Tag Archives: runkeeper
This week we’re taking a look back at our 2014 Quantified Self Public Health Symposium and highlighting some of the wonderful talks and presentations. We convened this meeting in order to bring together the research and toolmaker communities. Both of these groups have questions about data, research, and how to translate the vast amount of self-tracking data into something useful and understandable for a wider audience.
As part of our pre-conference work we took some time speak with a few attendees who we thought could offer a unique perspective. One of those attendees was Margaret McKenna. Margaret leads the Data & Analytics team at RunKeeper, one of the largest health and fitness data platforms. In our conversation and in her wonderful talk below Margaret spoke about two important issues we, as a community of users, makers, and researchers, need to think about as we explore personal data for the public good.
The first of these is matching research questions with toolmaker needs and questions. We heard from Margaret and others in the toolmaker community that there is a near constant stream of requests for data from researchers exploring a variety of questions related to health and fitness. However, many of these requests do not match the questions and ideas circulating internally. For instance, she mentioned a request to examine if RunKeeper user data matched with the current physical activity guidelines. However, the breadth and depth of data available to Margaret and her team open up the possibility to re-evaulate the guidelines, perhaps making them more appropriate and personalized based on actual activity patterns.
Additionally, Margaret brought up something that we’ve heard many times in the QS community – the need to understand the context of the data and it’s true representativeness. Yes, there is a great deal of personal data being collected and it may hold some hidden truths and new understanding of the realities of human behavior, but it can only reveal what is available to it. That is, there is a risk of depending too much on data derived from QS tools for “answers” and thus leaving out those who either don’t use self-tracking or don’t have access or means to use them.
Enjoy Margaret’s talk below and keep an eye out for more posts this week from our Quantified Self Public Health Symposium.
On July 4th, 2009 Jan Szelagiewicz decided to make a change in his life. After taking stock of his personal health and his family history with heart disease he began a weight-loss journey that included a variety of self-tracking tools. Over the course of a few years Jan tracked his diet, activities such as cycling, swimming, and running, and his strength. In this talk, presented at the Quantified Self Warsaw meetup group, Jan describes how he used self-tracking to mark his progress and stay on course.
Tracking diet and weight is nothing new and we’ve seen plenty of talks on the influence of carbohydrate intake on weight and metabolic values. But what about other pieces of daily life that could be influenced by what we eat? Adrienne Andrew Slaughter was testing out a new diet that included carbohydrate restriction. At the same time she was commuting to work on a bike. She started to notice feeling tired and slow during her commutes and wondered if her dietary changes had anything to do with it. Luckily, Adrienne was tracking her commutes and her diet and was able to run detailed data analysis to find out what happens when she goes carbless. You can watch her talk below, see her slides, and read her answers to our three prime questions.
You can also view the slides here [pdf].
We also asked Adrienne to answer the three prime questions:
What did you do?
I tracked two things: my bike commutes to work, and my adherence to a very low carb diet.
How did you do it?
I used RunKeeper to capture my rides, Strava to extract an uphill segment with no red lights, and Lift to track my adherence to a low carb diet.
What did you learn?
I learned that, especially for the first episode of eating very low carb, it took me longer to climb the hill on my way to work. When I increased my carb intake, I was able to climb the hill at my original speed. However, during the second episode of eating very low carb, I didn’t get as slow, and I returned to baseline fairly quickly– my body adapted to the change faster.
We’ve covered weight tracking here many times. It’s a very popular topic, and one of the easiest ways to get started with self-tracking. In this insightful talk from Matthew Ames we learn how weight tracking, in conjunction with diet and activity tracking, positively impact his weight loss and improved his fitness. (filmed at the Boston QS Meetup).
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:
“I was starting to feel a little bit out of control.”
Robert Carlsen used to be an amateur bike racer. When he moved to New York and stopped racing he found that his weight was slowly creeping up. He was still leading an active lifestyle, but he soon realized that most of daily food choices were the result of guess work. In this video, filmed at the New York City QS Meetup, Robert explains how he used different apps and tools to track his caloric inputs and outputs in order to move towards his goal weight.