Tag Archives: steps
Jamie Williams found himself with almost two years of self-tracking data including physical activity, blood pressure, and weight. Because of his interest in data visualization and coding he decided to learn how to access it the data and work on visualizing and understanding some of the trends and patterns. In this talk, presented at the QS St. Louis meetup group, he takes a deep dive into his activity and step data as well as his blood pressure data to learn about himself and what affects his behavior and associated data.
What Did Jamie Do?
Out of pure interest in seeing what the data would reveal, Jamie utilized a combination of devices to track his physical activity, blood pressure, heart rate, weight, numbers of drinks, and automobile travel. He then went on to explore ways in which he could pull down, integrate, visualize, and ultimately make sense of what he collected.
How Did He Do It?
In order to obtain his data on a minute-level resolution, Jamie had to email FitBit for a specialized use of their API. He then employed Mathematica to develop a number of (beautiful) visualizations of his activity – along with other key moments in his life (moving to St. Louis, changing job location, preparing for a Half Marathon, etc.). Jamie was able to compare his data not only to his peers through FitBit, but also to others of his demographic in the U.S. using the publicily available NHANES data set.
What Did He Learn?
Through Jamie’s Quantified Self collection and analysis efforts, he learned a lot not only about the patterns and changes in his activity, but why they were the case. He also presented great feedback about one’s mindset when comparing to peers vs. the general population.
Withing Blood Pressure Cuff
Thank you to QS St. Louis organizer, William Dahl, and Jamie for the original posting of this talk!
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)
We recently released our QS Access app, which allows you to see HealthKit data in tabular format. Not very many tools feed data into HealthKit yet, but Apple’s platform does pick up step data gathered by the iPhone itself. I have step data on HealthKit going back about two weeks. When Ernesto Ramirez and I were playing around with QS Access, loading the data into Excel and looking at some simple charts, I learned something: Even when I’m active, I’m sedentary.
My daily step totals ranged from a depressing 3334 steps on Thursday, September 18 to an inspiring 21,634 steps on Friday, September 25, but – as these charts clearly show – even on the extreme days my activity was concentrated into relatively short periods when I got up from my desk and went out to do something. Most hours, every day, were spent with hardly any movement at all. I’m sitting at my desk, and sitting at my desk some more, and sitting at my desk still more. That’s probably not good. No, not good at all.
Pulling my data out of HealthKit and seeing a few simple charts gave me a bit of insight that I hope will lead to a change in how much I sit. It was a great to be able to easily make some simple analysis of my data. I hope you’ll find QS Access useful also (you can learn more about it here). Please share what you learn in the QS Access thread in the QS Forum or by emailing us about your projects: email@example.com.
In response to the much anticipated reveal of the Apple Watch I did a bit of digging around to find out where we stand with wrist-worn wearable devices. I found over 60 different devices. The following list focuses on self-tracking tools, I intentionally left out those that work only as notification centers or secondary displays for your phone. I’m sure this isn’t all of them, but it’s as good a place to start as any. If you’re using one of these devices to learn something about yourself, or you’re just interested in these type of wearable tools we invite you to join us in San Francisco on March 13-15, 2015, for our QS15 Conference & Exposition.
(Thank you to all those who commented here, on Twitter, and on our Facebook group pointing us to additional devices to add!)
Sensors: Accelerometer, Heart Rate (optical), Blood Oxygen, Temperature
Sensors: Accelerometer, Pulse Oximeter, Temperature
Sensors: Accelerometer, Gyroscope, Heart Rate (optical)
Sensors: Materials state the ZenWatch houses a “bio sensors and 9-axis sensor.” I assume optical heart rate, accelerometer, and gyroscope.
Sensors: Accelerometer, Gyroscope, Heart Rate (optical)
Sensors: Accelerometer, Temperature, Pressure
Epson Pulsense Band/Watch
Sensors: Accelerometer, Heart Rate (optical)
Fatigue Science Readiband
In 2013 Eric Boyd started using a Nike FuelBand to track his activity. Not satisfied with the built in reporting the mobile and web applications were delivering he decided to dive into the data by accessing the Nike developer API. By being able to access the minute-level daily data Eric was able to make sense of his daily patterns, explore abnormalities in his data, and learn a bit more about how the FuelBand calculated it’s core metrics. Watch Eric’s talk from our 2013 Quantified Self Europe Conference to hear more about Eric’s experience.
There is nothing quite like having to unexpectedly deal with your own mortality. Mark Leavitt experienced that “wake up call” when he spent Thanksgiving in 2007 at a cardiac cath lab. While he didn’t have a heart attack, he left with 3 stints and new outlook. He decided to take a hard look at his “comfortable life” and see what he could change. After a successful diet and exercise regimen helped him lost 35 pounds he found himself slowly re-gaining weight. What happened next was amazing. Watch this great talk and Q&A to learn how he tracked and hacked his computer time to make it a much healthier endeavor.
This talk was filmed at our 2012 Quantified Self Global Conference. We hope that you’ll join us this year for our 2013 Conference where we’ll have great talks, sessions, and discussions that cover the wide range of Quantified Self topics. Registration is now open so make sure to get your ticket today!
Here at QS Labs we’ve been curious about the differences between two of the most popular devices among self-trackers: The Nike+ FuelBand and the FitBit. I’m the latest experimenter on this topic, and since January I’ve been wearing a FuelBand on my left wrist and a FitBit (original model) in the right hand coin pocket of my jeans. The FitBit almost always counts significantly more steps than the FuelBand.
The details are interesting. When Bastian compared his FuelBand vs his Fitbit, he found a slight correlation between his activity level and the difference in the number of steps they counted. In other words, them more active he was, the more the two devices disagreed. When Ernesto did his FuelBand vs Fitbit test, his numbers closely matched. My data is more like Bastian’s, but with the effect of high activity even clearer. Look at the graph below. On the vertical axis is the difference in step count, by day. On the horizontal access is the number of daily steps Fitbit counted. The higher the number of “FitBit steps,” the more likely it is that “Fuelband steps” are much lower.
We are not the only ones curious about whether our activity level looks different when seen with different trackers. Bastian Greshake, co-founder of OpenSNP.org, has been comparing his FuelBand and his Fitbit for months. Here’s what he found.
Inspired by Ernesto’s post I wanted to take a look at how my data for the Fitbit and the FuelBand compare to each other. I started wearing the FuelBand in October of last year. Since then it has only left my wrist to recharge the battery. I was already carrying a Fitbit Ultra, which I’ve had since May 2012. I wear the FuelBand on my dominant arm. The Fitbit is usually clipped to the pocket of my jeans and I have it on my non-dominant arm while sleeping. From my day-to-day experience I have a sense that FuelBand steps are usually a good way below the Fitbit steps. But I also thought that the difference was getting smaller, probably due to firmware updates on the FuelBand.
Using the Fitbit-API (and it’s integration into openSNP) it’s quite easy to get a file that contains all step counts measured with the Ultra. If you have an openSNP account you can download the complete file, also including sleep data and body measurements here. Unfortunately the Nike+ API isn’t ready yet, so one needs to manually scrape the data. As this is boring work that can’t easily be automated I only got FuelBand step data back to 2013/11/16. Still, that should be enough to get a first insight on how both devices compare.