Tag Archives: FitBit
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.
We’re back with another great set of articles, show&tells, and visualizations for you.
How to Make Government Data Sites Better by Nathan Yau. Government entities are some of the largest holders of interesting data. Nathan focuses this article on the difficulties of accessing and making sense of data from the United States Centers for Disease Control and offers some good ideas on how to make it better.
Project Eavesdrop: An Experiment At Monitoring My Home Office by Steve Henn. What happens when you start monitoring yourself in the same manner the NSA might be doing? The author employs some technical help to learn what data leaks are possible and what you can figure out from your digital trails.
Sitting is Bad for You. So I Stopped. For a Whole Month. by Dan Kols. As a past frequent user of a treadmill desk and a sedentary behavior researcher I found this article intriguing. Yes, a bit silly in nature, but an interesting look at what happens when you go to the extreme. I especially enjoyed the integration of personal tracking in the piece.
Analyzing Squash Performance Using Fitbit by Ben Sidders. Ben sought out to see if he could learn anything from his step data to improve his squash playing. In this post he explains how he used R to access his data and plot it against his squash records, which he also records.
My Life As Seen Through Fitbit. Reddit user, VisionsofStigma, plots a year and a half of Fitbit data to find out what is related to the rise and fall of his activity.
Freeing My Fitbit Data by Bonnie Barrilleaux. Bonnie used our instructions for accessing Fitbit data in Google Spreadsheets then used Python to visualize her data. I especially like the histogram pictured at left. If you want to visualize your Fitbit data, she’s included her code in the post.
Diurnal Plot of Fitbit Data by Matthew Gaudet. Matthew was inspired by the diurnal plots from Stephen Wolfram’s in-depth personal data review. He implemented the same methods to better understand his activity.
Iconic History by Shan Huang. As part of a the Interactive and Computational Design class at Carnegie Mellon University, Shan created a Chrome browser extension that visualizes your browser history. More about the project here.
Visualizing Last.fm History by Andy Cotgreave. Andy has been using Last.fm since 2006 to track his music listening activity. As a data scientist he was interested in what he could learned from all that data. In this four-part series, he explores his data along side data from eight of his friends. (If you want explore your Last.fm data you can export it using this awesome CSV export tool.)
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Maria Benet began tracking her activity a few years ago as a way to lose weight and take control of her health. What started with a simple pedometer and a few custom Access databases has morphed into a multi-year tracking project that includes news apps and tools. Her progress and data has even spurred her on to new experiences and athletic endeavors. Watch her talk, filmed at the Bay Area QS meetup group, and read the transcript below.
(Editors Note: We’re excited to have Maria attending the 2014 Quantified Self Europe Conference where we hope to hear an updated version of this wonderful talk.)
What did I do?
Hi, my name is Maria Benet and I am happy to tell you that only about two-thirds of me is here to talk about my tracking project. I mean that literarily, because in the 10 years since I’ve been self-tracking I lost over 50 pounds while getting fitter.
In my early 50s, I was overweight, out of shape, with bad knees, and when not cranky, depressed. I was already on meds for high blood pressure and was looking at the prospect of more prescriptions down the road.
So, what did I do to change my situation? I set about tracking my activity levels, my weight and my food intake with the help of apps, wearable devices – plus — in databases and Excel spreadsheets that I designed. Until late 2011, I tracked inconsistently, but once I discovered mobile apps and wearable devices — I became more systematic and consistent about tracking weight, food intake, and fitness data.
How did I do it?
When I first started — losing 50 pounds seemed daunting, but going for a walk at least 5 days a week seemed less formidable. To track walks I was going to take in the hilly neighborhood where I live, I created a simple Access database.
I bought a pedometer, hiking shoes, and off I went. After walking, I recorded the duration, the number of steps, and calculated the distances I covered. I also charted my routes by naming the streets, and made notes about the weather and my mood during the walk.
