Topic Archives: Discussions
If you’ve seen the announcement for our 2015 QS Conference & Expo and you’ve never been to a QS event before you may be asking yourself what our conferences are all about. From our very first meetup in 2008 through our six conferences and numerous events we’ve emphasized the role of the personal story and real-world experience. We do this in a variety of ways.
First, we run our conferences as a carefully curated unconference. When you register, you’re asked to tell us about the self-tracking projects you’re working on and other QS-related ideas you have. Our conference organization team goes through every registration, diving deep into personal websites, Twitter feeds, and blog posts. We love seeing individuals using self-tracking in new and different ways to find out something interesting about themselves and we work hard to surface truly unique and inspiring stories.
How does that manifest itself in the program? The core of our conference program is made up of the nearly two dozen show&tell talks where self-trackers get up and tell their story by answering our three prime questions: What did you do? How did you do it? What did you learn? It may seem simple, but these three questions provide a stable and consistent narrative to inspire you to learn and engage with your own tracking practice in new and different ways.
We’ve spent some time combing through our vast video archive to showcase some of our favorite talks from our previous conferences. We hope you find them enjoyable and they inspire you to join us on March 13-15 in San Francisco for our 2015 QS Conference & Expo. Who knows, maybe you’ll be on stage and we’ll be learning from you!
Sara Riggare on ‘How Not To Fall’
Sara Riggare is co-organizer of Quantified Self Stockholm. She is also an engineer, a PhD student and a tireless researcher of Parkinson’s disease. In this fascinating talk, Sara describes using body sensors to help her control her gait.
Vivian Ming on Tracking Her Son’s Diabetes
Vivienne Ming is an accomplished neuroscientist and entrepreneur. Two years ago her son, Felix, was diagnosed with Type 1 Diabetes. In this talk, presented at the 2013 Quantified Self Global Conference, Vivienne explains what they’re learning as they track and analyze his data
Chris Bartley on Understanding Chronic Fatigue
While on a research trip, Chris contracted Reiter’s Syndrome. After his recovered, something still didn’t feel right. Chris consulted his physician and started tracking his wellness along with his diet and supplement intake. What follows is an amazing story about what Chris learned when he started applying his knowledge of statistics to his own data.
Adrienne Andrew Slaughter on Tracking Carbs and Exercise
Adrienne Andrew Slaughter was testing out a new diet that included carbohydrate restriction. At the same time she was commuting to work on a bike. She started to notice feeling tired and slow during her commutes and wondered if her dietary changes had anything to do with it. Luckily, Adrienne was tracking her commutes and her diet and was able to run detailed data analysis to find out what happens when she goes carbless.
Bob Troia: Understanding My Blood Glucose
Bob Troia isn’t a diabetic and he’s not out of range, but he wanted to see if he could lower his fasting glucose levels. He started a long-term tracking experiment where he tested his blood glucose and began to explore the effects of supplementation and lifestyle factors.
Sacha Chua on Building and Using A Personal Dashboard
Sacha Chua started tracking her clothes to make sure she was varying her wardrobe on daily basis. This led he to ask, “What else can I track?” As she added time tracking, food, library books, and so much more (you can view the whole set on QuantifiedAwesome.com)
Robby Macdonnell on Tracking 8,000 Screen Hours
For the last six years Robby Macdonnell has been tracking his productivity and how he spends his time on his various computers (home and work) and even how he uses phone. Over those years he’s amassed 8,300 hours of screen time. Watch his great talk to hear what’s he learned about his work habits, productivity and how he’s come to think about time.
Sky Christopherson on Self-Tracking at the London Olympics
Sky Christopherson first shared his experience with tracking and improving his sleep in 2012. That tracking led him on a path to achieving a world record as a mastars level track cyclists. Later that year, Sky began helping other athletes us self-tracking and personal data to obtain their best performances, culminating in a surprise silver medal for the 2012 women’s olympic track cycling team, on which he served as a training advisor. In March of this year, Sky and his wife Tamara gave another QS talk at our Bay Area Meetup in which they told the wonderful story of how the 2012 Olympic team rode to their medal, a journey captured in the documentary, Personal Gold.
These are only a small sample of the amazing talks and self-tracking projects that are shared at our Quantified Self Conferences. We’d love to hear your story. Register today and let us know what you’re working on!
