Tag Archives: qstop

Sky Christopherson: Personal Gold

Our friend Sky Christopherson first spoke at a Bay Area QS meetup in 2012, when he unveiled an interesting discovery about sports performance, deep sleep, and room temperature, made while he was training for a cycling competition in which he set a new world record.

(You can watch Sky’s QS show&tell talk here: The Quantified Athlete.)

Sky’s experience led him on a new journey of 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 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.

 

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QSEU14 Breakout: The Future of Behavior Change

Today’s post come to us from Lukasz Piwek. Lukasz is a behavioral science researcher at the Bristol Business School, University of West England. We were happy to welcome Lukasz, who led an well attended breakout session at the 2014 Quantified Self Europe Conference where conference attendees discussed current issues and new dimensions of behavior change. We encourage you to read his description below (which first appeared on his cyberjournal, Geek on Acid) and join the conversation in our forum

The Future of Behavior Change
by Lukasz Piwek

I gave a short talk, and moderated a breakout discussion, on the future of behaviour change in the context of quantified self approach. It was an inspiring session for me so I summarised my slides here with the discussion that followed.

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First, I highlighted that behaviour change interventions require multidisciplinary approach in order to target a broad range of behaviours related to health (e.g. healthy eating, alcohol & drug use, stress management), sustainability (e.g. travel habits change, energy saving, recycling) or education.

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Health interventions are good example where behaviour change can enormously benefit from smart technology. Currently we have what we call a “sick care” model: when we notice a specific symptoms of illness we share it with our GP, and we get prescription, or we’re referred for more detailed diagnosis. This classic and dominant “sick care” model focuses on relatively passive way to manage illness “after” it occurs.

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However, in the future we can envision ourselves being empowered by smart devices that track various variables in our daily life (such as heart rate, body temperature, activity levels, mood, diet). This variables will get combined in sophisticated analysis merged with our illness history and DNA screening. This continuously provides us with information about “risk factors” for illnesses, which enables us in turn to act and change our behaviour before the onset of a disease. This is what we call a real “preventive care” model of healthcare. Clearly we’re not there yet.

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The key question we discussed was: “what critical features or solutions we are missing to make a breakthrough in behaviour change interventions with quantified self approach?” I started the discussion with giving two possible answers.

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First, we lack long-term user engagement for smart wearables and self-tracking solutions. A recent study showed that 32% of users stop using wearables after 6 months, and 50% – after just over a year. Similarly, there is a high drop rate amongst smartphone apps users: 26% of apps being used only once and 74% of apps are not used more than 10 times (although discussion pointed out that we might not need long-term engagement for many interventions).

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Second, existing devices for self-tracking lack data validity and reliability. Proprietary closed platforms and limited access APIs make it difficult for us scientists to validate how well self-tracking devices measure what they intend to measure. This is a major problem from the perspective of methodology for behaviour change interventions in clinical context.

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In the discussion that followed my presentation, the major reoccurring theme was a lack of robust and reliable feedback provided to users/clients. We agreed that new model of feedback would incorporate such concepts as: narratives, actionable advices on specific consequences of behaviour, and personalised, rapid, relevant data visualisation.

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Another problem highlighted was related to psychological resistance towards smart technologies in our lives, especially in the groups that are not motivated to use wearables/self-monitoring.

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Finally, it seems clear that we’re currently focusing on “exploratory” side of quantified self, and its important we start moving towards more “explanatory” and predictive approach, like in the healthcare example described above. This requires a development of new methodology for n=1 research and creation of data bank of personal analytics. Such bank would enable better generalisation and evaluation of results for larger-scale interventions.

I’m totally on it.

If you’re interested in the intersection of Quantified Self and behavior change we invite you to join the conversation in our forum.

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Debbie Chaves: A Librarian in Numbers

Debbie Chaves is a science and research librarian at Wilfred Laurier University and was interested in understanding her job and the various demands placed on her time. Using methods she’d employed previously she set about tracking different aspects of her work. The data she gathered allowed her to advocate for new changes and policies within her library. In this video, presented at the 2014 Quantified Self Europe Conference, Debbie explains her tracking, what she found, and what she was able to accomplish.

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This Week's QS Meetups

If you’re interested in learning more about Quantified Self, meeting new and interesting people, and being inspired by unique self-tracking projects we invite you attend a local QS meetup in your area. This week there are nine QS meet ups planned all over the world. Follow the links below to learn more. You can also find the full list of the over 100 QS meet ups in the right sidebar. Don’t see one near you? Why not start your own!

