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Today’s post comes to use from our friend and co-organizer of the Bay Area QS meetup group, Rajiv Mehta. Rajiv and Dawn Nafus worked together to lead a breakout session that focused on self-tracking in the family setting at the 2014 Quantified Self Europe Conference. They focused on the role families have in the caregiving process and how self-tracking can be used in caregiving situations. This breakout was especially interesting to us because of the recent research that has shed a light on caregivers and caregiving in the United States. According to research by the Pew Internet and Life Project, “39% of U.S. adults are caregivers and many navigate health care with the help of technology.” Furthermore, caregivers are more likely to track their own health indicators, such as weight, diet and exercise. We invite you to read the description of the breakout session below and then join the conversation on the forum.
Families & Self-Tracking
by Rajiv Mehta
In this breakout session at the Amsterdam conference, we explored self-tracking in the context of family caregiving. In the spirit of QS, we decided to “flip the conversation” — instead of talking about “them”, about how to get elderly family members to use self-tracking technologies and to allow us to see their data, we talked about “us”, about our own self-tracking and the benefits and challenges we have experienced in sharing our data with family and friends. These are the key themes that emerged.
Share But Not Be Judged
Feeling like you’re being judged, and especially misjudged, by someone else seeing your data is a very negative experience. People want to feel supported, not criticized, when they open up. Ironically, people felt that reminders and “encouragement” by an app, knowing that it is based on some impersonal algorithm, was sometimes easier to accept than similar statements from family. The interactions we have with family members aren’t neutral “reminders” to do this or that; they’re loaded with years of history and subtext. One participant commented “What I really want is an app that trains a spouse how not to judge.”
Earn The Right
So much is about learning how to earn the right to say something—that’s an ongoing negotiation, and both people and machines have to earn this. Apps screw it up when they try to be overfamiliar, your “friend.” I recalled a talk from the 2013 QS Amsterdam conference of a person publicly sharing his continuous heart rate monitoring, whose boss had noticed that the person’s heart rate had not gone up and demanded to know why he was not taking a deadline seriously! Such misjudgments can kill one’s enthusiasm for sharing.
Myth Of Self-Empowerment
Just because you’re tracking something, and plan to stick to some regimen or make some behavioral change, doesn’t mean you’re actually empowered to make it so. Family members need to be sensitive to the fact that bad data (undesirable results, lack of entries, etc.) may be a “cry for help” rather than an occasion for nagging.
Facilitating Dialog and Understanding
On the positive side, sharing data can lead to more understanding and richer conversations amongst family members. One participant described his occasional dieting efforts, which he records using MyFitnessPal and shares the information with his mother. This allows her to see how he is able to construct meals that fit the diet parameters (and so learn from his efforts), and also to just know that he is eating okay. I described the situation of a friend with a serious chronic disease who was tracking her energy levels throughout the day. In considering whether or not to share this tracking with her family she realized that they had very little appreciation of how up-and-down each day is for her. So, before she’s going to get benefits from sharing continuous energy data, she’s going to have to help her family understand the realities of her condition.
Sense of Control
Everyone felt that one key issue was that the self-tracker feel that s/he is the one making the decision to share the data, and has control over what to share, when to share, and who to share with.
We hope that before people design and deploy “remote monitoring” or “home tele-health” systems to track “others”, they first take the time to share their own data and see what it feels like.
If you’re interested in reading further about technology and caregiving we suggest the recently published report from the National Alliance for Caregiving, “Catalyzing Technology to Support Family Caregiving” by Richard Adler and Rajiv Mehta.
At our 2014 Quantified Self Europe Conference, as with all our events, we sourced all of our content from the attendees. During the lead up were delighted to have some amazing interactions with attendees Alberto Frigo and Danielle Roberts, both of whom have been engaged with long-term tracking projects. This theme of “Tracking Over Time” was nicely rounded out by our longtime friend and New York QS meetup organizer, Steven Dean. Steven has been tracking himself off and on for almost two decades. In the talk below, Steven discusses what led him to self-tracking and how he’s come to internalize data and experiences in order to create his sense of self.
