Tag Archives: sleep
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.
This guest post comes to us from Konstantin Augemberg who covers many interesting Quantified Self topics and his personal tracking experience on the wonderful MeasuredMe blog.
On Monday, September 30, Quantified NYC group has held its 23th meetup. The event was graciously hosted by Projective Space which offers collaborative community space to over 60 startups. With over a hundred people in attendance, interesting demos and inspiring presentations (quantifying Starcraft gaming skills, predicting choice of clothes based on weather forecast, and other self-quantified awesomeness!), it turned out to be a great evening. Here is my brief report on what I saw and loved:
We started with our Demos session during which QS entrepreneurs showcased their products and services:
- David Joerg (@dsjoerg) presented his GGTracker, web service that uses advanced analytics to help Starcraft players to track their stats and quantify and improve gaming skills
- Paula Murgia presented Personal Beasties app that helps people to cope with anxiety, fatigue and stress by using simple breathing exercises
- Stefan Heeke (@Stefan_Heeke) showcased My Online Habits, a webapp that uses Gmail and Google data to help analyze your productivity and communications habits
- Mike McDearmon (@Mike_McDearmon) demoed an awesome online dashboard that he built to visualize his outdoors activities.
The Show & Tell session was opened by Mette Dyhrberg (@mettedyhrberg) and her “The Pomodoro Recovery” presentation. Following the bouncing castle accident, Mette has been diagnosed with concussion and was recommended to rest and avoid using electronic devices in order to recover. She started tracking her symptoms, diet, and resting and working habits using Pomodoro method and Mymee app. The lack of progress has prompted her to look at her tracking data, after which she realized that she may have been misdiagnosed. The visit to another doctor has revealed that she sustained a neck injury, which luckily, could be fixed right on the spot. The treatment procedure helped her to feel better almost immediately. You can watch Mette’s presentation here.
In “Quantifying What to Wear”, Andrew Paulus (@andrewcpaulus) shared how he used self-tracking to measure impact of weather on his choice of clothes. It started when Andrew noticed that one of his morning habits included checking weather on his phone in order to decide what to wear on that day. That led to an idea to measure efficiency of this process, by tracking his choice of clothes and then assessing at the end of the day, if the choice was correct. His first attempt at quantifying weather and wardrobe was unsuccessful, due to some flaws in methodology and measurement (e.g., the weather data was collected at different times of the day; the clothes data was not very well structured). Andrew then has revised the methodology, by subscribing to more reliable and comprehensive weather data from Farmer’s Almanac, and logging wardrobe data in a more consistent manner. His girlfriend kindly agreed to co-participate in this experiment. After six months of tracking, Andrew looked at their data. He found that the overall, he tended to be slightly more accurate in choosing what to wear, compared to his girlfriend: his accuracy rate was 78%, vs. her rate of 74%. Another interesting finding was that his choices were more weather appropriate. The correlation between the clothes and weather was nearly 0.7 for him, and nearly 0 .1 for his girlfriend, which suggests that her choices are often influenced by many other factors, not just weather. You can see the full presentation here.
Amy Merrill (@amyjmerrill) shared her experiences with “Sleep Tracking with Jawbone Up”. Since April 2013, she has been tracking her sleep (deep sleep phase, in particular) using Jawbone Up, as well as social and work related activities using Google Calendar. By analyzing the patterns in her data, she was able to see how certain activities affect her deep sleep. In particular, she learned that more physical activity and sleep deprivation led to more deep sleep, where as restful days tend to result in more light sleep. Certain social activities like attending wedding and taking trips on tour bus have also had a considerable impact on quality of her sleep. For the next phase, she plans to include some aspects of the diet, including consumption of alcohol, caffeine and over-the-counter drugs. You can watch Amy’s presentation here.
The session was concluded by Andrew Tarvin’s (@HumorThatWorks) funny and inspiring presentation “The Perfect Day”, in which he discussed the tracking system that he used to build some new habits. Andre has been rating each day based on the number of goals that he achieved (e.g., waking up without snoozing the alarm, do something active for 20+ minutes, eat at least 4 fruites a day, etc.) The days with at least 3 goals met were defined as “quality days”, and the days with all 5 goals accomplished were rated as “perfect”. Andre learned that the strive for perfection was the most demotivating factor: missing one goal earlier in the day often resulted in giving up on all other habits as well. Waking up without snoozing was the most influential habit in that regard. He also learned that the “streaks” of quality and perfect days was the most motivational factor; once he had several consecutive successful days in a row, it was much easier to continue meeting the goals. Andre has been using this system for three years, and plans to continue using it to acquire new habits. You can read more about his system on his site. You can watch video of the presentation here.