Tuesday (7/15/14)
Southern Oregon Quantified Self  (Ashland, OR)

Auckland QS Show&Tell #6

Malta QS Show&Tell #2

Wednesday (7/16/14)
Dallas/Fort-Worth QS Meetup

Cincinnati QS Hangout #1

Triangle QS Meetup (Raleigh, NC)

Washington DC QS Meetup

Thursday (7/17/14)
Silicon Valley QS Meetup #12 (Mountain View, CA)

Melbourne QS Show&Tell #6

 

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What We Are Reading

Enjoy this week’s reading list. If you’d like to submit something for future What We’re Reading posts we invite you to get in touch!

Articles
Data Journalism Needs to Up Its Own Standards by Alberto Cairo. The influx of new data-based journalistic endeavors seems to grow by the day. In this great piece Alberto Cairo presents four suggestions for those practicing that art and science of data-based reporting.

Big Data Should Not be a Faith-Based Initiative by Cory Doctorow. The idea of “big data” as a miraculous fountain of new knowledge is widespread. In this article Cory Doctorow brings to light some of the major concerns about personal data and the true possibility of de-identification.

Data Privacy, Machine Learning, and the Destruction of Mysterious Humanity by John Foreman. This is a long read, but definitely worth the time. If you’re like me you’ll spend the next few hours (day?) thinking about yourself, the various companies and organizations consuming your data, and how your life may (or may not) be shaped by the information you willingly hand over.

Privacy Behaviors of Lifeloggers using Wearable Cameras [PDF] by Roberto Hoyle, Robert Templeman, Steven Armes et al. This research paper paper offers a good glimpse into the the concerns and real behaviors of people using photo lifelogging systems. This is an area we’ve previously explored (see Kitty Ireland’s great write-up about our lifelogging town hall at QSEU13) and we expect to continue discussing.

Show&Tell
Battery Life, 6mo Checkup By James Davenport. It may seem odd to have a post about tracking battery life from a laptop here in the Show&Tell section, but this is a really neat post. As part of tracking his laptop battery he also tracked his usage and led to some interesting data about his sleep. (Don’t forget to check out the post that kicked off his battery tracking.)

Bringing My Data Together by John T. Moore. John is on a journey of improving his health and being more active through self-tracking/monitoring. In this post he pulls together some of his most important data, but I also suggest reading his summary of how he got started with self-tracking.

Visualizations

carsharing
Seven Days of Carsharing by Density Design. Not exactly personal data here, but some beautiful visualizations based on one week of data from the Enjoy, a carsharing service in Milan.

aprilzero
Aprilzero by Anand Sharma. I stumbled on this website recently via the #quantifiedself feed on Twitter. The visualizations and interactivity on this personal data site are really nice.

LR_annualreports
Lee Rogers’ Annual Reports by Lee Rogers. Lee has been tracking different aspects of his life for more than three years. Since 2011 he’s put together Annual Reports detailing his personal data. You can view his 2011, 2012, and 2013 reports on his website.

From the Forum
Devising Experiments
Looking for a General QS Device
Masters Thesis: Self-Tracking Motivations
Greetings From Germany

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QSEU14 Breakout: Best Practices in QS APIs

Today’s post comes to use from Anne Wright and Eric Blue. Both Anne and Eric are longtime contributors to many different QS projects, most recently Anne has been involved with Fluxtream and Eric with Traqs.me. In our work we’ve constantly run into more technical questions and both Anne and Eric has proven to be invaluable resources of knowledge and information about how data flows in and out of the self-tracking systems we all enjoy using. We were happy to have them both at the 2014 Quantified Self Europe Conference where they co-led a breakout session on Best Practices in QS APIs. This discussion is highly important to us and the wider QS community and we invite you to participate on the QS Forum.

Best Practices in QS APIs
Anne Wright 

Before the breakout Eric and I sorted through the existing API forum discussion threads for what issues we should highlight. We found the following three major issues:

  • Account binding/Authorization: OAuth2
  • Time handling: unambiguous, UTC or localtime + TZ for each point
  • Incremental sync support

We started the session by introducing ourselves and having everyone introduce themselves briefly and say if their interest was as an API consumer, producer, or both. We had a good mix of people with interests in each sphere.

