We’ve just returned from the latest Quantified Self Conference in Amsterdam and we’re working on the slides and videos of talks to share with everybody. It always takes a few weeks of work to get them ready for posting, so be patient.
If you are already nostalgic (or wishing you’d been there) you can check out some random highlights using the QS17 tag on Twitter. Official documentation coming soon!
Today we’re publishing the program for QS17: The Quantified Self Conference, which will be held this year at Casa 400 in Amsterdam on June 17 and 18. Looking over the 60+ sessions, I’m struck again by how different our meetings are. We do some things that are contrary to common sense, and we keep doing them because they work so well. I don’t want to take away from the excitement of publishing our program, which includes an almost unfair number of deeply original projects. But I figure that the list mainly speaks for itself, so I can use this announcement as a way to highlight some of what we’ve learned about doing meetings differently.
Here are five rules we follow when making our program.
1. No paid speakers. All the talks come from registrants who share they work they’ve been doing over the past year.
2. Don’t rely (only) on serendipity. Not everybody who has something important to present has the combination of self-confidence and extroversion that makes it easy for a person to self-recommend. We ask our registrants to tell us something about their work, and then we actively follow up, reading their posts and looking at their project URLs to identify who might contribute, then contacting them and encouraging them to present.
3. One person at a time. We’ve learned to avoid panels, which tend to be poorly prepared, disjointed, and lacking in authentic back-and-forth. Instead, we ask people to present their own projects, then moderate a short discussion with the audience. That way everybody has an incentive to think in advance about what they want to say, and anybody can jump in with a word during the Q&A.
4. Help with the hard stuff. Being clear and interesting in a talk to a large group is not easy, so we make ourselves available to help with preparation before the meeting, whether that involves listening to practice talks or editing slides.
5. Hour long sessions, half hour breaks. A talk will only be as good as its audience. Tired, hungry, restless people cannot respond. So when you see our program, know that this is just the part we can easily make explicit; the rest is the useful absence of program, with coffee, bikes, and the streets of Amsterdam. Sixty minutes in chairs, then 30 minutes up.
The program this year is fantastic, with talks ranging from a 9 year-old’s self-collected data on the impact of cancelled recess on his activity to a story about microdosing psilocybin and its effect on social interaction. There is no way to sum it up in a blog post, so I encourage you to look for yourself.
The book discussed in this post is Making Time: Lillian Moller Gilbreth — A Life Beyond “Cheaper by the Dozen”.
When we repurpose tools of science and management for distinctly personal ends, we’re extending a path laid down for us by many ingenious predecessors. I want to take advantage of the last hours of the day to honor one of the greatest early biometricians, Lillian Moller Gilbreth, and to revive a question posed, at least implicitly, by her work.
Gilbreth began her career in the early part of the last century as a disciple of the founder of scientific management, Frederick Taylor. Even before she got her PhD, she was doing time motions studies with her husband Frank Gilbreth with the aim of improving worker efficiency. Frank Gilbreth died in 1924. By the time Lillian died, in 1972 at age 94, she’d taken what began as Taylorist dogma and turned it into a practice of close observation and participatory learning that almost turned it on it’s head. Instead of seeing human beings as a factor of production, to be exploited like any other resource until worn and replaced, she asked about the human factor in production: what was work for, what were its conditions and benefits, and how could it be improved.
Gilbreth was very well known in her day, so she’s easy to learn about and there’s no need to crib from sources you can consult yourself. Perhaps my favorite biographical detail is that, after being denied a PhD in 1912 by the University of California, Berkeley because her family and business responsibilities prevented her from being on campus during the last year of her studies, Gilbreth published her research as a series of articles in Industrial Engineering and Engineering Digest, and then as a book, and then just went ahead and got a PhD from Brown. Although it’s quite something to become a towering figure in a new field, developing many novel research methods, and it’s of course no small honor to be a member of the National Academy of Engineering (she was the first woman elected), my academic friends will surely bow in awe before somebody who deals with a recalcitrant and small minded graduate department by marching off to a competing school and writing a second dissertation.
She was like that all her life. Jane Lancaster’s biography of Gilbreth, Making Time, gives a sympathetic but critically aware portrait of a person who embodied, challenged, compromised with, exploited, and suffered from conflicting ideals and demands of women’s work. Gilbreth made her living consulting for corporations, especially those whose employees and customers were women. She was a key link between scientific management and consumer culture, taking techniques developed for studying workers on the shop floor and applying them to home life. In 1927 she wrote a practical guide called The Home-maker and Her Job, and for many years after she continued to do close observational studies and produced a nearly endless stream of advice for coping.
