Apple’s announcement of ResearchKit is strong evidence that Quantified Self practices are emerging as a major influence on medical research and other forms of knowledge making.
Apple talked about how their new effort focused on opening up health research is designed to combat five main current issues:
- Limited Participation
- Small sample sizes limit our understanding of diseases
- Reliance on subjective data
- Infrequent data provide only snapshots through time
- One-way communication from researcher to participant (and only at the end of the study, if at all)
Furthermore, the design of ResearchKit allows the participant to decide how data is shared. Apple will not see the data. Participants are allowed to be involved in the data collection in real-time, using the data they’re collecting to understand and inform their own health improvement plans.
In light of today’s announcement we wanted to highlight some of our favorite and most powerful examples of taking the research process into one’s own hands, making their own knowledge through thoughtful data collection and reflection. We invite you watch what’s possible now, and imagine with us what could be accomplished tomorrow.
Last year we gather a fantastic group of researchers, toolmakers, and science leadership at the 2014 Quantified Self Public Health Symposium to discuss how personal data can impact personal and public health. That meeting culminated in a great report that touches on many of the aspects discussed today regarding ResearchKit. We invite you to download, read, and share that report. For a more nuanced look into how ResearchKit may impact the research community, we’re highlighting four great talks from the the meeting.
Susannah Fox shares research from the Pew Internet and Life Project and describes the challenges ahead for promoting self-tracking.
Margaret McKenna explores the issues, challenges, and ideas large scale self-tracking applications have in mind when they consider working with the research community.
Jason Bobe talks about the lessons learned from involving research participants in the data ownership and discovery process.
Doug Kanter describes what he’s learned from tracking and visualizing his diabetes data.
If you’re interested in how ResearchKit will be affecting self-tracking, personal data, and access to information, research and knowledge making, then stay tuned to our Access Channel here on QuantifiedSelf.com and on Medium.
We are sure to have many great talks and sessions that focus on ResearchKit at our QS15 Conference and Actrivate Exposition. We invite you to join us.
In the lead up to our QS15 Global Conference and Activate Expo, we’re going to highlight our partners and sponsors that help us produce our events. If you’re interested in sponsoring our work or events, please get in touch.
Most of us spend a large percentage of our time at work. Next to sleeping, it’s likely the activity we do the most. Just like tracking sleep or exercise, there are a lot of things to be learned from tools that help an individual examine their time at work. RescueTime is such a tool.
RescueTime was co-founded by Robby Macdonell, a long time contributor to the QS community. Robby and his co-founders developed RescueTime to answer questions like: How much time do I spend on Twitter each day? Is Outlook my main time sink? Am I coding or daydreaming?
We’ve collected a few of our favorite examples of individuals using RescueTime to understand themselves and their work, starting with Robby’s own show&tell talk from our 2013 Quantified Self Global conference.
Robby Macdonnell: Tracking 8,300 Screen Hours
Robby works on product at RescueTime and has been tracking how he uses his computer and even his phone for over six years. In the fall of 2013 he presented his data and what he learned from tracking over 8,000 hours of screen time including how to do what we all only dream about - spending less time in email.
Robby also wrote up a fantastic blog post detailing a few different ways you can use RescueTime for interesting self-tracking projects: Getting the most out of RescueTime for your Quantified Self Projects
Buster Benson: How I use RescueTime
In 2011 Buster presented his “no input required” data capture using RescuTime. In this talk he describes how he used the data to better understand how he worked, what constitutes good and bad weeks, and how this data has become “a meaningful reflection of what I’m actually doing.”
Jamie Todd Rubin: How I Used RescueTime to Baseline My Activity in 2014 and Set Goals for 2015
In this excellent blog post, Jamie writes about his methods for using RescueTime to understand how he spent his time while working on his various computers. He describes how he used RescueTime data to better understand his time spent writing and how that data is helping him plan for the future. Jamie is a great resource for ideas related to exploring RescueTime data. Make sure to check out how he used it to find out what time of day he was actually writing.
