Tag Archives: qstop

What We Are Reading

Enjoy this week’s list!

Articles

Cell Phones Help Track Flu on Campus by Karl Bates. In 2013, Duke University students participated in a unique research trial to track the spread of influenza. Using sensors from their mobile phones and a few medical tests, researchers were able to see how personal habits and their social networks affected who got the flu.

How San Diego is Using Big Data to Improve Public Health by Mallory Pickett. A nice article here on some new research efforts being led by our friends at the University of California, San Diego.

“You Get Reminded You’re a Sick Person”: Personal Data Tracking and Patients With Multiple Chronic Conditions by Jessica S Ancker and colleagues. A very interesting research study examining the role of self-tracking and health technology in the lives of individuals with chronic conditions.

Next Steps in Developing the Precision Medicine Initiative by DJ Patil & Stephanie Devaney. After a few months of meetings and feedback, the folks helping steer the Precision Medicine Initiative are looking for new ideas and leading examples.

Show&Tell

0*7MPPXFrgXIfXEi06 My 40-Day Journey into Meditation with Muse (the brain-sensing headband) by Kal Mokhtarzada. An interesting post examining meditation and the data provided by the Muse. Kal dives deep into his data, and gives a few examples of why things tended to work, and when they didn’t.

 

6713040-3x2-940x627 What reporter Will Ockenden’s metadata reveals about his life by Will Ockenden and Tim Leslie. A fascinating look into what you can learn from someone just from the metadata their phone collects.

 

Visualizations

RW_Dating 8 Years of Dating Data by Robin Weis. Robin details her dating history, starting when she was 15, in this wonderful visualization.

 

image02 See it, believe it: The Web Visualization Library by Jasper Speicher. Our friends over at Open mHealth are building a great set of open source tools to work with personal health data. In this post, they describe why they built their visualization library.

From the Forum

Cholesterol Monitoring
Sleep Tracker and Sleepwalking

 

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Maggie Delano: Building Myself Back Up

Maggie Delano hit her head while helping a friend move. She was diagnosed with a concussion and, later, post-concussion syndrome. In order for her to heal, she had to give her brain a break from cognitively stimulating activities. In this show&tell talk, presented at the 2015 Quantified Self Conference, Maggie discusses how she tracked her progress toward recovery with Habit RPG (recently renamed Habitica) and improved her sleep with Sleepio.

To see great presentations like Maggie’s in person and get the chance to talk with the speakers, come to our Quantified Self Europe Conference on September 18 & 19. Our early-bird tickets (€149) expire in less than 24 hours, so get yours now!

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Meetups This Week

We have a trio of tremendous QS meetups occurring this week. In Portland, I will be giving a presentation on the best of the Quantified Self Global Conference in June. Los Angeles will feature toolmaker talks. And in Atlanta, they will be discussing their experiences with the Apple Watch, as well as, reviewing the recent QS Public Health Symposium.

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! If you are a QS Organizer and want some ideas for your next meetup, check out the myriad of meetup formats that other QS organizers are using here.

Tuesday, August 25
Los Angeles, California

Thursday, August 27
Portland, Oregon
Atlanta, Georgia

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What We Are Reading

Have you registered for our 2015 Quantified Self Europe Conference? If not, this weekend is your last chance to take advantage of our special early bird rate (€149!). We’d love to see you there so register today!

Our friends at Oura are currently crowdfunding their amazing heart rate, sleep, and activity tracking ring. Check out their Kickstarter to learn more.

Now, on with the show!

Articles
You may just have updated the map with your RunKeeper route by Alex Barth. Short post here describing a fascinating use of publicly available data from Runkeeper users around the world.

A Six Month Update on How We’ve Been Using Data, and How it Benefits All Americans by DJ Patil. A nice update on some of the current initiatives being championed at the federal level to make data more available and beneficial for all Americans. I can’t wait to see what happens next.

