We hope you enjoy this week’s list!
Are Google making money from your exercise data?: Exercise activity as digital labour by Christopher Till. Christopher describes his recent paper, Exercise as Labour: Quantified Self and the Transformation of Exercise into Labour, which lays out a compelling argument for considering what happens when all of our exercise and activity data become comparable. Are we destined to become laborers producing an expanding commercialization of our physical activities and the data they produce?
How Big is the Human Genome? by Reid J. Robinson. Prompted by a recent conversation at QS Labs, I went looking for information about the size of the human genome. This post was one of the most clear descriptions I was able to find.
Visualizing Summer Travels by Geoff Boeing. A mix of Show&Tell and visualization here. Geoff is a graduate student and as part of his current studies he’s exploring mapping and visualization techniques. If you’re interested in mapping your personal GPS data, especially OpenPaths data, Geoff has posted a variety of tutorials you can use.
Symptom Portraits by Virgil Wong. For 30 weeks Virgil met with patients and helped them turn their symptoms into piece of art work and data visualization.
Data Visualization Rules, 1915 by Ben Schmidt. In 1915, the US Bureau of the Census published a set of rules for graphic presentation. A great find by Ben here.
As part of the Quantified Self Public Health Symposium, we invited a variety of individuals from the research and academic community. These included visionaries and new investigators in public health, human-computer interaction, and medicine. One of these was Jason Bobe, the Executive Director of the Personal Genome Project. When we think of the intersection of self-tracking and health, it’s harder to find something more definitive and personal than one’s own genetic code. The Personal Genome Project has operated since 2005 as a large scale research project that “bring together genomic, environmental and human trait data.”
We asked Jason to talk about his experience leading a remarkably different research agenda than what is commonly observed in health and medical research. From the outset, the design of the Personal Genome Project was intended to fully involve and respect the autonomy, skills, and knowledge of their participants. This is manifested most clearly one of their defining characteristics, that each participant receives a full copy of their genomic data upon participation. It may be surprising to learn that this is an anomaly in most, if not all, health research. As Jason noted at the symposium, we live in an investigator-centered research environment where participants are called on to give up their data for the greater good. In Jason’s talk below, these truths are exposed, as well as a few example and insights related to how the research community can move towards a more participant-centered design as they begin to address large amounts of personal self-tracking data being gathered around the world.
I found myself returning to this talk recently when the NIH released a new Genomic Data Sharing Policy that will be applied to all NIH-funded research proposals that generate genomic data. I spent the day attempting to read through some of the policy documents and was struck by the lack of mention of participant access to research data. After digging a bit I found the only mention was in the “NIH Points to Consider for IRBs and Institutions“:
[...] the return of individual research results to participants from secondary GWAS is expected to be a rare occurrence. Nevertheless, as in all research, the return of individual research results to participants must be carefully considered because the information can have a psychological impact (e.g., stress and anxiety) and implications for the participant’s health and well-being.
It will not be surprise to learn that the Personal Genome Project submitted public comments during the the comment period. Among these comments was a recommendation to require “researchers to give these participants access to their personal data that is shared with other researchers.” Unfortunately, this recommendation appears not to have been implemented. As Jason mentioned, we still have a long way to go.
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.
Personal data, personal meaning. That’s the guiding principle of much of the work we do here at QS Labs. From our show&tell talks and how-to’s, to our worldwide network of meetups and carefully curated unconferences, we strive to help people make sense of their personal data and inspire others to do the same. However, over the last few years we’ve started to see that there is a third actor in the Quantified Self space. Data collected in the ordinary course of life can hold clues about some of our most pressing questions related to human health and wellbeing. Personal data might be a resource for public good.
On April 3, 2014 Quantified Self Labs with support from the Robert Wood Johnson Foundation, the US Department of Health and Human Services, and Calit2 at UCSD hosted the first Quantified Self Public Health Symposium. We gathered over 100 researchers, toolmakers, science leaders, and pioneering users to open up a discussion about what it means to use personal data for the public good. Over the course of the day we hosted a variety of talks, discussions, and toolmaker demonstrations. This week we’ll be highlighting some of the outstanding talks delivered at the symposium and we’re kicking it off with one of our favorites.
Susannah Fox has been a friend and colleague for many years. Her pioneering work at the Pew Internet and Life Project has inspired us many times over and remains the standard for research pertaining to self-tracking. We asked Susannah to help us open up the meeting by discussing some of her research findings as well as her thoughts on self-tracking in the broader landscape of health and healthcare.
(A transcript of Susannah’s talk can be found on her website here.)
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.
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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.
Lee Rogers has been collecting data about himself for over three years. The daily checkins, movements, and other activities of his life are capture by automatic and passive systems and tools. What makes Lee a bit different than most is that he’s set up a personal automation system to collect and make sense of all that data. A big part of that system is creating an annual report every year that focuses on his goals and different methods to display and visualize the vast amount of information he’s collecting. In this talk, presented at the Bay Area QS meetup group, Lee explains his data collection and why he values these annual snapshots of his life.
At our recent Bay Area QS meetup, Kevin Krejci presented a short update about the ongoing self-tracking and treatment projects he’s undergoing as part of living with Parkinson’s Disease. Back in January, Kevin first presented his tracking journey and how he’s using different tools to understand and improve his life. Watch his short talk below to see how he’s progressing.
In 2013 Eric Boyd started using a Nike FuelBand to track his activity. Not satisfied with the built in reporting the mobile and web applications were delivering he decided to dive into the data by accessing the Nike developer API. By being able to access the minute-level daily data Eric was able to make sense of his daily patterns, explore abnormalities in his data, and learn a bit more about how the FuelBand calculated it’s core metrics. Watch Eric’s talk from our 2013 Quantified Self Europe Conference to hear more about Eric’s experience.