Tag Archives: running
Enjoy this week’s list!
Effect of Self-monitoring and Medication Self-titration on Systolic Blood Pressure in Hypertensive Patients at High Risk of Cardiovascular Disease by Richard McManus et al. An interesting research paper here about using self-monitoring to reduce blood pressure. The paper is behind a paywall, but since you’re nice we’ve put a copy here.
Apple Prohibits HealthKit App Developers From Selling Health Data by Mark Sullivan. Some interesting news here from Apple in advance of their new phone and possible device release in a few weeks. I applaud the move, but would like to see more information about data portability in the next release.
Science Advisor, Larry Smarr by 23andMe. Great to hear our friends 23andMe and Larry Smarr are getting together to help work on understanding Inflammatory Bowel Disease. If you’ve been diagnosed with Crohn’s disease or ulcerative colitis consider joining the study.
Personal Health Data: It’s Amazing Potential and Privacy Perils by Beth Kanter. A lot of people have been talking recently about the privacy implications of using different tracking tools and technologies. In this short post Beth opens up some interesting questions about why we might or might not open up our personal data to others. Make sure to read through for some insightful comments as well.
Let’s Talk About 3 Months of Self-Quantifying by Frank Rousseau. Frank is one of the founders of Cozy Cloud, a personal could service. He’s also designed Kyou a custom tracker system built on top of Cozy. He’s also been using the services to track his life. In this post he explain how tracking his activity, sleep, weight, and other habits led to some interesting insights about his behavior.
The iPhone 5S’ M7 Predictor as a Predictor of Fitbit Steps by Zach Jones. A great post here by Zach as he explores the data taken from his iPhone 5S vs. his Fitbit.
Using Open Data to Predict When You Might Get Your Next Parking Ticket by Ben Wellington. Not strictly a personal data show&tell here, but as someone who suffers from street sweeping parking tickets somewhat frequently I found this post fascinating. Now to see if Los Angeles has open data…
What Time of Day Do People Run? by Robert James Reese, Dan Fuehrer, and Christine Fennessay. Runners World and Runkeeper partnered to understand the running habits of runners around the world. Some interesting insights here!
What Happens When You Graduate and Get a Real Job by Reddit user matei1987. A really neat visualization of min-by-min level Fitbit step data.
Data + Design by Infoactive and the Donald W. Reynolds Institute. A really interesting and unique take on a data visualization book. This CC-licensed, open source, and collaborative project represents the work of many volunteers. I’ve only read through a few chapters, but it seems to be a wonderful resource for anyone working in data visualization.
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!
Enjoy this week’s list!
The Five Modes of Self-Tracking by Deborah Lupton. One of our favorite sociologists, Deborah Lupton, explores the typologies of self-trackers she’s identified for an upcoming paper. A very nice and clear explanation of the self-tracking practices in regards to different “loci of control.” (Make sure to also read Deborah’s great post, “Beyond the Quantified Self: The Reflexive Monitoring Self“)
In-Depth: How Activity Trackers are Finding Their Way Into the Clinic by MobiHealthNews. An interesting look at the recent influx FDA-cleared activity and movement trackers and how clinicians are looking to use them. Surprising to me is the lack of data access for the patient in these devices (at least on first glance).
The Reluctantly Quantified Parent by Erin Kissane. As a new mother, Erin was hesitant to use what she deemed “anxious technology.” After some hard nights of little sleep she began to slowly incorporate some self-tracking technology into her routine with her newborn daughter. A great read about using tools then putting them away once they’ve served their purpose. (Reminded me of this great talk by Yasmin Lucero.)
Returns to Leisure by Tom VanAntwerp. Tom was interested in his return on investment from his leisure time actives. He tracked his time spent in different non-work activities for two weeks and calculated the cost of participating in those activities.
The Quantified Microbiome Self By Carl Zimmer. The great science writer, Carl Zimmer, writes about a recent experiment and journal article by two MIT researchers who tracked their microbiome every day for a year. Fascinating findings, including a successful self-diagnosis of salmonella poisoning. You can also read the original research paper here.
Better Living Through Data by James Davenport. We recently highlighted one of James’ posts on how his laptop battery tracking led him to understand his computer use habits. In this post he dives deeper into the data.
