Barriers to Patient Engagement in Mobile Health Apps and Devices

The need for hyper-personalization.

Healthcare in the U.S. is shifting toward a personalized model that emphasizes patient engagement.[1] Analysts claim that greater patient participation will improve health outcomes and lower costs.

More and more, wearable devices and mobile health (mHealth) apps are driving this paradigm shift. A recent Accenture survey shows that the use of mobile health apps and devices has more than doubled in the last two years (see Figure 1).

Despite mHealth’s popularity and promise, other research suggests that many of today’s health apps have low patient engagement. Why is that? In part, it’s because health apps and devices don’t know what their users do in the real world. As a result, they fail to personalize and engage users at times when they’re most likely to respond.

Figure 1. Data taken from the “Accenture 2016 Consumer Survey on Patient Engagement: U.S. Report,” p. 15.

What mHealth Does Well and Where It Falls Short

At the core, mHealth technologies collect and analyze data. For example, a person with chronic high blood pressure (hypertension) can regularly check their blood pressure with a smart monitor. A mobile app that pairs with the device records the user’s readings, then machine learning algorithms dig through the user’s scores to find and share relevant health insights. It’s remarkable technology.

But the efficacy of a product like this depends entirely on a patient picking up the device and actually using it.

Mobile health companies try to encourage patients to “pick up the device” by sending push notifications or causing the device to vibrate periodically. Though well-intentioned, these alerts get ignored because they call upon users at bad times.

The Problem with Poorly Timed Reminders

What happens when a person’s smart glucometer buzzes while they’re driving to work? They ignore it. Or worse, they get into a car accident.

People often neglect poorly timed notifications about their health for the same reasons they ignore less critical reminders. This morning, for instance, I set an alert on my phone to remind me to book a dinner reservation for my wife’s birthday. Somewhat arbitrarily, I chose 2 p.m. as the time at which I’d like to be reminded. This alert has hounded me all afternoon, and I’ve still yet to make the reservation.

A person living with diabetes can just as easily ignore ill-timed prodding from their glucometer as I ignored the dinner message. And therein lies the problem with our smart health products: they don’t adapt to users’ actions in the physical world.

“And therein lies the problem with our smart health products: they don’t adapt to users’ actions in the physical world.”

Missed opportunities to engage users can be costly. The pharmaceutical industry alone loses $637 billion in revenue every year due to chronically ill patients not taking their pills.

User Awareness and Personalization

Products vie for a user’s attention amid a vortex of activity — grocery shopping; CrossFit training; work commutes; dropping kids off at school; doctor appointments; the list piles up. In essence, these activities make up a person’s context. A product that knows and adapts to such context will see much better engagement.

Let’s imagine a glucometer endowed with user awareness. In this case, the device can sense that a user (a person living with diabetes) is driving to the gym and will vibrate as soon as the user pulls into the parking lot. Because this reminder is perfectly timed, the user checks their blood sugar and realizes that, to avoid hypoglycemia, they need to eat an apple before lifting weights.

Personalization of this caliber raises patient engagement and improves health outcomes.

Path to Personalization

At Neura, we use artificial intelligence (AI) to bring hyper-personalization to health apps and devices. Our machine learning algorithms distil sensor data from a user’s phone and connected devices into insights about the user’s habits and whereabouts. With this user awareness, products like smart spirometers, fitness tracking devices, and medication adherence apps can tailor their service to each user. The result? Patients engage with the product more and stay healthier.

Pill reminder sent from an app powered by the Neura SDK.
The Neura SDK improves health outcomes and increases patient engagement by personalizing health apps and devices.

Any app, device, or Internet of Things (IoT) product can gain actionable user insights by embedding the Neura software development kit (SDK). Not only that, companies save months of costly development time by choosing Neura over an in-house solution.

Firmly committed to protecting user data, Neura employs a rigorous privacy policy. We give users total control over their profiles within Neura, while making it easy for developers to request access to those profiles. Neura assumes the data privacy risks so that mHealth companies don’t have to.


Thanks to innovations in healthcare technology, more people are taking an active role in their health. Without question, apps and wearable devices have helped increase medication adherence and reduce hospitalizations. Even so, they lack critical user intelligence.

Neura fills this knowledge gap. Apps and devices that integrate Neura — and so become user aware — can expect to see patient engagement rates climb.

To learn more, schedule a product demonstration with us!

We’d love to connect with you on social media. Follow our pages to stay apprised of the latest developments in AI, machine learning, IoT, mobile health, and healthcare technology.

Neura LinkedIn
Neura Facebook
Neura Twitter

[1] Nitesh V. Chawla, PhD and Darcy A. Davis, PhD, “Bringing Big Data to Personalized Healthcare: A Patient-Centered Framework,” Journal of General Internal Medicine, (June 2013): 661.