Bob Tabor: Productivity, the Quantified Self and Getting an Office
Bob used RescueTime to analyze his productivity after becoming curious about the quantity and quality of his work while working at home. The ability to measure meaningful and productive work prompted him to find an office after he realized that he wasn’t as productive at home as he assumed.
Tamara Hala: On Using RescueTime to Monitor Activity and Increase Productivity
Tamara has been using RescueTime since 2012, sometimes even forgetting it was running in the background while she worked! In this excellent post she describes what she found out on a year-by-year basis and how it has impacted her work and productivity.
We hope to see you at the upcoming QS15 Conference and Activate Expo where you can meet with members of the RescueTime team and learn more about their tool in person.
There are excellent opportunities for getting involved in the QS15 Global Conference and the QS Activate exposition as a sponsor, including very affordable sponsor tickets, sponsored demos, and exhibit activations produced in collaboration with QS Labs and our production partner e2k Events. For more info, please get in touch.
We hope you enjoy this week’s selection of links, show&tell posts, and visualizations!
Hacking Your Brain by The Economist. Increasing performance and cognitive functioning, reducing depression, improving memory – if you could use a simple tool to get all these done, would you? What if that device was delivering electrical current to your brain? That’s the promise of transcranial direct current stimulation.
Talking Next-Gen Diabetes Tools with Dexcom Leaders by Mike Hoskins. Wonderful interview here with Terry Gregg (chairman) and Kevin Sayer (CEO) of Dexcom. Particular focus is given to their reaction and ideas regarding the open source Nightscout project.
Scientists threatened by demands to share data by Victoria Schelsinger. An older article (2013) about the shift towards open data and data sharing in academic science and it’s potential impact and possible pitfalls.
”’I think the public thinks that we’re all learning from everyone else’s work. That’s not true, and furthermore, it’s not true in ways that are even worse than you might think.’” – Heather Piwowar
Changing Representation of Self-Tracking by Deborah Lupton. It’s always great to hear that Deborah has released new writing. Her thoughtful analysis about self-tracking, data as culture, and data as object is consistently fantastic. Great addition to her growing body of work here.
Why Pets Are the Future of Fitness Wearables by Annie Lowrey. An interesting take on how the rise of tracking tools for pets may impact pet owners. Reminds me of research conducted by my old colleagues at San Diego State University: Physical activity, weight status, and neighborhood characteristics of dog walkers (Spoiler: Having a dog is associated with being more physically avtive.)
This guy is the Mark Zuckerberg of open-source genetics by Daniela Hernandez. A few weeks ago we highlighted an article by Daniela that focused on the fantastic openSNP project. She’s back with a profile of one of the founders, Bastian Greshake. (Full disclosure: I am openSNP member #610.)
Personal Sleep Monitors: Do They Work? by Christopher Winter. Superb experiment here to try and understand the accuracy of different sleep trackers.
What I’ve learned after 10 years of quantifying myself by Maxim Kotin. The title says is all.
A History of Checkins: Facebook Checkin Stats by Octavian Logigan. Octavian breaks down three years of his location checkin history and describes what he learned through examining seasonal trends, category breakdowns, and travel patterns.
I love the sleep tracker, so I can quantify this kind of information! (I have a 2yo and a 5yo….) by reddit user EclecticBlue. Fun visualization here of Fitbit sleep data. Also, great comments in the thread.
Locals & Tourists by Mapbox & Eric Fischer. I could spend hours exploring this interactive map of tweet locations by “tourists” and “locals”. (Special thanks to Beau Gunderson for point out that Eric also did a similar project with geotagged Flickr photos)
The Impact of Weather on Human Activity by Paul Veugen. The team at Human “1.9M activities in Boston & NYC to see the impact of weather on Human activity.” Make sure to click through for the full visualization.
FCC & FDA moving connected health forward by establishing wireless medical test beds
Nike+ Running Expand Global Partnerships
Will Our Fitness Data Be Used Against Us?
As the “quantified self” industry explodes, who will control the data — us or them?
