What Big Data Can Tell You About Useful mHealth

Maria Wolters, Alan Turing Institute / University of Edinburgh and Henry Potts, University College London
mHealth that Works
“If the user can’t use it, it doesn’t work at all.” This is how Susan Dray summarises her decades of user ex
perience work with clients around the world. If we want to harness the promise of Big Data to draw conclusions about the usability and usefulness of an mHealth app, Dray’s Law is an ideal starting point, because it givesus the fundamental variable we need to measure – how often people use an app.

An mHealth app can only work as intended if people use it, and if they keep using it over the intended period of time. Take food diary apps, such as the ever popular MyFitnessPal. If people don’t open it and log their food, it is of no use.  While regular use is necessary for an app to fulfill its purpose, it is not sufficient. For example, people may only record meals in MyFitnessPal that conform to guidelines and fail to log sweet or fatty foods, or they may use MyFitnessPal to support an eating disorder. Both of these patterns of using the app are contrary to the original goal, which is to help people reach and maintain a healthy bodyweight.

As app analytics 101 tells us, in order to get a good picture of app use, it is not enough to just aggregate the number of downloads, the number of reviews, and the app ratings themselves.

Metrics to Evaluate By

How can app developers achieve that? First of all, developers need to be clear about the time frame for using an app. Stop smoking apps have a natural endpoint – when users feel that they have been successful in kicking the habit. Weight management apps such as MyFitnessPal also often have natural end points (when the goal weight has been reached and maintained), but can be used long-term for people who want to maintain their goal weight or gain and lose weight depending on their sport.

We also need to acknowledge that this time frame can vary from person to person. A person who wants to lose over 20% of their bodyweight is looking at months and years of regular use, while somebody who wants to lose a couple of pounds might be done in a month.

Finally, in order to use the app meaningfully, people will need to spend a certain minimal amount of time in it – be it to track their mood, check the remaining calories or steps for the day, or enter a meal.

With these considerations out of the way, let’s look at the key indicators that can help us leverage Big Data to assess the usefulness of mHealth apps.

Number of Unique Active Users

Do people use your mHealth app once they have downloaded it? Whether this is the number of daily, weekly, or monthly users (or a combination of the three) depends on the goal of your app, but at least one of these numbers should be tracked regularly.

Session Frequency

Do people use your app as often as they should in order to get a benefit? How many of your active users are regulars? Again, the target depends on the goal of your app.

Time in App

How long do people actively spend in your app? Is this long enough to do something meaningful? In a second step, you can track what people actually do in the app, but time itself is a useful, if crude, approximation.

Retention Rate

Do people stick with your app for the amount of time they need to see a difference? If your app is about smoking cessation, you have a problem if people return to your app for years in yet another doomed attempt to kick the cigarettes, but if your app is about helping people maintain a healthy bodyweight, retention over months and years is good.

From Small Data to Big Data

As you start out with a great idea  and a small app, the data streams we have described above will be small and easy to manage. But if you believe in the promise of your app, and keep tracking, hopefully these data streams will grow and allow you to learn more about your customers, their habits, and the innovative ways in which they use your app.

What data streams do you use to measure whether people are actually using your app? What are the benefits and pitfalls you have discovered?