Skip to Content

Graphing Your Health

Runkeeper aims to bring together data from a diverse array of devices.
June 23, 2011

Runkeeper began life as an app to track runs using mobile phone’s GPS system. Three years later, Jason Jacobs, Runkeeper’s founder, aims to transform the nascent self-tracking movement.

Runkeeper’s Health Graph will allow users to aggregate and analyze data from different sources. Credit: Runkeeper

Jason Jacobs

RunKeeper started out three years ago as a mobile fitness platform for runners. We have since grown into a vibrant community of more than 6 million fitness enthusiasts, and our growth continues to accelerate. Early on, we noticed a couple of interesting trends:

● While many of our users are running, our core users also track of a lot of other health and fitness data; they go to the gym, watch their weight, keep track of how they sleep at night, go on bike rides, log the food they eat, and keep track of a host of other activities and types of data.

● RunKeeper was initially focused primarily on running; comparable sites also emerged focused on cycling, weightlifting, diet and weightloss, sleep, and many more individual aspects of the health and fitness landscape.

● As these highly-focused health sites proliferate, users increasingly have to log into several different sites to keep track of the different aspects of their health that they care about. The experience is tedious, and trying to correlate the information across sites to identify trends and areas for improvement cumbersome and time consuming.

We came to believe that, while these different sites are very important building blocks on the path to a healthier world, a means of aggregating and correlating the information from different sources is critical in helping people keep track of their overall health and fitness.

To test this theory, over the last two years, we integrated with a handful of 3rd party devices, including Fitbit, Withings, Zeo, Garmin, Polar, and Wahoo. We gave them access to our 6 million plus user community and incorporated a Facebook-like FitnessFeed into our website, which includes every activity and data point posted to RunKeeper.com and indicates the device or application from which it posted. We also set up an online store, where any app or device that integrates with us can be made available to our users. We wanted to make it easier for the RunKeeper community to track all of the health and fitness data that’s critical to their well-being in one consolidated place.

The results have been incredible; we learned that as users track more aspects of their health, they become more engaged and are more likely to become premium RunKeeper Elite subscribers. We also realized that we are an effective channel for apps and devices to get their products into more people’s hands. And finally, we built out an underlying correlation engine to make sense of this data across categories, so we can identify the factors that affect people’s fitness and health the most. We hope this will help predict the steps they should take to best improve their health over time.

Two weeks ago, we launched the Health Graph API. Any 3rd party app/device/web service can now easily tie into the underlying Health Graph that RunKeeper was built on. Our hope is that we can provide them with access to our users to increase their distribution. But more importantly, these types of services will improve the overall experience for users by providing them with a holisitic view of their health, across all of the different types of health data that they’re tracking.

The Health Graph API also enables developers to build innovative new apps and devices, drawing on the aggregated health data contained in the Health Graph. For example, developers could create apps that find links between different aspects of your health history, such as improved sleep when you work-out regularly. Or faster weight loss when you consistently track food intake.

We’re only a couple of weeks in, but we have been blown away by the response from the health and fitness developers community. People are lining up to gain access to the Health Graph (it is in private alpha now), and we’ll be announcing a steady stream of exciting new tools in the weeks/months to come. If you’re a developer that wants to learn more about the Health Graph, you can do so here.

Tomorrow’s post: A Physician’s Perspective on Self-tracking

Keep Reading

Most Popular

Large language models can do jaw-dropping things. But nobody knows exactly why.

And that's a problem. Figuring it out is one of the biggest scientific puzzles of our time and a crucial step towards controlling more powerful future models.

The problem with plug-in hybrids? Their drivers.

Plug-in hybrids are often sold as a transition to EVs, but new data from Europe shows we’re still underestimating the emissions they produce.

How scientists traced a mysterious covid case back to six toilets

When wastewater surveillance turns into a hunt for a single infected individual, the ethics get tricky.

Google DeepMind’s new generative model makes Super Mario–like games from scratch

Genie learns how to control games by watching hours and hours of video. It could help train next-gen robots too.

Stay connected

Illustration by Rose Wong

Get the latest updates from
MIT Technology Review

Discover special offers, top stories, upcoming events, and more.

Thank you for submitting your email!

Explore more newsletters

It looks like something went wrong.

We’re having trouble saving your preferences. Try refreshing this page and updating them one more time. If you continue to get this message, reach out to us at customer-service@technologyreview.com with a list of newsletters you’d like to receive.