with external sensors for tracking health information [106,107]
This is somehow contradictive to strong user needs for tracking function on mHealth applications on smartphones [106].
As scientific research, a recent review by Fiordelli et al. [10] gives a very good overview of application area and trend of mHealth research including study designs and technologies used.
They found that major application area of mHealth research is chronic conditions among
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which diabetes leads in the number of studies. Diabetes is targeted in a great deal of studies with mHealth interventions as well [108]. Fiordelli et al. also found the shift of study interest in the last five years; from split interests in evaluation of technology itself and of impact on health outcomes to mostly evaluation of the impact of mobile technology on health outcomes [10]. Given that a review was conducted in 2012, it is surprising that they found that few studies applied native mobile-phone applications created ad hoc for research purpose. On top of that, none of the reviewed studies applied already existing and publicly available
smartphone applications [10]. The major technology used in mHealth intervention research is still short message service (SMS) [10,108].
Incorporation of HBTs and of strategies employed in persuasive technology into eHealth including mHealth intervention is considered to be beneficial. This is not only for intervention using messaging service as its delivery channel but also in technology design and
development [90,99,109–113]. This notion is reflecting the growing recognition that knowledge alone is insufficient to produce significant changes in behavior and that
psychological and behavioral intervention is necessary [42,114]. However, Chomutale et al.
found that still only a small number of smartphone-based mHealth applications for self-management of diabetes are equipped with personal education features and social network features [115].
2.3.3 Research gaps and challenges
The biggest research gap is that there is very limited clear evidence of effects by mHealth. It is not only in the domain of diabetes as described in Introduction chapter but also other purposes within domains of disease management and health behavior change.
mHealth intervention with SMS or other types of messaging seems to have positive effects on improvement in self-management skills, behavior change or health outcome measures [116–
119]. However, the strength of evidence is fairly limited. This is because of heterogeneity of study design. Few studies are with high quality of evidence while many studies suffer from lack of reporting of process measure, demonstrated causality, and risk of bias [116,118,120].
The word ”black box” is used to express the current situation of studies with many potentially effective factors included in intervention [121,122]. This is not only limited to mHealth research but applicable to a part of eHealth research. Morrison et al. [123] found that studies of eHealth intervention with self-management features, activity planning and self-monitoring, often lacks specifications regarding how activity was planned, what behaviors were monitored, or how behavior was monitored. In their review of Web 2.0 self-management intervention for older adults, Stellefson et al. [124] also point out that “impact evaluations assessing Web 2.0 engagement were lacking”. Although almost all reviewed studies monitored web log activity, they did not take advantage of the information to assess users’ usage. Lehto and Oinas-Kukkonen [125] also revealed that program utilization was rarely assessed (3 out of 23 reviewed articles). They also revealed that incorporated persuasive features were not explicitly tested in Web-based smoking and alcohol intervention studies [125]. Effect of incorporation of HBTs in ICT-based intervention is not clearly shown either, because of the considerable variety in the effectiveness and study designs [126–128]. Evidence of tailoring intervention is also unclear for long-term effects on adherence to medication because of low-quality adherence measurement [129]. In Web-intervention studies for weight-management [130] and for people with mild and moderate depressive symptoms [131], it was found that participants did not read tailored message. They suppose that lack of trigger to let a user visit the web site and lengthy contents were the possible reasons for that the users did not read tailored messages.
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Lack of information and lack of clear organization of results make it difficult to conduct rigorous review. The current status provides very limited clear evidence regarding effects of mHealth in terms of not only health outcome but also cost effectiveness. This is considered to be the major barrier to the implementation of mHealth in the health care setting [104].
Another great challenge in mHealth is the “law of attrition”, which was defined by Eysenbach [17] as “the phenomenon of participants stopping usage and/or being lost to follow-up, as one of the fundamental characteristics and methodological challenges in the evaluation of eHealth applications”. As the factors influencing attrition, Eysenbach listed up the followings: relative advantage, compatibility, complexity, trialability, observability [17]. Additionally, he listed up hypothetical factors of usage attrition such as; ease of drop out, ease of stopping using an application, usability and interface issues, and lack of “push” factors such as reminders [17].