A Personal Analysis of 1 Year of Using Citibike by Miles Grimshaw. Miles was interested in understanding more about his use of the Citibike bike share system in New York City. Using some ingenious methods he was able to download, visualize, and analyze his 268 total trips. I especially appreciate his addition of a simple “how-to” so other Citibike users can make the same visualizations.
Visualizing Runkeeper Data in R by Dan Goldin. In 2013 Dan ran 1000 miles and tracked them using the popular Runkeeper app. Runkeeper has a quick and easy data export function and Dan was able to download his data and use R to visualize and analyze his runs. (Bonus Link: If you’re a Runkeeper user you might be interested in this fantastic how-to for making a heatmap of your runs.)
This Week on Quantifiedself.com
Natty Hoffman: The Enlightened Consumer
QSEU14 Breakout: Passive Sensing With Smartphones
Jenny Tillotson: Science, Smell, and Fashion
Paul LaFontaine: We Never Fight on Wednesdays
Vanessa Sabino on Tracking a Year of Sleep
Last year Alex Collins was diagnosed with Type 1 diabetes. Prior to his diagnosis Alex was frequently engaged in different types of exercise and physical activity. After his diagnosis his doctor mentioned that he might have a hard time exercising and controlling his blood sugar to prevent hypoglycemia. In this talk, presented at the London QS meetup group, Alex described his process for tracking and understanding the data that affects his day-to-day life so that he could “live my life normally without a high risk of complications.” This process of collecting and analyzing data has even pushed him to continue to explore his athletic boundaries, resulting in a running a ultramarathon and setting the world record for the fastest marathon while running in an animal costume.
Slides are available here.
Science. Someone makes an observation, creates a hypothesis, tests it, then analyzes the results against the hypothesis. Hopefully once a conclusion is reached it is tested again and again for validity and reproducibility. With self-tracking, the world of personal science and experimentation is opening up real-world personal laboratories to test the findings, claims, and promises available through the popular and scientific literature.
Nick Alexander is one of these self-experimenters. When he started to hear about thermodynamics and the effect of temperature on exercise and energy expenditure he decided to set up his own experiment:
I had been introduced to thermodynamics exercise research by former NASA scientistRay Cronise via Wired and the Four Hour Body. Ray makes an extraordinary claim (i.e. that exercising in a cold environment, especially in cold water, causes a large increase in calorie burn), and I was curious to see if it would work for me.
In this talk, given at the 2013 Quantified Self Global Conference, Nick explains his experimental setup and what he found after tracking over 30 runs and crunching the numbers. For a more in-depth discussion about his methodology and his findings I recommend reading his recaps.
This video is from our 2013 Global Conference, a unique gathering of toolmakers, users, inventors, and entrepreneurs. 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.
There are no shortage of apps and devices to track our various physical activities. Going for run? A few laps at the pool? An early morning hike? All of these are trackable with data delivered and archived in a variety of different ways. Mike McDearmon loves to get outdoors, and he also loves tracking his activities. What started as a project to document his runs by taking a picture every time he went running has evolved into a fascinating mixed-media project. Since 2011 Mike has been taking a picture every time he exercises outdoors. In this talk, presented at the New York QS meetup group, Mike explains his methods, and digs a bit deeper into what this means to him.
For me, the real value in this whole project hasn’t necessarily come from the data at all, but from the process of getting outdoors, exploring my surroundings, taking photographs, and then reflecting on my experiences through documentation. This is what I feel is at the heart of the Quantified Self movement – it’s the passion and enjoyment in certain aspects of our lives that makes us want to document them in the first place. – from 300 Outings.
Download slides here.
I highly suggest taking the time to peruse Mike’s wonderful website where he documents his running, cycling, hiking, walking, and the pictures he’s talking along the way. He’s also built a really neat data dashboard that is worth perusing.
Julie Price began running marathons in 2002. While training and learning about running she began to pick up new “rules of thumb” to help guide her training and performance, but something was still missing. How did she know that she was sticking to these rules? Was there any evidence that training was working or that she was accomplishing what she wanted to? Julie started tracking her running using a variety of tools to help answer these questions and start understanding her running. Watch Julie’s presentation from the 2013 Quantified Self Global Conference to hear more about what she learned when she started tracking.
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.