This Week on QuantifiedSelf.com
Gordon Bell: Every Beat of My Heart
QS15 Conference Preview: Stephen Cartwright on 17 Years of Location Tracking
What’s in My Gut
This past fall we learned about a unique study, conducted at Stanford University, designed to contribute to the understanding of the human microbiome. This study also has a component not common to academic research — data is being returned to the participants. Intrigued, I contacted the principle investigator, Les Dethlefson, to learn more.
Ernesto: Tell me about the Dynamics of Human Microbiota study.
Les Dethlefsen: Since I joined the Relman Lab at Stanford, I’ve been looking at the human gut microbiota, focusing on what affects it and how it changes over time. In our study, we are looking at three different perturbations, deliberate changes to the gut ecology, to see how the microbiota population is affected.
We are very interested in the patterns that emerge. In people who have very stable gut microbiota, does their microbiota remain that way when they undergo diet shifts, a colon cleanout, or an antibiotic? Or maybe people who have a stable gut microbiota most of the time are the ones who are most affected by something unusual such as taking antibiotics. We just don’t know enough to understand these patterns right now. So, we’re really looking for basic ecological information.
Ernesto: If you look at the popular press, it seems the microbiome is the new golden child of biological life sciences. We’re even seeing companies in Silicon Valley get involved with this kind of work.
Les: It is broader than that. It really is a worldwide interest on the parts of both the scientific community and the public. And unfortunately, we are probably going to see some overhype, just as we did with the Human Genome Project. But I do believe this is a very important area. I think there will be a lot of payoffs and health impacts from this research, although it’s not going to be everything.
The shift that, I think, would be good for us to make intellectually is to get rid of the “us vs. them” thinking, because we are symbiotic organisms.
We have evolved with a native gut microbiota, and native microbiota is pretty much everywhere. We have evolved together, so it’s fallacious — an artifact of our past ignorance — that we don’t think of our microbes as part of our physiology.
Ernesto: It seems like exploring the deep sea, an unknown world that we’re just starting to peek into.
Les: It’s along those lines. You’re not wrong about that. But unlike, let’s say, the deep waters surrounding an undersea hydrothermal vent, we already know a lot about human physiology. There are a lot of molecular details and genetic pathways that we already have worked out. The context is somewhat understood.
And now, we have a reasonable start on the initial research: What microbes are present, and where? What’s the range of what we think is the normal distribution? We certainly don’t know enough, because we only know about people in the developed world. However, this may not represent all of human diversity or a very natural state of the gut microbiota.
Ernesto: Let’s get back to your study. You are asking participants to send microbiome data in the form of fecal matter and urine to your lab. What are you doing with those samples?
Les: We ask participants to provide both stool and urine samples. With the stool sample, we apply four different methodologies to turn it into data. One is the very common 16S ribosomal RNA (16S rRNA) gene sequencing approach. It’s relatively standard and inexpensive. It acts like an ID card for microbial taxa — telling us approximately what strains are present and in what relative abundance. We have a lot of data like that already for comparison.
The second approach we will be applying is metagenomic sequencing, wherein we will be sequencing a random selection of all the genomes of the microbial types that are present. We can’t take this to completion, even with the dropping cost of sequencing, especially because there are some very, very rare microbes that we barely even have the chance to see at all. But we can get a pretty good swathe of genetic sequence data from all the microbes.
The third approach is even more ambitious. It’s called metatranscriptomics. Genes can be carried by any critter, you and I included, but not expressed. Knowing which genes are turned on, and to what extent they’re turned on is a better measure of the biological activity that is actually happening. The metagenome is a measure of potential activities, what the bugs can do. The metatranscriptome shows what the microbes are actually doing. Metatranscriptomics is even more challenging than metagenomics partly because of the nature of messenger RNA (mRNA). It’s a highly unstable molecule. There are technical challenges, but we’re ambitious enough to try to collect information on gene expression.
