Tracking the Recovery Process: Learn from Maggie Delano at QS15

Join us at our upcoming QS15 Global Conference and Exposition on June 18-20 in San Francisco to learn how to use self-tracking tools to aid in recovery and get back into a productive lifestyle without overdoing it.

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Maggie Delano was diagosed with Post Concussion Syndrome (PCS) after hitting her head over Labor Day Weekend. To recover, she had to give her brain a break from anything too cognitively stimulating, such as using screens, reading, intense physical exercise, and loud music. Maggie was able to return to work after several weeks off. She has developed strategies that balance getting work done with giving her brain a rest and preventing burnout. She’s been using apps such as Awareness to keep her mindful of how much time she’s spending on her computer, HabitRPG to slowly build back up her prior habits and self-tracking, and Beddit to help figure out what allows her to sleep best.

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Maggie’s session is just one of the many hands-on, up-to-date, expertly moderated sessions we’re planning for the QS15 Global Conference and Exposition. This year, QS15 is going to be two full days of self-tracking talks, demos, and in depth discussion, followed by a third day for a grand public exposition of the latest self-tracking tools. Join us at the Fort Mason Center on the San Francisco Waterfront. We’ve made some early bird tickets available for readers of the Quantified Self blog (for a limited time): Register here!

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Meetups This Week

There’s a great group of Quantified Self meetups occurring this week. The original QS group will be convening in Mountain View. The event will include talks on tracking punctuality and measuring heart-rate variability during an n-back test. If you live in the Bay Area, be sure not to miss it!

Tuesday, December 2
Bay Area, California

Wednesday, December 3
Pittsburgh, Pennsylvania

Thursday, December 4
Köln, Germany

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What We Are Reading

Articles
LifeLogging: Personal Big Data by Cathal Gurrin, Alan Smeaton, and Aiden Doherty. A wonderful overview of the field of lifelogging. Special attention is given to how information retrieval plays a role in how we can understand and use our lifelogs.

What happens when patients know more than their doctors? Experiences of health interactions after diabetes patient education: a qualitative patient-led study by Rosamund Snow, Charlottle Humphrey, and Jane Sandall. In this qualitative study, the authors engaged with 21 patients with type 1 diabetes who had developed expertise about their condition. Some interesting findings about how healthcare providers may be uncomfortable with patient who understand themselves and their condition. (Thanks to Sara Riggare for sharing this article with us!)

Internet of You: Users Become Part of the City-as-a-System by Tracy Huddleson. An good look into how wearables and personal technology might have an impact on the public infrastructure, institutions, and spaces.

Welcome to Dataland by Ian Bogost. Not sure how I missed this one piece from late July, but glad I stumbled across it this week. Ian Bogost takes a tour through the actual and imagine implications of the Disney Magic Band. I especially enjoyed the historical context describing the history of futurism at Disney.

Show&Tell
Gary Wolf on Cool Tools Show #15. QS co-founder, Gary Wolf, speaks with Mark Frauenfelder and Kevin Kelly on the Cool Tools Podcast about his favorite self-tracking tools and what he’s learned from using them.

My Life Analysis Spreadsheet by Reddit user batting_1000. A really interesting example of using Excel to track various aspects of daily life. Make sure to click through for the example screen shots.

Visualizations
BasisInterstellarMy heart rate during Interstellar (via Basis Peak) by Reddit user javaski. An nice use of the BasisRetreiver tool to download and analyze heart rate data from the new Basis Peak device.

Seasonal-compliance1-1024x576Activity Time vs. Device Wear Time by Shannon Conners. Shannon plotted her actual wear time using the BodyMedia Fit against the activity data to show that low activity numbers are probably caused by hotter summer months when wearing the armband caused unwanted tan lines.

“If I had not explored my activity and usage data first to remind me of this usage pattern, I could have created any number of plausible explanations for why my activity levels were so much lower during the hot North Carolina summer months.”

From the Forum
Wearable for Stress – Olive
Necklace worn fitness trackers?
MoodTraces App
New Heart Rate Variability and Coherence Papers
Artistic Apps

This Week on QuantifiedSelf.com
Ben Finn: Improving My Sleep
Nan Shellabarger: 26 Years of Weight Tracking

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Nan Shellabarger: 26 Years of Weight Tracking

“When I look at this, this is the story of my life in these years.”

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Nan Shellabarger has been tracking her weight for 26 years, including almost daily tracking since 1998. In the talk embedded below, presented at the Washington DC QS meetup group, Nan describes her experience with diving deep into how she’s making sense of her weight data. By looking over her complete history and layering in her personal contextual data she was able to find how different life events played a role in weight loss and gain. For example, she found that physical challenges and events were “tremendous motivation to get out there and doing things as well as helping me focusing on my eating.” Nan has also used a variety of activity trackers since 2010, starting with the Body Media Fit and now the Garmin Vivofit and Jawbone UP. These devices helped her explore calorie expenditure as it relates to her weight loss. On the other side of the equation, she also explored how diet tracking influenced her weight. Watch her great talk below to hear the whole story.

