Tag Archives: runkeeper
Yesterday evening I laced up my running shoes, connected my bluetooth headphones, turned on my Spotify playlist, and most importantly, hit “Go Running” on my Runkeeper app. About an hour later and I had run 6 miles at a decent pace of around 8 minutes per mile. And I knew this thanks to Runkeeper.
Founded in 2008, Runkeeper is designed to assist individuals who want to track their activities with GPS precision, whether that is walking, running, hiking, or cycling. If you’re moving outdoors, Runkeeper and similar apps, such as Strava or MapMyRun, use your smartphone’s GPS to pinpoint exactly where you are and how fast you’re moving. With all that data, you can train for your next marathon, discover new routes, and now, thanks to efforts by New York University researchers, take part in advancing public health research.
“We know from the existing literature that spatial characteristics like walkable neighborhoods and green spaces encourage exercise, but a lot of the details are still unknown.”
Last week, Dr. Rumi Chunara and her colleagues launched the Keeping Pace study. Over the next few months they hope to enroll participants who are willing to share their geo-located exercise data from Runkeeper. Because Runkeeper keeps a log of not only what you did, but where you did it, researchers hope to use the large amount of aggregated data to better understand physical activity patterns in communities around the United States.
“Typically, this type of research takes a long time and includes long, ardorous surveys or giving out GPS devices to participants,” said Dr. Chunara. “But with this type of data from apps people already use, we will be able to understand how the environment and exercise are related over more rapid and longer time periods.” With this data being contributed, the research team hope to understand differences in exercise choice between commuting and recreational activities, variation in activities among neighborhoods, and where people spend their time while being active.
Participants who enroll in Keeping Pace will be asked to complete a short demographic survey and then connect their Runkeeper account so researchers can access the type of activities they do and the GPS-based map associated with the activity. The Runkeeper data connection is being handled by a unique research platform, Open Humans.
A few years ago, Dr. Rumi Chunara was at a meeting hosted by the US Department of Health and Human Services. She was there to present and speak with colleagues about the growing importance of citizen science and crowdsourced data. There she met Jason Bobe, Executive Director of PersonalGenomes.org. They got to talking about some of their common insterests in open data, research, and new models for research participation. Later, when Dr. Chunara was designing GoViral, a project to examine how to leverage crowdsourced flu symptom information and diagnostics to predict illness risk, she ran into some issues with hosting and handling the amount of data participants were contributing. “It was obvious we needed some sort of platform to handle data,” said Dr. Chunara. She got back in touch with Jason, who helped her think about the issues and how to solve them.
This year, when it came to build out the infrastructure for the Keeping Pace study, Dr. Chunara decided to get back in touch with Jason and his colleagues, who were now developing OpenHumans.org. As we’ve written before, Open Humans represents a new way of thinking regarding researcher studies, participants, and the data being transferred between the two. The two teams, Open Humans and Dr. Chunara’s lab at NYU, worked together to develop an easy method for individuals to simultaneously allow researchers access to their Runkeeper data, and also maintain control over where and how that data flowed. Specifically, each individual who chooses to participate in the Keeping Pace study will be asked to create an Open Humans account, connect their Runkeeper account, and then authorize the Keeping Pace study to access their data. It sounds like a lot of work, but thanks to the designers and the use of Runkeeper’s API, it takes no more than five minutes to complete.
But why go through that trouble at all? Why not just have participants export their Runkeeper data and send it to the researchers? Why didn’t Dr Chunara and her colleagues build that data connection themselves?
Thanks to the proliferation of sensors, wearables, and smartphones, the ability to generate data about our lives is rapidly expanding. Pair that data with new efforts like the Precision Medicine Initiative and it’s easy to see the potential for researchers to understand our lives and our health in new and interesting ways. But what about the people who create that data? People, like myself, who strap on their phones when they go out for a run or log onto a website to report their flu symptoms. What do they have a right to in regards to their data? This is the question many researchers and scientific institutions are grappling with. But some have already taken a stand.
“What data are collected and how varies across research studies, but the question remains, ‘Who owns it?’ If someone is spending time generating then they should have control over it.”
Dr. Chunara and her colleagues chose to work with Open Humans because they shared the same perspective — participants should be in control of their data. “Open Humans has created an infrastructure that makes it easy to share and learn while respecting the participant and their data. That’s a noble motive, and it’s important,” said Dr. Chunara. Today, Keeping Pace is the first study to use Open Humans for data access and management for a research study. If successful, researchers may not only learn about exercise and the environment, but also about how studies that place an emphasis on participants’ data access and control may engage the public in new ways.
Keeping Pace is currently enrolling participants. If you’re a Runkeeper user and want to contribute your data to research, please visit the study website to learn more.
Keeping Pace was funded as part of the Agile Projects grants by the Health Data Exploration Network. If you’re a researcher, company, or individual interested in personal health data, sign up to become a network member. Membership is free.
Quantified Self Labs is dedicated to sharing stories and insights about the role of data access for personal and public health. We invite you to share your data access stories and this article. Then follow along on quantifiedself.com and @quantifiedself.
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.