From today’s vantage point we easily see that increasing the efficiency housework didn’t bring about the general emancipation it promised. For many people, time saved washing dishes is lost to doing paid work at stagnant wages; while savings from the lower cost of manufactured goods is eaten up by the price of healthcare and childcare. The cheery scientism of late Victorian elites looks naive from a century’s distance; that is, when its unhesitant racism doesn’t make it simply revolting. Gilbreth, at least at the beginning of the century, hoped that “positive eugenics” could improve the human species. Lancaster doesn’t go very deeply into this side of the rationalist ethos, except to note that Gilbreth wasn’t in favor of sterilization or murder, instead believing that people of high intelligence should have as many children as possible. She lived her faith, giving birth to twelve, one of whom wrote a memoir, Cheaper By the Dozen, in which she is reduced to a feminine caricature.
You can read Cheaper By the Dozen and watch both of the movies made from it without learning any of the most interesting things about Gilbreth’s research. For instance, in 1926 she undertook an unprecedented study on menstruation and menstrual pads, for which she canvassed, Lancaster writes, “a long list of potential informants, ranging form the Women’s Bureau through the American Federation of Labor to gynecologists, prison workers, and laundries.” In the early 1930’s she launched a project involving over 100 interviews and 20,000 questionnaires collecting data on sex and age discrimination. Before Frank died, he and Lillian Gilbreth carried out some of the very first, and certainly the most thorough, studies of how people with disabilities can benefit from kitchens designed specially for them, and after he died she continued to advocate passionately for better design to support independent living. Paid by Macy’s to improve the efficiency of their cashiers, she went to work on the sales floor herself, coming to understand in an intimate way the different meaning of “tiredness” for women of different ages working for different reasons.
Gilbreth didn’t merely link scientific management to consumer culture through her research, she also embodied – through her seemingly supernatural productivity – it’s greatest tensions. Gilbreth was a non-conforming rationalist engineer, and a bourgeois advocate of domesticity. (She argued for what she called a “50-50 marriage” of shared domestic labor but backed down in the face of ridicule, paying at least lip service the idea of a woman’s sphere.) She spent a good part of her life addressing the problems of working women, while of course working herself, and yet her greatest public fame came from the “biological wonder” of her twelve children.
Gilbreth understood efficiency, and yet her work leaves us with a question: what does efficiency cost? The promise is mastery, sufficiency, ease of accomplishment, when unnecessary friction has been eliminated. Do things the “one best way” and look what you get: Two dissertations. Twelve children. A long shelf of original and useful publications. She made it look easy. But as Lancaster makes clear, the ease is an illusion. Automation and routinization works best under controlled circumstance, but controls fail, and someone has to clean up the mess. Gilbreth mainly cleaned her own messes, though not all of them. The book’s most provocative minor character is the man who worked for decades as her main domestic servant, Tom Grieves. He’s presented as grumbly but affectionate, a practical person with a cigarette always on his lips, doing dishes and straightening rooms, chasing children around, and generally needed to maintain the conditions of predictability required for rational management to function. Grieve’s comment on his employer’s obsession with efficiency was succinct: She was, he said, trying to “make it easy for folks to work hard.”
Gilbreth exposed the realities of women’s work both inside and outside the home, but always with the promise that good technique could lift the burden. The promise is still with us; but, then again why is it still just a promise? I think it honors Gilbreth’s legacy to keep asking this question, even as it takes us outside the domain of efficiency she pioneered.
Let’s start 2016 with a very interesting talk by Randy Sargent about how to visualize the very large data sets produced by some kinds of self-tracking. Randy’s idea about using spectrograms, normally used for audio signals, to create a portrait of your own time series data, is completely novel as far as I know. If you have tried something similar, please get in touch.
In this fascinating short talk by geneticist Jim McCarter, we see detailed data about the effects of a ketogenic diet: lower blood pressure, better cholesterol numbers,and vastly improved daily well being. Jim also describes the mid-course adjustments he made to reduce side effects such as including muscle cramps and increased sensitivity to cold.
Jim begins: “When I tell my friends I’ve given up sugar and starch and get 80% of my calories from fat, the first question I get is: Why?”
The rest of the talk is his very clear answer.
I talked with Vinod Khosla over the summer about machine learning and the Quantified Self.
Khosla was a founder of Sun Microsystems and is one of Silicon Valley’s most experienced investors in Quantified Self companies. His portfolio includes AliveCor, Ginger.io, Jawbone, Misfit, Narrative, and many other toolmakers that people doing QS projects will recognize. In our conversation, I ask Vinod about the role machine learning can realistically play in QS practices.
Below are links to three papers Vinod mentions in the interview:
Beck, Andrew H., Ankur R. Sangoi, Samuel Leung, Robert J. Marinelli, Torsten O. Nielsen, Marc J. van de Vijver, Robert B. West, Matt van de Rijn, and Daphne Koller. “Systematic analysis of breast cancer morphology uncovers stromal features associated with survival.” Science translational medicine 3, no. 108 (2011): 108ra113-108ra113.
