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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.
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.
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.
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,
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.
In the age of advanced medical treatments, a significant obstacle to improve outcomes is the failure of patients to adhere to medication prescribed by their physicians. Medication adherence and compliance can be defined as the “act of (the patient) conforming to the recommendations made by the provider with respect to timing, dosage, and frequency of medication taking” [
A World Health Organization report on adherence to long-term therapies suggests that patients adhere to only 50% of drugs prescribed for chronic diseases in developed nations, a figure that is even lower in developing countries. The same report also highlights two major consequences of nonadherence: (1) suboptimal health outcomes for patients and (2) rising health care costs [
The rapid growth of mobile technologies and their uptake by consumers worldwide presents opportunities and solutions that attempt to address the problems within health care systems. This use of portable technology in health care is called mobile health (mHealth) [
Previous studies on adherence apps have focused on the prevalence of behavior change techniques, ideal features, health literacy, content, and usability [
The aim of this study was to review the currently available medication adherence apps in the two largest app repositories, the Apple App Store and the Google Play Store, in terms of their evidence base, medical professional involvement in development, and strategies used to facilitate behavior change and improve adherence.
Relevant medication adherence apps were identified by interrogating the Apple App Store and Google Play Store using the primary search terms, which are “medication,” “medicine,” “pill,” “drug,” and “tablet,” combined with secondary search terms, which are “reminder,” “alarm,” “manager,” “tracker,” “list,” “organizer,” “helper,” “compliance,” “adherence,” and “accordance.” The search and review took place in April 2015.
Any identified app designed to facilitate patient adherence to medications was included. The term
Data were extracted for each app from the app repository overview and the developer’s website. Not all apps provided a website address; therefore, for a number of apps, information was gleaned from testing alone. Relevant data items included (1) documentation during the development of the app, and (2) availability of evidence base pertaining to the app (either relating to its design and development, or its efficacy). Other datasets were collected but found irrelevant to analysis; these are stated in
HCP involvement was defined as any individual working within the health care industry who was directly involved with the distribution or prescription of medication to patients. Hence, this included physicians, pharmacists, and nurses.
Evidence base was defined as an app providing data on trials or studies that are carried out utilizing the app to indicate effectiveness. This was only accepted once a report, study, or trial was seen by testers to validate the claim.
Free apps were downloaded for further testing to explore the specific adherence strategies utilized by apps to promote medication adherence (eg, alarms and push notification reminders). Any additional feature not contributing specifically to adherence but designed to enhance user experience was also documented (eg, pharmacy locator function and refill reminder). In the case of inaccessible and paid apps, the identification of features was based on the app description and publisher website. Inaccesible apps were those that could only be accessed with authorization provided by a specific health care organization, pharmacy or health care provider.
Four researchers performed the data extraction. They identified the adherence methods used by apps and within those features, which subsets were utilized. Once a feature was identified, it was placed within an Excel spreadsheet alongside the app’s name, which all reviewers had access to.
To provide reliability throughout testing, definitions for each adherence feature were established and agreed upon by all 4 reviewers.
A devised medication regime was input into all identified apps, and this was used by all 4 reviewers to test the apps in terms of adherence mechanisms utilized. If there was any uncertainty or doubt about an app’s adherence mechanisms, it was resolved by consensus among the 4 reviewers.
All 4 reviewers tested the first 10 apps identified within the Apple App Store and the Google Play Store independently. Results of individual reviewers were then compared, and the interrater reliability was determined using the Fleiss Kappa coefficient.
The remaining apps were then equally allocated among reviewers. Data were extracted and placed into a spreadsheet for analysis.
During testing, any app that did not function was excluded, and details were kept in a separate spreadsheet, including the reasons for nonfunctioning. Only apps that functioned and fulfilled an adherence function were included for testing.