The fourth approach is not based on gene sequences, but on chemical composition. Metabolomics is the name given to a number of these approaches that are not directed to a specific chemical. These are techniques that try to measure a broad swathe of chemicals present in the environment and their relative abundance. This is a technology that we, in the Relman Lab, know very little about. We’re collaborating with the Nicholson Lab in Imperial College in London, and they will be doing the metabolomic analyses on the stool samples. That may be even closer to where the rubber meets the road — knowing not just the gene expression but also the resulting chemical changes that are happening in the environment.
Metabolomics takes us to the other type of sample we’re collecting: the urine samples. We aren’t doing this because we have an interest in the urinary microbiome itself, but because, as the Nicholson Lab suggested, the urine provides a more complete, integrated picture of the co-metabolism between the human host and most of the gut microbiota. So while metabolomics for the stool samples would primarily measure the gut microbial activity and what they contribute to the host’s physiology, the urine provides a more integrated picture about how the host metabolism works in concert with the gut microbiota.
Ernesto: If a participant is going to be contributing all of that data, will they have access to it?
Les: As someone with similar interests, I certainly knew that a huge motivation for people to join the study would be the access to their own data. We offer monetary compensation, but for the amount of time that will be spent in contributing samples, it is probably trivial. We knew we would attract the curious, scientifically inclined, and practising scientists. Of course, they would want to see their data.
The Institutional Review Board (IRB) was quite open to us sharing information with the participants about their own microbiota. It probably helped that there’s publicity about ways people can get this information. There is the American Gut project, offering an assessment of your microbiota for a donation, and uBiome, a private company offering the same kind of service.
I, or another staff member of the study, are going to share this microbiota data with each participant in a conference call. So in effect, I’m going to be a microbiota counselor. It’s nowhere near as high-stakes as sharing genome information. We don’t know enough to say, for example, that this microbiome is definitively healthy, or that it’s unhealthy, or what the exact risks of diseases are due to this particular composition. So we will be putting this information in context, and we will be available as interpreters of the scientific literature. We may be able to say that there is a statistical association between a particular microbial group that someone may have in their gut and some health-related outcome.
Ernesto: Will participants be getting a copy of their data as well?
Les: Yes, we will provide that. I have an open source mentality. Added to that is the fact that there are many practicing scientists signing up for the study and saying they want data, not just a PDF summary. I am happy to provide the data in as raw a format as people want. They can get the raw sequence information, a low-level summary (which is the result of the first pass of data processing), or the final summary. I have permission and full intention to share all the data derived from a person’s samples with that person.
Ernesto: Do you think we will see this happening more in the future?
Les: I think we will probably see more of it in the future. We’re moving in the direction of access to information. The open source movement has reached the health and medical realm from its origins in tech and computing. I think the participatory nature of access to data and scientific information is a good thing. It has started, and I don’t see any way of reversing the trend. I would hope that it becomes the norm that there is some appropriate level of sharing, that research participants have access to their data if they wish, and in a way that lets them interpret that data appropriately.
I believe that people have a right to that level of knowledge about their bodies, and if we, scientists, are generating that knowledge, there’s no reason not to share it with the individuals.
The Dynamics of Human Microbiota study is currenlty recruiting participants. If you’re interested in learning more about the ecosystem within read more about the study and check to see if you’re eligible to participate here.
On June 18-20 we’ll be hosting the QS15 Conference & Activate Expo in San Francisco at the beautiful facilities at the Fort Mason Center. This will be a very special year with two days of inspiring talks, demos, and discussion with your fellow self-trackers and toolmakers, plus a third day dedicated to the Activate public expo. As we start to fill out our program we’ll be highlighting speakers, discussion leaders, sponsors, and attendees here.
Stephen Cartwright has been attending the QS Conferences since 2012, where he first spoke about his ambitious geolocation tracking project. As an associate professor at the School of Art and Design at the University of Illinois at Urbana-Champaign, where he teaches sculpture, digital fabrication, and furniture design, Stephen brings an interesting and welcomed point of view and set of experiences to our show&tell program.