In response to the much anticipated reveal of the Apple Watch I did a bit of digging around to find out where we stand with wrist-worn wearable devices. I found over 60 different devices. The following list focuses on self-tracking tools, I intentionally left out those that work only as notification centers or secondary displays for your phone. I’m sure this isn’t all of them, but it’s as good a place to start as any. If you’re using one of these devices to learn something about yourself, or you’re just interested in these type of wearable tools we invite you to join us in San Francisco on March 13-15, 2015, for our QS15 Conference & Exposition.
(Thank you to all those who commented here, on Twitter, and on our Facebook group pointing us to additional devices to add!)
Sensors: Accelerometer, Heart Rate (optical), Blood Oxygen, Temperature
Sensors: Accelerometer, Pulse Oximeter, Temperature
Sensors: Accelerometer, Gyroscope, Heart Rate (optical)
Sensors: Materials state the ZenWatch houses a “bio sensors and 9-axis sensor.” I assume optical heart rate, accelerometer, and gyroscope.
Sensors: Accelerometer, Gyroscope, Heart Rate (optical)
Sensors: Accelerometer, Temperature, Pressure
Epson Pulsense Band/Watch
Sensors: Accelerometer, Heart Rate (optical)
Fatigue Science Readiband
Here at QS Labs we’re very interested to see what Apple will be announcing today. The following post with reactions and discussion by Gary Wolf & Ernesto Ramirez will be updated as we learn more.
Someday, you will have a question about yourself that impels you to take a look at some of your own data. It may be data about your activity, your spending at the grocery store, what medicines you’ve taken, where you’ve driven your car. And when you go to access your data, to analyze it or share it with somebody who can help you think about it, you’ll discover…
Now is the time to work hard to insure that the data we collect about ourselves using any kind of commercial, noncommercial, medical, or social service ought to be accessible to ourselves, as well as to our families, caregivers, and collaborators, in common formats using convenient protocols. In service to this aim, we’ve decided to work on a campaign for access, dedicated to helping people who are seeking access to their data by telling their stories and organizing in their support. Although QS Labs is a very small organization, we hope that our contribution, combined with the work of many others, will eventually make data access an acknowledged right.
The inspiration for this work comes from the pioneering self-trackers and access advocates who joined us last April in San Diego for a “QS Public Health Symposium.” Thanks to funding support from the Robert Wood Johnson Foundation, and program support from the US Department of Health And Human Services, Office of the CTO, and The Qualcomm Institute at Calit2, we convened 100 researchers, QS toolmakers, policy makers, and science leaders to discuss how to improve access to self-collected data for personal and public benefit. During our year-long investigation leading up to the meeting, we learned to see the connection between data access and public health research in a new light.
If yesterday’s research subjects were production factors in a scientist’s workshop; and if today’s participants are – ideally – fully informed volunteers with interests worthy of protection; then, the spread of self-tracking tools and practices opens the possibility of a new type of relationship in which research participants contribute valuable craft knowledge, vital personal questions, and intellectual leadership along with their data.
We have shared our lessons from this symposium in a full, in-depth report from the symposium, including links to videos of all the talks, and a list of attendees. We hope you find it useful. In particular, we hope you will share your own access story. Have you tried to use your personal data for personal reasons and faced access barriers? We want to hear about it.
You can tweet using the hashtag #qsaccess, send an email to firstname.lastname@example.org, or post to your own blog and send us a link. We want to hear from you.
The key finding in our report is that the solution to access to self-collected data for personal and public benefit hinges on individual access to our own data. The ability to download, copy, transfer, and store our own data allows us to initiate collaboration with peers, caregivers, and researchers on a voluntary and equitable basis. We recognize that access means more than merely “having a copy” of our data. Skills, resources, and access to knowledge are also important. But without individual access, we can’t even begin. Let’s get started now.
An extract from the QSPH symposium report:
[A]ccess means more than simply being able to acquire a copy of relevant data sets. The purpose of access to data is to learn. When researchers and self-trackers think about self-collected data, they interpret access to mean “Can the data be used in my own context?” Self-collected data will change public health research because it ties science to the personal context in which the data originates. Public health research will change self-tracking practices by connecting personal questions to civic concerns and by offering novel techniques of analysis and understanding. Researchers using self-collected data, and self-trackers collaborating with researchers, are engaged in a new kind of skillful practice that blurs the line between scientists and participants… and improving access to self-collected data for personal and public benefit means broadly advancing this practice.
