What’s Wrong with Mobile Apps for Medication Adherence

Health experts weigh in.

Not too long ago, Walgreens put out a pair of case studies that showed signs of progress in using mobile apps to reduce medication non-adherence. Other research also gives a glimpse into how health apps could crack the non-adherence riddle — a 100-300 billion dollar problem for the U.S. each year. [1–4]

This is encouraging news. And it tells of the positive strides mobile health (mHealth) solutions are already making in the medication adherence space. But it doesn’t reflect the wider reality.

Health experts point out gaps in today’s medication adherence apps that thwart the end goal of improving health outcomes.

In a previous post, we outlined the costs and causes of non-adherence. For this article, we’ve compiled health experts’ main critiques of mobile apps for medication adherence.

Our intent is not to bash these technologies. On the contrary, we firmly believe that mobile health apps will bring long-term gains in adherence. Instead, our aim is to share information that app makers can use to enhance their products.

Health Apps Conflict with Behavior Change Research

A 2016 report in JMIR mHealth and uHealth captures the essence of health experts’ critiques of medication adherence applications. Upon reviewing apps for people with chronic arthritis (CA), the research team had this to say:

“We found a remarkable lack of persuasive techniques to engage patients in digital management of their disease.” [5]

Others give a similar diagnosis.

After evaluating over 400 apps and user testing 100 of the highest-rated ones, Dr. Seth Heldenbrand and his colleagues observed:

“It is concerning that nearly 25% of the top-scoring adherence apps could not perform a basic function (i.e. issue reminders) to mitigate non-adherence, could not be installed by a student healthcare professional, or possessed other barriers to using them.” [6]

He adds: “Despite the continued growth in the adherence app marketplace, our data show that many of the available apps lack many basic adherence app attributes.” [7]

“Despite the continued growth in the adherence app marketplace, our data show that many of the available apps lack many basic adherence app attributes.”

In Heldenbrand’s eyes, app makers are hurling new products into the market while neglecting in some cases the most basic tenets of adherence research. Health apps ignore health science.

A report out of the American Journal of Preventive Medicine sketches an even clearer picture of apps’ shortcomings by grading them based on established Behavior Change Techniques (BCTs). Researchers in the study held 166 medication adherence apps up to scrutiny using the BCT scale — an evidence-based list of ways to change people’s behavior that has been used to test fitness and dieting apps in the past. [8]

Of a possible 96 different BCTs that apps could employ to alter users’ behavior for the better, researchers only found 12 in all the apps in the study. The authors aptly noted that “BCT is limited in these apps.”

Researchers in AJPM study only found 12 Behavior Change Techniques (BCTs) of a possible 96 in all the apps they reviewed.

As their peers in other studies observed, this same research team concluded:

“The development of medication adherence apps may not have benefited from advances in the theory and practice of health behavior change.” Again, known strategies for affecting human behavior (e.g. getting someone to take a pill) appear to be missing in many of our health apps.

“The development of medication adherence apps may not have benefited from advances in the theory and practice of health behavior change.”

Another group of researchers looked at medication adherence apps for patients with diabetes and reported that the patients in the study didn’t like the functionality of existing apps. [9]

The research team concluded: “Both functionality and user engagement could be improved by including relevant stakeholders in future app development, which should be driven by clinical and user need as opposed to what is easiest to develop.”

“…future app development…should be driven by clinical and user need as opposed to what is easiest to develop.”

In yet another study just published in December by the University of Michigan Medical School, researchers were short on praise for medication adherence apps. They looked at 137 of the best-rated apps for people with chronic conditions and found that “few apps address the needs of the patients who could benefit the most.”

They determined that an app’s consumer ratings have little bearing on its ability to help with adherence. [10]

What to Fix First

In one of the above cited studies, researchers made a suggestion that speaks to an important product enhancement for app developers. The researchers said that medication adherence apps should leverage sensor data within a person’s phone to track their physical activity and help them better manage their conditions. [11]

This observation hints at far more than just fitness tracking; it points to the much bigger idea of making our mobile health apps user aware.

Of all the product enhancements mHealth companies could make in 2017, equipping their apps with user awareness should be at the top of the list.