Interrater reliability between the 4 testers was calculated using the Fleiss Kappa coefficient (reproducibility between more than 2 testers). A sample of 20 apps (10 from each respective app store) was used, which resulted in a coefficient of .61 (SE 0.078; 95% CI 0.46-0.76). This suggests good reproducibility between the reviewers according to the Landis and Koch rules for interpreting Fleiss Kappa coefficient values [
The app repository search identified 5888 apps, of which 5207 apps were excluded, leaving 681 apps for analysis (see
The majority of those excluded were medically not relevant; these included various apps, for example, video games, magazine apps, to-do list, and wall paper apps.
Where possible data were extracted through app testing and from developer websites, where apps had a linked website. Of the free apps, 260 apps provided a website, with 160 apps providing no website.
Flowchart of identification of applications.
Moreover, 186 apps were solely found in the Google Play Store, 136 apps originated from the Apple App Store, and 98 apps were found in both repositories.
Download Stats were only available for Google Play Store apps. Of the 284 apps available for analysis, 168 (59.2%) had fewer than 10,000 downloads (<10,000), 63 (22.2%) apps had over 10,000 downloads (>10,000), and 53 (18.7%) apps had no available Download Stat.
Of the 420 free apps, 13.6% (57/420) of the apps were developed with involvement from HCPs in the medical or pharmaceutical industry.
Meanwhile, mention of an evidence base (either in relation to the development process or of app effectiveness) was identified in only 1.0% (4/420) of apps. One app referenced trialing and testing by a patient panel from myhealthapps.net (network). Another app described following evidence-based patient safety practices recommended by the Minnesota Alliance for Patient Safety. The final 2 of the 4 apps specifically highlighted patient pilots and clinical trials in which their apps were used and have published the data.
Of the paid apps, 4.4% (9/203) of apps had HCP involvement in development, 0.5% (1/203) of apps had a documented evidence base, and 0.5% (1/203) of apps had both. The single evidence-based app was subjected to a randomized controlled trial and proved to be beneficial with 95% of participants adhering to medication. There was also one app, which was supported by the National Health Service Health Apps Library.
In addition, 31% (18/58) of inaccessible apps were produced with HCP involvement, whereas 3% (2/58) of apps had a documented evidence base. One of the 2 apps had produced a case study based on their app; however, this was not available for access. The other had developed a case study with a partnered company using their work, detailing the benefits of the companies offering. There were no clinical trials.
A total of 420 free apps were downloaded and further analyzed to identify strategies used to improve medication adherence. This led to the identification of three broad categories of adherence strategies:
It was apparent following the download and testing of apps that the behavioral and reminder categories could be further subdivided in line with the various identified techniques used by apps. This allowed the development of a taxonomy of adherence strategies utilized by apps (
The reminder classification was subdivided into three subcategories: (1)
The subcategories for the behavioral classification were (1)
Almost all apps utilized a reminder function of some sort to facilitate adherence to medications; the number totaled 387 apps, amounting to 92.1% (387/420) of all apps tested. The largest subcategory was
Reviewing the reminder function according to the number of downloads revealed in the <10,000 downloads group that 88.1% (148/168) of apps utilized a reminder function. In the over >10,000 downloads group, 90% (57/63) of apps possessed a reminder function, and in the group where download data were unavailable, 100% (53/53) of apps utilized a reminder function (
Comparison of apps according to app repository revealed that 170 (91.4%) apps of 186 Google Play Store only apps, 129 (94.9%) apps of 136 Apple App Store only apps, and 88 (89.8%) apps of 98 apps in both store utilized a reminder function (
Numbers of apps adopting the various adherence strategies.
Strategy | Number of apps |
Reminder | 220 |
Behavioral | 28 |
Education | 1 |
Reminder, behavioral | 133 |
Reminder, education | 12 |
Behavioral, education | 4 |
Reminder, behavioral, education | 22 |
Total | 420 |
Taxonomy of identified adherence strategies.
Number of apps adopting reminder strategies.
