Four Challenges for mHealth Apps

A quick search of the Google Play and iTunes stores returns thousands of health and fitness apps that aim to help people with all sorts of behavior from tracking exercise to improving sleep habits. mHealth apps are not only popular, but they are also very flexible in what they can do. These features have made health care providers increasingly optimistic about using apps to help patients manage chronic diseases like diabetes and heart diseases, which often require major lifestyle changes.

Although mHealth apps hold a lot of promise to help people manage chronic diseases, there are several important challenges they have to overcome in order to fulfill this potential. To better guide app development for managing chronic diseases, researchers from CHIDS outlined four major challenges for mHealth apps based on their experiences as a multidisciplinary team developing and evaluating an app for diabetes management, DiaSocial.

Challenge 1: Driving User Demand Through Usable and Personalized Interactions

Designing mHealth apps in a way that people want to use them is a major challenge. User characteristics like familiarity with technology and health literacy shape what people want from their their mHealth tools, and there is no “one size fits all” approach that will work for everyone

For example, DiaSocial researchers found that some potential users liked the idea of extensive interactions with healthcare providers through the app while others did not want micromanaging of their health, even from their physician or care coordinator.

The challenge for developers, then is in developing interfaces and features that can dynamically adapt to individual user characteristics, matching the app experience to each user’s preferences.

Challenge 2: Streamlining Data Input

Most health apps can only be helpful if they have good quality data on their users, so it’s important that apps make data input easy and reliable. Health apps usually collect user data in two main ways: a) through passive import of data (steps taken, sleep quality, etc.) from a smartphone, wearable sensor or other monitoring device; and b) with the user manually entering data. Both ways of data collection present unique challenges.

While almost all consumer health sensor vendors provide apps that allow users to view their data, support for export is often problematic, with buggy or restrictive experiences. Frustration with syncing data like this can quickly lead to user disengagement.

For user input, the challenges are even more severe. App designers aim to reduce the input load so that users do not feel overwhelmed by the level of effort just to use an mhealth app. Even then, some user education may be required to make sure people can accurately monitor their health behavior like calorie intake. Some solutions may also lie in sophisticated technology, like in the development of image analysis software that can calculate calories and carbs (even approximately) from a picture of a plate.

The adoption rate and successful use of mHealth will be a function of how seamless data capture is, and this is reflected in the growing interest in research to reduce or eliminate manual data entry.

Challenge 3: Analyzing and Presenting the Data

mHealth apps produce an enormous amount of data, particularly when connected to wearable sensors. To handle this data, apps have to be able to both store large amounts of information and analyze the data such that meaningful information can be provided to the user at the appropriate time in a relevant, understandable, and engaging format.

One challenge lies in aggregating data from different types of body sensors, medical devices (such as smart glucometers), and apps since each stream of data has unique formats. Being able to aggregate information like this could be very helpful in cases of disease management, where apps might be able to find sources of health problems. For instance, an app might be able to link high glucose readings to a pattern of eating too much sugary food or lack of exercise. However, a lack of widely adopted data standards and little interoperability between apps currently constrain the utility of mHealth app analytics.

Challenge 4: Security, Privacy, and Regulations

mHealth apps that pull data from multiple sources (e.g. from electronic health records, phones and wearable sensors) store a lot of sensitive information about their users, potentially making them targets of unintentional or malicious compromise.

Given these privacy concerns, apps need to make sure they not only meet the standards of any government regulations related to health data, but that they also clearly explain privacy and security features to their users. Transparency is essential to making users feel comfortable using apps that store personal health and in reducing opportunities for breaches in security.

Looking Ahead

These four challenges can limit the impact of mhealth applications for chronic disease management but they also apply to mHealth more generally. As the field continues to grow at a rapid pace, developers will gain more insight into how to address these challenges and create more and more effective mHealth tools, making for a very bright future ahead.  


Crowley, K., et al. (May, 2015). Helping grandma thrive: Four challenges for mobile apps aimed at wellbeing and chronic disease. CHI Annual Conference, Developing Emotional Skills and Wellbeing Workshop, Seoul, Korea.




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