We hope to see an update of this great talk when Nan joins us at our QS15 Global Conference and Exposition next June in San Francisco. Early bird tickets are available for a limited time. Register now!

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Ben Finn: Improving My Sleep

Benn Finn has been battling issues with his sleep ever since he was a teenager. His sleep was suffering from the usual problems we’ve all faced: taking too long to get to sleep, waking up too often, waking up late, and being tired during the day. He made plan to fix his issues by researching what affects sleep and then experimenting to find out what worked for him. For four months he tracked his sleep using Sleep Cycle along with 21 factors that he thought might affect his sleep. He also created a “sleep quality” score based on 5 different data points, including data from the Sleep Cycle app. In this talk, presented at the London QS meetup group, Ben describes his experiments, what he learned from analyzing his data, and how he finally ended up fixing his sleep issues. (Special thank you to Ken Snyder for his valuable work documenting the talks at QS London.)

BenFinn_Sleep
Slides are also available here.

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Meetups This Week

There will be two Quantified Self meetups this upcoming Thanksgiving week (that is, if you are in the United States).

To see when the next meetup in your area is, check the full list of the over 100 QS meetup groups in the right sidebar. Don’t see one near you? Why not start your own!

Tuesday, November 25
Houston, Texas
Vienna, Austria

Here are photos from meetups last week in Tokyo, Washington D.C. and Portland:
Tokyo600_431698740Tokyo600_431698738 Tokyo600_431698716Tokyo600_431698718 DC600_431710176PDX600_431775992

 

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What We Are Reading

Have a great time exploring these links, posts, and visualizations!

Articles
At Quantified Self, I forget I have Parkinson’s by Sara Riggare. Sara is a longtime member of our worldwide QS community and this heartfelt post about her experience at our conferences was wonderful to read. Experience the conference yourself and meet Sara at our QS15 Global Conference and Exposition. Register here 

Standards for Scientific Graphic Presentation by Jure Triglav. Jure is a doctor, developer, and researcher interested in how data is presented in the sciences. In this post he goes back in time to look at previous standards for presenting data that have largely been forgotten.

Painting with Data: A Conversation with Lev Manovich by Randall Packer. In this great interview, researcher, artist, and visualization expert, Lev Manovich, explains his latest work on exposing a window onto the world through photos posted to popular social apps.

Big Data, LIke Soylent Green is Made of People by Karen Gregory. A thoughtful essay here on automation, algorithmic living, and the change in value of human experience.

“In the production of these massive data sets, upon which the promise of “progress” is predicated, we are actually sharing not only our data, but the very rhythms, circulations, palpitations, and mutations of our bodies so that the data sets can be “populated” with the very inhabitants that animate us.”

When Fitbit Is the Expert Witness by Kate Crawford. I almost didn’t include this article in this week’s list. The story has been circulated so many times around the web this week, mostly without any real thought or examination. However, I found that Kate Crawford did a good job putting this news in context without resorting to sensationalism.

How California’s Crappy Vaccination Policy Puts Kids At Risk by Renee DiResta. A bit of a sensational title, but a great post that uses a variety of open data sources to showcase a growing concern about childhood vaccination policies in California.

Show&Tell
How I Used RescueTime to Baseline My Activity in 2014 and Set Goals for 2015 by Jamie Todd Rubin. I’ve been a big fan of Jamie’s writing since I found it earlier this year. He’s voracious self-tracker, mostly related to his tracking and understanding his writing, and this post doesn’t disappoint.

Sleeping My Way to Success with Data by Pamela Pavliscak. A great post by Pamela here about her experience starting tracking her sleep with the Sleep Cycle app. A great combination of actual data experience and higher-level thoughts on what it means to interface with personal data. I especially love this quote referencing her experience interacting with other sleep trackers,

“And they are doing the same thing that I’m doing — creating data about themselves, for themselves.”

Visualizations
IntoTheOkavango
Into the Okavango by The Office for Creative Research. A really neat interactive project by researchers, scientist, and the local community to document an expedition into the Okavango Delta in Botswana.

Strava
A Day in the Bike Commuting Life by Strava. The data science team at Strava put together a neat animation comprised of one-day of cycling commutes in San Francisco. Unsurprisingly, the Golden Gate Bridge is quite popular among cyclists.