Ioannidis, John PA. Why Most Published Research Findings Are False. PLoS Med 2, no. 8 (2005)
We haven’t gotten the full report yet, but this looks like a wonderful meeting!
For people who take insulin, self-measurement is a matter of life and death. No wonder, then, that people with diabetes who track their blood glucose have been so important in advancing techniques of visualization,and understanding data. At the Quantified Self Europe conference in Amsterdam this year, we were honored to host a panel discussion on Data Visualization and Meaning with Joel Goldsmith (Abbott Diabetes Care), Jana Beck (Tidepool), Doug Kanter (Databetes), and Stefanie Rondags (diabetes coach and blogger).
This discussion strikes me as widely important for self-trackers whether or not we have diabetes. Many of us will be tracking blood glucose in the near future. And the issues of data access, understanding, and clinical relevance that people with diabetes are working on resemble challenges commonly faced by anybody who is tracking for health.
For instance, Jana Beck was asked during the Q&A about her health care providers. How receptive are they to the important experiments she’s done to improve her health based on the data she’s collected? ”None of my endocrinologists have been very receptive to this approach,” she answered. “My A1C tends to fall within the range of what’s considered the gold range for people with Type 1. But I’m interested in optimizing that further. Often, I don’t even see them more than twice a year.”
Jana, Stefanie, and Doug all showed their own data in the context of discussing experiments and decisions that have had a major impact on their wellbeing. All were clear that the domain of these experiments and decisions is not healthcare as traditionally understood; but nor is it a matter of general fitness or lifestyle. The domain of these experiments is different and perhaps still unnamed. Self-collected data can and should essential health decisions, but the most advanced techniques of understanding this data are still being developed in an ad-hoc, grassroots way, by knowledgeable and open minded individuals who have a strong interest in learning for themselves.
At the end of the session I asked Joel Goldsmith, of Abbott Diabetes care, about the future prospects of the Freestyle Libre, a minimally invasive wearable blood glucose monitor that is not yet available in the US. (Disclosure: Abbott Diabetes Care was one of the sponsors of the QS Europe Conference.) The Freestyle Libre has a sensor in the form of a patch worn on the arm, and a touchscreen reader device that you lift close to the sensor to get a reading. There is no finger prick involved. While this and competing minimally invasive or non-invasive glucose monitors will almost certainly continue to be regulated as medical devices and understood as part of the health care system, many other people will also use them, and the flood of data and the questions that go with it will challenge our understanding of where this type of information should live.
The video above contains the full session, including the Q&A.
Do you have a Quantified Self idea that can help ease the burden of pain?
On November 5th, 2015, we’re convening the first QS Symposium on Pain and Innovation Challenge on the campus of Singularity University at the NASA Ames Research Center in Mountain View, California. For this meeting, we’re trying a new kind of innovation challenge, designed to advance your ideas for helping people who are dealing with acute and chronic pain. If you have an idea that can help people in pain, please join us for an intense and inspiring one day workshop with some of the world’s leading experts to advance your idea and connect with collaborators who can support its development, from prototyping to reaching the market.
At the end of the day, we’ll award a $10,000 cash prize to the idea that has most challenged and inspired us to look beyond what is already known about reducing the burden of pain.
We’re looking for ideas based on deep insight into the practical challenges faced by people dealing with acute and/or chronic pain, with a particular focus on tools that enhance self-awareness, self-efficacy, and empower people of all types to better understand themselves and live joyful lives. We welcome participation from all innovators interested in sensing, devices, apps, services, and social innovations.
This is a unique challenge, designed to unfold from start to finish over the course of a single day. Instead of competition, co-operation. Instead of obscure judgments made behind closed doors, an open conversation about what we are learning. Instead of long lead times and uncompensated design work, a short, intense, inspiring immersion among the makers of the most innovative tools of tracking and learning emerging from the Quantified Self movement today.
We are toolmakers, pain sufferers and clinical experts, united by a common intention to make a difference in the lives of people who suffer from pain every day.
Include some details about your idea and reference links, and we will follow up with you.
A year ago we released QS Access, a simple app that allows you to see your healthkit data in a table. Our idea was to make it easier for people to explore their data using familiar tools, such as Numbers, Excel, or any spreadsheet program that can open a .csv file. We’ve really enjoyed hearing its been useful, and we’ve received lots of good feedback. This week we released a new version of the QS Access App that contains some commonly requested features. You can now:
- See raw data from individual elements, such as running.
- Store the query details, so you don’t start from scratch each time.
- Choose units for many quantities.
- Get a table of your sleep data.
We’re still listening, so if you are using QS Access and have feedback for us please let us know by emailing email@example.com.