Quantified Sense of Self
by Steven Dean
Twenty years ago, I was in grad school getting an MFA. I was making a lot of objects that had very strong autobiographical component to it. Some I understood the source of. Many I did not. Continue reading
Today’s post comes to use from Anne Wright and Eric Blue. Both Anne and Eric are longtime contributors to many different QS projects, most recently Anne has been involved with Fluxtream and Eric with Traqs.me. In our work we’ve constantly run into more technical questions and both Anne and Eric has proven to be invaluable resources of knowledge and information about how data flows in and out of the self-tracking systems we all enjoy using. We were happy to have them both at the 2014 Quantified Self Europe Conference where they co-led a breakout session on Best Practices in QS APIs. This discussion is highly important to us and the wider QS community and we invite you to participate on the QS Forum.
Best Practices in QS APIs
Before the breakout Eric and I sorted through the existing API forum discussion threads for what issues we should highlight. We found the following three major issues:
- Account binding/Authorization: OAuth2
- Time handling: unambiguous, UTC or localtime + TZ for each point
- Incremental sync support
We started the session by introducing ourselves and having everyone introduce themselves briefly and say if their interest was as an API consumer, producer, or both. We had a good mix of people with interests in each sphere.
After introductions, Eric and I talked a bit about the three main topics: why they’re important, and where we see the current situation. Then we started taking questions and comments from the group. During the discussion we added two more things to the board:
- The suggestion of encouraging the use of the ISO 8601 with TZ time format
- The importance of API producers having a good way to notify partners about API changes, and being transparent and consistent in its use
One attendee expressed the desire that the same type of measure from different sources, such as steps, should be comparable via some scaling factor and that we should be told enough to compute that scaling factor. This topic always seems to come up in discussions of APIs and multiple data sources. Eric and I expressed the opinion that that type of expectation is a trap, and there are too many qualitative differences in the behavior of different implementations to pretend they’re comparable. Eric gave the example of a site letting people compare and compete for who walks more in a given group, if this site wants to pretend different data sources are comparable, they would need to consider their own value system in deciding how to weight measures from different devices. I also stressed the importance of maintaining the provenance of where and when data came from when its moved from place to place or compared.
On the topic of maintaining data provenance, which I’d also mentioned in the aggregation breakout: a participant from DLR, the German space agency, came up afterwards and told me that there’s actually a formal community with conferences that cares about this issues. It might be good to get better connections between them and our QS API community.
The topic of background logging on smartphones came up. A attendee from SenseOS said that they’d figured out how to get an app that logs ambient sound levels and other sensor data on iOS through the app store on the second try.
At some point, after it seemed there weren’t any major objections to the main topics written on the board, I asked everyone to raise their right hand, put their left over their heart, and vow that if they’re involved in creating APIs that they’d try hard to do those right, as discussed during the session. They did so vow.
After the conference, one of the attendees even contacted me, said he went right to his development team to “spread the religion about UTC, oAuth2 and syncing.” He said they were ok with most of it, but that there was some pushback about OAuth2 based on this post. I told him what I saw happening with OAuth2 and a link to a good rebuttal I found to that post. So, at least our efforts are yielding fruit with at least one of the attendees.
We are thankful to Anne and Eric for leading such a great session at the conference. If you’re interested in taking part in and advancing our discussion around QS APIs and Data Flows we invite you to participate:
Justin Timmer is a student in human movement science and a fitness instructor. He was interested in exploring what he could do to increase his strength. Rather then starting with a typical strength training program Justin wanted to test if isometric muscle contraction alone could increase his strength. This type of exercise involves just squeezing the muscles without using any weight. He even went so far as to only target one side of his body so that he could test against his non-squeezing muscle groups. In this talk, presented at the 2014 Quantified Self Europe Conference, Justin explains his process and the results of this 4-week experiment.
What did you do?
For four week, I was “squeezing” (isometric contractions) my muscles four times a day. I trained my right leg, abdominals, and right chest and arm.
How did you do it?
During every quiet moment during the day I contracted my muscles as long and hard as possible. I quantified my progress by completing maximum repetitions on a fitness machine every week.
What did I learn?
I learned that in four weeks I almost doubled my force on the right side of my body. But I also learned that this training was going too fast, I got a lot of issues with little unexplained pains in my legs, and rising fluids whenever I contracted my abdominals. Overall I learnt this was a very effective training that was very easy to implement in my daily life.
