King leads the center’s efforts to help people manage weight via mobile health apps, and she sees a huge opportunity to use that kind of continuous data to provide more targeted, dynamic interventions to people who are headed down a wellness dead end. “We can catch people on their way toward obesity, and provide them feedback through smartphone apps, so they can actually do something about it in the moment.”

For now, though, using smartphone-based data to build public health research and guidance is still problematic. Reason number one: Step-tracking data is actually pretty unreliable.

“In particular, steps that come out of commercial devices like the Apple built-in step counters are not very accurate,” says Bruce Schatz, head of Medical Information Science at the University of Illinois-Urbana Champaign. “They’re tuned for making physically active people feel good.” The issue, he says, isn’t with the measurement device. Smartphones are equipped with accelerometers that measure tiny variations in location, and they do it well.

But the handful of algorithms that Apple and other phone manufacturers and app developers employ to package that raw data into easy-to-use step counts can't accurately capture the huge variety in people's walking mechanics. They don’t have enough flexibility to account for, say, old people who shuffle instead of stride. And not all steps are created equal. Strolling in the park burns fewer calories than sprinting up stairs. Which matters for people trying to manage their weight (though not as much as what people eat). Detecting those distinctions requires raw, not pre-packaged accelerometer data. That's why Schatz, who has worked with the NIH and NSF on their population-scale mobile health initiatives, says raw is the way to go if data is going to be used for health interventions.

The downside is it’s a lot harder to work with. Most app developers don’t keep raw data themselves because the storage costs would be huge. And constantly pulling that data from your phone (think 60 times every second instead of 60 times every hour) would knock out its battery in about an hour or two. Algorithms that store inferences about what you’re doing—walking, biking, sitting—cut down all that data and save battery power. That’s the kind of information Althoff and his Stanford collaborators got from Azumio: 1,440 data points per person per day as opposed to 5 million.

That data was constrained in a less technical way, too. By only looking at the steps of people who bought iPhones and downloaded Azumio's app, the researchers limited themselves to a self-selected group—more likely to be wealthier and healthier than average. Azumio doesn't collect data on things like income and race, and while some app users do keep track of daily food logs and calorie intakes, the company didn't share those for this study. So researchers couldn't test any other hypotheses about lifestyle variations that could impact obesity other than steps. Building accurate models with which to detect, monitor, and predict obesity will require more information than most smartphones readily give up.

Getting population-scale raw accelerometer data from phone manufacturers like Apple and Google isn't impossible. It's just wildly impractical. Researchers who wanted to do it would need to either partner with a developer or build an app themselves, then get loads of people to download it despite the battery drain. Neither Apple nor Google are just giving away data pulls on the billions of phones they have circulating the globe because of its value to paying customers, like online advertisers. And that makes the best information for building accurate predictive models for public health issues like obesity, for all intents and purposes, beyond the reach of most scientists.

“Mobile data really is good enough now to be actionable,” says Schatz. “But nobody has done it except for targeted ads.” Which means that for smartphone data to be able to tackle public health problems, it may first have to become a public good.