The team used a convolutional neural network to find the "biologically relevant" motion patterns in a large set of US health survey data and correlate that to both lifespans and overall health. It would look for not just step counts, but how often you switch between active and inactive periods -- many of the other factors in your life, such as your sleeping habits and gym visits, are reflected in those switches. After that, it was just a matter of applying the understanding to a week's worth of data from test subjects' phones. You can even try it yourself through Gero Lifespan, an iPhone app that uses data from Apple Health, Fitbit and Rescuetime (a PC productivity measurement app) to predict your longevity.
This doesn't provide a full picture of your health, as it doesn't include your diet, genetics and other crucial factors. Doctors would ideally use both mobile apps and clinical analysis to give you a proper estimate, and the scientists are quick to acknowledge that what you see here isn't completely ready for medical applications. The AI is still more effective than past approaches, though, and it could be useful for more accurate health risk models that help everything from insurance companies (which already use activity tracking as an incentive) to the development of anti-aging treatments.