Conclusion

These critiques may be a tough pill to swallow for some in the mobile health space. But pearls of opportunity lie lodged within these criticisms. Namely, app creators can walk away knowing that if they want to increase engagement and improve health outcomes, then they must incorporate science-based, behavior-changing features in future app developments. Their apps must align with clinical healthcare research.

In our final post of this three-part series, we will explain what adding user awareness to a mobile health app or device looks like, how artificial intelligence (AI) enables such capabilities, and how this enhancement will breed the highest gains in user engagement at a low cost.


At Neura, we help medication adherence apps increase user engagement with machine learning and artificial intelligence (AI). To learn more, visit Neura’s medication adherence page.

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Citations

[1] “Avoidable Costs in U.S. Healthcare.” IMS Health. 2016. Accessed December 29, 2016. http://www.imshealth.com/en/thought-leadership/quintilesims-institute/reports/avoidable-costs.

[2] Benjamin, Regina M. “Medication Adherence: Helping Patients Take Their Medicines as Directed.” Public Health Reports (2012):127(1): 2–3.

[3] Osterberg, Lars, and Terrence Blaschke. “Adherence to Medication.” New England Journal of Medicine 353, no. 5 (2005): 487–97. doi:10.1056/nejmra050100.

[4] “Adherence Facts.” CVS Health. 2015. Accessed December 16, 2016, https://cvshealth.com/sites/default/files/Adherence%20Facts.pdf.

[5] Geuens, Jonas, Thijs Willem Swinnen, Rene Westhovens, Kurt De Vlam, Luc Geurts, and Vero Vanden Abeele. “A Review of Persuasive Principles in Mobile Apps for Chronic Arthritis Patients: Opportunities for Improvement.” JMIR mHealth and uHealth 4, no. 4 (2016). doi:10.2196/mhealth.6286.

[6] Heldenbrand, Seth, Bradley C. Martin, Paul O. Gubbins, Kristie Hadden, Catherine Renna, Rebecca Shilling, and Lindsey Dayer. “Assessment of medication adherence app features, functionality, and health literacy level and the creation of a searchable Web-based adherence app resource for health care professionals and patients.” Journal of the American Pharmacists Association 56, no. 3 (2016): 293–302: 300. doi:10.1016/j.japh.2015.12.014.

[7] Heldenbrand et al, 301.

[8] Morrissey, Eimear C., Teresa K. Corbett, Jane C. Walsh, and Gerard J. Molloy. “Behavior Change Techniques in Apps for Medication Adherence.” American Journal of Preventive Medicine 50, no. 5 (2016): e143. doi:10.1016/j.amepre.2015.09.034.

[9] Conway, N., I. Campbell, P. Forbes, S. Cunningham, and D. Wake. “MHealth applications for diabetes: User preference and implications for app development.” Health Informatics Journal 22, no. 4 (2015): 1111–120: 1119. doi:10.1177/1460458215616265.

[10] Singh, K., K. Drouin, L. P. Newmark, J. Lee, A. Faxvaag, R. Rozenblum, E. A. Pabo, A. Landman, E. Klinger, and D. W. Bates. “Many Mobile Health Apps Target High-Need, High-Cost Populations, But Gaps Remain.” Health Affairs 35, no. 12 (2016): 2310–318. doi:10.1377/hlthaff.2016.0578.

[11] Geuens et al.

Other Citations:

Michie, Susan, Michelle Richardson, Marie Johnston, Charles Abraham, Jill Francis, Wendy Hardeman, Martin P. Eccles, James Cane, and Caroline E. Wood. “The Behavior Change Technique Taxonomy (v1) of 93 Hierarchically Clustered Techniques: Building an International Consensus for the Reporting of Behavior Change Interventions.” Annals of Behavioral Medicine 46, no. 1 (2013): 81–95. doi:10.1007/s12160–013–9486–6.

Santo, Karla, Sarah S. Richtering, John Chalmers, Aravinda Thiagalingam, Clara K. Chow, and Julie Redfern. “Mobile Phone Apps to Improve Medication Adherence: A Systematic Stepwise Process to Identify High-Quality Apps.” JMIR mHealth and uHealth 4, no. 4 (2016). doi:10.2196/mhealth.6742.