From the Forum
Sleep tracking for new parents
Different Approach to ZEO Headband
Hello Everybody!
MyStress
New Self-Quantifier

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Bryan Ausinheiler: Diet and Digestion

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Bryan Ausinheiler was experiencing gastrointestinal issues for years and decided it was time to figure out what was causing it. By precisely controlling his diet – eating exactly the same quantities at exactly the same time – for a month and then measuring the quality of his stool in a self-designed spreadsheet he was able to create a baseline dataset to better understand his issues. Bryan then developed an experimental protocol that included “elimination and diet variations to figure out the cause of my frequent (3-5x/day) loose stools.” It turns out that “eating too many sunflower seeds was the main culprit.” Watch Bryan’s fascinating talk, presented at the Bay Area QS meetup group, to learn more about his process, and how he tackled self-experimentation and data collection.

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Heart Rate Variability and Stress: Learn from Paul LaFontaine at QS15

PLF_StressSessions

Join us at our upcoming QS15 Global Conference and Exposition on June 18-20 in San Francisco to learn how heart rate variability can indicate how relaxed or stressed you feel when meeting with other people.

PaulLFWe’re pleased to have a self described heart rate variability hacker and veteran QS’er Paul LaFontaine share how he uses heart rate variability readings to improve his effectiveness when engaging in discussion with others. Paul used off the shelf technology including a Polar Heart Rate Belt and a Heart Rate Variability Logger app to record his heart rhythms during different stressful and relaxing activities.  Once he had these baseline readings, he compared them to hundreds of hours of meetings to find patterns in the data and to pinpoint what was associated with relaxed, productive discussions or stressful, less productive interactions. Some of the source of stress may surprise you!

Paul’s Heart Rate Variability session is just one of the many hands-on, up-to-date, expertly moderated sessions we’re planning for the QS15 Global Conference and Exposition. This year, QS15 is going to be two full days of self-tracking talks, demos, and in depth discussion, followed by a third day for a grand public exposition of the latest self-tracking tools. Join us at the Fort Mason Center on the San Francisco Waterfront. We’ve made some early bird tickets available for readers of the Quantified Self blog (for a limited time): Register here!

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Quantifying in the Classroom

VL_improvingdata

Victor Lee makes data fun for kids. Victor is an assistant professor at Utah State University, where he’s been working on ways to bring the Quantified Self experience into high school classrooms to improve data literacy and expose students to “more authentic forms of inquiry.”

I first met Victor at the 2013 Quantified Self Global Conference, where we had a great conversation about QS and education. A few weeks ago, I saw this wonderful presentation of his research (a video is embedded below). I couldn’t resist the chance to ask him a few questions about his work.

If you are a teacher or a parent interested in using QS as a way to get kids into math and science, we want to especially invite you to QS15: The Quantified Self Global Conference and Exposition, where QS and Education is an important theme.

QS: What inspired you to work on data literacy as a subject area?

victor_profileVictor Lee: On the one hand, the first time I had a wearable device (a heart rate monitor, several years ago), I was impressed with the data I could receive and what kind of inferences I could make from them. This made me aware of an opportunity with the technology. At the same time, I know from my training and work as a learning scientist that data can be notoriously difficult for students (and adults) to understand and use. I have done a lot of work in the area of science education previously, and finding ways for kids to meaningfully work with and learn from data is one of the big challenges that have been documented for quite some time. Plus, the topic of data literacy is timely in that we have much greater access to data in so many forms, whether it is charts showing how the Earth’s climate is changing or if it is infographics that make the rounds on the internet. There is a lot of talk about “big data”, and if we are going to have the next generation of workers and citizens be ready to work with all of that data, we really need to work on supporting that literacy now.

QS: Your research involves students using wearable devices to generate and explore their own data. Why is having their own data an important part of this process?

VL: Informally, I talk about the benefit of wearables as helping to make it so that students have some “skin in the game”. Data that is about you is consequential because at some level, it reveals something about who you are and the things you do. In some sense, there is something inherently interesting about learning more about yourself and also comparing yourself against others or against the goals that you have set. But beyond that, there is a lot to leverage in the way of learning. One of the big ideas out of education and the learning sciences, and one that I can’t reiterate enough, is the importance of building on prior knowledge. When you look at data about yourself, you don’t strictly see points or dots or bars. You see a depiction of an experience or activity that is already intimately familiar. In some sense, you know why the data look the way that they do. If it’s exercise data, you remember what certain moments felt like. You have some expertise on how your body works and that creates some expectations and support for thinking with data. That is a really important bit of personal knowledge to leverage.

Wearables are also useful in that they expose students to some of the messier things that come along with data. You have to realize that a tracking device is doing some form of measurement and measurements are prone to some error. You get so much data that you can start to see typicality and patterns. Data are hard to collect, but being able to wear something that collects data in the background means you can collect a lot of data and see what regularity looks like. You get to see what noise looks like. You get to know what is an outlier and how that fits against the larger set of data. Too often in school, we are given these very sanitized data experiences and students don’t get to think through these things or experience them in a familiar or meaningful way.

