We’ve been working for many months to organize our next Quantified Self conference, and now we’re ready to open QS17 for registration. We’re going to Amsterdam, and we hope you’ll join us.
This will be the fourth time we’ve held the conference in Amsterdam at our favorite venue just outside the Amsterdam city center. Those of you who have already been to a QS conference here will understand why: This calm, beautiful, and extremely well located hotel, just a short walk from the canal ring, is a perfect place to work together and learn from each other.
QS17 is what we call a “Carefully Curated Unconference.” We’ll have over 100 individual sessions, all of which are proposed and lead by conference attendees. We work closely with all the participants in advance, based on what we know of your projects, work and interests. The final program lineup is released a few days before the event. So please let us know what you’re working on when you register.
Due to the size of the venue, attendance is strictly limited to 350 people. The first hundred people to register can sign up for €250 for the two day conference. So please don’t delay.
See you in June!
We recently announced that we’re collaborating several other editors to edit a special “focus theme” on N-of-1 experiments for the established informatics journal, Methods of Information in Medicine.
Here’s an extract from our justification for the call:
Scientific progress in medicine and public health during the last century has been dominated by studies performed with groups of people. Today many people collect data their own data to help investigate a health problem, make progress towards a goal, or simply because we are curious. Such investigations need not be conducted on groups. Often, they involve just a single person who is both the subject and the investigator. They are “N-of-1” trials, where data are generated by the individual, normally making use of self-quantification systems, including mobile apps and portable monitoring devices. This focus theme of “Methods of Information in Medicine” on single subject research encourages submission of original articles describing data processing and research methods using a “N-of-1” design where the questions and analysis are guided by the interests and participation of the subject. We encourage submissions that focus on challenges and questions involving data collection, processing, integration, analysis and visualization in the context of single subject research.
AREAS OF FOCUS MAY INCLUDE, BUT ARE NOT LIMITED TO:
Personal health and well-being * Chronic disease management * Mental health * Autonomous self-experimentation in the context of health and well-being * Health education and autodidactic learning * Privacy, ethics and regulation issues
Larry Smarr’s major contributions to scientific progress are well known. A physicist and the founding director of the National Center for Supercomputing Applications (NCSA), he helped bring the power of computing to scientific research at a time when computers will still highly specialized instruments. Today he is the Director of the California Institute for Telecommunications and Information Technology (Calit2), one of the most innovative research institutes in the world. He’s also an avid self-tracker, using his own data to correctly self-diagnose the onset of Crohn’s disease. In preparation for our upcoming Quantified Self Public Health Symposium I asked Larry about his idea for a large scale, non-commercial, broadly accessible infrastructure for improving access to self-collected data for both personal and public benefit.
Gary Wolf: What’s the role of the public health sector and of the academic research community in a world where individuals and consumer-oriented tech companies are taking on increasingly complex questions of personal and population health?
Larry Smarr: The fundamental role is bridging the gap between N=1 and N=a lot. Any time in the last 30 years when I’ve seen a technical innovation that mattered, like a software tool, the first approaches aren’t ready for prime time. They are not developed with professional-level software engineering, version controls, documentation and all that. Similarly, scaling up of biomedical observations made by N=1 quantified individuals is going to require the professional methodologies of the public health sector.
GW: Can this be left to industry?
LS: Not entirely, although startups are doing a fabulous job of getting tracking tools into the hands of tens of millions of individuals. The problem is how to do research on the data produced by that broad population. Too often these days I see researchers from the university going to tracking companies and asking for access to the company’s raw data feeds, for instance to heart rate or exercise time series, and the company says no. They will give you the weekly or daily average, but you can’t get to the raw data. If you go to them and say, I’ve got this really great innovation that can be used to understand this data, more often than not they decline. They have an installed base and market share to protect, which naturally tends to make them conservative. I think there is a real opening for companies to make this anonymized broad population data available to academic researchers. That’s when a raft of scientific discoveries will be made from the quantified population.
GW: Those are the consumer fitness companies, but what about the healthcare IT world?
LS: Again there is a disconnect between the consumer fitness cloud-based apps for millions of individuals and the electronic health records in your healthcare provider. If you’re a doctor in a medical office, unlike a data science researcher, you don’t want all this data. What you want to know is: did my patient do 1000 steps or 10,000 steps today, did you get aerobic exercise or not, are they getting enough sleep? So it’s not like you need a vast dumping place inside electronic health records. Again, I think pilot experiments are the way to get started.
GW: You’re arguing that the incentives aren’t there.
LS: These are currently major structural barriers. Who is going to work on the bridging we are discussing? There aren’t incentives for the commercial tracking companies to work on it. Neither are there incentives for the electronic medical record companies to work on it. NIH isn’t going to support bridging between commercial companies. It falls between the stools. You need to have the research community, and health care IT experts, the commercial tracking companies, and the individual self-trackers all come together and collaborate.
GW: You envision some kind of technical system so that individuals and health care providers and researchers could all benefit from access to data. What does your experience tell you about how long this would take to have a working prototype that would be practically useful?
LS: It’s a three-to-five year project. I think if a major funder did a call for proposals requiring a health care provider, university research community and the self-tracking community to come together a prototype a solution, I think they would get some very interesting proposals.
GW: In a talk you gave in 2011, you said “science is not enough.” You pointed out that we’ve known the link between smoking and cancer for over half a century, and yet global cigarette consumption has tripled during this time. So we have all this possibility for new discoveries with self-tracking data, but how is that going to help make people healthier?