It is implied that tailoring message [116,132] and more extensive employment of dialogue support such as reminders and suggestions [133] have a potential effect on users’ retention, but the evidence is not yet strong enough to be generalized [122]. Another issue about attrition is that more engagement with program does not automatically mean more improved outcome. A part of participants benefit from a brief exposure to Web-based interventions.
However such participants are likely to be included among dropouts [134] or if not, they will decrease usage of or be less engaged with eHealth program. This is due to “ceiling effect”, which illustrates the status that a participant finds no more effect by further engagement with the program [135].
2.3.4 Research agenda
In order to overcome the barrier against implementation of mHealth, one way of potential approaches is to employ rigorous research methods such as Randomized Controlled Trial (RCT) to provide with high-quality evidence on efficacy of mHealth [112,118,121,136].
However, there are opinions that research methods such as RCT are not necessarily
appropriate considering rapid evolution of mobile technology and too many uncontrollable confounding factors that cannot be anticipated [105,137,138]. Regardless of the types of evaluation, the most essential requirement throughout the course of design, development and evaluation is to clearly and thoroughly describe information so that the results can be
comparable with others [12,14,139]. CONSORT-EHEALTH [140] offers a solid guideline to follow when planning, conducting and reporting eHealth interventions. This guideline clearly states the necessity of detailed description about intervention and patients’ utilization of the program. As described previously, non-usage of or drop outs from eHealth program are inevitable, and thus it is important to report non-usage and drop-outs over time [17]. Kelders states “understanding how the content, system and service of an intervention are used and experienced, may be the key to understanding why eHealth technologies suffer from large non-adherence rates” [122]. Whittaker et al. argue that qualitative investigation should be carried out to the participants of RCTs in order to obtain more in-detail understanding about how participants experienced the intervention tool [136]. Mixed methods research is
considered having a high potential to understand user experience in relation to how actual usage influenced the outcome [105]. It is also important to investigate what level of engagement is necessary to benefit from the investigations and how it varies among users [112].
Taking multidisciplinary approach is also considered essential. In CeHRes Roadmap, integrating persuasive technology, UCD and business modeling is considered necessary to give theoretical background for the development process [105]. However, in eHealth and mHealth research in general, there is a certain gap between domains - HCI and healthcare - in
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terms of focus and thereby study design and reporting styles. This gap is confirmed by the finding of a review of Web-based dietary interventions [141]. Another review of Web-based intervention programs for diabetes revealed that only six percent of tools were free of usability errors [142]. As a matter of fact, it is considered a great challenge to realize patient-participation in design from an early stage [143]. Based on actual experiences, many
researchers recently presented practical guidelines or implications about UCD when users are patients and/or how to involve healthcare consumers, such as patients as users [21,144–149].
However, most of them are published within HCI domain. In order to fill this gap, a research team should be consisting of members with multidisciplinary backgrounds including ones from HCI domain [150]. Especially for ensuring usability of a mHealth application, which is considered to be one of factors of attrition or low uptake [17,135,151], UCD is essential [19,104]. The importance of UCD is illustrated by successful experiences in developing engaging mHealth tools by implementing UCD from an early phase and thereby being able to identify a number of key system requirements [152]. It is also illustrated by lessons that usability problems with an mHealth application were found when tested by people who were novice to the application and without technical background [153]: When the same mHealth application had been tested for three months by users with technological background and with previous experience of using the same type of the mobile phone, the usage remained high over time and users’ impression on the usability was also good [154].
As described in Introduction chapter, evaluation of mHealth from HCI perspective should therefore focus on understanding usage and uncovering potential problems that stem from design of an employed mHealth or eHealth technology for intervention [20]. And again, evaluation of usability and of how design features influence effects should be made publicly available [137]. With regard to integrating persuasive technology, it is a challenge to ensure that a user would use the program with a positive attitude. For example, Crutzen et al. [155]
found that although participants appreciated having control on web sites so that a user could skip pages, the knowledge obtained and exposure to pages were higher among those who used pages with less user-control. Together with ethical issues of persuasive technology described in the previous section, such challenges should be tackled with active involvement of
stakeholders, especially the end users.
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