Recording the data turned out to be a form of reward in itself. At the start of this tracking project, I enjoyed seeing the database grow a little more than I enjoyed the actual walks themselves.
Over time, the walks got longer, steeper, and eventually included actual hikes. I also took up the practice of Yoga regularly, and then added Pilates to my exercise repertoire.
Along the way, I also started to lose weight. Though I didn’t weigh myself every day, I began to pay attention to the kinds of foods I ate and tried to wean myself off processed foods in general.
They say you get fit in the gym, but lose weight in the kitchen. In September 2011, when I discovered LoseIt, it became my virtual kitchen: LoseIt helped me see what foods I ate regularly, which of these spiked my weight, even if my calorie intake stayed the same. I noticed these relationships anecdotally, rather than by finding statistical correlations between them.
Tracking in LoseIt helped me realize that as much as I love bread and beer, they are not my friends. Two years ago, an allergist confirmed my wheat sensitivity through blood tests and an elimination diet.
I added Endomondo to my tool box a few months later, since I liked having the maps and stats it offered, in addition to the other data it showed. By December I also added a Fitbit, as with it I could track more accurately how many steps I took and approximate better the number of calories I burned. The Fitbit was like going back to the pedometer, but to one on steroids.
With the Fitibit, I focus mostly on the Very Active Minutes it claims to measure. Increasing that number over time became a game. In 2012, I was averaging about 57 minutes a day, which put me in the 98th percentile. Increasing to 69 minutes only brought me to the 99th percentile, as the Fitbit population also has increased over time.
The Fitbit turned out to be a catalytic tool, because it spurred me on to push the perceived limits of my fitness abilities and possibilities further. It ended up putting wheels under my dreams.
In the spring of 2012,I took up cycling to increase my active minutes and challenge a mental habit of opting out of things because of a fear of failure or thinking of them as not age appropriate. Biking, in turn, added to my collection of gadgets and apps for tracking the metrics involved.
By 2012 then, in addition to LoseIt and Fitbit, I was tracking workouts with a Garmin GPS watch with a HR monitor and my bike rides with a Garmin Edge computer, uploading the data to the Garmin site, to Endomondo and Strava, as each had strengths the other lacked, from my perspective.
To complicate data gathering, back in January 2012, I started a basic Excel spreadsheet that tracks highlights from each of these apps in an application-independent reference for me. In Excel I track the type of activity, duration, distance, if applicable, average and maximum heart rate, Strava suffer points, (a measure of exertion), the hours I slept and how that sleep seemed to me, and additional notes about the day I might think relevant.
The plethora of my gadgets and apps might earn me an entry into the next edition of The Diagnostic and Statistical Manual of Mental Disorders. But exploring these tools was, and still is, my way of looking for a comprehensive and personalized way to track the quantities in my habits and activities that make for a qualitative difference in my life … which brings me to what I learned so far:
What did I learn?
I learned that small quantitative changes in particular daily habits add up to a big difference in quality of life in general.
The incremental additions in my tracking methods and number of gadgets I added produced a lot of data, which I haven’t analyzed closely, because I was already getting a lot of return from them in the form of new experiences in my life.
The most memorable of these experiences is my having completed the metric century ride on the Tour de Fuzz in Sonoma last September. In the space of a little over a year I went from covering barely 8 miles in an hour on my first rides to completing 63 miles in 5 and ½ hours and feeling ready to ride a lot more.
It has been said that motivation is what gets us up and going, but it’s habit that keeps us going. So it is with my tracking: though the motivation was to lose weight, the habit of tracking and keeping an eye on the numbers are what allowed me to go from daily small changes to a much bigger transformation from the overweight, depressed, and achy person I was 10 years ago to who I am now: someone interested in health and fitness and setting goals I can meet.
I learned that for me the act of tracking is the project itself. Although the data I generate can be charted and described in numerical relationships the number that brings me the information that makes a difference in my life, is a simple 1 – or tracking one day at a time.
