Editor’s Note: This is the seventh installment of The Decoder, a column that’s part of the Living With Data series exploring how our online data is tracked, collected and used. Do you have questions about how your personal data is being used? Curious to learn more about your daily encounters with algorithms? Email the Decoder at firstname.lastname@example.org or submit your question via the form here. Screen shots and links are helpful clues!
Activity trackers provide individuals valuable data about their health and fitness. But companies like Jawbone are starting to show that there is value in the aggregate view of distributed activity data from its users. Even though we pay for the device, that doesn’t mean we are guaranteed an exclusive, private data experience.
A reader wrote with this question this summer when Jawbone shared some interesting data insights:
While Brandon may be interested in his fitness, tracking steps and reminding himself to get up and move throughout the day, Jawbone is interested in what can be gleaned from its device’s more than 1 million users, in what is perhaps one of the largest sleep studies ever conducted.
According to Jawbone’s privacy page, the Up fitness-tracking band collects information about micromovements indicating “when you are asleep, when you are awake, when you are idle and your activity intensity and duration.” As illustrated in the earthquake and circadian rhythm studies, Jawbone also collects location information, not from the activity band but from its Up mobile app.
Brandon’s activity is certainly contributing to that data, but Jawbone confirms on its blog that it anonymizes all data before it is analyzed and presents it only in aggregate form. According to the company’s FAQ, “Jawbone believes your data belongs to you.” But how is that claim reflected in its actual data science practices?
Most sleep studies in a university or medical setting require ample institutional review board attention, addressing the concerns of the human subjects involved. The processes of consumer data research have come under scrutiny, especially in light of the Facebook emotional contagion study this summer.
Cases like this might be counterintuitive to even the savviest consumers. We might understand the trade-offs we make using services like Google and Facebook for free in exchange for the usage data that monetizes their services. When you use Jawbone and other quantified self devices, paying for the hardware doesn’t necessarily mean that your data from them is private.
How meaningful is this corporate data science, anyway?
Given the tech-savvy people in the Bay Area, Jawbone likely had a very dense sample of Jawbone wearers to draw from for its Napa earthquake analysis. That allowed it to look at proximity to the epicenter of the earthquake from location information.
Jawbone boasts its sample population of roughly “1 million Up wearers who track their sleep using Up by Jawbone.” But when looking into patterns county by county in the U.S., Jawbone states, it takes certain statistical liberties to show granularity while accounting for places where there may not be many Jawbone users.
So while Jawbone data can show us interesting things about sleep patterns across a very large population, we have to remember how selective that population is. Jawbone wearers are people who can afford a $129 wearable fitness gadget and the smartphone or computer to interact with the output from the device.
Jawbone is sharing what it learns with the public, but think of all the public health interests or other third parties that might be interested in other research questions from a large scale data set. Yet this data is not collected with scientific processes and controls and is not treated with the rigor and scrutiny that a scientific study requires.
Jawbone and other fitness trackers don’t give us the option to use their devices while opting out of contributing to the anonymous data sets they publish. Maybe that ought to change.
Are you surprised to be a part of a large-scale data science project? Are you fascinated by the findings? Share your thoughts in the comments or with @smwat and #TheDecoder on Twitter.
Do you have questions about how your personal data is being used? Curious to learn more about your daily encounters with algorithms? Email the Decoder at email@example.com or submit your question via the form here. Screen shots and links are helpful clues!