At the QS15 Conference he will be sharing his process and what he’s learned from tracking his location every hour using a GPS for the last 17 years. He will describe how his practice has changed and adapted to new technologies over the years, including how active versus passive tracking techniques have impacted this project.
My tracking informs my life and especially my art, so I will consider my tracking through the lens of my 3D data visualization sculpture. The artistic aspect of my work allows the data visualization to become more than informative graphs, they become new landscapes of data.
We’re excited to have Stephen joining us and asked him a few questions about himself and what he’s looking forward to at the conference.
QS: What is your favorite self-tracking tool (device, service, app, etc)?
Stephen: This is a difficult question, I use different tools for different stages of my work. My practice would be nowhere without a GPS. It took me a long time to replace my Garmin stand-alone GPS but I now use the MotionX GPS app for my iPhone. My requirements for these apps/devices is that the waypoints have to be saved with the date and time attached.
QS: What are you most looking forward to at the conference?
Stephen: The conference is a great place to be among like-minded people and share ideas and inspiration. Although all the attendees have a lot in common everyone comes to self-tracking from a different angle and seeks different outcomes. I love to see how similar practices result in improvements in performance and health, self-help, and even art.
QS: What should people come talk to you about at the conference?
Stephen: Come talk to me about the intersection of art and science, data-visualization, and GPS/location tracking.
QS: What tools, devices, or apps do you want to see at the conference?
Stephen: I am looking for the best smart phone based step and movement tracker.
QS: What topic do you think that Quantified Self community is not talking enough about?
Stephen: I would like to hear more about the relationship between individual trackers and larger data studies. How well do we know ourselves as compared to what can be inferred about us by our data footprint or studies of people in similar circumstances?
Stephen’s session is just one of the many hands-on, up-to-date, expertly moderated sessions we’re planning for the QS15 Global Conference and Exposition. We’ve made some early bird tickets available for readers of the Quantified Self blog (for a limited time):
Bonus Video of Stephen’s Data:
Gordon Bell has been involved with self-tracking for over a decade. From his ground-breakign MyLifeBits project to his popular book on the possibilities of a fully digital life he is constantly thinking about new ways we can understand ourselves through the data we collect. We are always excited to see him at our QS events and were especially happy to have him reach out to us about presenting at our last Bay Area QS meetup.
Gordon has experienced two heart attacks, one in 1983 and another in 1996, two double bypasses, and currently is living with his third pacemaker. It probably isn’t surprising given his medical history that he has a keen interest in understanding his heart. In this talk Gordon describes what he’s been learning from the data collected from his pacemaker and the 320 days of heart rate and activity data he has collected with his Basis watch.
We’re also excited to have Gordon joining us at our upcoming QS15 Conference & Activate Expo. We’ve made some early bird tickets available for readers of the Quantified Self blog (for a limited time): Register here!
We have a great list for you today. Special thanks to all those who are reaching out via Twitter to send us articles, links, and other bits of interestingness. Keep ‘em coming!
Self-Experimentation: Crossing the Borders Between Science, Art, and Philosophy, 1840–1920 by Katrin Solhdju. This brief essay lays out a great foundation for anyone interesting in the history and philosophy of science, with an obvious focus on the self-experiment. This essay is hosted at the Max Plank Institute for the History of Science, at which I highly recommend spending some time clicking around and reading the wonderful essays and articles.
After the Data Confessional: interview with Ellie Harrison by Stephen Fortune. A very interesting and thought-provoking interview with artist Ellie Harrison. For six years self-tracking data was the core component of Ellie’s work as an artist. Then she decided to stop and reconsider her tracking practices and what it meant to her and her work.
Data is the New “___” by Sara M. Watson. “What do we talk about when we talk about data?” is the question Sara posses here to frame a wonderful piece on how our use of metaphors influences our view of data.
A brief history of big data everyone should read by Bernard Marr. If we’re going to talk about how we talk about data it is probably useful to have some historical context. Great timeline here of data in society.
