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Designed for the long run: Maximizing the effectiveness of mHealth in supporting long-term physical activity change

Contents

I Introduction
II mHealth
III Designed for the long run
IV Physical activity and mHealth
V From evidence to practice
VI Designed for everyone?
VII Practical implications
VIII Conclusion

--Submitted by Sophie Rosa, Health Promotion Consultant, Health Promotion Capacity Building, Public Health Ontario

I Introduction

The use of mobile devices in various health sectors has increased significantly during the last decade. In 2015, there were over 165,000 mobile health applications (apps) on the market, however, many showed limited effectiveness, uptake and functionality. [1] In fact, only about 36 health apps account for 50 per cent of total health app downloads. [1] A preliminary review of the few synthesis and meta-analysis focusing on the effectiveness of health apps in supporting behaviour change indicated mixed results. In one systematic review of health app efficacy, Philips et al. found that apps can have a positive impact on short-term changes in various health and disease management behaviours. However, few studies have examined long-term-effectiveness. [2] In this article we explore how to use the Unified Theory of Acceptance and Use of Technology (UTAUT) to maximize the effects of mobile devices to support public health efforts. We also examine how public health practitioners can design multi-disciplinary interventions to maximize the effects of physical activity mobile apps and tracking technology (devices to monitor and track fitness-related metrics e.g., Fitbit, Jawbone). [3]

II mHealth

The World Health Organization (WHO) defines mHealth as “medical and public health practice supported by mobile devices, such as mobile phones, patient monitoring devices, personal digital assistants (PDAs), and other wireless devices.” [3, page 6]  WHO goes on to state that mHealth has the potential to revolutionize public health practice. [3] In their systematic review, Payne et al. reported significant improvements in levels of physical activity as a result of mHealth use. [4] As well Krebs et al. reported that 65 per cent of mHealth users reported significant improvements in their health and confidence in the apps’ effectiveness and accuracy. [5] However, to ensure long-term maintenance of new healthy behaviours continual use of health apps and tracking devices is essential. [4]

Unfortunately, two major surveys of mHealth uptake and usage show that 26 per cent of health apps downloaded are not used for a second time [6] and 33 per cent of activity tracker owners stop wearing their devices after six months resulting in a noticeable decrease in level of physical activity. [7] Similarly, Krebs et al. reported that 46 per cent of respondents no longer used a downloaded health app. [5] They also reported that the main barrier to maintain use were cost, disinterest over time, and privacy concerns. [5] As such, the design and deployment of mHealth should take into account the leading health promotion approaches (health communication, education, program development, etc.) to ensure a meaningful impact on public health outcomes.

III Designed for the long run

The current evidence highlights the need to design mHealth initiatives using the guiding principles of major health behaviour change theories (e.g., Theory of Planned Behaviour, Social Learning Theory, etc.) in order to support sustained behaviour change and improve health outcomes.[8–20] Health promotion research suggests that goal setting, feedback, social support, self-monitoring and prompts are all essential components of interventions that successfully generate behaviour change in a sustainable manner. [8–13] Various in-depth explorations of current health apps show that the strong majority lack behaviour change principles in their design limiting their effectiveness. [14–20] On the other hand, evidence reveals that electronic tracking devices use evidence-informed behaviour change theories and clinical best practices. [11,13] As such, how the use of electronic tracking devices can lead to positive health outcomes should be closely examined. 

IV Physical activity and mHealth

The topic with most traction when it comes to mHealth is physical activity, accounting for 53 per cent of the mHealth market, [5] as it has been found to significantly improve levels of physical activity in users. [21–27] Further in-depth examinations revealed that the most effective mHealth products integrate several health promotion strategies such as self-monitoring through the use of mobile diaries, which tend to keep users focused on their behavioural goals. [28–30] Other tactics include mobile displays encouraging users to maintain activity levels [31,32], as well as goal prompts, reminders and text messaging, which are most effective for challenging behaviour changes that require a high level of self-control. [33–34] Other effective behavioural change strategies include customized messaging, feedback and goal setting. [28,33,35–38]

In terms of the technology features of mHealth products, key design elements such as apps that fit within users’ busy schedules, increase behaviour awareness and provide opportunity for social network sharing, competition or support lead to continued use and increased levels of physical activity. [30,31,33,39–42]

V From evidence to practice

The evidence reviewed for this article indicates there is a wide range of considerations for the design and deployment of effective mHealth products. However, public health practitioners can rely on various models to guide their efforts such as the UTAUT. This theory was developed by Ventakesh et al. (2003) and is a synthesis of the majority of technology acceptance and use models and leading behaviour change models. [43] The model identifies four key factors (i.e., performance expectancy, effort expectancy, social influence and facilitating conditions) and various moderators (age, gender, experience, etc.) that predict behavioural intention to use a technology and actual technology use. The model proposes that performance expectancy, effort expectancy, and social influence impact behavioural intention to use technology while behavioural intention and facilitating conditions predicts technology use.