Discovering Google Maps New Location History Features by Mark Krynsky. Mark walks us through the new features embedded within Google Maps and Location tracking. Want to find out where you spend most of your time or how often you visit your favorite coffee shop? Google may already know!

Drowning in Data, Cities Need Help by William Fulton.

No city government, university or consulting firm can possibly figure out how best to use all the data we now have. The future lies in having everybody who understands how to manipulate data — from sophisticated engineering professors to smart kids in poor neighborhoods — mess around with it in order to come up with useful solutions.

Just Talking with Maggie Delano by Christopher Snider. Take a listen to a great conversation with our friend and QS Boston and QSXX organizer, Maggie Delano. Well worth your time.

Show&Tell

1112195 HRV Measurements: Paced Breathing by Marco Altini. Marco is back at it again with a in-depth post about his experiments on how breathing rate affects HRV and heart rate measurements. Starting with a great review of the current literature, he then dives in to his own data and what he’s found through various experimental protocols.

 

tumblr_inline_n4fxiifr6T1r6gaqp Resuming Quantified Self Practices by Emily Chambliss. A short post here on using Excel to track and understand food consumption. Make sure to check out the slides from a talk she gave in 2012 at a New York QS Meetup.

Visualizations

9aXr5Mm My Sleep Quality of the last 2 Years by Reddit user Splitlimes. A beautiful visualization of just over two years of sleep data tracked with the Sleep Cycle app.

 

3 - nqfnVD8 Time-histogram of 10 Million Key Strokes by Reddit user osmotischen.

These are plots of 10 million key strokes and about 2.4 million mouse clicks logged over a bit more than a year’s time on my computer. (Make sure to click through for more visualizations.)

 

From the Forum
Descriptives and visualizations for large numbers of variables
I created this site to make decisions better with an algorithm. I’d love feedback!
HRV apps for Polar H7 that include SDNN

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QSEU15 Preview: Morris Villarroel on Slowing Time with a Lifelog

Morris Villarroel at QS14

The 2015 Quantified Self Europe Conference will commence in less than four weeks, bringing together the QS community to share what they’ve been learning with personal data.

Anyone who engages in any sort of self-tracking discovers that the data collected is not a mere recording of some aspect of your life. Rather, engaging with and reflecting on that data can change the way that you relate to an aspect of yourself. Something as simple as getting on a scale each morning can change the way you think about weight. Morris Villarroel has discovered a novel way that this relationship can develop. At this year’s conference, Morris will talk about how using a Narrative camera to keep a visual record of his days, along with detailed notes, has changed his subjective experience of time, “bringing it closer to the present.”

I experienced something similar when I used a spaced repetition system to memorize entries from my daybook. Frequently recalling recent events kept the past distinct and novel. When a month passed, it no longer seemed like a blur, but a container filled with distinct experiences that differentiated itself from any other month.

You can find out more about how Morris gleans value from his lifelog at the 2015 QS Europe Conference. In addition to his show&tell talk, Morris will be leading a breakout discussion on how we can learn more from our lifelogs. We invite you to join us in Amsterdam on September 18th & 19th for two full days of talks, breakout discussions, and working sessions! Early bird tickets are still on sale. Register today for only €149!

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QSEU15 Preview: Ellis Bartholomeus on Doodling Mood

EllisMoodFaces

In just four short weeks we’ll be kicking off the 2015 Quantified Self Europe Conference, and we are so excited to hear from old friends, learn from new members, and interact with some wonderful toolmakers. It’s going to be a great time.

As you may know, we build our conference programs from the ground up with attendees submitting their projects and ideas when they register. It’s always fun to read about someone’s new self-tracking project or experiment, especially when it involves something we haven’t seen before. Today we’re going to begin our conference previews with one of those novel and interesting talks.

EllisB

 

Ellis Bartholomeus is no stranger to our QS Conferences, having given an excellent talk on using photos for food tracking at our 2013 Europe Conference. At this year’s conference Ellis will be sharing her experience with a very interesting type of mood tracking. For six months Ellis tracked her mood by drawing a face every day. This simple act of using a quick doodle to track how she was feeling led to some unexpected benefits:

 

This inspired and engaged me more than expected with other quantifications. The faces triggered my curiosity and provided many insights, which continue to motivate me.