Strategy | Number of apps |
Alarm | 48 |
Push notifcation | 248 |
Short messaging service | 2 |
Alarm, push notification | 85 |
Alarm, short messaging service | 0 |
Short messaging service, push notification | 3 |
Alarm, short messaging service, push notification | 1 |
Total | 387 |
Chart comparing reminder function percentage according to downloads.
Chart comparing reminder function percentage among apps in different app stores.
This category was the second largest, with 44.5% of apps (187/420) utilizing one or more of the three behavioral technique subcategories. A total of 42.4% of apps (178/420) used the
Comparatively, 22 apps (5.2%) used a form of
Comparing by number of downloads (Google Play Store available apps): in the <10,000 group, 45.2% (76/168) of apps; in >10,000 group, 49% of apps (31/63); and in apps where download data were not available, 37% of apps (20/52) utilized a behavioral function (
Comparison of apps according to app store revealed that 46.2% (86/186) of Google Play Store only apps, 43.4% (59/136) of Apple only apps, and 43% (42/98) of apps in both stores utilized a behavioral function (
A total of 39 apps used education as a method. Comparing by number of downloads (Google Play Store available apps): in the <10,000 group, 7.7% of apps (13/168); in >10,000 group, 3% of apps (2/63), and in apps where download data were not available, 8% of apps (4/53) utilized education as a method (
Comparison of apps according to app repository revealed that 2.7% of (5/186) Google Play Store only apps, 14.7% (20/136) of 136 Apple only apps, and 14% (14/98) of apps in both stores utilized education as a method (
Through testing, various additional user features were identified; these are listed in
Number of apps adopting behavioral strategies.
Strategy | Number of apps |
Gamification | 1 |
Personal tracking | 161 |
External tracking | 8 |
Gamification, personal tracking | 3 |
Gamification, external tracking | 0 |
Personal tracking, external tracking | 13 |
Gamification, personal tracking, external tracking | 1 |
Total | 187 |
Chart comparing behavioral function percentage according to downloads.
Chart comparing behavioral method percentage among apps in different app stores.
Chart comparing education method percentage according to downloads.
Chart comparing educational method percentage among apps in different app stores.
User features offered by apps.
User feature | Description of feature | Number of free apps with user feature |
Track other health metrics | Such as blood pressure | 67 |
Pharmacy information | Information relating to nearby pharmacies, such as contact information or location | 52 |
Important contacts | Can input information relating to pharmacist, doctor, or emergency contact in the app | 34 |
Refill reminder | An alarm or reminder relating to when the user requires refilling of their medication | 31 |
Photo of medication | Add a picture of the medication or select image from existing gallery to place next to medication on app | 30 |
Export information from app | Can email or send information on medication or adherence record to another person, such as a health care provider | 24 |
Appointment reminder | Reminds you of medical appointments | 19 |
Record medical history | Can act as an electronic medical record by inputting medical history | 17 |
Hospital information | Information relating to nearest hospital, contact information, and location | 8 |
Barcode scanner | Scans barcode and automatically inputs medication according to the barcode | 6 |
Work with wearables | Compatibility with wearable technology | 5 |
Chart comparing user features across payment modalities.