This is a visualization of one month of my blood sugar readings from October 2012. I see that my control was generally good, with high blood sugars happening most often around midnight (at the top of the circle). -Doug Kanter
Richard Bernstein, an engineer with diabetes, pioneered home blood glucose monitoring. What he learned about himself contradicted the medical doctrine of his day, but Bernstein went on to become an MD himself, and established a thriving practice completely devoted to helping others with diabetes. We think of Dr. Bernstein as a hero because he used self-measurement to support his own learning, and shared what he learned for general benefit.
Tracking personal metabolism is a necessity for diabetics, and it is also something that will become increasingly common for many people who want to understand and improve their metabolism. Diabetics are also leading the fight for personal access to personal data, and we’re looking forward to meeting inspiring activists and toolmakers today at the DiabetesMine D-Data Exchange meeting in San Francisco. In honor of this meeting, we’ve put together an anthology of sort of QS Show&Tell talks about diabetes and metabolism data.
Jana is a Type 1 diabetic and data visualization practitioner who has been working on creating new techniques for understanding that data from her Dexcom continuous blood glucose monitor. In this talk, she described some of her newest techniques and her ongoing work with Tidepool.org. You can also view her original QS show&tell talk here.
Doug has been featured here on the QS website many times. We first learned about Doug through his amazing visualizations of his own data (like the image above). At the 2013 QS Global Conference, Doug shared what he learned from tracking his diabetes, diet, activity, and other personal data and his ongoing work with the Databetes project.
We spoke with Doug about his experience with tracking, visualizing and understanding his diabetes data. You can listen to that below.
James is a graduate student, professional cyclist, and a Type 1 diabetic. In this talk at the QS San Diego meetup group he talked a bit about how he manages his diabetes along with his near super human exercise schedule and how he uses his experience to inspire others. (Check out this great article he wrote for Ride Magazine.)
Brooks, a Type 1 diabetic, was tracking his blood glucose manually for years before switching to a continuous blood glucose meter. In this talk he describes what he’s learned from his data and why he prefers a modal day view.
Bob tracked his fasting blood glucose, diet, and activity to find out what could help him lower his risk of developing type 2 diabetes.
Vivienne’s son was diagnosed with Type 1 Diabetes two years ago and she’s applied her scientific and data analysis background to understand her son’s life.
Today’s post comes to us from Rajiv Mehta, our longtime friend and co-organizer of the Bay Area Quantified Self Meetup group. Rajiv is also leading the team behind UnfrazzledCare, a media and application development company focused on the caregiving community.
“What lessons have we learned through Quantified Self meetings and conferences that would benefit entrepreneurs looking to enter this space?” That’s what I was asked to comment on at a recent event on Quantified Self: The Next Frontier in Mobile Healthcare organized by IEEE and TiE. The workshop took place on September 19, 2013, almost exactly five years after the first QS meetup, naturally leading to a theme of 5 years and 5 lessons.
The 5 themes I discussed were:
- How difficult it is to get an accurate measure on the “market size” for self-tracking, though according to some measures it is a very common activity.
- The importance of and excitement surrounding new sensor technologies, but also what we have learned about our in-built human sensors and the challenges of making sense of the data.
- The need to treat feedback loops with caution; that thoughtful reflection is sometimes better than quick reaction.
- About engagement and motivation, about how so many are drawn to QS through a desire to change their own behaviors, and how QS experiences match behavior science research.
- The value of self engagement, and how self-trackers often learn something even when their experiments aren’t successful.
My slides include my talking points, in small text below the slides. If you view this full-screen, you should be able to read the small text.
Several other QS regulars participated in this workshop. Rachel Kalmar, who runs the Sensored meetup group and is a data scientist with Misfit Wearables, gave a keynote on some of the technology challenges facing those working on the sensing devices. These ranged from the fundamental (“What exactly is a step?”) to prosaic (batteries!), and from business issues (data openness vs competitive advantage) to human issues (accuracy vs wearability). Dave Marvit, of Fujitsu Labs, shared some of their work on real-time stress tracking and his thoughts on the issue of “quantifying subjectivity”. Sky Christopherson, of Optimized Athlete, told the audience of his own health-recovery through self-tracking and how he helped the US women’s track cycling team to a dramatic, silver-medal performance at the London Olympics. QS supports his passion for “data not doping” as a better route to athletic excellence. And Monisha Perkash showed off Lumoback.
Earlier today John Wilbanks sent out this tweet:
— John Wilbanks (@wilbanks) December 11, 2013
John was lamenting the fact that he couldn’t export and store the genome interpretations that 23&Me provides (they do provide a full export of a user’s genotype). By the afternoon two developers, Beau Gunderson and Eric Jain, had submitted their projects. (You can view them here and here).
