Medication Adherence Apps: Review and Content Analysis

Imran Ahmed, Niall Safir Ahmad, Shahnaz Ali, Shair Ali, Anju George, Hiba Saleem Danish, Encarl Uppal, James Soo, Mohammad H Mobasheri, Dominic King, Benita Cox, Ara Darzi, Imran Ahmed, Niall Safir Ahmad, Shahnaz Ali, Shair Ali, Anju George, Hiba Saleem Danish, Encarl Uppal, James Soo, Mohammad H Mobasheri, Dominic King, Benita Cox, Ara Darzi

Abstract

Background: Medication adherence is an expensive and damaging problem for patients and health care providers. Patients adhere to only 50% of drugs prescribed for chronic diseases in developed nations. Digital health has paved the way for innovative smartphone solutions to tackle this challenge. However, despite numerous apps available claiming to improve adherence, a thorough review of adherence apps has not been carried out to date.

Objective: The aims of this study were to (1) review medication adherence apps available in app repositories in terms of their evidence base, medical professional involvement in development, and strategies used to facilitate behavior change and improve adherence and (2) provide a system of classification for these apps.

Methods: In April 2015, relevant medication adherence apps were identified by searching the Apple App Store and the Google Play Store using a combination of relevant search terms. Data extracted included app store source, app price, documentation of health care professional (HCP) involvement during app development, and evidence base for each respective app. Free apps were downloaded to explore the strategies used to promote medication adherence. Testing involved a standardized medication regimen of three reminders over a 4-hour period. Nonadherence features designed to enhance user experience were also documented.

Results: The app repository search identified a total of 5881 apps. Of these, 805 fulfilled the inclusion criteria initially and were tested. Furthermore, 681 apps were further analyzed for data extraction. Of these, 420 apps were free for testing, 58 were inaccessible and 203 required payment. Of the 420 free apps, 57 apps were developed with HCP involvement and an evidence base was identified in only 4 apps. Of the paid apps, 9 apps had HCP involvement, 1 app had a documented evidence base, and 1 app had both. In addition, 18 inaccessible apps were produced with HCP involvement, whereas 2 apps had a documented evidence base. The 420 free apps were further analyzed to identify strategies used to improve medication adherence. This identified three broad categories of adherence strategies, reminder, behavioral, and educational. A total of 250 apps utilized a single method, 149 apps used two methods, and only 22 apps utilized all three methods.

Conclusions: To our knowledge, this is the first study to systematically review all available medication adherence apps on the two largest app repositories. The results demonstrate a concerning lack of HCP involvement in app development and evidence base of effectiveness. More collaboration is required between relevant stakeholders to ensure development of high quality and relevant adherence apps with well-powered and robust clinical trials investigating the effectiveness of these interventions. A sound evidence base will encourage the adoption of effective adherence apps, and thus improve patient welfare in the process.

Keywords: medication adherence; mobile apps; patient compliance; reminder systems; smartphone; telemedicine; treatment outcome.

Conflict of interest statement

Conflicts of Interest: None declared.

©Imran Ahmed, Niall Safir Ahmad, Shahnaz Ali, Shair Ali, Anju George, Hiba Saleem Danish, Encarl Uppal, James Soo, Mohammad H Mobasheri, Dominic King, Benita Cox, Ara Darzi. Originally published in JMIR Mhealth and Uhealth (http://mhealth.jmir.org), 16.03.2018.

Figures

Figure 1
Figure 1
Flowchart of identification of applications.
Figure 2
Figure 2
Taxonomy of identified adherence strategies.
Figure 3
Figure 3
Chart comparing reminder function percentage according to downloads.
Figure 4
Figure 4
Chart comparing reminder function percentage among apps in different app stores.
Figure 5
Figure 5
Chart comparing behavioral function percentage according to downloads.
Figure 6
Figure 6
Chart comparing behavioral method percentage among apps in different app stores.
Figure 7
Figure 7
Chart comparing education method percentage according to downloads.
Figure 8
Figure 8
Chart comparing educational method percentage among apps in different app stores.
Figure 9
Figure 9
Chart comparing user features across payment modalities.

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