Baby Lucent: Pitfalls of Applying Quantified Self to Baby Products [PDF] by Kevin Gaunt, Júlia Nacsa, and Marcel Penz. An interesting article here from three Swedish design students that looks at current baby and parenting tracking technology. They also conducted a design process to develop a future tracking concept to better understand parent’s reactions to baby tracking. I thought there were a few interesting finding from their interviews.
Hey, Nate: There Is No ‘Rich Data’ In Women’s Sports by Allison McCann. It only seems fitting that a few days before this weekend’s MIT Sloan Conference on Sports Analytics Conference, the “it” place to learn about and discuss sports data, that we learn about the amazing dearth of data collected and published about women’s sports.
Analyzing Email Data by Austin G. Waters. A great deep dive into the 23,965 emails that Austin has collected in his personal account since 2009. I won’t spoil it, but this post just keeps getting better and better as you scroll. Bonus points to Austin for describing his methods and open-sourcing the code he used to conduct this analysis.
The App That Tricked My Family Into Exercising by Adam Weitz. Not a lot of data in this post, but I enjoyed the personal and social changes Adam described through his use the Human activity tracking app.
Smart Art by Natasha Dzurny. Using IFTTT and a few littleBits modules Natasha created a piece of artwork that reflects how often she goes to the gym. Would love to seem more DIY data reflections like this!
How does weather affect U.S. sleep patterns? by Sleep Cycle. Sleep Cycle analyzed 142,272 sleep reports from their users (recorded in January of 2015) to explore mood upon awakening, stress levels before bed, and sleep quality. Fascinating stuff.
HHS Expands Its Approach to Making Research Results Freely Available For the Public
Many Patients Would Like To Hide Some Of Their Medical Histories From Their Doctors
Doctors say data fees are blocking health reform
From the Forum
Best ECG/EKG Tool for Exercise
BodyMedia API – Anyone have an active key/application?
Sleep monitor recommendations for research on sleep in hospitals
Simplified nutrition, alertness, mood tracking
Greg Kroleski has been tracking his time for the past six years, starting when he was 20-years old. Using a spreadsheet he designed himself he collects how much time he spends in eight different categories: Survival, Labor, Social, Spiritual, Mind, Expression, Body, and Distraction. In this talk, presented at the San Francisco QS meetup group, Greg describes the data he’s collected and what he’s learned about where his time goes. If you’re interested in applying his tracking methodology he’s graciously put his spreadsheet template online here.
In our Access Channel we’re trying to expose ideas, efforts, and insights about personal data access and it’s role in both generating personal and public insights. The last time we wrote about data donation we mentioned a few different projects that allowed you to collect and/or publish your self-tracking data for others to view and access. Today we’re going to showcase a few research-focused projects that collect personal data, but also allow participants to access the data they contribute. This seemingly minor addition, participant access to data, is actually a process not commonly employed by research studies. We’re very interested in new participatory models of research that respect participant’s rights to fully understand and access the data they contribute. If you know of others please get in touch and we’ll add them to the list.
Personal Genome Project: Harvard
Probably the most well-known of these research projects is the ongoing Personal Genomes Project based at Harvard University (PGP). Led by George Church and an outstanding team, the PGP is an ongoing research project recruiting participants to “share their genetic, health, and trait data in a public and non-anonymous manner. Participation is free.
Much like the project above, the American Gut project is an open call for participant to collect and share their data. In this case it is human microbiome data. Although enrollment is not free (they request donations starting at $99 to participate) data is returned to participants. (If you’re interested in participating in microbiome research, but live in Europe see the British Gut project)
Dynamics of the Human Microbiota
This new project, based out of Stanford, is also exploring the human microbiome. This study includes a variety of different perturbations and longitudinal data collection. Participants are compensated for their participation, their data is made accessible to them, and they have the opportunity to discuss their results with the study staff.
For those of you interested in research methods and ethics we recommend reading this brief article by Jeantine E. Lunshof, George M. Church, and Barbara Prainsack: Raw Personal Data: Providing Access