Tag Archives: sleep
It’s a long one today, so buckle in and get ready for some great stuff!
The Quantified Self: Bringing Science into Everyday Life, One Measurement at a Time by Jessica Wilson. This piece, from the Science in Society Office at Northwestern University, explores the Quantified Self movement, with a particular focus on the local Chicago QS meetup. Always interesting to see how individuals draw distinctions between self-tracking projects and “real science.”
Diversity of Various Tech Companies By the Numbers by Nick Heer. Recently Apple released data about the diversity of their employee workforce. This marked the last major tech company to publish data about diversity. In this short post Nick takes that data and shows how it compares to data from the US Bureau of Labor Statistics. Interested in more than just the big six listed here? Check out this great site for more tech company diversity data (Hat tip to Mark Allen for finding that link!)
Intel Explores Wearables for Parkinson’s Research by Christina Farr, Reuters. Intel is in the news lately based on their interest in developing and using their technological prowess for qs-related activities. In this post/press release, they describe how they’re partnering with the Michael J. Fox Foundation to explore how they can use wearable devices to track and better understand patients with Parkinson’s Disease. It appears they’re also working to get their headphone heart rate tracking technology out to market.
Spying on Myself by Richard J. Anderson. I’m always interested in how people talk to themselves about self-tracking. This short essay describes the tools that Richard uses and why he continues or discontinues using them. His follow up is also a must read.
Dexcom Mac Dance by Kerri Sparling. You know we’re fascinated by the techniques and tools developed and refined by the the diabetes community. In this short post, Kerri highlights the work of Brian Bosh, who developed a Chrome extension to access and download data from Dexcom continuous glucose monitors on a Mac. (Bonus link: Listen to Chris Snider’s great podcast episode where he talks to John Costik, one of the originators of the CGM in the Cloud/Nightscout project.)
The Three-Year Long Time Tracking Experiment by Lighton Phiri. Lighton is a graduate student at the University of Capetown. In 2011 he became curious about how he was spending his time. After installing a time-tracking tool on his various computers, he started gathering data. Recently, after 3 years of tracking, he downloaded and analyzed his data. Read this excellent post to find out what he learned.
Experimenting with Sleep by Gwern. One of our favorite self-experimenters is back with some more detailed analysis of his various sleep tracking experiments. Read on to see what he learned about how caffeine pills, alcohol, bedtime, and wake uptime affects his sleep.
QS Bits and Bobs by Adam Johnson. Adam gave talk at a recent QS Oxford Meetup about his lifelogging and self-tracking, his custom tools for importing data to his calendar, and what he’s learned from his experiences. Make sure to also check out the neat tool he’s developed to log events to Google Calendar.
FuelBand Fibers by Variable. A design team was given Nike FuelBand data from seven different runners and created this interesting visualization of their daily activity.
I don’t Sleep That Well: A Year of Logging When I Sleep and When I’m at Work by Reddit user mvuljlst. Posting on the r/dataisbeautiful subreddit, this user tracked a year of their sleep and location data using Sleepbot and Moves. If you have similar data and are interested in exploring your own visualization the code is also available.
In the City that We Love by Brian Wilt/Jawbone. The data science team at Jawbone continues to impress with their production of meaningful and interesting data visualizations based on data from UP users. In this post and corresponding visualizations they explore the daily patterns of people from around the world. Make sure to read the technical notes!
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Vanessa Sabino was curious about how well she was sleeping. By using the Sleep as Android app, she was able to track a year of sleep data. Before she was able to dig into the data she ran into a problem with the data export format and had to write her own custom data parser to create usable CSV files. Vanessa was then able to use the data to explore her question, “When do I get the most amount of deep sleep?” In this talk, presented at the Toronto QS meetup group, Vanessa explains her process and what she learned from analyzing 340 days of sleep data.
Today’s Tidings dispatch is from Daniel Gartenberg, co-organizer of the Washington DC meetup group. Read below to hear about their recent meetup. It sounds like a great time and we can’t wait to share the videos from these interesting talks.
