Adam Johnson: QS Bits & Bobs

AdamJohnson_LifeLogs

This is Adam Johnson’s third QS talk. Previously he’s discussed the lifelogging tool he developed and uses and how he re-learned how to type in order to combat RSI. In this talk, Adam gives an update to his self-tracking focused on three areas: tracking an long-distance cycling trip, his streamlined lifelogging process, and how he’s using the Lift app to track his habits.

What Did Adam Do?
In general, Adam is dedicated lifelogger who’s been tracking what he’s doing for over a year. Adam cycled 990 miles from Lands End to John O’Groats with his father and brother over 14 days and tracked it along the way. Because he wasn’t able to “lug around his Mac” to complete his regular lifelogging he decided to update his custom system to accept photos and notes. Lastly, he added habit tracking to his daily lifelogging experience by using the Lift app.

How Did He Do It?
Adam tracked his long distance cycling journey by using Google location history and a Garmin GPS unit. He was able to export data from both services in order to get a clear picture of his route as well as interesting data about the trip.

He also updated his lifelogging software so that it could accept photos and notes he hand enters on his phone. The software, available on GitHub, gives him an easy way to track multiple event such as how often he drinks alcohol and how much he has to use his asthma inhaler.

Lastly, Adam tracked the daily habits he wanted to accomplish such as meditating, reading, making three positive observations, and diet, using Lift.

What Did He Learn?
Everything Adam learned is based on his ability to access and export his data for further analysis. From his cycling trip he was able to make a simple map to showcase how far he traveled based on Google location history (which did have some issues with accuracy). He also was able to see that he traveled 1,004 miles, cycled for 90 hours, burned 52,000 calories, but didn’t lose any weight.

Using his updated lifelogging system, he was able to explore his inhaler use and after a visit to the doctor was able to “find out a boring correlation” that a preventative inhaler works and his exercise induced inhaler usage went to almost zero.

Finally, because Lift supports a robust data export, Adam was able to analyze his habit data and began answering questions he was interested in, but aren’t available in the native app experience. He found that seeing a visualization of his streaks as a cumulative graph was inspiring and motivating. He also explored his failures and found that Saturdays, Sundays, and Mondays were the days he was most likely to fail at completing at least one of his habits.

Slides of this talk are available on Adam’s GitHub page here.

Tools
Google Location History, Garmin GPS, Lifelogger, Lift, Photos, Notes

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Meetups This Week

There is one QS meetup this week and it is a good one. The group in Zürich will feature a talk on using movement, EKG and EEG sensors to better understand one’s sleep phases.

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 (October 14)
Zürich, Switzerland

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What We Are Reading

A long one this time. Enjoy the words, numbers, and images herein.

Articles
New biometric tests invade the NBA by Pablo S. Torre and Tom Haberstroh. Data and statistics are nothing new in professional sports. They’ve even made Academy Award nominated movies based the idea that data can help a team win. Until now data on players and teams has come from analysis of practices and gameplay. This great piece opens another discussion about collecting even more personal data about how players in the NBA live their lives off the court. Recall that athletes, coaches, and owners have been talking about out of game data tracking since 2012.

Misleading With Statistics by Eric Portelance. We’ve featured these type of articles before, but the example used here by Eric is not to be missed. So many times the data visualization trumps the actual data when a designer makes editorial choices. After reading this piece you’ll think critically the next time you see a simple line chart.

Handy Tools & Apps by Ray Maker. A great resource for athletes and exercisers who use a variety of tools to capture, export, and work with the activity and workout data we’re collecting.

You are not your browser history by Jer Thorpe. If someone you didn’t know was given a record of every ad served to you in your browser, what would they say about you? Who would they think you were? Jer Thorpe actually put these questions to the test as part of his work with the Floodwatch project. Floodwatch is a very interesting tool (browser extension) that saves and visualizes the adds you see while you browse. They also have a clear privacy policy including giving you access to your data.

Show&Tell
Happiness Logging: One Year In by Jeff Kaufman. A great post here about what Jeff has learned about himself, what is means to log something like “happiness”, and the power of tagging data. After looking at his data, and a commenter’s from the r/quantifiedself subreddit, I’m wondering about the validity of 10-point scales for this type of self-tracking.

Redshit/f.lux Sleep Experiment by Gwern. Our esteemed friend and amazing experimenter is back with another analysis of his sleep data. This time he explains his findings from using a program that shifts the color temperature on his computer away from blue and towards red.

I ran a randomized experiment with a free program (Redshift) which reddens screens at night to avoid tampering with melatonin secretion and sleep from 2012-2013, measuring sleep changes with my Zeo. With 533 days of data, the main result is that Redshift causes me to go to sleep half an hour earlier but otherwise does not improve sleep quality.

Make sure to join the discussion on the forum!

Visualizations

Joost_3yr
3 Years of computing by Joost Plattel. Our good friend and Amsterdam QS co-organizer, Joost Plattel takes a look at three years of running Lifeslice.