In 2016, Venkatesh et al. published an extensive review of the literature pertaining to the UTAUT and presented a revised framework to guide the development of mHealth interventions. [44] The framework presents the predicting factors and individual level moderators for behavioural intentions and for acceptance and use of mobile technology. While facilitating factors – factors that influence the actual health behaviour – and established mobile use habits predict behavioural intentions, they also directly predict acceptance and use. Five individual factors also predict behavioural intention to use.

The first factor is performance expectancy which is defined as perceived usefulness or utilitarian value. The second is effort expectancy—the degree of easiness to use. The third and fourth are social influence and price value, respectively. The fifth is hedonic motivation, defined as the degree of fun, entertainment and pleasure associated with usage. The framework also includes user attributes –age, gender, culture, ethnicity, employment, income and education – and technology attributes – usability, privacy, task difficulty – as moderators. [44]

In the context of health promotion, Cho et al. reported that both health consciousness (the extent to which one takes care of his or her health) and health app efficacy (the degree of cognitive ability in using apps to improve health) significantly predicted behavioural intentions to use an app with health app efficacy mediating the relationship between health consciousness and actual app use. [45] The study also found that eHealth literacy (the ability to seek, find, understand and appraise health information from electronic sources and apply knowledge gained to addressing or solving a health problem ) had a positive effect on health app efficacy and app use. [46] 

VI Designed for everyone?

EHealth literacy is creating a new level of health inequity and directly affecting how information is perceived, assimilated and put into practice. [47-49] Today, individuals increasingly rely on mobile technology and the internet to gather health information and use it to inform their personal health practices. Also, organizations use both to provide timely health information and promote their services and programs.  However, without adequate skills, individuals and organizations can mis-navigate the mHealth market and internet and inadvertently be given inaccurate and potentially dangerous information or end up using ineffective apps.

As highlighted by the WHO, the social determinants of health, from age to country of birth, are significant predictors of health literacy and eHealth literacy. [50] In their systematic review, Neter and Brainin (2012) reported that people with higher levels of eHealth literacy use more information sources, employ more search strategies, conduct more frequent searches and assess the information more carefully than people with lower eHealth literacy. [46]  The authors also reported that younger and more educated respondents tend to have higher levels of eHealth literacy, and that highly eHealth literate individuals gain more positive outcomes from their searches in terms of self-management, behaviour change, better interactions with their physicians, fulfilling health care needs, and better use of health insurance.[46]

The demonstrated link between eHealth literacy and the social determinants of health indicate how the reliance on internet and technology-based approaches in public health likely reinforces health inequity. [51] Daily mHealth users, who derived significant health benefits, tend to be younger, more educated and with a higher social economic status. [1,5] As such, online campaigns and services must be tailored to the needs of lower eHealth literate groups, aim to increase skills relevant to eHealth literacy and use mHealth technology that is both user-friendly and accessible. 

VII Practical implications

Evidence suggests various roles for public health and health care practitioners. First, an interdisciplinary approach in both design and marketing of mHealth is essential. Evaluation research highlights that most apps have limited effectiveness and recommend that developers partner with behaviour change experts to increase app quality, improve research design and undertake more rigorous evaluation. [51,52] Second, practitioners can play a key role in assessing existing mobile apps, or designing apps tailored to their specific region and clients to guide their target audience in choosing the right mHealth products for their situation. Without such guidance app popularity is the determining factor in mHealth choice. Aitken et al. reported an increasing interest in health app use in health practitioners for the purpose of chronic disease management with more than one-third of physicians reporting they had recommended a specific app to their patients. [1] Third, public health organizations could develop evidence-informed standards to identify, assess, evaluate and recommend a selection of mHealth, such as the UK’s National Health Service (http://apps.nhs.uk).   

VIII Conclusion

Based on the limited evidence available on the effectiveness of mHealth in creating positive behaviour change and maintenance, public health practitioners should consider using mHealth as part of a mix of health promotion strategies. As further research focuses on the moderating effects of various individual attributes on behavioural intention to use mHealth and its actual use, a better understanding of how key health promotion strategies (e.g., health communication, social marketing, policy development, etc.) can maximize the effectiveness of its use on various public health outcomes will be achieved.

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