What Digital Health Apps Must Do to Increase Medication Adherence
New AI can personalize mHealth apps like never before.
In our latest post of the three-part series on mobile apps for medication adherence, we presented health experts’ main criticisms of mobile health apps. Researchers, by and large, say apps don’t conform with scientifically proven methods for changing human behavior.
App creators likely hear that and say, “Great, but what specific changes can we make to better engage and influence users?”
One research team begins to answer this question when they suggest that medication adherence apps leverage sensor data within a person’s phone to track relevant physical activities. 
This observation hints at far more than just fitness tracking; it points to user awareness.
In this post, we argue that to achieve the highest gains in user engagement at a low development cost, digital health companies should invest in making their products “user aware.”
What Is User Awareness?
User awareness fits into the broader personalized healthcare migration. Experts argue that a preventive, patient-centered healthcare model will improve health outcomes and lower costs. A patient-centered approach entails greater patient participation. Ultimately, that’s what mobile health (mHealth) companies want: higher participation and engagement.
Thus, to improve health outcomes and increase adherence, app creators must tailor their apps to each user. They must personalize. And to personalize, apps must know the user. They need to know who the user is and what they do in the real world.
User awareness is knowing who the user is and what they do.
Who the User Is
An app may want to know if a user is a workaholic, an early riser, a travel lover, a serial marathon runner, a video game addict, a techie, and so on. These identifying traits help it adapt to the user.
What the User Does
At a given time, an app may want to know if a user is waking up, leaving the house on foot, driving to work, arriving at a restaurant, lifting weights at the gym, grabbing drinks at a bar with friends, or sleeping soundly at home. Knowledge of the user’s habits, activities, and context helps the app respond to users at optimal moments.
User-aware apps learn more and more about their users over time. They know how to keep users engaged despite the chaos of daily life.
A Day in the Life of a User-Aware App
An example: Consider someone living with diabetes. Care plans for this chronic illness vary from person to person and are riddled with complexity.
The patient has to regularly check their glucose (blood sugar) levels, inject insulin and/or take pills, and follow a strict diet and exercise plan. What’s more, they must coordinate these steps with near perfection. Otherwise, if they forget to check their blood sugar before exercising, for example, they could suffer a hypoglycemic reaction mid-workout and end up in the ER. Or, if they mix up short- and long-acting insulin, they could overdose. In addition, stress levels, sleeping patterns, and other factors affect glucose balance.
Existing apps struggle to navigate this logistical maze. User-aware apps find their way. Here’s how.
- Knowledge: The app knows the user’s historical health patterns and senses when they are driving to the gym.
Result: The app prompts the user to check their glucose levels and/or to eat an apple before exercising (when the user needs it most).
- Knowledge: The app knows when the user is going to sleep versus waking up versus driving 45 minutes to work.
Result: It tells the user to take long-acting or short-acting insulin at the appropriate times. It also sends accurate, perfectly timed pill reminders.
- Knowledge: The app knows key data about the user such as their sleeping patterns, exercise habits, average work hours each week, and more.
Result: Based on this data, it creates an activity report that enables the app to share tailored insights with the user, helps users see where they need to change behaviors or reveals causes behind certain symptoms, and/or gives the user’s healthcare provider information that they can use to help the user manage their condition.
These examples alone show how user-aware medication adherence apps could lift engagement rates and improve health outcomes.
Bear in mind, user awareness is not just a means to enable a few cool features. It’s a foundational enhancement. It’s the acquisition of a new, rich body of knowledge that seeps into and amplifies every part of an app.
How to Make Apps User Aware with Neura
The technology behind user awareness is extremely difficult to build. For an app to both detect a user’s actions and know their significance in real time, requires advanced levels of machine learning and artificial intelligence (AI). At Neura, we’ve torn down this barrier to entry by creating our own user awareness engine for apps and devices.
Instead of spending months — or even years — writing user awareness into their products, app makers can now integrate the Neura software development kit (SDK) and become user aware immediately.
Neura’s team of data scientists has spent years perfecting our AI. With machine learning algorithms, we transform raw sensor data from a user’s phone and connected devices into precise, real-time insights about their habits and activities. We do this while maintaining top-tier data privacy and security. Apps draw on Neura’s user insights to unlock engagement-boosting features.
In a recent example, one of our customers My Days — a fertility awareness app developer — saw an 18 percent rise in user engagement after only 30 days with the Neura SDK integration. They expect this number to keep climbing.
Read full case study: My Days: Fertility Awareness App Increases Engagement by 18 Percent with Neura
With breakthroughs in AI, now more than ever, digital health companies have the opportunity to make personalized healthcare a reality.
Personalize Your App Today
Personalization should be a top priority for creators of medication adherence mobile apps. Adding Neura’s user awareness engine is a bounding step toward that end. Neura is easy to implement, and it brings sweeping gains in engagement and adherence. Furthermore, it enables mHealth apps to better align with evidence-based approaches to behavior change.
 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.