But the research community has been able to pull in information from Fitbits and other connected, wearable devices for four years with the help of a research platform called Fitabase.
This week, Fitbit announced that Fitabase, made by San Diego-based startup Small Steps Labs, has now collected more than 2 billion minutes of Fitbit data for research purposes. Fitabase also has supported more than 200 research projects since its 2012 founding, the company also disclosed.
“What we’ve built is kind of the missing piece for research,” said Fitabase CEO Aaron Coleman. The platform collects and de-identifies data from Fitbit users and offers data pools to academic researchers, including many in healthcare. “This removes a lot of privacy concerns,” including those around HIPAA, Coleman said.
“This is a technology that bridges a consumer device like Fitbit with the needs of research,” Coleman said. “Researchers are loving this new paradigm of research.”
That’s important because millions have purchased and regularly use activity trackers. The data these wearables collect provide insights about movement, heart rate and sleep patterns that previously had not been available, plus people actually enjoy wearing their Fitbits.
“It was really difficult to get people to use pedometers,” Coleman noted. That made it tough for researchers and clinicians alike to collect good data and, more importantly, improve health.
“Devices help people better tailor their activities and their health,” Coleman said. “Interventions shouldn’t be the same for everyone.”
For example, Fitabit is helping researchers determine how quickly people regain their previous level of activity following surgery. “They can tailor interventions to people who need it most,” Coleman said.
So what about the “2 billion minutes” of Fitbit data? “We provide the researcher with de-identified data at the minute level,” Coleman explained. Each person’s activity levels can vary at different times in the day. Having this insight allows researchers — and, ultimately, healthcare professionals and caregivers — to schedule interventions when they are most likely to be effective, according to Coleman.
Coleman pointed to a research project at Arizona State University, where Eric Hekler, director of the school’s Designing Health Lab, is applying engineering strategies to study what Hekler calls “precision behavior change,” a complement to precision medicine. Hekler and research partner Daniel Rivera, director of the ASU Control Systems Engineering Laboratory, are testing “health interventions that are adaptive and individualized, versus static and generalized,” according to a Fitbit statement.
Coleman himself also has applied individual Fitbit data to control the level of difficulty in an app called Tappy Fit, a Flappy Birds-like mobile fitness game.
Photos: Fitabase, Fitbit