Among Quantified Self devotees who log their every step, calorie and heart beat, Stephen Wolfram, the scientist and innovator behind Wolfram Alpha and Mathematica, is something of a self-tracking superstar. For decades, he’s been collecting all kinds of personal information, from emails sent and received to meetings and phone calls and all of his daily steps. Earlier this year, he published a blog post with graphs visualizing the personal analytics of his life, arguing that, one day, he expects everyone to collect their personal data as a matter of routine.
As more self-tracking apps and devices – like the FitBit, Nike Fuelband and apps that track sleep, mood, meals and other activities – find their way to consumers, that future certainly seems more likely. But how we actually interact with all those streams of data is an open and interesting question, particularly given the fact that a minority of consumers currently take advantage of health tracking platforms and even tracking our finances (which can sometimes have more immediate consequences) can feel like a chore.
One of the visualizations of Wolfram’s activity.
At the Wired Health Conference Tuesday in New York, Wolfram was asked by Wired writer and author Steven Levy about how data from sensors and self tracking software could be incorporated into the clinical experience of the future.
“How am I going to find out what’s wrong with me… is my doctor a Genius Bar now?” Levy asked.
Wolfram replied: “People will watch their health in a way that’s a little closer to the way that they watch their financial portfolios.”
Just like fluctuations in financial data can be symptomatic of a problem with a stock, he said, patterns from personal data from sensors and devices could help alert individuals and healthcare providers to medical problems. A combination of human intelligence, experience and data could be used to diagnose a problem and then algorithmically-determined drugs and devices could be used for treatment, Wolfram added.
Already, in a less comprehensive way, that model of data interpretation is emerging to help people manage chronic conditions and achieve specific goals.
Healthrageous, a Boston-based startup we covered this week, uses data from blood glucose monitors, blood pressure cuffs, accelerometers and other health-tracking devices to provide personalized, day-to-day support for patients with diabetes, hypertension and other conditions. Tictrac, a UK-based company, aggregates a wide range of personal analytics – from social media engagement, travel and spending to physical activity and meals – to help consumers complete personal projects (such as losing weight or monitoring the development of a new baby). Ultimately, the site plans to pair consumers with data-savvy coaches who can help analyze their information to help individuals progress.
But some say self-tracking won’t make its full mark on health until it’s combined with artificial intelligence that can monitor data for us and let us know when we really need to pay attention.
At the Wired conference Monday, Kevin Kelly, founding executive editor of Wired and one of the early creators of the Quantified Self movement, said that Quantified Self technologies are still at the phase of the early computer, which wasn’t very powerful until it was married with the Internet.
“In Quantified Self, a lot of the tools that we’re talking about will have a limited kind of effect until they’re brought on to the Internet and until we have some kind of artificial intelligence to give attention to… the data we’re accumulating and alert us when the patterns are there,” he said.
Image from argus via Shutterstock.