We’ve doing some exploration and research about QS APIs over the last two years and we’ve come to understand that having data export is key function of personal data tools. Being able to download and retain an easily decipherable copy of your personal data is important for a variety of reasons. One just needs to spend some time in our popular Zeo Shutting Down: Export Your Data thread to understand how vital this function is.
We know that some toolmakers already include data export as part of their user experience, but many have not or only provide partial support. I’m proposing that we, as a community of people who support and value the ability to find personal meaning through personal data, work together to provide the tools and knowledge to help people access their data.
Would you help and be a part of our Personal Data Task Force*? We can work together to build a common set of resources, tools, how-to’s and guides to help people access their personal data. I’m listening for ideas and insights. Please let me know what you think and how you might want to help.
*We’re inspired by Sina Khanifar’s work on the Rapid Response Internet Task Force.
Earlier this summer we found out that the Knight Foundation was launching a challenge centered on funding “innovative ideas to harness information and data for the health of communities.” We decided that this would be a great opportunity to propose a program idea we’ve wanted to work on for a long time: A Quantified Self Civic Festival. The idea of the festival is that the highest value in personal data lies in its usefulness for self-discovery, both individually and in our communities.
Traditionally, research questions about health and wellness are addressed from the top down. Professionals choose which health measures are important, while citizens are seen mainly as sources of data and recipients of expert advice. We’d like to help turn this world upside down, inspiring individuals, families, and communities to define what they’d like to track, and why, while enlisting experts as servants to a broadly popular adventure in making knowledge. (A guiding principle of the festival would be that participants have maximum control over their own data.)
We’d love your feedback. You can comment here, but it would be very helpful if you commented on the challenge website. While you’re there, take a look at some of the other wonderful entries. There is a wealth of inspiration and we’re excited to see what comes out of this work.
Earlier this year we discussed some very interesting research from the Pew Research Center’s Internet & American Life Project about the role of technology and the Internet in health and healthcare. We were lucky to have Susannah Fox, Associate Director at Pew, talk to us a bit about what it means when 21% of people who track are using some form of technology. Of course, that conversation and that research spawned a few more questions and some interesting insights.
Today we’re looking at some brand new research results coming from Pew that are derived from that same research data set. This time Susannah and her team have focused on a particularly important set of individuals in the health and healthcare space: caregivers. In their recently released report, Family Caregivers are Wired for Health, they found that 39% of adults in the U.S. are caring for child or adult. So why talk about this here? What does that have to do with Quantified Self? Well, it turns out that the people who spend their time and energy caring for the health and wellbeing of others may actually be more engaged in tracking than their non-caregiving counterparts:
- 72% of caregivers track their health (weight, diet, exercise, blood pressure, sleep, etc.) while 63% of non-caregivers track their health.
- 44% of caregivers who track say they track their most important indicator “in their heads” (non-caregivers = 53%).
- 43% of caregivers who track say they track their most important indicator using paper (non-caregivers = 28%).
- 31% of caregivers track the health of someone other than themselves.
“When controlling for age, income, education, ethnicity, and good overall health, being a caregiver increases the probability that someone will track a health indicator.”
- 41% of caregivers who track share their data with someone else (non-caregivers = 29%).
- 52% of caregivers who track say it has changed their overall approach to maintaining their health or the health of someone for whom they provide care (non-caregivers = 41%).
- 50% of caregivers who track say it has led them to ask a doctor new questions or to seek a second opinion (non-caregivers = 32%).
- 44% of caregivers who track say it has affected a decision about how to treat an illness or condition (non-caregivers = 26%).
We asked our friend and fellow QS organizer, Rajiv Mehta to comment on this report. When he’s not helping organize our Bay Area QS Meetup, Rajiv has been working on exploring and understanding caregiving.
“Given the prevalence of caregiving (40% of adults) and that 30% of caregivers track something about the person they’re caring for, there’s a lot of opportunity for appropriate tracking and analysis tools. However, caregiving often involves tracking a wide variety of medications, biometrics, symptoms, etc., and design and developing appropriate tools is not easy. I recently wrote about my own experiences in “Self-Care and Caregiving Apps Development.” After all these years of QS meetups and conferences, I can only recall one talk of caregiver tracking (a mother tracking the progress of her baby). Hopefully we’ll see much more over time.”
Please take some time to read the full report and for the data savy, take a look at the preliminary survey data and see what you can find. We would love to hear your thoughts on this new report here in our comments or on our forum.