You can also view Justin’s slides here.
A bit of a change this week. Today we’re posting some of our favorite academic and scholarly articles dealing with many different aspects of Quantified Self tools and methods. If that’s not for you, make sure to scroll down for some great self -tracking projects and visualizations. (Make sure to click [pdf] for the full article.)
Understanding Physical Activity through 3D Printed Material Artifacts [pdf] by Rohit Khot, Larissa Hjorth, and Florian Mueller. A fascinating paper on what happens when you transform digital physical activity data into representative physical objects.
Personal Tracking as Lived Informatics [pdf] by John Rooksby, Mattias Rost, Alistair Morrison and Matthew Chalmers. The authors of this research paper interviewed users of self-tracking tools to better understand how they incorporate personal data into their lives. From the abstract, “We suggest there will be difficulties in personal informatics if we ignore the way that personal tracking is enmeshed with everyday life and people’s outlook on their future.”
Persuasive Technology in the Real World: A Study of Long-term Use of Activity Sensing Devices for Fitness [pdf] by Thomas Fritz , Elaine M. Huang, Gail C. Murphy and Thomas Zimmermann. The authors of this study interviewed thirty individuals who had been using different activity tracking tools for different amounts of time (3-54 months). Those interviews unearthed some of the reasons why people starting using and continue to find activity trackers useful in their lives.
Using MapMyFitness to Place Physical Activity into Neighborhood Context by Jana Hirsch, Peter James, Jamaica Robinson et al. What can you find out about a population by partnering with a QS toolmaker? Jana Hirsch and colleagues tried to answer that question by partnering with MapMyFitness to better understand where and how individuals in Winston-Salem, North Carolina were exercising.
Visualized and Interacted Life: Personal Analytics and Engagement With Data Doubles [pdf] by Minna Ruckentstein. Don’t let the the title fool you, this article is not about new analytical methods for personal data. Rather, it is an thorough examination of the phenomenology of self-tracking and how people construct understanding of themselves through personal data collection.
Stress Trigger Personal Survey by Paul LaFontaine. We were lucky to hear about Paul’s stress tracking at the 2014 QS Europe Conference. While we work on getting that talk edited and posted online we thought this would be a great sneak preview.
Data, Pictures, and Progress by Chris Angel. Chris found out about QS while he was thinking about figuring out how to best lose weight. This post is his “first quarter” report from 2014.
Google has most of my email because if has all of yours by Benjamin Mako Hill. Benjamin has been running his own email server for 15 years. After a conversation with a friend he began wondering about how much email Google has a copy of. What followed was an amazingly in-depth analysis.
Strava Labs Global Heatmap. You can explore over 220 billion data points from almost 100 million different running and cycling activities tracked with the Strava app. (If you’re interested in the engineering side of this visualization they’ve written a great blog post here.)
From the Forum
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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.
Yesterday we posted our first opening plenary talk from the 2014 Quantified Self Europe Conference. Today we are happy to post our second talk from the opening plenary session.
Kaiton Williams is PhD student at Cornell in the department of Information Science. Over the last few years he’s been interested in how people use technology to understand and create the stories of themselves. As we were exploring our 2014 Quantified Self Europe Conference registrants to see what they were involved in we were immediately drawn to Kaiton’s paper from the 2013 CHI Personal Informatics Workshop, The Weight of Things Lost. We asked Kaiton to talk about his experience with self-tracking and the mental and social tension inherent in the numerical definition of life. Kaiton’s plenary talk is available below as is a transcript of the talk.
First, thank you all for welcoming me here. I do take it as a privilege to be here. This is a surreal, and a little bit frightening, experience for me. It feels in many ways like the end of a pilgrimage.
I’m a Ph.D. candidate at Cornell University and over the last few years, I’ve been working to understand how we’re harnessing our devices, our applications and our algorithms to figure out just who, when, what, & why we are. I’m particularly interested in the ideologies and values that inform the things we discuss in rooms like this one, and go on to create and use.
I’m going to talk a little but about my experience with self-tracking and self-transformation and how it brought me here to this room, and then I’ll pose some ideas and questions on how we might use personal experiences like mine as a platform from which to influence the developing relationships between companies, markets, health, and our data.