As always, before and after the sessions, I had a chance to mingle and meet a lot of interesting people. Special shout out to Stefan Heeke, Mike McDearmon, Sylvia Heisel, Michael Moore and Dave Comeau.
If you’re a loyal, or even infrequent user of the Zeo sleep tracking device then you’ve probably heard the sad news that the company has shut down. This opens up a lot of questions about what is means to make consumer devices in this day and age, but rather than focus on those issues we’ld like to talk a bit about data.
Zeo has been unfortunately a little quiet on the communication front and there are quite a few users out there who are wondering about what will happen to all those restless nights and sound sleeps that were captured by their device. This has been compounded by the fact that the Zeo website went down for a short time (it is up as of this writing) closing off access to user accounts and the data therein. Lucky for you there have been quite a few enterprising and enthusiastic individuals who have taken the time to create or highlight ways to capture and store your Zeo data.
Use The Zeo Website
You can’t fault Zeo with making it hard to access your own data. As long as their website is up you can easily download your sleep data from by logging into your user account at mysleep.myzeo.com. After logging into your account you will see a link on the right hand side labeled “Export Data.” Click that link and you’ll be able to download a CSV file containing all your sleep data. They’ve even provided a description of the data and formats that you can download here.
Eric Blue’s FreeMyZeo Data Exporter
QS Los Angeles Meetup Organizer and hacker extraordinaire whipped up a simple data export tool using the Zeo API. The great thing about Eric’s is that even if the myZeo web portal goes down this tool should continue to work.
Download Data Directly From the Device
If you’re using a Zeo bedside device then you can continue to use it and download the data directly from the memory card without relying on uploading it to the Zeo website. In order to do this you’ll have to read the documentation and use the Data Decoder Library. These files are hard to find as they’ve been removed from the Zeo developer website, but you can access them from our Forum thanks to our friend Dan Dascalesu. Zeo also created a viewer using this library that you can use via this Sourceforge page.
If you’ve found another way to download Zeo data please let us know. You can also participate in the great forum discussion that inspired this post.
At its core, Quantified Self is a community-driven effort to extract personal meaning from personal data. Our conferences reflect that by providing opportunities to learn what others are doing in their Quantified Self practice. Through our Show & Tell presentations you get to see first-hand accounts of how data is being collected and put to use in order to understand and investigate personal phenomena, but that’s not all our conference have to offer. In the spirit of collaborative learning we also schedule “Breakout Sessions” alongside our wonderful Show & Tell talks. These sessions, like all our conference programming, are developed and and facilitated by our wonderful attendees. Here’s a preview of just a few of the many fantastic Breakouts we have scheduled.
Title: The Self in Data
Breakout Leader: Sara Watson
Description: In my research on the QS community, I’ve found that we talk a lot about our technical requirements of data, and about how we want to use data. What we don’t often talk about is what it means to know ourselves through data. This breakout is an opportunity to discuss what data tells us about ourselves and how we relate to our data.
Title: On Sleep Tracking
Breakout Leader: Christel De Maeyer
Description: Does self-monitoring with devices like myZeo, Body Media create enough awareness and persuasion to change behavior and to maintain new habits? We would like to use this session to learn and share our experiences.
Title: Tracking breathing as a Unifying Experience
Breakout Leader: Danielle Roberts
Description: During this session we can exchange experiences on the tracking of respiration and tracking and visualising of life group data in general. You’ll have the opportunity to take part in a demo using custom breath tracking wearables and real time visualisation of breath data.
Title: Activity trackers
Breakout Leader: Michael Kazarnowicz
Description: We’ll take a look at the most common activity trackers on the market today. We will look at the trackers (maybe even play around with them hands-on) and compare the functions and the data you can get from them.
Title: QS as a Catalyst for Learning?
Breakout Leader: Hans de Zwart
Description: In this session we will explore whether quantifying yourself can act as a catalyst for learning. Can it speed up the learning process? Can it help us in achieving the holy grail of learning, a personalized tutor? What perverse effects might it have in the context of learning?