Mood tracking is something that continues to intrigue our community. Understanding our happiness, what affects our mental state, and how to improve our moods is a common theme at meetups around the world. We’re interested to learn more from Ellis and her experiences at the 2015 QS Europe Conference. If you’re tracking your mood we invite you to join us in Amsterdam on September 18th & 19th for two full days talks, breakout discussions, and working sessions! Early bird tickets are still on sale. Register today for only €149!

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Oura Ring on Kickstarter: Sleep and Activity Tracking On A Finger

OuraPrototypes

I first got a look at the Oura ring at the Quantified Self Public Health Symposium last May. I was surprised that the Oura engineers had managed to get sleep and activity tracking into  a bit of jewelry the size of a ring, and ever since I’ve been deeply curious to experiment for myself. Although a few samples showed up at QS15, there was nothing we can could take home with us. But the Oura ring campaign on Kickstarter launches today, with delivery estimated for November 2015. The company is a QS sponsor, and they’re offering readers here and our followers on Twitter a few hours head start on the campaign’s very limited number of $199 rings. (They have just 500 0f these, after which the minimum pledge to get a ring rises to $229).

The Oura ring has both optical sensors and an accelerometer, an increasingly common duo, used in the Apple watch and quite a few other devices. But I thought that the combination of sensors and battery demands would make a ring-size sleep and activity sensor challenging.

Of particular interest to me is the offer of “laboratory accurate” measurement of heart rate variability, or HRV, using the optical pulse sensor. Heart rate variability is the the variation in the time between heart rates, and it’s useful for Quantified Self experiments involving measurement of emotional arousal and stress. HRV is relatively easy to get, if you have an accurate heart rate monitor, but typically these have taken the form of elastic chest straps. Even Apple, with its relatively capacious watch, doesn’t yet promise accurate measurement of HRV. If the Oura ring ends up offering accurate HRV in a ring that is easy to keep on at all times, it will spark a lot of very interesting new projects.

Thank you to Petteri Lahtela and Hannu Kinnunen, the Oura founders, for giving us a few hours head start. We wish you good luck on your campaign!

For early access use this link: Quantified Self Access to Oura Kickstarter.

Note: both Petteri and Hannu will be at Quantified Self Europe conference in Amsterdam on September 18 & 19.

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Meetups This Week

We have two great Quantified Self meetups occurring this week. St. Louis will have talks on crowdsourcing clinical research and tracking for athletic performance. After Austin‘s show&tell talk on using eeg’s to improve everyday performance, they will have a roundtable discussion.

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! If you are a QS Organizer and want some ideas for your next meetup, check out the myriad of meetup formats that other QS organizers are using here.

Monday, August 17
Austin, Texas

Tuesday, August 18
St. Louis, Missouri

 

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What We Are Reading

Enjoy this week’s list!

Articles
When ‘Special Measures’ Become Ordinary by David Beer. What does it mean to have measurement, personal and institutional, as part of our everyday experience? A nice article that begins to expose what it means to operate in this new world.

Building smarter wearables for healthcare, Part 1: Examining how healthcare can benefit from wearables and cognitive computing by Robi Sen. In this article, Robi Sen describes what IBM sees as the “analytics gap” in current wearable technology. Specifically, that devices and the data they present don’t fully understand and utilize contextual information, and therefore are not providing meaningful information. What’s the answer? IBM’s Watson, of course.

Doping scandals, open data, and the emergence of the quantified athlete by Glyn Moody. A short and interesting piece that wonders if opening up athlete performance data might be a useful part of combating doping and illegal performance enhancement in professional athletics.

N of 1 Trials and Personal Health Data with Dr. Nicholas Schork. The Health Data Exploration Network hosted their inaugural webinar this past Friday. The focus was on N of 1 trials: why they’re important, how to conduct them, and the role of Quantified Self and self-tracking data.

