Topic Archives: Videos
“When I look at this, this is the story of my life in these years.”
Nan Shellabarger has been tracking her weight for 26 years, including almost daily tracking since 1998. In the talk embedded below, presented at the Washington DC QS meetup group, Nan describes her experience with diving deep into how she’s making sense of her weight data. By looking over her complete history and layering in her personal contextual data she was able to find how different life events played a role in weight loss and gain. For example, she found that physical challenges and events were “tremendous motivation to get out there and doing things as well as helping me focusing on my eating.” Nan has also used a variety of activity trackers since 2010, starting with the Body Media Fit and now the Garmin Vivofit and Jawbone UP. These devices helped her explore calorie expenditure as it relates to her weight loss. On the other side of the equation, she also explored how diet tracking influenced her weight. Watch her great talk below to hear the whole story.
We hope to see an update of this great talk when Nan joins us at our QS15 Global Conference and Exposition next June in San Francisco. Early bird tickets are available for a limited time. Register now!
Benn Finn has been battling issues with his sleep ever since he was a teenager. His sleep was suffering from the usual problems we’ve all faced: taking too long to get to sleep, waking up too often, waking up late, and being tired during the day. He made plan to fix his issues by researching what affects sleep and then experimenting to find out what worked for him. For four months he tracked his sleep using Sleep Cycle along with 21 factors that he thought might affect his sleep. He also created a “sleep quality” score based on 5 different data points, including data from the Sleep Cycle app. In this talk, presented at the London QS meetup group, Ben describes his experiments, what he learned from analyzing his data, and how he finally ended up fixing his sleep issues. (Special thank you to Ken Snyder for his valuable work documenting the talks at QS London.)
Slides are also available here.
Bryan Ausinheiler was experiencing gastrointestinal issues for years and decided it was time to figure out what was causing it. By precisely controlling his diet – eating exactly the same quantities at exactly the same time – for a month and then measuring the quality of his stool in a self-designed spreadsheet he was able to create a baseline dataset to better understand his issues. Bryan then developed an experimental protocol that included “elimination and diet variations to figure out the cause of my frequent (3-5x/day) loose stools.” It turns out that “eating too many sunflower seeds was the main culprit.” Watch Bryan’s fascinating talk, presented at the Bay Area QS meetup group, to learn more about his process, and how he tackled self-experimentation and data collection.
Cathal Gurrin is a researcher at Dublin City University and the University of Tsukuba. He’s also an expert in the field of visual and data-driven lifelogging. Since 2006 he’s collected over 14 million passively collected images from different wearable cameras. Add his other sensors and he’s nearing over 1TB per year of self-tracking data. In this talk, presented at our 2014 Quantified Self Europe Conference, Cathal describes what he’s learned over the last eight years and what he’s working on in his research group including search engines for lifelogging as well as privacy and storage issues.
“How can I define what makes me happy and what makes me sad, what is good for me?”
In 2012 Benjamin Bolland was finishing up his undergraduate degree and working on a new start-up. He found that his moods were constantly changing and wondered if there was something he could do to make sense of them as they moved “up and down.” He began tracking his mood with an simple self-designed Google Form. Each day at 11AM and 6PM he reported his mood on a 1-10 scale and wrote a quick descriptive note. After 1.5 years of doing this nearly every day he realized that he didn’t really know what was making him happy or sad so he decided to update his Google Form to include a variety of different categories that he thought might affect his mood including physical health, sport participation, and many others. In this talk, presented at the Berlin QS meetup group, Benjamin describes his process and how he’s used this mood tracking process to be more reflective and mindful during his daily life.
“There was nothing in my life pushing me to to have these more intimate relationships, the few people I actually care about.”
When Akshay Patil was putting together the guest list for his wedding he realized that it had been a long time since he’d spoken with some of the the people he was inviting. Even with his good friends, he surprised by his lack of communication, his inability to stay connected. As anyone faced with this realization he decided to try and change, but the realities of life quickly crept back and as they say, old habits die hard. When he left his last job and began looking for projects to work on, this troubling area of his life crept back to the fore. Maybe there was something he could do better track and change his communication and relationships. Using his development skills, and the ability to gather data from his Android phone, he decided to build a system that helped him stay in touch with the people that mattered most to him. In this talk, presented at the New York QS meetup group, Akshay talks about what’s he’s learned from using this app, including when it fails.
Siva Raj was interested in lowering his blood pressure. With a family history of cardiovascular disease and heart attacks he was worried about slightly elevated blood pressure (pre-hypertension). As someone engaged with understanding and building fitness applications he thought he would be able to lower his blood pressure by staying on track with a regular exercise program that focused on cycling. Interestingly his blood pressure measurement didn’t respond to his constant exercise or weight loss. After reading more research literature about the link between fitness and cardiovascular health Siva decided to change his training to improve his fitness. He decided to incorporate a increased intensity into his routine. After a short period of time he had increases in this fitness and was able to observe the reduction in blood pressure he was looking for. In the video below, filmed at the Boston QS meetup group, Siva explains his methods and talks about how he was able to track his body’s response to different fitness routines.
Jamie Williams found himself with almost two years of self-tracking data including physical activity, blood pressure, and weight. Because of his interest in data visualization and coding he decided to learn how to access it the data and work on visualizing and understanding some of the trends and patterns. In this talk, presented at the QS St. Louis meetup group, he takes a deep dive into his activity and step data as well as his blood pressure data to learn about himself and what affects his behavior and associated data.
What Did Jamie Do?
Out of pure interest in seeing what the data would reveal, Jamie utilized a combination of devices to track his physical activity, blood pressure, heart rate, weight, numbers of drinks, and automobile travel. He then went on to explore ways in which he could pull down, integrate, visualize, and ultimately make sense of what he collected.
How Did He Do It?
In order to obtain his data on a minute-level resolution, Jamie had to email FitBit for a specialized use of their API. He then employed Mathematica to develop a number of (beautiful) visualizations of his activity – along with other key moments in his life (moving to St. Louis, changing job location, preparing for a Half Marathon, etc.). Jamie was able to compare his data not only to his peers through FitBit, but also to others of his demographic in the U.S. using the publicily available NHANES data set.
What Did He Learn?
Through Jamie’s Quantified Self collection and analysis efforts, he learned a lot not only about the patterns and changes in his activity, but why they were the case. He also presented great feedback about one’s mindset when comparing to peers vs. the general population.
Withing Blood Pressure Cuff
Thank you to QS St. Louis organizer, William Dahl, and Jamie for the original posting of this talk!
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.