And while my talk is fancily titled in your program as “The Weight of Things Lost,” I really could have gone with “All I Wanted Was a Flat Stomach and Six- Pack Abs” It was this, more than any high-minded investigation into technology, or a community, or our practices, that got me started and kept me going.
My story began about 28 months and over 1.3M calories ago. Like many tales, it began at Christmas. I was experimenting with a polaroid camera one day, and as I watched my picture develop, I realized how out of shape I had gotten. Even though I thought I was in control of my diet and getting enough exercise I had been slowly but steadily gaining weight without paying much attention. I looked, in my own estimation, terrible. Granted, as physical problems go, this was a minor calamity but I wanted to do something about it. But I realized that I didn’t know how exactly to go about it. I wasn’t sure what good goals where or even what I was capable of. And I definitely had little formal idea of how to manage my consumption to meet them.
2 ½ years later and this remains something that I consider with a fair amount of irony. I was among a group of researchers who had been critical of the persuasive and reductive logic that powered many of the popular diet control and tracking systems. But now I found myself in need of them.
This was the time and place of my first conflict. As a researcher now seeking to modify my body how could I participate in systems like these and still champion the resistance against them? Would I be taking them down from the inside?
Maybe after I got my 6 pack, washboard abs. THEN, then it would be down with the tyranny of rational digital systems and self-surveillance.
What I told myself was that I would be able to develop a personal, inside understanding that was tied to a real personal need. Surely this was better than just critical analysis lobbed in from the outside? So I swallowed my pride and looked for help. And, as it turned out, there were many apps for that.
In the months that followed I assembled & auditioned a shifting conglomerate of tracking apps, sensors, and databases. I scheduled full body density scans, blood panels, and metabolic breath tests. It didn’t take very long before I began to read my life through the prism of my tools and data. I had found new units of measure, new ways of marking my time, my mind, and my body. For 18 months, not a single day passed where I did not enter, in almost excruciating detail, what I had eaten and planned to eat. My tools were my oracles, and I consulted with them regularly.
Their effect was strong even though I knew intellectually that I was reacting to numbers, colors and graphs based on rough estimates, or provisional theories. I knew that, by describing my body as a precise system that would go out of sync based on small discrepancies, an industry benefited by positioning their tools and systems as indispensable and necessary guide in my life.
But once I began to see successes, I felt a strong sense of fidelity to my system; an ordained from Logos desire to keep the record true. And, over the months I steadily made my life more calculable by streamlining my diet to in turn streamline how I input data into my tools. I avoided complex recipes and prioritized foods that best fit the capabilities of my databases and sensors.
Halfway in, I spent the better part of one morning trying to figure out what happens to the calories in baking powder once baked into a cake. For that matter actually, I swore off cake.
Surprisingly though, I found a freedom & spiritual joy in this calculation and control, and ample room in its reduction. It was, reassurance itself. Together, my conglomerate and I had constructed a digital model of my self that I fully bought into and managed. I was managing myself, it seems now, by proxy.
I became worried about going it alone though. What would I do without my systems? How would I maintain the goals that I had developed and now hit? I think a lot about the transformation.
The numbers showing my weight and fitness level fill me with as much pleasure as fear. Can I maintain this state without help from my system? And even if I do cast these systems aside, would doing so really lead to any better engagement with my self? What happens if these tools are no longer supported, or if the people behind them make business or ethical decisions that I can no longer support?
And this is how I ended up here: to get your help in answering the questions.
I had begun this journey this to feel in better control of my self and to be healthy and fit. I definitely feel healthier but am I really in control? It is this last move, from personal questions to broader political ones, that concerns me the most— particularly when being healthy no longer seems to mean just avoiding being sick but continuously optimizing our selves. Self-tracking habits are becoming mainstream and I believe that how we are globally perceiving and contesting our possibilities is being reshaped through discussions and design decisions made at conferences like this one.
Our conversations are already embracing holistic ideas of well-being that stretch beyond the easily quantifiable, but we should also incorporate and question how our personhood and our work is increasingly being defined not just by ourselves, but by an array of others that includes entrepreneurs, governments, institutions and corporations that are all building on our desire to optimize our selves. If we understand that the work done in this community affects practices in the wider world, how can we begin to explicitly shape those relationships?