Lifelog: Pilot Tasks of NTCIR–12. Our good friend and lifelogging researcher, Cathal Gurrin, is spearheading an innovative project to improve search and information access to lifelogging and self-tracking data. If you’re a researcher or information systems specialist you may want to take a look at data and see if you can help push the field forward!

Show&Tell
sleepwalking_beddit Sleepwalking. Rather than point to one post over another we’re going to highlight this entire blog by one anonymous scientist who’s exploring his sleepwalking. The whole blog is chock full of insights into measurements, devices, and experiments to see what may or may not affect their sleepwalking. Start here to get a good overview.

How I Hacked Amazon’s $5 WiFi Button to track Baby Data by Ted Benson. Have $5 to spend on an Amazon Dash button? With a little bit of programming you can turn it into your own DIY internet-connected tracker!

FullSizeRender-1 10,000 Steps at a Music Festival by Tim Hanrahan. A fun post about tracking physical activity at the Lollapalooza festival.

Visualizations

vjo_2015-Aug-13 Basis Data Analysis by Victor Jolissaint. I saw this Victor tweet this visualization and was immediately drawn in. Turns out he’s been exploring ways to analyze and understand his Basis watch data using R. Check out the link for his code and take a crack at analyzing your own data!

My-Steps_thumb Tableau: Helping Me See and Understand Myselfb y Craig Bloodworth. Craig pulled all his self-tracking data into Tableau and designed his own personal dashboard to better understand what was going on with his activity, personal finances, and other lifestyle information.

From the Forum
How to acquire info about sent e-mails using gmail?
How to quantify myself
We want to track you!

 

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2015 QS Visualization Gallery: Round 4

We’re excited to share another round of personal data visualizations from our QS community. Below you’ll find another five visualizations of different types of personal data. Make sure to check out Part 1Part 2, and Part 3 as well!

daily habits Name: Damien Catani
Description: This is an overview of how I have been doing today against my daily habit targets. Yes, I had a good sleep!
Tools: I used a website I’ve been building for the purpose of setting and tracking all goals in life: goalmap.com

 

tock_b_tock_goal_page Name: Bethany Soule
Description: This is my pomodoro graph. I average four 45 minute pomodoros per day on my work, and I track them here. This is where most of my productivity occurs! There’s some give and take.
Tools: The graph is generated by Beeminder. I use a script I wrote to time my pomodoros and submit them to Beeminder when I complete them. The script also announces them in our developer chat room, so there’s also some public accountability there as well.

 

 

qs1 Name: Steven Zhang
Description: This plot shows the time I first go to sleep, against quality of day (a subjective metric I plot at the end of every day). What this tells me is that if I get a full night’s sleep of 8 hours, for every hour I got to bed, I can expect a .16 decrease in my QoD rating, which, given my range of QoD around 2 to 4, is about a 5% decrease in quality of day.
Tools: Sleep as Android to track sleep and some python scripts for ETL.

 

qs2Name:Steven Zhang
Description: Log of all my sleep for the last 6 months, labeled by the types of sleep I most often encounter

  1.  Normal sleep
  2. Napping
  3. 3. Trying to achieve normal sleep, but failing to

Tools: Tableau for visualization. Sleep as Android for logging sleep.

 

Digits
Name: Eric Jain
Description: Benford’s Law states that the most significant digits of numbers tend to follow a specific distribution, with “1″ being the most common digit, followed by “2″ etc. But my daily step counts show a slightly different distribution: The fall-off from “1″ to “2″ is larger than expected, and the frequency of digits larger than “5″ increases rather than decreases. Is this pattern typical for step counts? Could suspicious distributions be used to detect cheaters?
Tools: Fitbit, Zenobase, Tableau

Stay tuned here for more QS Gallery visualizations in the coming weeks. If you’ve learned something that you are willing to share from seeing your own data in a chart or a graph, please send it along. We’d love to see more!

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