The Quantified Self European Conference will be held in Amsterdam on May 11th & 12th. Registration is now open. As with all our conferences our speakers are members of the community. We hope to see you there!
Ari Berwaldt wanted to better understand how his sleep affected his mental performance. In this great talk Ari explains his insights from tracking his cognitive skills using Quantified Mind and some surprising results about the lack of correlation between his Zeo data and his mental performance. Make sure to keep watching as Ari also explains some very interesting data and conclusions from blood glucose and ketone tracking during fasting. Filmed at the QS Silicon Valley meetup group.
Jules Goldberg is a snorer, and estimates that he has spent 1/8th of his life snoring. The noise was bothering his wife, so he built an app called SnoreLab to quantify his snoring (mild, loud, or epic?) and help him reduce it. In the video below, Jules shares how he identified where his snoring was coming from, remedies he tried, and which ones made it better and worse. (Filmed by the London QS Show&Tell meetup group.)
Leigh Honeywell has always had trouble getting herself to go to bed, so she started tracking her sleep to make sure she was getting enough rest. In the video below, filmed at Quantified Self Seattle, Leigh talks about the different ways she measured sleep, how she caught up on her sleep debt, and what she learned about her anxiety and crashing.
Sami Inkinen, triathalete, self-quantifier, and founder of Trulia, measures his mood on a five point scale every morning, within five minutes of waking up. This method fascinates me. I do something similar (though I use only a three point scale). Sami has found that this quick and easy measurement reliably correlates with his athletic performance, suggesting that it indeed measures something significant about his overall well being in the day ahead.
Read Sami’s full post here: What the first 2 minutes after waking up can tell you about the day ahead?
I’m typing this post while flying back to Southern California after spending a few days at a “Big Data” conference in San Francisco. One of the best things about the conference was meeting the subject of today’s round of Numbers From Around the Web. I first stumbled upon Bastian because he’s the main instigator and developer behind a great project called openSNP. Simply put, openSNP is a place you can host your direct-to-consumer genomic data for the world to see, understand, play with, download, well you get the point. This is a really interesting phenomenon that deserves it’s own post, but we’re going to explore some really neat QS experimentation and learning Bastian engaged in to better understand his sleep.
So, I found out about Bastian because openSNP announced that they also built a method to link and host Fitbit data (you can do that here if you’re so inclined). Turns out Bastian is an avid Fitbit user and has been using it to explore his sleeping patterns. His first major insight from his data indicated that in about 5.5 years he should be sleeping 24hrs per day:
So I downloaded my data from openSNPand started playing around with it: I did a simple linear regression over the time series and could indeed find a trend towards more sleep. The regression came out as y = 0.5x + 417, which ± says that for each two days that pass I will sleep a minute longer, which also means that it will be about 2000 days (or 5.5 years) until I will sleep 24 hours a day.
So yes, obviously regression may not be the best tool in the statistical toolbox to understand sleep so he decided to examine another question: “Do I sleep better or worse when there is someone in bed with me?” Using his sleep and calendar data he was able to identify nights he spend alone and nights he slept next to a warm body and found some pretty interesting stuff.
You can clearly see here in his table of 80 days of sleep (60 alone vs 20 with a companion) that he actually tends to sleep worse when he is sharing his bed. While he spends more time in bed, he takes longer to fall asleep, spends more time awake, and is awakened more often. For those of you who are not statically inclined those p-values indicate the probability that the difference in the two categories is due to chance (you can learn more about p-values here).
Like many good scientists he dug deeper to make sure what he was observing wasn’t related to other confounding variables such as the day of week:
Bastian didn’t find any significant differences in sleep quality between weekend and week days for his sleeping situation, but as one might expect he’s less active and sleeps more on weekends.
So, while this analysis might seem simplistic, one of the great things about Bastian and what he’s developing at openSNP is his willingness to be open with his data. Do you have some ideas about what you might find about Bastian from his Fitbit data? Have another hypothesis about sleep? Well you can test it out by downloading his data! You can start by reading his excellent post about this sleep analysis here.
Every few weeks be on the lookout for new posts profiling interesting individuals and their data. If you have an interesting story or link to share leave a comment or contact the author here.