Improving the uptake and continued use of mobile health technology: From behavioral change theory to contextual intervention design and the inclusion of the end-user

CITATION:

Georgsson, M. (Winter 2019). Improving the uptake and continued use of mobile health technology: From behavioral change theory to contextual intervention design and the inclusion of the end-user. Online Journal of Nursing Informatics (OJNI), 23(1).  Available at http://www.himss.org/ojni

The purpose of mobile health (mHealth) is to: “improve individuals’ health and well-being by continuously monitoring their status, rapidly diagnosing medical conditions, recognizing behaviors, and delivering just-in-time interventions, all in the user’s natural mobile environment” (Kumar, NIilsen, Pavel, & Srivastava, 2013, p. 28). In this sense mobile health technologies play an important role for those who utilize them in their care due to their convenience and availability, and perhaps even more so for those who have conditions that require monitoring over time or necessitate a behavior change. It is increasingly recognized that these technologies are vital nowadays to support patient self-management and most likely will continue to develop in importance and use in the coming years.

But, despite the recognition that these mHealth technologies offer several important benefits, and can be useful in self-management, there is limited uptake and continuous use of these technologies - why is this the case?

There may be several explanations for this. Those who have reviewed the literature on mobile health intervention usage in adherence and disease management studies have for example found, that there is limited use of behavioral change theory as a basis for these mobile interventions (Riley et al., 2011) despite the fact that others have reported that those studies that have used theory in their interventions have been found more effective (Michie, Johnston, Francis, Hardeman, & Eccles,2008, Webb, Joseph,  Yardley, & Michie, 2010).

It therefore seems that a greater use and application of behavior change theory in intervention development and design can be one important step to include (Riley et al., 2011, Michie et al., 2008, Davis et al., 2015). This may be especially the case if, as some authors point out, the intent is to be able to accomplish and then sustain behavior change in the patient with the intervention (Michie et al., 2008, Davis et al., 2015). In their view, these kinds of theories can aid in clarifying those determinants that are needed when it comes to targeting specific behaviors and can in turn be translated into specific behavior change techniques or strategies that patients can learn to use in their self-care (Michie et al., 2008, Davis Campbell, Hildon, Hobbs, & Michie, 2015).

A good starting point when it comes to increasing the intervention effect, achieve health behavior change or foster continuous use, seems to be that developers of the mHealth interventions make use of the theoretical underpinnings of behavior change theory to gain an insight into the different factors and mechanisms that have an influence (Michie et al., 2008, Davis et al., 2015). This may be part of it. Other authors also point out that even if current behavior change theories are applied more it may also be the case that these theories need to be developed to fit the mobile health interventions better; especially as these interventions become more interactive and adaptive to the individual user (Riley et al., 2011).

It is certainly the case that the health interventions of today are of a complex character, and there are many parts that are needed for them to be successful; for example, they should be developed with the right purpose in mind and applied in the right context (Campbell et al., 2007). Matthew-Maich et al. (2016) proposed that to fully encompass this complexity one must consider all the specific factors that are necessary in the design and implementation of mHealth technologies; ensure that these technologies fit the target user context and that the design and development teams are interdisciplinary and include health care practitioners. She and her colleagues also pointed out that regardless of who the target user of the mHealth solution is, it is vital that the design work is conducted together with the end-users and that they are included in the development (Matthew-Maich et al., 2016). This is also supported by Kumar et al. (2013) who pointed out that as mHealth systems become increasingly complex in their design, it is the transdisciplinary research collaboration - among computing, engineering as well as medical research - that will make the difference in these systems fulfilling users’ needs and hopefully also in contributing to their sustained use.

Author Bio

Mattias Georgsson, PhD, MSc, MPH, RN has a Master of Science degree in Nursing and  Master’s degrees in health informatics and Interaction Design. He completed his PhD in Applied Health Technology in the Spring of 2018. He currently holds a position as a Post-Doctoral Research Fellow in eHealth at University West, Sweden.

References

Campbell, N. C., Murray, E., Darbyshire, J., Emery, J., Farmer, A., Griffiths, F., Guthrie, B., Lester, H., Wilson, P. & Kinmonth, A. L. (2007). Designing and evaluating complex interventions to improve health care. British Medical Journal, 334, 455-459. https://doi.org/10.1136/bmj.39108.379965.BE

Davis, R., Campbell, R., Hildon, Z., Hobbs, L. & Michie, S. (2015). Theories of behaviour and behaviour change across the social and behavioural sciences: A scoping review. Health Psychology Review, 9, 323-344. https://doi.org/10.1080/17437199.2014.941722

Kumar, S., NIilsen, W., Pavel, M. & Srivastava, M. (2013). Mobile health: Revolutionizing healthcare through transdisciplinary research. Computer, 46, 28-35. https://doi.org/10.1109/MC.2012.392

Matthew-Maich, N., Harris, L., Ploeg, J., Markle-Reid, M., Valaitis, R., Ibrahim, S., . . . Isaacs, S. (2016). Designing, implementing, and evaluating mobile health technologies for managing chronic conditions in older adults: A scoping review. JMIR mHealth and uHealth, 4, e29. https://doi.org/10.2196/mhealth.5127

Michie, S., Johnston, M., Francis, J., Hardeman, W. & Eccles, M. (2008). From theory to intervention: Mapping theoretically derived behavioural determinants to behaviour change techniques. Applied Psychology, 57, 660-680. https://doi.org/10.1111/j.1464-0597.2008.00341.x

Riley, W. T., Rivera, D. E., Atienza, A. A., Nilsen, W., Allison, S. M. & Mermelstein, R. (2011). Health behavior models in the age of mobile interventions: Are our theories up to the task? Translational Behavioral Medicine, 1(1), 53-71. https://doi.org/10.1007/s13142-011-0021-7

Webb, T. L., Joseph, J., Yardley, L. & Michie, S. (2010). Using the internet to promote health behavior change: A systematic review and meta-analysis of the impact of theoretical basis, use of behavior change techniques, and mode of delivery on efficacy. Journal of Medical Internet Research, 12, e4. https://doi.org/10.2196/jmir.1376