A couple of weeks ago I had the opportunity to attend the very first Quantified Self Conference. The 2-day event took place in the Computer History Museum, in Mountain View, CA.
Quantified Self is a community of people including hackers, academics and others who are riding the wave of ubiquitous sensing and computing (e.g. mobile phones) to track various aspects of their lives. The focus of many of these tracking efforts centers on improving health, sometimes in the context of assessing health risks, sometimes encouraging reflection and behavior change, and sometimes with the goal of extending life. As the name suggests, quantification is a core element of the movement. Kevin Kelly and Gary Wolf, the duo who brought the community together and organized the event with many collaborators and volunteers, have been applying the Homebrew Computer Club model to the Quantified Self community and I think it’s working very well at the moment.
The conference was organized around talks in the main auditorium and breakout sessions spread out throughout the day covering a wide range of topics. The organizers planned the event well, and left plenty of time in-between sessions for attendees to chat, and for the more engaging sessions to keep going for an extra 30 minutes if necessary. Healthy lunches and snacks were provided.
Needless to say, I met large number of interesting people working on interesting projects such as David Duncan (journalist and author who’s been writing about the absolute state of the art in human testing, genomics, biotech and health innovation, usually undergoing the procedures he writes about as well), Greg LeMond (former professional bike racer, won the Tour de France three times and is interested in self-tracking for exercise performance, usually on the bike), Patricia Brennan (professor at the University of Wisconsin-Madison and leader of Project HealthDesign, an initiative that aims to bring observations of daily living into clinical care), Hugo Campos (who would like to hack his implanted defibrillator and have access to the data the device sends to Medtronic and his doctors over a phone line), Bill Jarrold (computer scientist working on language analytics for assessing brain health) and Tito (who is producing an open-source PCR machine).
There were many startups and established companies showcasing their products, some of which haven’t even been released yet. In general these startups fell into three categories, (1) activity tracking, (2) activity data aggregation, visualization and analysis and (3) health-oriented communities.
Under activity tracking were companies like Zeo, RunKeeper, BodyMedia, Phillips and GreenGoose, selling (or planning to sell) various types of devices for activity and physiological data tracking. I was particularly impressed by Zeo and the uptake they’ve got so far. In one of the talks, Zeo’s CEO or CTO mentioned that they currently have the largest repository of sleep data in the world, and a portion of it is made public and available to researchers. This makes sense, since sleep research is typically confined to research labs and the Zeo device allows anyone to collect sleep pattern data.
In the space of data aggregation, visualization and analysis were startups such as Sen.se, Quantter and Scanadu. I left the event with an intriguing impression of Scanadu. They plan to open their system so that users can input data from devices like Zeo and many others. At that point, Scanadu will help users understand the impact of their data and offer health recommendations. It’s an ambitious goal and to be seen whether they will succeed.
Finally, companies like MedHelp and Genomera are all about bringing people together. MedHelp is a portal for health-communities and Genomera is in the new space of personal science, offering an application that lets users join or initiate health studies. In my opinion, Genomera and some of the other companies in the field are behind of the most transformative trends that I am observing in what some people call Health 2.0. In the (really) long term, the bet that Genomera is making is that the future of health is crowdsourced and participatory, and increasingly less reliant on centralized government entities.
At a meta-level, I noticed a number of trends in this field. Now that I’ve had the chance to synthesize all the talks, conversations and interactions that I had, here are some of my observations:
The Future of Logging
Whether it’s the food you eat, your mood, your thoughts or your exercise, tracking activities and physiological signals is at the core of the quantified self movement. Not surprisingly, it was represented at the conference en masse, with speakers sharing logging techniques and startups showcasing tools that facilitate logging and visualization. In terms of actual sensing technology, I didn’t see anything tremendously novel. GreenGoose does seem to have a promising approach to tracking physical activities, but because the product is not available yet, they weren’t able to provide details. The team behind the upcoming Basis watch was attending the event, but again, we couldn’t see the product in its final form. We will certainly see new sensors and human tracking modalities next year. This area will keep evolving, without a doubt.
In my view, one of the key points has to do with the value and analysis of tracked data. I expect that in the years to come, the quantified self movement will evolve from not only tracking data but making sense of it and validating it as well. What kinds of patterns emerge from this data? How can these patterns be turned into best practices that transform them from merely interesting to indicators you can’t live without. In other words, sensing is fairly easy to do, analysis is much harder in comparison.
Health Policy, Urban Planning
At the moment the focus is on the individual. Are there any opportunities for tracking and aggregating activity data at the population level? Can you improve health policy at the neighborhood, city level when you collect this type of data for large numbers of people who are similarly geo-located? If city officials had access to public self-tracking data and could see that large numbers of people in a neighborhood are avid bikers, would that facilitate investment in bike lanes and similar infrastructure?
My impression was that at least half of the participants at the conference have gone through some type of genetic testing, 23andMe being the most popular service. By and large, the motivation for personal genetic testing is mostly at the curiosity stage at the moment, but I expect that it will become much more of a health tool as we move forward. Consequently, I imagine that in the years to come, we will see more personal applications that rely on genetic data. The same way that groups of people are experimenting with treatments for conditions today, personal science-style, we will see genetics coming into the mix as genetic-wide association studies (GWAS) become yet another tool available to citizen scientists. Also, in terms of GWAS, many of the studies center on genotype/phenotype correlations. I see an opportunity to introduce additional quantification to capture the (extended) phenotype, even in high-profile projects like George Church’s PGP. Without a doubt, there will be many more discussions around this topic in the future, as these projects and techniques evolve.
Personal (Social) Health Science
My grandmother is in her late eighties. She suffers from intermittent blackouts, usually when walking to church or the grocery store, that result in fainting and falls. She’s visited multiple doctors and undergone numerous tests, and all that medicine has been able to say is that her circulatory system is probably not as effective as it used to be and that under rigorous exercise, such as walking, her brain ends up not being oxygenated enough and turns itself off momentarily. What if my grandmother discovered, by experimentation or chance, that eating half a stick of butter a day made her blackouts go away? That would be tremendous for her well being. Now imagine if there was a way for her to share her discovery and improve the condition of hundreds, maybe thousands of people around the world that also suffer from blackouts.
Scenarios like this are starting to happen, even though most times we don’t understand the cause/effect health science behind them. The truth is, my grandmother doesn’t care whether an effective treatment for her condition comes from the Harvard Medical School, the FDA or from a neighbor next door. As long as it works, she can go to church and that’s all that matters. That’s the idea behind personal (social) health science, and I bet that we are not even at the tip of the iceberg yet. We can just barely see it under water.
As we begin to have more access to information about our health and our activities, through personal devices, the value of this data will become clear. Therefore we will demand more openness with regards to the legacy medical procedures that have traditionally kept medical data out of our hands. This in turn will accelerate the adoption of personal health records. Does it make sense for a private company like Medtronic to not give individuals access to their own heart analytics? The answer might be yes from a financial and legal stand point but Medtronic and others like it will begin to feel the heat to change the way they operate.
It is exciting to be part of a new field as it emerges, and that’s exactly what happened in earnest in Mountain View, despite the fact that Quantified Self Meetups have been taking place all over the world for a while now. The energy in the room was palpable. In his closing talk, Kevin Kelly discussed how quantification has all the ingredients to become part of the evolving scientific method. I can’t help but agree.