Tag Archives: sleep
A long one this time. Enjoy the words, numbers, and images herein.
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.
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!
Schedule Abstracted by Mike McDearmon.
Even a hectic schedule can have a sense of serenity with all text, labels, and interface elements removed.
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?
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!
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.
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.
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.
It’s About Time by Hunter Whitney. A nice post here about the different methods of visualizing temporal data.
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)
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.
Like anyone who has ever been bombarded with magazine headlines in a grocery store checkout line, Kouris Kalligas had a few assumptions about how to reduce his weight and improve his sleep. Instead of taking someone’s word for it, he looked to his own data to see if these assumptions were true. After building up months of data from his wireless scale, diet tracking application, activity tracking devices, and sleep app he spent time inputing that data into Excel to find out if there were any significant correlations. What he found out was surprising and eye-opening.
This video is a great example of our user-driven program at our Quantified Self Conferences. If you’re interest in tell your own self-tracking story, or want to hear real examples of how people use data in their lives we invite you to register for the QS15 Conference & Exposition.
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!
Want to receive the weekly What We Are Reading posts in your inbox? We’ve set up a simple newsletter just for you. Click here to subscribe. Do you have a self-tracking story, visualization, or interesting link you want to share? Submit it now!
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 firstname.lastname@example.org 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.