ScheduleAbstracted_MMcD
Schedule Abstracted by Mike McDearmon.

Even a hectic schedule can have a sense of serenity with all text, labels, and interface elements removed.

LocationHistory
Location History Visualizer by Theo Platt. The data above is actually my full Location History from Google Takeout. Theo made this simple and fast mapping visualization tool. Try is out yourself!

Lifelogging Lab. No visualizations here, but if you’re a designer, visualizer, or just have some neat data then you should submit it to this sure to amazing curated exhibition.

From the Forum
The ethics of QS
Call For Papers: HCI International 2015 Los Angeles
Pebble for Fitness Tracking
QS Business Models
QS, Light, Sleep, Reaction Timing, and the Quantified Us
Are you using your data to write a reference book or tell a story?

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Cliff Atkinson: Storyboarding the Psyche

CliffA_Storyboard

Cliff Atkinson is a consultant who helps people tell their stories and showcase their data in clear and understandable ways. It’s no surprise that when he became interested in understanding himself he turned to his experiences with visual storytelling. In 2012, at a New York QS meetup, Cliff spoke about how he’s embarked on a project to “quantify the “unconscious.”

What Did He Do?
Cliff began this project because he was noticed that there were “recurring patterns of procrastination and motivation” going on in his life. He began trying to understand them by turning to the large body of literature on human psychology. Then he asked himself, “Would it be possible to use some quantitative methods to track what was happening.” Using what he’d learned in his research and his experiences he decided to track his body, emotions, and mind.

How Did He Do It?
Cliff used his expertise and knowledge around visual storytelling to create an interesting system of visual diaries with which he could record information in his three areas of interest: the body, emotions, and the mind. Using Penultimate, and iPad app for sketching and notation, along with some clip art, he tracked physical, emotional, and cognitive events.

What Did He Learn?
The process of creating a space to reflect and record how he’s feeling across these three chosen domains has created a space for Cliff to better understand himself and how his mind works. This is still a work in progress and it sounds like Cliff is still exploring how to better understand the data he’s capturing over a longer period of time and even correlating it with other information such as his work and speaking engagements.

“One of the models for therapy is that somebody else helps you. I think with the quantified self and the things we’re doing we can take some of that power into our own hand and start to come to some personal understanding of what’s going on in our own lives.”

Tools
Penultimate

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Steve Zadig: Life in the Fast Lane

Steve Zadig is the COO of Vital Connect, but when he’s not busy with his job he’s out racing high performance vehicles. In this talk, presented at our 2013 Global Conference, Steve explains how he uses data to help him achieve his racing goals.

What did Steve do?
Steve wanted to get more information about how his body was reacting during racing. Frustrated that he was getting a lot of diagnostic data from his car and not any from himself he sought to track different biometrics to see what he could learn about what happens while he’s behind the wheel.

How did he do it?
Steve wore a Vital Connect patch to record and transmit his respiration rate, heart rate, and stress levels while he was was racing.

What did he learn?
After the race Steve was able to match the data with specific points and events during the race. He learned how something major, like spinning out of control, caused a large spike in stress, and how when he’s feeling in the zone his body responds with a lower heart and breathing rate.

“It’s about knowing. It’s about the knowledge of what’s happening with your body and how to deal with that.”

The QS15 Conference and Exposition is fast approaching. We invite you attend and give show&tell talks just like this one about your tracking and personal data experiences.

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Meetups This Week

This week, there will be three wonderful Quantified Self meetups in three countries on two continents. A couple of the groups will try a different format from the classic Show&Tell program. The Indianapolis group will get lunch and watch Show&Tell videos. Meanwhile, the Oxford group will have a working session where the attendees will explore a person’s data set. I love to see this experimentation, as organizers figure out what kind of format works for their group.

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!

Monday (October 6)

Oxford, England

Thursday (October 9)

Oslo, Norway

Saturday (October 11)

Indianapolis, Indiana

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What We Are Reading

We’ve put together an nice list of articles for you to enjoy this weekend. As always, please get in touch if you have something you’d like us to share!

Articles
Finding Patterns in Personal Data by Kitty Ireland. Another great post from Kitty about using personal data to uncover interesting, and sometimes surprising, patterns. Some great examples in this post!

The Tale of a Fitness-Tracking Addict’s Struggles With Strava by Jeff Foss. Just because you can track, and you can get something out of it, might not mean you should. (I had a similar experience on a recent trip to Yosemite so this article was quite timely.)

Algorithmic skin: health-tracking technologies, personal analytics and the biopedagogies of digitized health and physical education by Ben Williamson. Quantified Self and self-tracking tools are not limited to only being used by conscious and willing adults. They’re also being developed for and used by a growing number of children and adolescents. What does this mean of health and fitness education, and how should we think about algorithms in the classroom and gym?