After introductions, Eric and I talked a bit about the three main topics: why they’re important, and where we see the current situation. Then we started taking questions and comments from the group. During the discussion we added two more things to the board:

  • The suggestion of encouraging the use of the ISO 8601 with TZ time format
  • The importance of API producers having a good way to notify partners about API changes, and being transparent and consistent in its use

One attendee expressed the desire that the same type of measure from different sources, such as steps, should be comparable via some scaling factor and that we should be told enough to compute that scaling factor. This topic always seems to come up in discussions of APIs and multiple data sources. Eric and I expressed the opinion that that type of expectation is a trap, and there are too many qualitative differences in the behavior of different implementations to pretend they’re comparable. Eric gave the example of a site letting people compare and compete for who walks more in a given group, if this site wants to pretend different data sources are comparable, they would need to consider their own value system in deciding how to weight measures from different devices. I also stressed the importance of maintaining the provenance of where and when data came from when its moved from place to place or compared.

On the topic of maintaining data provenance, which I’d also mentioned in the aggregation breakout: a participant from DLR, the German space agency, came up afterwards and told me that there’s actually a formal community with conferences that cares about this issues. It might be good to get better connections between them and our QS API community.

The topic of background logging on smartphones came up. A attendee from SenseOS said that they’d figured out how to get an app that logs ambient sound levels and other sensor data on iOS through the app store on the second try.

At some point, after it seemed there weren’t any major objections to the main topics written on the board, I asked everyone to raise their right hand, put their left over their heart, and vow that if they’re involved in creating APIs that they’d try hard to do those right, as discussed during the session. They did so vow. :)

After the conference, one of the attendees even contacted me, said he went right to his development team to “spread the religion about UTC, oAuth2 and syncing.” He said they were ok with most of it, but that there was some pushback about OAuth2 based on this post. I told him what I saw happening with OAuth2 and a link to a good rebuttal I found to that post. So, at least our efforts are yielding fruit with at least one of the attendees.

We are thankful to Anne and Eric for leading such a great session at the conference. If you’re interested in taking part in and advancing our discussion around QS APIs and Data Flows we invite you to participate: 

You can sign up for the QS Toolmakers List
You can take part in ongoing discussions in the API Forum Thread .
And lastly, you can comment on this particular breakout discussion here

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Justin Timmer: A Lazy Workout

Justin Timmer is a student in human movement science and a fitness instructor. He was interested in exploring what he could do to increase his strength. Rather then starting with a typical strength training program Justin wanted to test if isometric muscle contraction alone could increase his strength. This type of exercise involves just squeezing the muscles without using any weight. He even went so far as to only target one side of his body so that he could test against his non-squeezing muscle groups. In this talk, presented at the 2014 Quantified Self Europe Conference, Justin explains his process and the results of this 4-week experiment.

What did you do?
For four week, I was “squeezing” (isometric contractions) my muscles four times a day. I trained my right leg, abdominals, and right chest and arm.

How did you do it?
During every quiet moment during the day I contracted my muscles as long and hard as possible. I quantified my progress by completing maximum repetitions on a fitness machine every week.

What did I learn?
I learned that in four weeks I almost doubled my force on the right side of my body. But I also learned that this training was going too fast, I got a lot of issues with little unexplained pains in my legs, and rising fluids whenever I contracted my abdominals. Overall I learnt this was a very effective training that was very easy to implement in my daily life.

You can also view Justin’s slides here.

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Ellis Bartholomeus on Tracking Food with Photos

At the start of 2013 Ellis Bartholomeus decided to start keep track of her life. Since her friends were always asking about her eating habits (she was a consistent traveler and rarely at home) she decide to start tracking her food. Instead of entering in her food into a calorie counting app she started taking pictures of everything she ate. In this talk, presented at the 2013 Quantified Self Europe Conference, Ellis describes her process and some of the interesting things she learned along the way. I was especially interested to hear how these pictures served to act as “anchors” for other things going on in her life:

It became a great way to remember how I spent my days, where I was, with whom. These pictures are very clear reference, they work like anchors in my memory.  It is very joyful to browse through the month food-wise since dinner and breakfast are so often a social occasions, and I was reminded of great conversations and situation while looking at the picture.