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:
There is something really magical about taking data and turning it into a compelling visual image. Even though I’ve already written a bit about the importance of making data visual, I am consistently amazed at how data can be made more appealing and informative by creating eye-popping graphics. Today we are devoting this NFATW post to some amazing projects with beautiful data.
Tom MacWright is an engineer for MapBox and Development Seed and spends his time creating and using amazing visual representations of his data. Here are just two of many wonderful projects.
A New Running Map
Tom wasn’t happy with the data visualization he was getting from his Garmin GPS and heart rate watch so he decided to build his own using tools he works with every day. What came out was a really interesting interactive website that visualizes his running routes along with his heart rate. Click on the image above to play around with him data.
He’s also created a unique representation of the same time of running data (GPS + HR) that anyone can play with called Ventricle. Ventricle allows you to plot your own running data if you have .gpx files.
I’ve had a long standing interest in how I spend my time interacting with my computer. As a long time RescueTime user I’ve gotten used to having something watching my computer use and informing me about my habits. Tom was also interested in his computer use, but wanted something that had less functionality while still giving him information that was important. So, he developed Minute, a keystroke counter and visualization system that constantly records and displays the keystroke frequency over time.
By using a heat map he is able to better understand the pattern of his technology usage. Interestingly, he is also able to make inferences about his sleep and leisure time as he treats them as the inverse of his keystroke time:
Minute is an open-source application hosted on github so if you’re interested in understanding your own computer use or want to contribute to the project go take a look at the source code.
We’ll wrap up today with a quote from Tom’s post on what he learned from developing and using Minute:
Tracking nearly anything you do is alarming and humbling. The aggregates of our actions are lost on us: we can watch hundreds of hours of television and write it off as a small time commitment. How much is too much? It’s hard to make pretty charts without learning something and thinking about what they should look like.
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.
Alex Grey is developing a better kind of muscle sensor, to help people see their muscle activity patterns and change behaviors like typing or running to be more effective and less painful. The sensors are wireless, stick to your skin, and can measure different kinds of muscle activity including arm/leg (EMG) and heart muscles (ECG). In the video below, Alex describes how he used these sensors to find his optimal stride rate as a runner, as well as to detect when he was starting to fatigue or compensate on one side for an old injury. Fascinating talk with lots of great data! (Filmed by the Bay Area QS Show&Tell meetup group.)
The first speaker at last week’s QS meetup in San Francisco was Alexander Grey. He told us about the muscle-activity sensor he had developed and the fascinating things he had learned about himself from using it. The result of many years of thinking and work, he’s now eager to find collaborators, so he jumped at my suggestion to participate in this series.
Q: How do you describe Somaxis? What is it?
Grey: We have developed a small, wireless sensor for measuring muscle electrical output. The sensors stick onto the body adhesively (like Band-Aids) and transmit data to our smartphone app. One version “MyoBeat” uses a well established heart metric to provide continuous heart rate measurement (like a “chest strap” style sensor). A second version “MyoFit” uses proprietary algorithms to measures the energy output of other muscles. For instance, one on your quads while running can give you insight into how warmed up you are, how much work you are doing, fatigue, endurance, and recovery level. If you use two at the same time, it can show you your muscle symmetry (when asymmetry develops during exercise like running or bicycling, it can indicate the onset of an injury). Our goal is to get people excited about understanding how their bodies work.
Grey: My parents used to run a clinic that used muscle energy technology (sEMG) along with a special training method called Muscle Learning Therapy to cure people with RSI (Repetitive Strain Injury) and other work-related upper extremity disorders involving chronic pain. Each sEMG device they bought cost them $10K. I started to develop early symptoms of TMD (Temporomandibular Joint Disorder) when I was only 10, and my father used sEMG to teach me how to control and reduce my muscles’ overuse. The training worked, and I still have it under control today.
Years later, I decided to start a company to develop and commercialize more accessible / less expensive sEMG technology, with my mom as my investor. (My father has passed away, but I think he would have supported the idea.) At first we were going after a workplace safety service — I developed an algorithm that quantified people’s likelihood of developing an RSI injury in the future, and envisioned a prevention-based screening/monitoring service to offer to progressive companies. The feedback I got from VCs was that we needed to start with a bigger market. So we redesigned the product to make it small, cheap, and completely wireless. I also started working on a new set of sports-related algorithms to interpret muscle use into useful metrics.
