mHealth: It’s not about the technology, it’s about the patient

Medical education and/or care delivered via mobile-based or digitally enhanced solutions (mHealth) is a rapidly evolving area and a common component of patient support programmes. However, just because a person is digitally literate or routinely uses mobile devices, does not necessarily mean that they will adopt mHealth initiatives to help facilitate management of a specific condition.

In this article, we examine emerging evidence on the psychological and behavioural traits that make some people more pre-disposed to adopting mHealth initiatives, and how screening tools can be used to personalise the patient experience.

mHealth is not for everyone

mHealth applications (mHealth apps) have the potential to support high-need, high-cost populations in managing their health. In practice, apps have been used to help people living with various health conditions, including mental health,^1,2 heart failure,³ smoking cessation,^4,5 and diabetes.⁶ But while the number of mHealth apps has grown substantially, there is a notable absence of research on the behavioural traits linked to mHealth adoption. The research to date has largely focused on the digital apps themselves and has done little to expand our knowledge about patient characteristics that influence mHealth adoption.

Understanding the individual needs and drivers of adoption is essential for the uptake and ultimately, the success of mHealth initiatives. Simply recommending a mobile device or a certain app to someone who is diagnosed with a condition is unlikely to be of benefit. The nuances of the condition, especially when combined with other health challenges, mean that individuals must navigate a journey based on their own priorities, needs and motivations. It stands to reason that mHealth initiatives should be targeted to those individuals who are most likely to adopt them, while acknowledging that others may benefit from alternative modes of information delivery. However, in the absence of such information, many poorly targeted mHealth apps fail to meet the needs and expectations of prospective users, nor achieve their market potential.⁷

By understanding the barriers and enablers associated with mHealth adoption, it is possible to integrate screening tools into the design and implementation of patient support and educational programmes that employ such technologies. Providers of mHealth apps would then have the ability to strategically target the programme experience to the needs of the individual, including whether (or not) they are likely to embrace mobile device self-management strategies.

Targeting the educational needs of patients

Emerging data from one of Australia’s leading universities goes some way to helping expand our understanding of the digital-health divide. Researchers, led by Mr Morris Carpenter from the University of Canberra, recently performed a mixed-methods, cross-sectional study with the aim of identifying behavioural factors that augment the uptake of mHealth by individuals diagnosed with type 2 diabetes mellitus (T2DM).⁸ The study was conducted in two distinct phases. In the first, semi-structured, face-to-face interviews were conducted with 17 adults (7 males, 8 females) living with T2DM to identify important components of effective self-management and successful mHealth adoption. All participants were independently selected by a practice nurse from a local GP clinic and all provided signed informed consent. The qualitative results established a framework (Figure 1) that placed participants into one of four quadrants based on levels of mHealth engagement and self-management proficiency.

Information from the first phase was then used to develop a patient-centered questionnaire using validated methodology to quantify the degree of mHealth adoption together with key concepts associated with mobile digital engagement and successful self-management [e.g. self-efficacy proficiency; online health information seeking behavior (OHISB) and multidimensional health locus of control (MHLOC), mobile device digital literacy, and frequency/sub-categories of mobile device use]. Patients with T2DM registered with the National Diabetes Services Scheme (NDSS) were invited to participate.

Completed questionnaires were received from 382 adults (220 males, 159 females). Using multivariable regression analysis, the results demonstrated statistically significant positive correlations between successful mHealth adoption and following domains:

• self-efficacy (β=.132; p<0.05),
• (β=.299; p<0.001), and
• frequency of mobile device use (β=.231; p<0.001).

While digital literacy is an essential element of mHealth adoption, in isolation, the correlation with mHealth engagement was not statistically significant (β=.11; p=NS).

Implications for the healthcare industry

The research project identified three significant predictors of mHealth adoption in a sample of adult patients with T2DM: self-efficacy, OHISB and frequency of mobile device use. The first domain, self-efficacy, refers to an individual’s belief in his or her capacity to execute behaviours necessary for specific tasks.⁹ This belief leads to a greater sense of confidence and control over one’s own motivation and social environment, translating into a greater theoretical likelihood of both intending to perform the behaviour and actually doing so.¹⁰  In the context of the current study, the data suggests that individuals with good diabetes management self-efficacy, those who frequently use mobile devices in their everyday lives, and those who search the internet for information that is relevant to their own health status, are more likely to adopt mHealth technology to facilitate their disease management.

The individual domains of OHISB and the frequency of mobile device use were also strong indicators for mHealth adoption. Importantly, it is the frequency of mobile device use for factors other than communication, namely information, entertainment and e-commerce, that is most predictive of mHealth adoption.

Although the research project described herein is relatively small in nature and confined to a cohort of individuals with T2DM, there are several important implications that can be drawn from the results:

• The factors most predictive of mHealth adoption (i.e. self-efficacy, OHISB and frequency of mobile device use) can all be measured using validated tools, thereby providing a simple and practical approach for assessing individuals’ likelihood of embracing such technologies. With regards to patient support and education programmes, such information could be used to deliver targeted learning experiences that engage and retain participants based on their specific needs.
• The findings reinforce the concept that mHealth is not embraced by everyone and poorly targeted mHealth apps can make the difference between a flourishing initiative and a failed experiment. As part of any support or education programme, different representations of content should be provided, taking into account individual preferences and abilities.

For more than a decade, the concept of ‘individualising treatment for patients’ has been a key theme in healthcare circles. With the opportunities and flexibility afforded by today’s mHealth technologies, there is no reason why our approach to patient support, and to educational initiatives in general, should not be the same. Service providers to the healthcare industry should not avoid the attractive innovations afforded by mobile technology. However, it is important to remember that mHealth is not about the technology, it is about the end user – the patient. The patient is mobile and is at the centre of the experience, and the technology allows the patient to access the experience in any context.¹¹

Key points

• mHealth apps hold great potential, provided that are appropriately targeted to patients who are most likely to adopt such initiatives.

• While digital literacy and access to the internet are clearly essential elements of mHealth adoption, in isolation they are insufficient to trigger engagement with mobile devices to enhance health outcomes.

• Screening tools are necessary to identify skills for adequate use of mHealth services.

• Patient support services and/or education programmes should offer functionalities tailored to the individual’s likelihood of adopting specific information.

For more information, or to discuss how the screening tools mentioned in this article can be incorporated into your current or future programmes, contact medScript.