Topic Archives: Videos
This is Adam Johnson’s third QS talk. Previously he’s discussed the lifelogging tool he developed and uses and how he re-learned how to type in order to combat RSI. In this talk, Adam gives an update to his self-tracking focused on three areas: tracking an long-distance cycling trip, his streamlined lifelogging process, and how he’s using the Lift app to track his habits.
What Did Adam Do?
In general, Adam is dedicated lifelogger who’s been tracking what he’s doing for over a year. Adam cycled 990 miles from Lands End to John O’Groats with his father and brother over 14 days and tracked it along the way. Because he wasn’t able to “lug around his Mac” to complete his regular lifelogging he decided to update his custom system to accept photos and notes. Lastly, he added habit tracking to his daily lifelogging experience by using the Lift app.
How Did He Do It?
Adam tracked his long distance cycling journey by using Google location history and a Garmin GPS unit. He was able to export data from both services in order to get a clear picture of his route as well as interesting data about the trip.
He also updated his lifelogging software so that it could accept photos and notes he hand enters on his phone. The software, available on GitHub, gives him an easy way to track multiple event such as how often he drinks alcohol and how much he has to use his asthma inhaler.
Lastly, Adam tracked the daily habits he wanted to accomplish such as meditating, reading, making three positive observations, and diet, using Lift.
What Did He Learn?
Everything Adam learned is based on his ability to access and export his data for further analysis. From his cycling trip he was able to make a simple map to showcase how far he traveled based on Google location history (which did have some issues with accuracy). He also was able to see that he traveled 1,004 miles, cycled for 90 hours, burned 52,000 calories, but didn’t lose any weight.
Using his updated lifelogging system, he was able to explore his inhaler use and after a visit to the doctor was able to “find out a boring correlation” that a preventative inhaler works and his exercise induced inhaler usage went to almost zero.
Finally, because Lift supports a robust data export, Adam was able to analyze his habit data and began answering questions he was interested in, but aren’t available in the native app experience. He found that seeing a visualization of his streaks as a cumulative graph was inspiring and motivating. He also explored his failures and found that Saturdays, Sundays, and Mondays were the days he was most likely to fail at completing at least one of his habits.
Slides of this talk are available on Adam’s GitHub page here.
Google Location History, Garmin GPS, Lifelogger, Lift, Photos, Notes
Cliff Atkinson is a consultant who helps people tell their stories and showcase their data in clear and understandable ways. It’s no surprise that when he became interested in understanding himself he turned to his experiences with visual storytelling. In 2012, at a New York QS meetup, Cliff spoke about how he’s embarked on a project to “quantify the “unconscious.”
What Did He Do?
Cliff began this project because he was noticed that there were “recurring patterns of procrastination and motivation” going on in his life. He began trying to understand them by turning to the large body of literature on human psychology. Then he asked himself, “Would it be possible to use some quantitative methods to track what was happening.” Using what he’d learned in his research and his experiences he decided to track his body, emotions, and mind.
How Did He Do It?
Cliff used his expertise and knowledge around visual storytelling to create an interesting system of visual diaries with which he could record information in his three areas of interest: the body, emotions, and the mind. Using Penultimate, and iPad app for sketching and notation, along with some clip art, he tracked physical, emotional, and cognitive events.
What Did He Learn?
The process of creating a space to reflect and record how he’s feeling across these three chosen domains has created a space for Cliff to better understand himself and how his mind works. This is still a work in progress and it sounds like Cliff is still exploring how to better understand the data he’s capturing over a longer period of time and even correlating it with other information such as his work and speaking engagements.
“One of the models for therapy is that somebody else helps you. I think with the quantified self and the things we’re doing we can take some of that power into our own hand and start to come to some personal understanding of what’s going on in our own lives.”
Steve Zadig is the COO of Vital Connect, but when he’s not busy with his job he’s out racing high performance vehicles. In this talk, presented at our 2013 Global Conference, Steve explains how he uses data to help him achieve his racing goals.
What did Steve do?
Steve wanted to get more information about how his body was reacting during racing. Frustrated that he was getting a lot of diagnostic data from his car and not any from himself he sought to track different biometrics to see what he could learn about what happens while he’s behind the wheel.
How did he do it?
Steve wore a Vital Connect patch to record and transmit his respiration rate, heart rate, and stress levels while he was was racing.
What did he learn?
After the race Steve was able to match the data with specific points and events during the race. He learned how something major, like spinning out of control, caused a large spike in stress, and how when he’s feeling in the zone his body responds with a lower heart and breathing rate.
“It’s about knowing. It’s about the knowledge of what’s happening with your body and how to deal with that.”