We had our biggest meetup yet at 1776 – a start-up hub located in the heart of our nations capital. At the meetup there were three great talks, fun socializing over sandwiches, and lively QS Discussions. We had three wonderful talks:
James Norris – serial entrepreneur and avid self-experimenter gave a captivating talk about tracking his “firsts”. This included everything from his first kiss to his first time meditating on a train. One thing that James found was that traveling was one of the key factors that impacted his “firsts” – but only up to a limit – where after some time traveling, there are diminishing returns to “firsts”.
Next, Daniel Gartenberg gave a talk on his new efforts to evaluate and improve sleep. He described a study that he is conducting with the QS community where participants can receive $50 for tracking 2 weeks of their sleep data. Some participants will even have the opportunity to use a Hexoskin, actiwatch, and galaxy gear. However, users must have an iPhone and be willing to take 10 minutes out of their day for cognitive testing. Please contact Daniel Gartenberg at email@example.com if you are interested in participating in the study.
Finally, Daniel Martinez showed off an amazing visualization of more than 1800 days of his sleep data that he calculated using pencil and paper and inputting the data into Mathemetica software. Daniel created a new tool for evaluating sleep, which included categorizing time as “up and at em”, dozing, sleeping, and awake while trying to sleep. Using these categories he presented visualizations of sleep and showed a bimodal distribution in his bedtime and a new way to evaluate his sleep quality.
If you’re in the Washington, DC area we invite you to join this great meetup group!
Jan-Geert Munneke has had an issue with snoring for quite a while. He started off his self-tracking journey by tracking his snoring with the Snore Lab app. Having this data led him to think about how he could understand what was going on while he was sleeping. So, he decided to incorporate more sensors to better track his sleep. In this talk, from our 2013 Quantified Self Europe Conference, Jan-Geert describes what he found from combining data from different devices and how it’s inspired him to think about how he could track other aspects of his sleep.
Max Gotzler wasn’t feeling his best during a long German winter. He decided to visit his physician to see if anything might be going on. This included various blood and micronutrient tests. When he received his results he noticed that his testosterone levels were on the low end of the acceptable range. Intrigued by this, he set out to figure out what affects his testosterone levels. Using a variety of self-tracking tools and methods he experimented with diet and lifestyle factors while tracking his testosterone values. Watch Max talk about what he found below and make sure to read his responses to our three prime questions.
Slides are also available here.
What did I do?
I explored how diet changes influenced my level of free testosterone. In addition, I observed how changes in testosterone related to my mood, sleep and energy level.
How did I do it?
Over the course of one year, I regularly checked my level of free (active) testosterone in saliva and correlated the results to other data I had collected using apps and tracking devices.
What did I learn?
I learned that eliminating carbs from my diet resulted in lower testosterone and adding carbs together with fat and protein increased testosterone. I also learned that sleep was closely tied to my level of testosterone. After good nights of sleep (usually more than 8 hours), my level was elevated the next morning.
Eric Jain stumbled upon a study published in 2013 that found the a full moon was associated with less sleep. Being an avid self-tracker and a toolmaker he decided to find out if that was true for him as well. Eric used his tool, Zenobase, to import, aggregate, filter, and then analyze his sleep data in a few unique ways. While he found some evidence that a full moon was associated with less total sleep he wasn’t able to make any statistically significant results. Watch his short video below, filmed at the Seattle QS meetup group, then take a look at his great screencast where he walks through all his steps to complete this analysis.
This guest post comes to us from Mark Moschel and Eugene Granovsky, the co-organizers of the Chicago Quantified Self meetup group. At their recent meetup on March 26, 2014 they had three great talks from community members. If you live in the Chicago area why not join the group!
Dan Abreu on GeoTracking
Dan travels a lot. I mean… a LOT! He stepped through an airport well over 300 times in 2012. He started documenting his travel a few years back and has used a variety of tools since: TripIt, Track My Life (discontinued), Google Latitude (discontinued), QStartz, and myTracks. During that time, his technique for tracking evolved and gained complexity. He’s now able to develop very detailed maps of his trips (see below). What has he learned from all this? “Not much” he said. However, he enjoys the practice and consistency of it and is excited to continue finding more uses for this data in the future.
