Tag Archives: activity
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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Today’s post comes to us from Josh Berson. Josh is a anthropologist and researcher at the Max Plank Institute. In the fall of 2014 Josh and his colleagues will be embarking on an ambitious research program to explore how we understand and engage in activity and rest. This research project, housed at the Hub at Wellcome Collection, is still in it’s early stages and Josh sought to discuss and gain insight into how people think about measuring activity and rest, as well as how they perceive participatory research projects. You’re invited to read Josh’s description of the session below and then join the discussion on the QS Forum.
Cartographies of Rest
by Josh Berson
In the Cartographies of Rest breakout, we were looking for practical guidance on setting up a large study using self-tracking technologies to elicit a synoptic picture of contemporary rhythms of rest and activity in densely inhabited urban spaces. The response was great. We had participants with backgrounds in voice analysis, geovisualization, and the design of participatory public health interventions. Some of these conversations will probably lead to formal working relationships with the Hub at Wellcome Collection, the umbrella project under which Cartographies of Rest is being carried out.
But the key insight we came away with had little to do with the technical apparatus or tracking channels for the study. Rather, it was that we would do well to present the demands we’re making of our research participants not as an inconvenience but as an opportunity to be part of an innovative collective approach to generating knowledge about how we move (and stop moving) through shared space. We need to make sure the design of the study, and the way we communicate the study’s aims to participants, reflects our core conceit, to wit, that activity and rest are social phenomena, and their physiological and behavioral correlates are conditioned by social context, not the other way around. In contrast to previous “reality mining” studies, we’re not looking to study the evolution of a preexisting social network, and in fact we’d been thinking recruitment would need to stratify against too many preexisting relationships among participants, since that might confound the rest:activity timelines they generate as individuals. We were also concerned with how to control for the fact that participating in the study would lead participants to change their behavior.
But if, instead, we focus on creating a community among the participants, and on letting the fact that participating in the study will inevitably change their behavior (through new relationships, through new concern, individually, for how active and restful they’re being) be part of what we’re looking at, we will end up with results that have greater translational value and are truer to the aspirations to self- (as well as social) improvement out of which self-tracking methods originate
If you’re interested in exploring tracking activity and rest we invite you to head to our forum to join the discussion on the QS Forum.
Image by Ian Forrester.
Bob Troia was interested in his blood glucose. While he’s not a diabetic and he’s not out of range, he wanted to see if he could lower his fasting glucose levels. He started a long-term tracking experiment where he tested his blood glucose and began to explore the effects of supplementation and lifestyle factors. In this talk, presented at our 2014 Quantified Self Europe Conference, Bob talks about his experiment and what he learned from analyzing his data. Make sure to read his take on what he did, how he did it, and what he learned below.
You can also view the slides here [pdf].
We also asked Bob to answer the three prime questions:
What did you do?
After learning via my 23andMe results that I had an elevated risk for Type 2 diabetes (and having an interest in the longevity benefits of maintaining low blood glucose levels), I began tracking my daily fasting glucose and the effects that diet, exercise, supplements, and stress have on glucose levels so I could take whatever steps I needed to proactively understand, control, and optimize it.
How did you do it?
Over the course of 7 months, each morning I would take a fasting glucose reading using a handheld glucose meter. After establishing a 30-day baseline of daily fasting glucose readings, I began to take supplement called oxaloacetate. It’s been shown to lower and more tightly regulate fasting glucose by mimicking the effects of caloric restriction. It’s a naturally occurring compound found in lots of foods, such as spinach, potatoes, or apples, and it’s as safe as Vitamin C. After several weeks, there was a noticeable improvement in my average values! I then started looking at day-of-week trends in addition to how exercise (in my case, playing soccer) and other things such as travel affected my glucose.
What did you learn?
I learned that I could indeed improve and better stabilize my fasting glucose levels using oxaloacetate – but only in conjunction with intense, interval-type exercise like soccer. My average fasting glucose levels are highest on Mondays (stress of a new work week) and lowest on the weekends. Long airplane travel can adversely effect my glucose levels for several days. Surprisingly, alcohol consumption did not have an effect.
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.
“I wanted to see what I could find when I tracked my electrocardiogram over time.”
Maggie Delano developed a wearable ECG and activity monitor as part of her masters thesis at MIT. She decided to test it out on herself to see what she could find out about her daily activities and her sleep. What she found out surprised her. Watch her talk to hear what she learned and be sure to stick around for the engaging Q&A that follows. (slides are also posted 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.
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!