• Health Communication
• Medical Informatics
• Public health informatics
• Global Health

With the widespread adoption of mobile technologies, including in the developing world, there has been enthusiastic exploration of ways that such devices can support care delivery and management in a wide variety of settings. mHealth was accordingly introduced as a general term for the use of such devices, and especially mobile phones, to support the practice of medicine and promotion of public health. The most common application of mHealth has involved the use of mobile devices to communicate with patients or healthy individuals. The goal has been to educate them about health promotion and disease prevention, or to assist with remote patient monitoring and care delivery, either through direct interaction with patients or with health workers. Mobile technology has recently appeared rapidly in low-income and middle-income nations (as defined by the World Bank economic criteria). Middle-income and (especially) low-income countries face various constraints in their healthcare systems, such as a severe lack of human, physical and fiscal resources, as well as highly significant burdens of disease and extreme poverty. Additionally, healthcare access to many parts of society is generally low in these countries.

The potential to lower informational and transactional health-related costs improves when the populace has greater access to mobile phones— typically available in urban settings but also important in rural areas where the communications infrastructure may be suboptimal or absent. These factors have motivated discussions regarding how greater access to mobile phone technology can be leveraged to mitigate the numerous pressures faced by healthcare systems in developing countries. There has been a substantial involvement of informatics professionals in discussions, both as researchers and as system builders. Their work has greatly enhanced our understanding of the optimal strategies and designs for building technical solutions that can be successfully introduced to, and adopted by, some of the most challenging user communities and healthcare settings on the globe.

There are several challenges in mHealth, which have included limited access to mobile devices and constrained cellular or internet connectivity. Even when these problems are addressed, data privacy and security, variations in literacy, cultural factors, and attitudes towards technology can profoundly constrain the use of mobile devices for health delivery and information management. Additionally, even when literacy concerns have been addressed (for both patients and health workers), the lack of standardisation in mHealth interventions can obstruct the best of intentions by those who seek to apply the technology. Literacy issues often mandate that the task undertaken on mobile technology be kept as simple as possible, enhancing efficiency to reach a larger portion of the needy population and reducing opportunities for error. In addition, new research often enlightens our understanding of the considerations that should guide ongoing work in the area.

Burka et al1 present their user-centred design (UCD) approach to designing a digital information system to support chronic disease management (hypertension) in four low-income and middle-income countries in South Asia and Africa. UCD is an iterative design process in which developers focus on the users and their needs in each phase to create usable and accessible products. Particular attention is paid to usability goals (crucial for acceptance), user characteristics, environment, tasks and the workflow surrounding use of the anticipated product. In the study, the authors applied this design approach to create a simple, offline-first, mobile application for providers to use when recording data during patients’ clinical visits, linked to a web-based dashboard that can be used to monitor programme performance. This offline application, focusing on data acquisition and simple guidance, ensures the continuous functionality of the application, even if there is a temporary loss of network connectivity. The article highlights the creation of a fast and easy-to-use hypertension management system, aimed at managing the providers' time constraints by minimising data entry and focusing on key performance indicators. Their goal has been to reach scale successfully in low-resource settings. The application did scale rapidly over 4 years to reach more than 11 400 primary care facilities in the four countries with over three million patients enrolled. This is an impressive result since such scaling usually takes much longer. The authors summarise four key design principles that they believe account for this success: speed/ease of use, minimal data entry, ability to do basic work offline and inclusion of minimalistic requirements designed to address key indicators of quality improvement.

In a second paper, Schretzlmaier et al2 conduct a cross-sectional validation study to evaluate (in two German-speaking countries, Germany and Austria) the extended Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model3 for predicting mHealth acceptance, using mobile diabetes applications as an example. The authors found that even though UTAUT2 has been well established in the information technology sector to predict a system’s acceptance by its intended users, the original UTAUT2 should be extended by two additional constructs: ‘perceived disease threat’ and ‘trust’. These allow the model to predict mHealth acceptance more effectively. The perceived disease threat is an individual’s awareness of the risk and limitations of the disease for their health, especially with chronic diseases like diabetes. They offer a detailed analysis, based on an extensive survey of patients with diabetes who were users of mobile applications, to show that awareness of risk is a significant driver for achieving consistent acceptance and use of mobile health applications. Trust in the technology (ie, that it would not fail if used in their care) was also shown to be a key factor in acceptance. However, the augmented model, with the two additional factors included, while improved over the base model, still could not consistently predict mHealth acceptance.

The two studies analyse user acceptance in very different ways that demonstrate the complexity of the task when one endeavours to introduce mHealth technology for routine use by either patients or providers. In one study, the emphasis is on providers in low-income to middle-income countries, demonstrating that successful implementation is possible if the four key factors are addressed. It uses minimum, immediately relevant patient information for data entry, with limited access to other health information. Sometimes simple technology suffices when attempting to reach a large population quickly. In the other study, the emphasis is on patients in more advanced countries, where they use mHealth to participate in managing their own care. Here, the emphasis of the analysis, and new insights, involve the importance of their own perceptions of the threats of the disease to their own health and their trust in the technology itself. The study offers methodological innovations that can be used to refine the current model for technology evaluation and acceptance.

The two studies are not contradictory, but they demonstrate the complexity of issues that need to be addressed when assessing and designing for user acceptance of mHealth technology. There will clearly be no single solution for all countries, cultures, levels of literacy, disease settings and fiscal environments. As a result, there remain myriad opportunities for study, assessment and development of targeted guidelines that will assist those who seek to engage a wide variety of healthcare communities with mHealth solutions.