I love to see the numbers my Garmin and Fitbit generate, but in the end, the quantified self for me is not so much about the measured life as it is about keeping those numbers coming through a well-lived and, more importantly, well-enjoyed life as I go from my fitter fifties into what I hope will be my sounder sixties.
Our show&tell talks usually give you insight into new and different self-tracking projects from a first person perspective. What we rarely hear about is how a self-tracking practice affects those around you, your family and friends. In this wonderful Ignite talk from our 2013 Global Conference Bill Schuller explains how his tracking has impacted his kids and what he’s learned from their experiences.
Vivienne Ming is an accomplished neuroscientist and entrepreneur. When she’s not conducting research or working on new ideas she’s busy taking care of her son Felix. Two years ago Felix was diagnosed with Type 1 Diabetes. Vivienne and her partner tackled his diagnosis head on and started tracking everything they could. In this talk, presented at the 2013 Quantified Self Global Conference, Vivienne explains what they’re learning together.
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.
How will children respond to a world where personal data is ubiquitous? Bill Schuller is starting to find out with his two young children and will be sharing his story at the upcoming 2013 Quantified Self Global Conference.
Bill started tracking his exercise and weight in 2010. His preschool-aged son, listening to his father talk about his daily metrics at the dinner table, began to imitate Bill’s tracking behavior, regularly stepping on the scale, not to watch his weight, but to “just check my numbers.” Bill then designed tracking games for him and his son. One of them involved putting things away in the house while tracking steps and gaining “clean-up points.”
This fun talk will feature more stories on the creative ways Bill and his children are playing with self-tracking. As a preview, we have a version of the talk that he gave in San Diego in March 2012. Watch the video and then find out at the conference what further data adventures Bill has had with his kids in the last year and a half.
The Quantified Self Global Conference will be held in San Francisco on October 10th and 11th. Registration is now open. As with all of our conferences, our speakers are members of the community. We hope to see you there!
Data gave me power to talk about the issue.
We highlight a lot of great show&tell talks here that focus on personal medical mysteries and understanding one’s own health. Well, this one really hit home for me. I’m a runner and I’m constantly battling minor injuries and recurring knee pain. It’s nothing terrible, but it’s at that level of annoying that really makes it hard to enjoy running as much as I should.
Mark Wilson was having similar issues. After running a half-marathon his knee started giving him trouble. The typical treatments didn’t work for him, but instead of giving up running he turned to self-tracking to understand his knee pain (you can see a snap shot of Mark’s running (blue) and knee pain (pink) over time in the header image of this post). In this show&tell talk, filmed at the QS San Francisco Meetup, Mark explains how he built a database that pulls information from different sources like Fitbit, Runkeeper, and his self-rated knee pain, and what he’s learned from that process.
I think most importantly putting all this data together and being able to look at it gave me power to talk about it. Because, I can’t really describe how much despair I was feeling just looking at my knee and thinking, “What the hell is wrong with you? Why is my knee hurting?” I felt like I was trying everything I could on my own and it just wasn’t working. So I wanted to collect a lot of evidence against my knee to indict it.
This data-backed indictment enabled him to have better and more productive conversations with his physical therapist and he began to understand how to move forward. Is it working? You’ll have to watch his great talk to find out:
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.
If you’re like me, then you’re always looking for new ways to learn about yourself through the data you collect. As a long time Fitbit user I’m always drawn back to my data in order to understand my own physical activity patterns. Last year we showed you how to access your Fitbit data in a Google spreadsheet. This was by far the easiest method for people who want to use the Fitbit API, but don’t have the programming skills to write their own code. As luck would have it one of our very own QS Meetup Organizers, Mark Leavitt from QS Portland, decided to make some modifications to that script to make it even easier to get your data. In this video below I walk you through the steps necessary to setup your very own Fitbit data Google spreadsheet.
Step-by-step instructions after the jump. Continue reading
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.