The QS15 Conference and Exposition is fast approaching. We invite you attend and give show&tell talks just like this one about your tracking and personal data experiences.
Like many people, Christel de Maeyer felt that her sleep could be better. Presenting at our 2013 conference in Europe, Christel shares what she learned from collecting over three years of sleep data.
What did Christel do?
Christel tracked her sleep for 2 years with various devices. She tested the effects of different variables on her sleep quality, including consumption of alcohol, keeping a consistent wake time and changing her mattress.
How did she do it?
She used the Zeo to track sleep for two years, before switching over to a BodyMedia device. While making changes she monitored how her sleep data changed, as well as how she felt.
What did she learn?
Before self-tracking, Christel felt that she woke up frequently during the night, and the Zeo confirmed this. On average she woke up around 8 to 9 times. She suspected the mattress could be part of the problem. After considerable research, she replaced her mattress (to one that had a foam top), successfully reducing her wake-ups to 4 or 5.
Christel discovered that her sleep patterns looked significantly different after just two glasses of alcohol. Her REM diminishes to nearly 0% (though deep sleep seems unaffected).
Christel also found that total sleep time was less important for how she felt the next day than the combination of REM and deep sleep. Even if she only sleeps for six hours, as long as she gets at least 2 hours of combined REM/deep sleep, she feels good.
In addition to these findings and others she explores in the video above, Christel has taken her lessons and now helps others with sleeping issues. You can find more at her website.
How many times during the course of the day do you find your mental state drifting into negativity, feeling like you’re lost, or just plain stressed? How could you even keep track of this, and why would you want to?
What Did Paul Do?
Paul LaFontaine has been tracking what he calls “upsets” to better understand himself, the way he works, and to see if he can improve his mental and physiological response and recovery.
Upsets are something physiological that were happening beneath the surface, and they’re trackable. It didn’t have to be emotional, but there had to be a signal. This project is part of an longer ongoing study. Before this current iteration I manually logged over 3,000 upsets and what I found is that most of my upsets were self-induced. I’d be in a calm environment, but then become upset about something. I wanted to use technology because I was afraid of bias and I know I was missing some upsets.
How Did He Do It?
I used the HeartMath EMWave2 that measures heart rate variability and indicates when you’re in and out of coherence. When I was out of coherence I captured that as an upset. I would stop what I was doing and use an audio recorder to keep track of the time, how long I was upset, the reason, and what method I used to recover. I tracked 71 sessions (each session was 25-45 minutes) totaling 42 hours of tracking time. I logged 1292 upsets during this period.
What Did He Learn?
Paul analyzed his data and found some very interesting insights about his upsets, his reasons for being upset, and the effectiveness of his recovery techniques.
I found that I was triggering an upset every 2 minutes. My wife said something must be wrong with me, but this stayed relatively constant through the tracking period. I started to think of it like skiing a mogul course. The moguls didn’t move, it was about how effective I could move through them. And, dealing with upsets is like playing whack-a-mole. They come fast and furious and every second counts.
For recovery I was able to find that my most effective technique was breathing. By returning to six breaths per minute routine I was able to improve recovery time from 33 seconds to 17.8 seconds. It was the primary way I could remove myself from being upset and make myself calmer.
We want to thank Paul for presenting this great QS project at the Bay Area QS Meetup group. Make sure to watch the full talk below to learn more about Paul’s methods and findings, then hop over to his website where you can read about how he tracked his stress during this talk.
Like many of us, James Norris remembers his first kiss. Unlike many of us, he also knows who it was with, where it was, and his age. How does he know this information? When he was 13, he realized that he forgot some detail about his life that he thought was important. To prevent that from happening again, he decided to carry around sticky notes to record important life events and has been doing it ever since. Fast forward 15 years and James has recorded 1,500 “firsts.” Watch this talk, presented at the Washington DC QS meetup group, to hear James talk about the data he collects, and the lessons he’s learned along the way.
Like anyone who has ever been bombarded with magazine headlines in a grocery store checkout line, Kouris Kalligas had a few assumptions about how to reduce his weight and improve his sleep. Instead of taking someone’s word for it, he looked to his own data to see if these assumptions were true. After building up months of data from his wireless scale, diet tracking application, activity tracking devices, and sleep app he spent time inputing that data into Excel to find out if there were any significant correlations. What he found out was surprising and eye-opening.
This video is a great example of our user-driven program at our Quantified Self Conferences. If you’re interest in tell your own self-tracking story, or want to hear real examples of how people use data in their lives we invite you to register for the QS15 Conference & Exposition.
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.