I spent the past weekend in Mountain View at the Quantified Self conference, a gathering of about 400 pioneers in the space of personal tracking and citizen science. I was one of the few at the conference, along with a handful of venture capitalists and healthcare types, who wasn’t experimenting with personal tracking. I came at the invitation of the Robert Wood Johnson Foundation, who are fascinated by the movement and invited a handful of thinkers and practitioners to the event, paying our way, in exchange for our reflections on the conference and the field. Once I’d registered, Gary Wolf (one of the co-organizers) was kind enough to invite me to give a short talk on what we might learn from tracking media consumption. But despite these generous efforts at inclusion, I felt like I likely had most in common with the anthropologist who introduced herself in one of the first sessions saying, “I’m trying to figure out what motivates people to self track.”
Me too. I’ve been an involuntary self-tracker for more than 25 years by virtue of being a type 1, insulin-dependent diabetic. Tracking my blood sugars was one of my most loathed activities through junior high and high school, and my most ignored activity in college, though I’ve come to see its utility in adulthood. But it’s still hard for me to understand why people would volunteer to track their steps, calories, caffeine consumption or REM sleep. Fortunately, when I confessed this confusion on stage in front of a group of self-trackers, I got good-natured laughter and I had a general sense that most of the people at the conference understood that their behavior was exceptional. Whether they’re early adopters or outliers seems like an open question to me. I’m convinced that what’s going on with quantified self experiments is helpful for the folks who are currently undertaking experiments. I’m less sure that this movement is going to become mainstream or change principles of scientific and medical discovery.
Here are three open questions I’m trying to work through:
– What’s the relationship between self tracking and citizen science? And between citizen science and laboratory science?
– How does the quantified self interact with the internet of things, and a wider proliferation of sensors?
– Is the quantified self broader than the quantified body? And what do we learn from quantifying other aspects of the self?
Seth Roberts gave the opening talk at the conference, and I had the pleasure of talking one on one with him Sunday morning as he accompanied me on my (ultimately fruitless) search for egg and chorizo tacos enroute to the conference. This gave me the chance to ask him several more questions about his working method and his discoveries. After Seth’s talk on Saturday, I found myself wondering whether he was proposing that everyone engage in the sort of self-tracking and experimentation he practices. I also wondered about the utility of his work to a broader scientific community – reacting to his talk, Ryan Calo of Stanford Center for Internet and Society described Roberts’s results as “rigorous anecdotes”, as distinguished from traditional scientific practices of double-blind studies.
A one on one conversation with Roberts helped me see that there’s a possible way in which personal science could help a much broader range of people, a tiered approach to the working method. Seth conducts novel experiments and looks for broader scientific principles that underly his findings about his own experiences. Those underlying principles – theories of nutrition or exercise – allow him to design interventions that he tests on himself, using the large set of data he’s collected on himself as a baseline. Not everyone will be able to (or want to) design interventions based on novel understandings of nutrition and sleep science. But a larger group may accumulate baseline data from self tracking and engage in experiments suggested by professional or citizen scientists. (As Sean Ahrens’s talk on hookworm and Chron’s disease demonstrated, it’s not easy as you might think to do this well.) And a yet larger group might benefit from discoveries made through this process and adopt interventions, even if they don’t have data on how effective the treatments are.
Roberts’s opening talk at the conference ended with a fairly aggressive critique of science as it’s generally practiced by trained professionals in the context of a university or corporate lab. Professional scientists are forced by grant length to use short timescales in their research, and Roberts worries that their motivations aren’t sufficiently personal to get them to consider practical solutions that might initially seem silly (like standing on one bent leg until exhaustion six times a day). I thought his critique was helpful and valid, up to a point… I know a lot of scientists who care passionately about the issues they study and would be happy to look silly if it helped them find a breakthrough in treating a chronic disease that had verifiable benefits for a group of people. I worried, that Roberts’s framing of citizen science in opposition to lab science was a false dichotomy. It’s possible for citizen science to inform lab science, and vice versa, and I think even Roberts acknowledges that we want more science, not a war between the citizen and professional camps.
Lots of people at the conference were conducting experiments on themselves, either designed to test the effectiveness of an intervention (does taking hookworms make my Chron’s disease symptoms more manageable?) or to monitor and understand the dynamics of a particular indicator (am I having a hard time at my current job? What’s my mood like when I’m working versus when I’m at home?). Fewer were sharing this data. The vertical integration of companies like Zeo (which manufacture sensors, sell products and collect and analyze data from users) means that a few actors have large sets of data – Zeo likely has more data on sleep than any other sleep lab simply because their sample size is so large compared to that in most lab experiments. But most self-trackers aren’t sharing their data very widely, both due to privacy concerns (will my health insurance provider cut me off if they discover I’m a restless sleeper? That I only walk 3000 steps a day?) and in part because sharing and aggregating data may not have easily apparent benefits.