Seeing Ourselves Through Technology: How We Use Selfies, Blogs and Wearable Devices to See and Shape Ourselves by Jill Walker Rettberg. I just started this book and it appears offer some interesting perspectives on the current cultural shift toward technically mediated representation. The book includes a chapter on Quantified Self and is available for download in PDF and EPUB under a CC BY license.

Show&Tell
Why Log Your Food by Amit Jakhu. Amit started tracking his food in March (2014) and has since learned a few things about his preconceived notions about his diet, food, and what it takes to keep track of it all.

Even When I’m active, I’m sedentary by Gary Wolf. Gary and I used our recently released QS Access app to download his historical step data. Using some simple charting in Excel we found some interesting patterns related to his daily movement.

Visualizations
SleepJewel
When Do I Sleep Best by Jewel Loree. Jewel presented her sleep tracking project at a recent Seattle QS Meetup. The image above is just a small piece of a great set of visualizations of her data gathered with SleepCycle and Reporter apps.

20min
Lightbeam Visualization by Simone Roth. Interesting tool described here to track how your data and web activity is being tracked. You can check out the Firefox extension here.

Minard It’s About Time by Hunter Whitney. A nice post here about the different methods of visualizing temporal data.

Respiration Machine 0.3 (bellows) by Willem Besselink. A neat physical visualization and art project that represents breath using a Hoberman sphere.

From the Forum

There has been a lot of great discussion on the forum lately. Check out some of the newest and most interesting topics below.

QS Access App
Hypoxic – An App for Breathing Exercises with HRV Tracking
Sleep Tracking & Hacking Google Hangout
Personal Analytics Service for Software Developers
Using Facial Images to Determine BMI
The Right Tool? (tracking and plotting sleep)

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QS Access App: See your HealthKit Data in a Table

On Wednesday this week we learned that the QS Access app we submitted to the Apple store was approved. This means you can download the QS Access app on iTunes. We hope you’ll find it useful. Our app is a very simple tool for accessing HealthKit data in a table so that you can explore it using Numbers, Excel, R, or any other CSV compatible tool.

It is still early days for HealthKit, but my conversations with toolmakers at Quantified Self events convinces me that there will be many device and software makers that integrate with Apple’s platform for collecting and analyzing personal data. I hope this will allow more people to learn from their own data by reflecting on changes over time and by combining multiple data streams – such as activity, sleep, and nutrition – into a single visualization for comparison.

To give you your HealthKit data in tabular format, we’ve had to simplify it. QS Access shows your data in either “hourly” or “daily” chunks.  These won’t be appropriate for all uses, but many interesting questions can be asked of data that is presented as a time series using hourly and daily values. This is just a starting point, and we’re looking forward to making it do more based on your feedback.

We very much hope that if you learn something from your data using QS Access, you’ll share your project by participating in a Quantified Self Show&Tell meetup and by joining us at QS15 Conference and Exposition next year in San Francisco. Suggestions about the app itself and interesting examples of usage can be shared with us directly by emailing us: labs@quantifiedself.com,

Read a short example of using QS Access to look at my activity data.
Find Support for QS Access in the QS Forum.

The QS Access App was authored by our long time QS Labs friend and collaborator Robin Barooah.

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Christel De Maeyer: My Journey With Sleep Monitoring

Like many people, Christel de Maeyer felt that her sleep could be better. Presenting at our 2013 conference in Europe, Christel shares what she learned from collecting over three years of sleep data.


What did Christel do?
Christel tracked her sleep for 2 years with various devices. She tested the effects of different variables on her sleep quality, including consumption of alcohol, keeping a consistent wake time and changing her mattress.

How did she do it?
She used the Zeo to track sleep for two years, before switching over to a BodyMedia device. While making changes she monitored how her sleep data changed, as well as how she felt.

What did she learn?
Before self-tracking, Christel felt that she woke up frequently during the night, and the Zeo confirmed this. On average she woke up around 8 to 9 times. She suspected the mattress could be part of the problem. After considerable research, she replaced her mattress (to one that had a foam top), successfully reducing her wake-ups to 4 or 5.

Christel discovered that her sleep patterns looked significantly different after just two glasses of alcohol. Her REM diminishes to nearly 0% (though deep sleep seems unaffected).

Christel also found that total sleep time was less important for how she felt the next day than the combination of REM and deep sleep. Even if she only sleeps for six hours, as long as she gets at least 2 hours of combined REM/deep sleep, she feels good.

In addition to these findings and others she explores in the video above, Christel has taken her lessons and now helps others with sleeping issues. You can find more at her website.

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Meetups This Week

This week there will be three meetups in three countries on three continents. The theme for the Bogotá group will be the quantification of emotions. The Brussels meetup will take a look at AmWell’s tools for monitoring stress, sleep and activity.  And in Pittsburgh, they will feature casual hands-on demos before their talks.

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!

Wednesday (October 1)
Pittsburgh, Pennsylvania
Brussels, Belgium
Bogotá, Colombia

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