Zak Boswell on Sleep
Like many of us, Zak was on a very inconsistent sleep schedule for most of his life and would often stay up too late. However, unlike many of us, Zak was experiencing severe fatigue during the day. In the span of just a couple years, he had 4 car accidents from falling asleep at the wheel (in two, his car was totaled). Realizing this was a problem, he started exploring traditional solutions. He saw a handful of doctors and participated in a very expensive (and ineffective) sleep study. During this time, he also started tracking his sleep and decided to go to bed at a consistent time each day (around midnight). In the data, he saw his sleep quality beginning to improve. He also stopped falling asleep during the day. At first, he struggled with the change, but he’s since changed his whole philosophy and loves it. You can view Zak’s presentation here [PDF].
Ovetta Sampson on how tracking helped her become an Ironman (or “The science of Faith”)
Let’s start with the end on this one. Here’s what Ovetta accomplished: 2.4 mile swim, 112-mile bike ride, 26.2 mile run – all in under 17 hours. Wow! Even more impressive is that she was never an athlete growing up and weighed 270 lbs in 2012. In just a year, she turned a seemingly impossible goal into a real accomplishment. How? She found faith in her data. “Tracking data helped me change my behavior” she said. By tracking her times, weight, speed, and distance, a few things happened: 1) she quickly saw progress and was motivated to keep going, 2) she became competitive with herself, always trying to beat her last score, and 3) she could ignore the thoughts in her mind. As she said, “you have to trust something and the mind is not to be trusted. Trust the data.” Her thoughts kept telling her to quit, but the data proved she was doing well. She didn’t quit and now she’s an Ironman. You can view Ovetta’s presentation here [PowerPoint].
For those of you in the Chicago area Elmhurst Art Museum is hosting a new exhibit called “Lifeloggers: Chronicling the Everyday.” Check it out here.
“If I look at this, I have these memories, and I remember this was a good year.”
Collect it and forget it. This could be be hidden mantra of many people engaged with self-tracking, myself included. I will readily admit to buying a device or application with the hope that I can collect enough information to generate a grand insight at some mythical point in the future where the intersection of free time, analytical knowledge, and sample size magically coalesce. Ulrich Atz encountered the same problem. He was tracking, but soon lost sight of the purpose. Rather than giving up he started a new tracking project.
Ulrich started by building on the popular habit and tracking theory, Don’t Break the Chain, based on consistency in behaviors you care about. He identified six major categories he wanted to understand and pay attention to: his evening ritual, fitness, nutrition, learning, sleep, and travel. Rather than using an passive tracking system like Foursquare of Sleep Cycle, he decided to keep track of it by writing on a large wall calendar. In this presentation, given at the London QS meetup group, Ulrich describes his methods and what he learned from this year-long process.
Even in a world of connected devices, wearable technology, and near ubiquitous data connections self-tracking and personal data collection can be difficult endeavor. Aaron Parecki has been tracking various aspects of this life for years – specifically location, weight, and sleep. We’ve covered some of Aaron’s work and his amazing geolocation visualizations here before and we were excited to have him speaking about his experiences at our 2013 Global Conference. Watch this fantastic talk to hear about Aaron’s tracking practices and his thoughts on why a personal data server is an important tool.
Update: Aaron let us know that his slides from this talk are also available and can be viewed here.
We’ll be posting videos from our 2013 Global Conference during the next few months. If you’d like see talks like this in person we invite you to join us in Amsterdam for our 2014 Quantified Self Europe Conference on May 10 and 11th.
This is concatenation of screenshots from my sleep app. Most sleep apps don’t let you zoom out like this and still see daily/nightly detail, so I just made it myself. I like that it shows how almost-consistent I am with my sleep, and made me ask new questions about the “shape” of a night of sleep for me.
Tool: Azumio Sleep Time
We invite you to take part in this project as we share our favorite personal data visualizations.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.