QS: Each of your examples that you discuss during your talk were student-led and defined experiments. What role does allowing students to ask the questions play in the learning process?

VL: Students are inherently curious. They have their own interests and things they care about that speak to their experiences and their concerns. A student-centered approach to instruction tries to capitalize on that. That means looking to students for questions. It also helps to put some more skin in the game. But beyond that, it helps in learning how to actually do science. If you ask most science educators, the big goal is not to memorize facts and terms but to know how science is done and how things move from questions to data to conclusions to new questions and so on. This is critically important, and there is an even greater push for new technologies and new models in education to support this. It certainly is not easy, but I think that it is worthwhile and that the kids who get to really do it find it worthwhile too.

QS: It appears that your central thesis is “When you give them the opportunity, kids can learn hard things.” Clearly from your examples this is true. Outlier analysis, data visualization, and pattern recognition are all present. What makes the methods you’re exploring so impactful?

VL: I suspect it has to do with how the students are able to leverage their own experiences and interests. It is memorable but also consequential to something that they already do or encounter. It lets them sit in the driver’s seat when they often are put in situations that makes them more of a passenger. In fact, I think that is one of the interesting things about the Quantified Self movement. In some respects, it is increasing access to data and making it possible for people to do aspects of science or mathematics or statistics in ways that are meaningful to them.

I do also want to credit some really remarkable teachers and schools that allow for activities like this to happen. Especially in this day and age with an intense focus on testing, students don’t get as many opportunities to be curious in this way. Having motivated and flexible teachers on board certainly helps make this impactful.

QS: I was really struck by the anecdotes you shared that showed how strongly the students were affected by the lessons plans (Note: fast forward to 42:40 in the video above for a great example.) Are there any other stories that come to mind that help illustrate how students engage with these type of personal data based curriculums?

VL: We recently finished a project with a school we had never worked with previously. There were some students at this school who were really disappointed that the unit ended and they would not be able to keep working with the wearable devices that we provided (in this case, Fitbit Flex wristbands). I know one student that we worked with who was so enthusiastic that he wanted to keep on doing data collection and analysis using the kinds of tools we provided and would pull a member of my research team aside to help him plan how to keep working with data after the unit had ended. I know another student was really distraught that he was going to be absent on a day that he was set to share what he had did with activity data with the rest of the class. The teacher ended up extending things another day so that student could be there and still share his discoveries.

I may have mentioned this in the presentation, but the students who discovered outlier sensitivity were so enthusiastic about what they learned that after we shut down the cameras and were leaving, they began to boast to the other students what they had figured out and proceeded to show them how outlier sensitivity worked.

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QS: You’ve done some work with trying to implement QS meetups in schools and in younger age groups. What have you learned from that experience?

VL: This has been an interesting side project. Basically, I wanted to give some students who did not have the opportunity to experience QS (in this case, some very talented and motivated high school Latina girls). Those students were terrific to work with, but the experience raised some interesting challenges and concerns about how much background infrastructure that needs to be in place to be a QSer. While they all had access to mobile devices, they were not the cutting edge. Some did not have wifi at home or bluetooth. They also felt that the latest and greatest wearable devices, while cool, didn’t fit with their aesthetic. And they had some constraints on their life circumstances that limited how much they could experiment with the devices. I presented some of these findings at a workshop as part of a recent ubiquitous computing conference. There is the potential for several potential benefits with QS being accessible to youth, but I think that this is a population with very different needs and concerns than those who are early adopters. If we want QS to be something that could be of value to youth beyond a classroom curriculum, we need to do some more targeted research and development. That’s generally something that I would be glad to pursue more in the future, as I imagine are many of my professional colleagues.

QS: Lastly, what new ideas and projects are you excited about?

VL: I am excited to do a more detailed analysis of what students learned from our most recent launch of a wearables-based data unit in the sixth grade. I am excited to potentially extend some of our findings to other grade levels and finding the best ways to address how self data could be useful in supporting rich student learning. I have been generally intrigued by the QS movement and have been trying to understand why people self-track and what they end up doing with the data they collect. There are other project in my field that I think are outstanding. At UC Davis, there is some work to get self data as input into digital games. We are actually starting to explore similar issues at Utah State. There is a neat project at CU-Boulder with kids building their own infographics, and I would love for self collected data to gradually become a part of that. I have had some side conversations with some organizations who have been thinking about wearable sensors in schools, and if those conversations continue and we are able to share what we have learned, I think there is much to be excited about in that area.

We want to thank Victor for taking the time to speak with us about his work. If you’re interested in learning more about Victor’s research we invite you to visit his faculty page and read some of this great research papers, a selection of which are linked below:

Quantified Recess: Design of an activity for elementary students involving analysis of their own movement data.
Integrating physical activity data technologies into elementary school classrooms
The Quantified Self (QS) Movement and Some Emerging Opportunities for the Educational Technology Field

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