Here’s a matrix I scrawled on the back of a napkin after the first day of the conference. (Because this is a slightly cleaned up napkin drawing, I reserve the right to modify or discard this model altogether when someone demonstrates its inadequacies to me…):
The vertical access considers whether the data we collect is useful by itself, or whether it’s useful primary through aggregation with lots of other data. The horizontal looks at the audience for the data: is this information primarily helpful to you or to others? Many of the projects we saw at the conference fit squarely into the lower left of the matrix. They’re personal experiments that rely on individual data for individual insights. Tracking my mood with MercuryApp when I’m writing may be very helpful in convincing me that I need a different career, but that data’s not especially useful to a broader audience. Other types of personal tracking may have aggregation benefits – information on my sleep cycle is helpful by itself, but likely more helpful if I can compare to how other people are sleeping, and especially to how happy and healthy people are sleeping. (Information that leads towards building a model has benefits for me as an individual and for a broader set of people as well, and positions on this matrix should probably be blurry, uncertain smudges, not fixed points.)
One of the coolest technologies I heard about at the conference was Asthmapolis, a tracking device attached to an asthma inhaler that sends GPS data to a central server when the inhaler is triggered. It might be useful to have a record of where my asthma attacks occurred, so I can avoid a particular part of my city, but this data is more likely to be helpful to public health officials, as they try to figure out what parts of cities are subject to asthma attacks and what factors might be mitigated. (David Van Sickle of Asthmapolis told me about this astounding piece of research that used emergency room records to asthma attacks in the city of Barcelona and trace those attacks to the offloading and storage of soybeans. Exciting as this research is, the hope is that we could figure out a correlation like this in weeks, not years, and make changes more rapidly.) It’s appropriate that Asthmapolis refers to the data they collect from their units as “surveillance data” – the continuum from tracking to surveillance (my horizontal axis) is the continuum from choosing to track yourself to being tracked by others. An extreme of surveillance might be the sort of tracking and profiling conducted by internet advertisers – you don’t choose to be tracked, and despite promises that targeted ads will be more useful to you than untargeted ones, most of us aren’t very fond of ads that guess at our identity and our desires.
Here’s another pass at this matrix. The experiments at Quantified Self that focus most heavily on personal tracking cluster in the bottom left. Having our individual movements tracked, not for our benefit but for the benefit of a third party, is the uncomfortable sphere of surveillance. When our individual movements are less interesting than the movements of masses of people, we move to the top right, distributed sensing. I don’t quite know what to call the top left, but I think it’s perhaps the most exciting sphere for many at the conference: community science. The promise here is that we can all track our sleep, productivity or mood and aggregate the data, making discoveries that help ourselves and the world as a whole. It’s an exciting vision, though I think we’re far from realizing it, not just due to shortcomings in tools and protocols for sharing data.
As I mentioned before, I’m not sure this is the best way to understand this space, and I’m certain it’s not the only way to model the interests and motivations of participants. But it did help me understand why there’s a gap between projects that focus on individuals changing their own behavior and projects that hope to map disease incidence through collecting many points of data.
If I had a complaint about the Quantified Self conference, it was that it focused heavily on quantified health and less on other aspects of the self. The projects and ideas I found most exciting were those that moved beyond brain waves and blood pressure and sought to understand less embodied aspects of the self through quantifying behaviors. I was most aware of this tension in a session on tracking location. Many of the projects we discussed were using location as a proxy for behavior, and using this data to refine other bodily measurements: if I can see that I was in the park, I was likely walking the dog, which means I was burning this many calories per hour. I was interested in tracking location because where I go and what I see is an interesting aspect of my existence. I’m curious to see whether my paths through a city are limiting me from certain types of encounters, and potentially in building systems that help me encounter the unexpected.
The bias towards health is an understandable one – if you’re not sleeping well, you might be willing to engage in far more self examination than if you were trying to diversify your media diet, or ensure you visit unfamiliar neighborhoods in your city. And because healthcare is such a huge market in the US, it makes sense that entrepreneurs and large corporations would be paying attention to the space, especially the intersection of the health and gadget space (two profitable tastes that taste great together!) But when I quantify myself, it’s often in terms of tracking my productivity (words written per day), my influence (who retweeted me? who quoted my posts?) and my attention (what did I read? was it candy, or did it influence my work?)
I think there’s something to be learned by using some of the ideas and techniques being applied to quantified health questions and applying them to other aspects of the self. Whether or not I find myself gravitating to Quantified Self meetups in Boston, I’m hoping to meet other people interested in questions of how we might self-track and understand ourselves in ways beyond the performance of our bodies: our moods, our work, our media, our interests, our movements.
I’m grateful to RWJF for making it possible for me to attend the conference and to Gary Wolf and Kevin Kelly for being such great hosts and giving me a spot on the program. And I’m grateful to everyone exploring these ideas for opening such interesting and provocative questions.