LS: Yes, just knowledge of what causes negative impacts on health is not enough. My former UC San Diego colleague Naomi Oreskes documents how economic interests slowed down the logical social reaction to smoking health threats and climate change in her Merchants of Doubt: How a Handful of Scientists Obscured the Truth on Issues from Tobacco Smoke to Global Warming (2010). We are seeing similar delaying and disinformation tactics in the obesity/diabetes epidemic, which has been building for four decades. It is sobering to me to see someone as politically skilled as New York City mayor Bloomberg defeated in his efforts to ban jumbo sugary drinks. My best guess is that we face a multi-decadal battle, just as we have had with tobacco and climate change, to get our society to move to healthy eating and drinking. The bright spots are subcultures of healthy living, often empowered by tracking and social media, that are developing across the country. My hope is that these will spread and scale over the next decade.
GW: It seems you are also pointing toward activism, since that’s been so important with smoking.
LS: Activism is essential given the enormous power of the entrenched economic interests. Activism can lead to regulatory reform, which over time can make huge social changes. For example, when I grew up in the 1950s and early 60s my father didn’t smoke, but he was embarrassed that you had to have ashtrays in your house, because he said you couldn’t tell people not to smoke in your own home. Socially, you just couldn’t. About that time the Surgeon General’s report on smoking was published. Fifty years later, huge chunks of society are smoke-free, such as all the University of California campuses, restaurants, and large social gatherings. Just think of what an enormous shift that has been! We are beginning to see similar activism in getting pension fund investors to boycott carbon fuel companies in order to slow down climate change. So can we imagine a boycott against sweetened beverages and high glycemic prepared foods? I believe that there is a huge role for health-related individual and organized activism in the near future.
GW: At the last Quantified Self Public Health meeting, you suggested that this emerging field needs a new kind of journal where individuals can report their discoveries. In light of the big challenges you’ve been describing, challenges that can’t be solved by academic and research publication alone, what kind of contribution could a new journal make?
LS: Let’s go back to the issue of scaling we discussed. Imagine the journal articles are fairly short, describing how the data was generated, but the back end is a publicly available cloud of data so that you could begin growing a large dataset of N=1 projects. Then the research community could pick up on the ideas coming out of the Quantified Self community, explore the data, and take it further. That’s how things grow.
GW: You want to be on the editorial board?
LS: No, I want to submit a paper!
Can’t resist just posting this photo from our friend Dana Lewis, taken today at the White House. She’s holding up her DIY artificial pancreas system as her fellow #wearenotwaiting activist Howard Look talks data access with – yes, that’s him – President Barack Obama.
For more on Dana, Howard, and the movement to build a new ecosystem for diabetes care, see the links below:
Today the New York Times published a fantastic story by Peter Andrey Smith about the Nightscout and OpenAPS projects: A Do-It-Yourself Revolution in Diabetes Care. People with diabetes and parents of kids with diabetes are self-tracking by necessity, and we’ve learned a lot from their talks about their projects at QS meetings and conferences. Their impact is growing. Reading Smith’s story inspired me to repost a talk by Nightscout pioneer Lane Desborough, along with links to additional people and resources that didn’t make it into the Times story.
Nightscout, which Lane describes in this wonderful talk, allows people with people with diabetes and parents of kids with diabetes the see real time data from a blood glucose monitor on a mobile device. While similar efforts are underway among manufacturers, leadership is coming from patients and caregivers.
The quality and commitment here can inspire anybody who is thinking about how QS tools fit into new forms of knowledge and cooperation. The projects Lane discusses in this talk have continued to grow and evolve. Supported by a remarkable group of activists and a technically expert community made up mainly of people with diabetes and parents of kids with diabetes, contributors to these projects have created a suite of tools that can dramatically improve self-care.
For instance, a couple of weeks ago I saw this tweet from Howard Look, founder of Tidepool:
Did you know that people with diabetes have been building their own artificial pancreas systems? Read more about Nightscout, the Open Artificial Pancreas System, and related projects at these links:
In this fascinating talk Rocio Chongtay shares her novel and thoughtfully designed experiments in using music to adjust her concentration and relaxation depending on what she’s doing. Using a consumer EEG device from Neurosky, Rocio tried different types of music while tracking the relaxation and concentration dimensions identified by the Neurosky algorithm. She had experience experimenting with Neurosky in her lab, and then turned these techniques on understanding something about her own mind.
As an English teacher Kyrill Potapov spends a lot of time working with 12 year old kids who are trying to improve their reading, writing, comprehension, and analytical skills. In this talk, he explores a remarkable method of speed reading, called Spritz, that promises to let you “read Harry Potter in three hours” with full understanding and recall. Could such a promise possibly be true? And, if the claim is true, another question arises. Is such a pace desirable and useful, or rather something quite alien to the activity of reading?
With his students, Kyrill decided to resolve these questions empirically, reading the same material in a book, on a screen using conventional scrolling, and on a screen using the novel method of Spritz, which displays words one at a time at a pace determined by the reader. They found high comprehension at the high speeds permitted by Spritz, but with some cost, which he outlines in this wonderfully clear and interesting talk.
Kouris Kalligas, a long time participant and contributor at Quantified Self meetings, is the creator of the very easy to use data aggregation service AddApp. AddApp is an iPhone app that makes it simple to gain insights from data gathered on dozens of different devices. While running his startup, Kouris has also been doing ongoing self-tracking experiments. At QS Europe 2014, he gave an excellent show&tell talk about his sleep, diet, and exercise data. In the talk below, he discusses using mood data in combination with calendar data to reflect on the relationship between emotion, experience, and self-image.
It’s been an honor to have Beeminder founders Daniel Reeves and Bethany Soule participating in Quantified Self meetings, giving us a chance to watch the evolution of their very useful tool for setting and achieving personal goals. These days they are working on the forefront of device and service integration. In this talk Daniel gives a brief explanation of how to bring data into Beeminder with minimal hassle.
(Note Daniel’s generous shout-out to another great QS toolmaker, Rescue Time.)