I think we can use our diverse store of personal knowledge to construct platforms for doing just that. Focusing on personal experience doesn’t have to be seen as a retreat from focusing on others, but instead can be a strong foundation from which to develop empathy for the experiences of others and to understand their implications for our joint lives.
And so to close, I’d like to pose these questions to you:
If our new abilities to measure and track our selves are forming the basis of what it means to be modern, healthy and connected, how can we use personal experiences like mine and the ones we’ll hear this weekend, to tackle not just the question of what does the collection and availability of data means for n=1/just me, but what it might mean for others? Particularly, others who might not be in the same circumstances, or might not have the same ability or availability to join this community? How do we incorporate the perspectives of the many who can’t participate here, are overlooked and marginalized, but whose lives will eventually be affected by practices that spiral out from ours?
Can we transform our wealth of personal and experiential data into a platform for improving our connection to those around us and to the broader world?
What did you do? How did you do it? What did you learn?
These are the three questions we ask people to answer in their Quantified Self Show&Tell talks. We’re thrilled to see the Show&Tell proposals coming in from registrants for the 2014 Quantified Self Europe Conference, with topics ranging from blood glucose tracking to novel uses of spaced repetition for memory training. The current Show&Tell lineup is previewed below. We hope you can join us!
Understanding My Blood Glucose
After learning he had an elevated risk for contracting Type 2 Diabetes, Bob starting tracking daily glucose measurements, exercise, diet, and supplements.
Connecting My Mind And Body
Juliana used data from activity, sleep, heart rate, and stress sensors to explore the effects of mindfulness on her physical condition.
Carbless in Seattle
Adrienne Andrew Slaughter
Trying out diets with different amounts of carbs, Adrienne saw unexpected effects on her athletic performance.
A Million Heartbeats
Can a day of heart beat data be accurately represented with a simple curve, for establishing baselines and comparisons?
Retraining My Body With Electrical Nerve Stimulation
Sara’s self-tracking data convinced her to try using Transcutaneous Electrical Nerve Stimulation to address her most troublesome Parkinson’s symptom, “freezing-of-gait”.
Me and My Log
Cathal lifelogging data comes from a camera that takes a constant stream of photos, wherever he is.
Fit 50s Sound 60s
Maria has been tracking for almost 10 years, developing strategies for improving and maintaining her health as she ages.
After many productivity experiments, Brian finally made progress with the Pomodoro method in 2011. He’ll show his continuing experiments to increase his focus and productivity.
Memorizing My Daybook
Steven wanted to see what happened when he memorized entries from his daybook using spaced repetition.
A Goal for Each Month
In the beginning of 2014 Florian set himself twelve goals, one per month. He’ll show data from the first three months.
A Testosterone and Diet Experiment
Blood tests showed Max he had low levels of Vitamin D and Testosterone. Could diet changes help?
Analyzing Changes in My Weight and Sleep
Kouris spent thirty hours combining his multiple data streams into one place, and learned what influenced his weight and sleep.
Does Diet Affect My Sleep?
Denise shows a year’s worth of data from her diet and sleep experiment and finds while food matters a little, other things matter more.
A Four Year Journal
Morris links a detailed handwritten journal to quantitative analysis and visualization.
A Librarian in Numbers
Academic librarians work in a complex environment. Debbie will describe how her tracking changed the way she worked.
A Lazy Workout
Does just squeezing your muscles make them stronger? Justin will talk about his experiments with an isometric training program.
We Never Fight On Wednesdays
Six months of tracking mood alongside events, time and people gave Paul some surprising lessons.
Meta-Effects of Happiness Tracking
How does asking yourself if you are happy change your happiness?
Science, Smell, Fashion
Jenny will tell a ‘science fashion’ story that introduces real-time biofeedback scent interventions as a means to complement orthodox treatments for chronic mental illness and to de-stigmatise mental health issues.
Lifelog as Self-Portrait
Using automatic lifelogging and visualization software Cors is experimenting with putting his computer in charge of his creativity.
Washing My Eyelids
Steve will demonstrate how he used self-tracking tools to get under atopic dermatitis.