Topic Archives: Videos
Sue Lueder had a mystery stomach ailment that started after a vacation to Spain in 2011. When she returned from her trip she was beset by consistent and frequent burping attacks. After visiting her physician and receiving a diagnosis for heart burn, which she didn’t trust. she began to track her attacks and her diet. In this talk, presented at our 2013 Global Conference, Sue how she tracked he symptoms and used the data to make sense of this mystery food allergy.
What Did She Do?
Sue tracked her diet and the frequency and severity of her attacks.
How Did She Do It?
Sue was able to explore the data she was entering in to her self-designed spreadsheet tracking system. She used a few of the analytical tools and visualizations built into Excel to explore her data.
What Did She Learn?
Her analysis was able to pinpoint that dairy was probably the main culprit responsible for her attacks. Sue found out that she was able to improve her “good” days from 32% to 51% of the days she was tracking when she reduce dairy in her diet. When she experimented with adding dairy her findings were confirmed.
In 2009 Tim Ngwena switched on Last.fm and he’s been running in across all his devices ever since. Earlier this year he decided to take a deep dive into his listening data to see what he could learn.
I realized that I was listening to the same old thing and I began to think about changing what I was listening to. But how can I change? Where can I start? I also wanted to learn something about my music, what I was listening to and who was behind the sounds. I decided to focus on music because it was doable.
In this talk, presented at the London QS meetup group, Tim explains how he was able to make sense of almost five years of data and learn more about himself and his listening habits.
What Did Tim Do?
Tim explored his music data along side additional information such as location data from Moves to learn about his musical tastes, listening habits, and explore new visualization and data analysis techniques.
How Did He Do It?
Tim exported his data, used the Last.fm API and some data cleaning and organizational tools to create a simplified and extensive database of his music listening history and associated data. He then visualized that data using Tableau.
What Did He Learn?
Tim learned a lot about himself and what the music he listens to says about him. He describes a few of the most interesting below,
Basically 80% of my listening comes form 10% of the artists that I have in my library.
I’ve listened to Erykah Badu for over a week (7.2 days). It led me to ask what is she saying to me?
Monday is my jam time. I’m listening from the morning into the evening.
I listen to music mostly when I’m walking.
Tim also learned a lot through the process of designing and creating his data visualization. The visualization, which you can explore here, made him think about being able to see the big picture when he has so much linked data.
I think context is important and you need to see all that information in one place and the tools I’m using allows me to do this.
Shawn Dimantha is always looking for easier ways to track his health. He uses a variety of self-tracking tools, but a few months ago he became interested in exploring what he could do given his engineering and health IT background. He was inspired by immersion, an MIT-developed email analysis tool, which helped him understand who he was communicating with, and by Wolfram Alpha’s Facebook analysis tool. Focusing on Facebook and the wealth of image-based data in his profile he asked himself if images could be a window into his health. After reading a research paper on the use of images to predict body mass index he decided to see what he could learn my implementing a similar procedure on his own images.
What Did Shawn Do?
I used photos from my Facebook account to track my health, the reason I did this because I wanted to see how a simple heuristic I used for tracking my health daily could be implemented in the online world given the huge amount of photos that are and have been shared on a daily basis. I notice when I gain or lose weight, am stressed or relaxed from my seeing my face in my mirror. I was partly inspired by the self-photo collages presented on YouTube.
How Did He Do it?
I selected photos of my face from my Facebook account, cropped out my face and used some software and manual tagging to measure the ratio of different fiducial points on my face (eye-eye length, and cheek to cheek length) over time to help serve as a proxy for my health.
What Did He Learn
Facial image data needs to be cleaned and carefully selected. Face shapes are unique and need to be treated as such. Data that is not present is often more telling than what is present. Life events effect my weight and should be put into context; however causation is harder to determine than correlation. By being more conscious of my score and I can change my behavior before things get off track.
Right now I’m turning this into a product at Enfluence.io where I’m focused on using it to help with preventive health.
Facebook (my own images)
Python / OpenCV
Slides from Shawn’s talk are available here.
One interesting aspect of personal data is how it can reveal what is unique about you. Nowhere is this more true than with genetic information coming from DNA testing kits. However, people are still at an early stage on how they apply that information to their lives. Ralph Pethica, who has a PhD in genetics, was interested in what his DNA could tell him about how to train more effectively. His findings were presented as an ignite talk at the 2014 QS Europe Conference.
What did Ralph do?
Ralph loves to surf. When it is the off-season, he trains so that his body will be in good condition for when the warm weather rolls back around. He used genetic research to inform how he designed his training plans.
How did Ralph do it?
Ralph used a 23andMe kit to find out his genetic profile. He researched those genes that have been found to have an impact on fitness to see his body should respond to exercise. For example, did he possess genes that gave him an advantage in building muscle with resistance training? He then modified his training routines to take advantage of this information and monitored his results (using the Polar watch and a Withings scale) to see whether his assumptions held up.
What did Ralph learn?
Ralph found out that he has genetic disadvantages when it came to strength training. This told him that progress in this area depended more on his lifestyle. In particular, he found that eating immediately after working out was important.
When it came to cardio exercise, he had a number of genetic advantages. The unexpected downside to this is that his body adapts quickly to any training regimen, resulting in a plateau. To get around this, he varied his training plan and monitored his results. On one day, he would cycle at a steady rate, while the next, he would use high-intensity intervals. His body seemed to respond to the varied training plan and he hit fewer plateaus. Without knowing which genes he possessed, and reading current research on those genes, it is unlikely that he would have discovered these effective customizations to his training plan.
Ralph has taken what he’s learned and built a tool called Genetrainer to help people use their genetic information to inform their fitness plains. You can check it out here.
Tools: Genetrainer, 23andMe, Polar RCX5, Withings Smart Body Analyzer
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.