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Jamie Aspinall on Learning From Location Data

Jamie Aspinall was interested in what his location history could tell him. As a Google Location user, his smartphone is constantly pinging his GPS and sending that data back to his Google profile. Using Google Takeout Jamie was able to download the last four years of his location history, which represented about 600,000 data points. In this talk, presented at the London QS meetup group, Jamie describes his process of using a variety of visualizations and analysis techniques to learn about where he goes, what causes differences in his commute times, and other interesting patterns hidden in location data.

You can also view his presentation here.

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What We Are Reading

We’ve assembled another great list of articles, posts, and other interesting ideas for you to enjoy.

Articles
Billy Beane’s Ascendant A’s Are Playing a Brand-New Brand of Moneyball by Will Leitch. I know what you’re thinking, “What’s an article about baseball doing in this list?” First, it’s about how the Oakland Athletics are using metrics to improve their team. And two, I was struck by the following:

“Instead, Beane and his front office have bought in bulk: They’ve brought in as many guys as possible and seen who performed. They weren’t looking for something that no one else saw: They amassed bodies, pitted them against one another, were open to anything, and just looked to see who emerged. Roger Ebert once wrote that the muse visits during the act of creation, rather than before. The A’s have made it a philosophy to just try out as many people as possible—cheap, interchangeable ones—and pluck out the best.”

Sounds a lot like our old friend, Seth Roberts, describing the value of self-experimentation - start small, do a lot of them, learn by doing.

Build Great Models . . . Throw Them Away by Mark Ravina. A digital humanities researcher makes the case for using data and statistical methods of modeling not to answer questions, but to come up with better questions. Really enjoyed the great examples in this post.

App data reveals locations, times and distances of Calgary’s runners and cyclists by Meghan Jessiman. A collaboration between RunKeeper and the local Calgary Herald newspaper led to some interesting findings and, of course, some activity heat maps.

A Digital Dose of Magic Medicine by Naveen Rao. Naveen connects the dots between the recent controversy surrounding Doctor Oz to the possibly misplaced hopes we’re putting in tools like HealthKit.

9-Volt Nirvana by RadioLab. This episode of the always interesting RadioLab tells the story of a journalist who was hooked up to a tDCS device for a sniper shooting exercise. The device helped her accuracy in the simulation, but then there was an unexpected after-effect. For three days afterward, the voices of self-doubt and self-abnegation receded from her consciousness. She talks about that experience directly on her blog. (Thanks to Steven Jonas for sending this one in!)

Tracking Sleep With Your Phone by Belle Beth Cooper. A great roundup here of iOS and Android apps you can use to track sleep. I especially appreciated the nice discussion of the current limitations of using mobile apps to track and understand sleep.

From Missiles To The Pitch: The Story Behind World Cup Tech by Melissa Block and NPR. If you’re wondering how FIFA is able to track the movement of individuals players during this year’s World Cup then this is for you. You can also check out all the data on FIFA’s website here.

Show&Tell
Productivity, the Quantified Self and Getting an Office by Bob Tabor. Bob works at home and was curious about how productive he really was. After using RescueTime he realized maybe he wasn’t getting the productive time he really need.

Basis to Roambi by Florian Lissot. Florian wanted to explore his Basis data. After using Bob Troia’s great data access script and some additional tools to aggregate multiple files he was able to create some great visualizations with Roambi and learn a bit more about his daily patterns of activity.

Do you have a self-tracking story you want to share? Submit it now!

Visualizations
losangeles-transport
How We Move in Cities by Human.co. It seems that making heatmaps based on movement is all the rage these days. Human has gone one step further than previous entries in this category by including motorized travel alongside cycling, walking, and running data. Don’t forget to check out the amazing GIFs as well.

cecinestunedataviz
This is Not a Data Visualization by Michael Thompson.

“[...] visualizations are not the data. The data is not the sum of the experience. We’ve been inappropriately using data visualizations as the basis for statements and conclusions. We’re leaving out rigorous statistical analysis, and appropriate qualifiers such as confidence intervals. It’s exciting that we’ve become more and more a society of pattern-seekers. But it’s important that we don’t become lazy and cavalier with what we do with those observations.”

MSFTdataviz
Reflections on How Designers Design With Data [PDF] by Alex Bigelow, Steven Drucker, Danyel Fisher, and Miriah Meyer. Researchers from Microsoft and the University of Utah sought out to understand how designers go the process of understanding data and creating unique visualizations.

Do you have a QS data visualization you want to share? Submit it now!

From the Forum
Best passive GPS Logger?
Quantified Baby
Android App for Self Surveys

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