Q: What impact has it had? What have you heard from users?
Grey: Having this new kind of tool at my disposal has really been a lot of fun, and has allowed me to run some new kinds of experiments that haven’t really been practical before.
For example, I wondered: for a given running speed, what cadence or stride rate would use the least energy, and so delay the onset of fatigue? I put sensors on my both quads, hamstrings, and calves. I created an audio track that increased from 120 – 170 bpm in increments of 5pm, 15 seconds on each. I kept my treadmill locked at 6.5 mph (my “comfortable pace”). By adding up the work done by all 6 muscles in the legs, I got a snapshot of the energy expenditure at each stride rate / cadence. The resulting curve [see graph above] answered my question: for me, at 6.5 mph, 130 bpm is my “sweet spot” that minimizes energy expenditure. It also showed a second trough in the graph, not as low as 130, but still pretty low, at 155 bpm. So if I need to run uphill or downhill, and want to keep the same speed but take shorter steps and still try to minimize energy burn as much as possible, I should shoot for 155 bpm.
Another test that these tools allow us to do is to figure out how recovered someone is from exercise. I did a test where I ran at a fixed speed every 24 hours (that’s not enough recovery time for me – I’m not in good shape). The first day, the muscle amplitude was about 1000 uV RMS (microvolts, amplitude). The second day, the amplitude started out at 500 uV and decreased from there. So the lack of sufficient recovery showed up in the data, which was quite interesting to see.
Whenever we have volunteers in the lab offering to help out (runners, usually) they geek out over these devices and the insight that they can get into the muscles of their bodies for the first time. We’ve had about 40 volunteers help out with muscle data gathering, and about 60 with heart rate testing.
Q: What makes it different, sets it apart?
Grey: Our design goals for our sensors are “good enough” data, wireless, long battery life, and comfort (wearability). Key to this is using a low-power, low-bandwidth radio. The trade-off is a much lower sample rate and a/d resolution than medical-grade sensors. Our sensor transmits processed data, not the raw data. However, our data is good enough for sports and fitness, where you want to see some predigested metrics and not raw graphs or frequency analysis. The benefit is that our battery life is 100 hours, and our sensor is small and light enough to attach using an adhesive patch. The up-side of an adhesive-based solution is that one-size fits all, it’s very comfortable, and there is no tight and annoying strap around your chest.
Q: What are you doing next? How do you see Somaxis evolving?
Grey: We are mainly focusing on improving the physical sensor itself: rechargeable battery, completely waterproof (current version is water resistant), and a smaller size. And maybe a medical-grade version with much higher sample rate and a/d resolution.
We also want to open up the hardware platform so that others can develop applications for it. For example, maybe someone wants to develop software for Yoga that uses muscle isolation to help do poses correctly. Or perhaps someone wants to focus on a weight-lifting application that assesses power and work done during lifting. We can envision many possibilities for sports, gaming, physical therapy, and health.
Q: Anything else you’d like to say?
Grey: I would love to hear from anybody who has ideas about potential uses of our technology! Also, we are fairly early-stage, so if anyone wants to work with us (individuals) or partner with us (companies) we definitely want to hear from you. You can reach me at firstname.lastname@example.org
Product: MyoLink platform: MyoBeat (heart) and MyoFit (muscle)
Website: www.somaxis.com (coming soon – there’s nothing there right now, but check back again soon)
Platform: Sensors stream data to an iPhone app (Android under development) and certain sports watches (Garmin, etc.)
Price: $25 for a starter set of 1 Module (MyoBeat or MyoFit) and 4 adhesive patches. Or you can buy 1, 2 or 3 Modules, with a one-year supply of patches, for $75, $125, or $170, respectively.
This is the 11th post in the “Toolmaker Talks” series. The QS blog features intrepid self-quantifiers and their stories: what did they do? how did they do it? and what have they learned? In Toolmaker Talks we hear from QS enablers, those observing this QS activity and developing self-quantifying tools: what needs have they observed? what tools have they developed in response? and what have they learned from users’ experiences? If you are a “toolmaker” and want to participate in this series, contact Rajiv Mehta at email@example.com.