To our knowledge, this is the first study to systematically and exhaustively review all currently available medication adherence apps on the two largest app repositories. Dayer et al [
One of the most important findings of this study is the concerning lack of HCP involvement in app development (84/681, 12.3%) and the limited evidence base related to the development and use of such apps (8/681, 1.2%). App reviews focusing on other medical fields have reported similar findings such as colorectal conditions [
Of the 8 identified evidence-based apps, only 3 apps related specifically to clinical trials investigating app efficacy (in terms of an improvement in medication adherence rates). In the current era of evidence-based practice, robust evidence supporting the use of app-based interventions is necessary if there is to be widespread HCP buy-in to apps or if apps are to be prescribed and reimbursed by health care systems in the future, in much the same way as drugs currently are. The limited prevalence of evidence-based apps may, in part, be explained by the inherent tension that exists between the slow-paced and arduous nature of gold-standard health care intervention evaluation methodologies (such as the randomized controlled trial) and the fast-paced and evolving nature of app technologies [
The testing of adherence apps undertaken in this study has enabled us to create a taxonomy of strategies that have been utilized by such apps to promote behavior change and adherence. The wider adherence literature describes two broad types of nonadherence among patients [
Educational strategies, which may be of potential benefit in both unintentional and intentional nonadherers, were also underutilized, despite evidence demonstrating that increasing patient knowledge regarding medicines and the importance of taking prescribed medicines improves adherence [
External monitoring was another poorly utilized adherence strategy. This strategy allows third parties to receive adherence information of the patient, giving them greater opportunity to become more actively involved and integrated with patient care. This may be of particular benefit in those with chronic conditions. Although the overall utilization of external monitoring was low, prevalence in the inaccessible groups of apps was much higher (28% [16/58] vs 5.2% [22/420]), highlighting how certain clinics and pharmacies are taking on the responsibility of monitoring and promoting adherence of their patient populations through the use of apps.
Gamification was the least commonly utilized adherence strategy, with just 1.2% (5/420) of apps utilizing this technique. It is an umbrella term used to describe “the use of video game elements in nongaming systems to improve user experience and user engagement” [
Aside from the various adherence strategies provided by apps, a large proportion also offered a host of additional user features and functionality, falling into one of 11 categories. The most common features were health metric tracking, medication refill reminders, pharmacy information, and directories of health care service contacts. The least prevalent features were barcode scanning, connecting with wearable technologies, and hospital information provision. In general, user features were found to be more prevalent among paid apps, offering a more comprehensive service for the individual downloading the app and justifying the cost price.
Although few identified apps provided barcode scanning (using digital quick response code technology to capture the relevant identifier on a drug packet), such technology has been demonstrated to reduce medical error rates, thereby promoting patient safety [
Finally, the literature highlights that nonadherence is particularly common among the elderly, who are often on multiple, life-long medicines [
Several limitations were identified in this study. First, although we were able to download and test free apps to identify the adherence strategies that they utilized, we were unable to download and test paid apps because of lack of funding. From app repository descriptions, it appears that paid apps offered additional features and functionality and the ability to download such apps may have yielded further useful insights around the strategies used by apps to promote adherence. Similarly, we were also unable to download and test inaccessible apps, which required log-in credentials from an affiliated health care organization or clinic.
As a consequence of the dynamic nature of the mHealth apps market and the rapid turnover of apps, several apps initially identified for inclusion in this review were subsequently withdrawn from app repositories rendering potentially influential data gleaned from such apps redundant.
Finally, because of the rapid production and release of new apps, we acknowledge that as this review was performed, new adherence apps will have been released that have not been included in this study.
We have highlighted two main potential areas for future research. First, although we have used HCP involvement as a surrogate market for app quality, other markets are also likely to be important such as patient involvement in the creation of apps. Further research involving focus groups and qualitative assessment of apps with patients will help in addressing this issue.
Second, we have focused on all medication adherence apps irrespective of disease condition to get a broad overview of the market. Future research may therefore focus on apps designed for adherence in specific disease contexts.
This app repository review demonstrates a concerning lack of HCP involvement in app development. Greater collaboration is required among app developers, HCPs, academics, behavioral scientists, and end users to ensure the development of high-quality, relevant adherence apps.
The results have also identified that the vast majority of current adherence app offerings on repositories lack any evidence base of effectiveness. In this regard, well-powered and robust clinical trials investigating the effectiveness of these interventions are needed going forward. Such evidence will enable HCPs to prescribe an adherence app whenever they are prescribing a medicine, thereby resulting in widespread adoption among patients.
Supplementary table.
health care professional
mobile health
short messaging service
We would like to thank Imperial College Business School for introducing us to our wonderful supervisors and supporting us throughout our intercalated BSc, which resulted in the production of this project.
None declared.