Tag Archives: videos

Steven Jonas: Spaced Listening

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It’s hard for me to like an album the first time I listen to it. I can almost feel some part of my brain reject the music, even from bands I like, because it’s not familiar. However, after a few listens, the album will grow on me and I’ll find myself humming melodies that I previously couldn’t sit through. That is, unless I turned off the album the first time around and never gave it a second listen.

I suspected that this behavior was having a negative impact on my ability to appreciate new music when I noticed that almost none of the music that I listen to has come out after 2006.

In this talk, given at a recent QS Bay Area meetup, I discuss the system I set up that scheduled when I should listen to an album to help me over the hump to appreciating an album on it’s own terms instead of rejecting it because it wasn’t familiar.

Spaced_Listening_by_Steven_Jonas_on_Vimeo

Click to watch Steven’s Show&Tell talk.

Tools used:
- Anki
- Google Forms

QS17 is coming soon

Our next conference is June 17-18 in lovely Amsterdam. It’s the perfect event for seeing the latest self-experiments, debating the most interesting topics in personal data, and meeting the most fascinating people in the Quantified Self community. There are a limited number of tickets left. We can’t wait to see you there.

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Laila Zemrani: Training for Strength or Endurance?

While it is clear that exercise is beneficial, how does one decide what to do to get and stay fit? When Laila Zemrani surveyed people at the gym, she found that a majority don’t decide at all. Sixty percent didn’t know why they were doing a particular exercise. And of those, 50% admitted to merely copying whatever their neighbor was doing.

Laila spoke recently at a QS meetup in Boston about how she tried to be more intentional in her choice in exercise. In reviewing the number of available exercises, she was able to put them into two buckets: strength and endurance. She decided to track the effectiveness of each training regimen by focusing on a single metric and watching its progress. For strength, she focused on body fat ratio. For endurance, she looked out how long it took her to run the same distance. She then alternated her training every three months or so, focusing on one or the other.

Laila Zemrani speaking at QS Boston

Laila Zemrani speaking at QS Boston

Here’s what she found. When she focused on strength training, her body fat ratio improved. For instance, in one three month period it went from 29% to 25%. This type of improvement repeated itself a number of times. However, when she focused on endurance, she did not see improvements in the time it took her to run a certain distance.

Using a Fitbit Aria, Laila tracked body fat while alternating training regimens.

Laila tracked body fat with a Fitbit Aria scale.

It’s hard to know what conclusion to draw from these results. Are these the right metrics for assessing performance? What does it mean to respond more to strength than endurance exercise? However, the question of why Laila seemingly responds better to strength-based exercises may be found in her genetics. She used a DNA test from 23andMe and the results suggested that she shows a propensity toward building fast-twitch fibers which allow for better performance at explosive activities, such as sprinting or weight-lifting. On the flip side, people who are more proficient at building slow-twitch fibers tend to do better at endurance-type activities such as running long distance. Everyone has a combination of the two types of muscle fiber, but the ratio seems to be correlated with performance, depending on the type of activity.

Screenshot from Laila's 23andMe account.

Screenshot from Laila’s 23andMe account.

With these results, Laila decided it made sense for her to focus on strength-building exercises, since it seems that her body was built for that type of activity. Laila feels that having this information is allowing her to personalize her regimen and be more intentional about how she exercises, rather than be too influenced by the latest fads in fitness.

It can be debated whether it makes sense to focus on strength as opposed to endurance, depending on which one you see progress in. For Laila, the appearance of progress is important psychologically, in that it is easier to motivate herself if she sees improvement. There could be a downside to appearance of quick improvement, though. Ralph Pethica also uses genetic data to inform his training. He is the opposite of Laila in that his body is better suited for endurance exercise. What he finds, though, is that he improves and adapts too quickly and sees his performance plateau. To overcome this, he found that switching between steady-state training sessions and high-intensity intervals minimized the time he spent plateaued.

Training with knowledge of your genetic background is still a nascent practice. It’s still unclear how this information can and should be used. Useful ways to take advantage of this genetic information is still being tested and developed, but progress could be hastened if more people knew if they had more slow-twitch or fast-twitch muscle fiber. If this awareness is increased, it could lead to better strategies to get more out of exercise and reduce frustration and, hopefully, abandonment of the gym.

You can watch Laila’s entire Show&Tell presentation that she gave at a QS Boston meetup. You can follow up with Ralph’s Show&Tell from the QS Europe conference.

Tools used:
Fitbit Aria Wi-Fi Smart Scale

23andMe DNA Test – Health + Ancestry

QS17 Tickets are Available

Our next conference is June 17-18 in lovely Amsterdam. It’s a perfect event for seeing the latest self-experiments, debating the most interesting topics in personal data, and meeting the most fascinating people in the Quantified Self community. There are only a few early-bird discount tickets left. We can’t wait to see you there.

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Stefano Schiavon: Using Data to Understand Personal Comfort

Stefano Schiavon is an assistant professor and researcher interested in sustainable building design. As he told us at last month’s Quantified Self meetup in Berkeley, California, “I am Italian. I love architecture. And I think buildings are beautiful.”

One goal of building design is to increase individual comfort. However, this poses a problem. Everyone is different. For instance, what should the temperature be set at? There is no one temperature that is comfortable for everyone. It doesn’t work to try find a temperature that is pleasant to the largest number of people. As Stefano puts it, it is like going around and measuring everyone’s foot to get an average, say 9, and then dictating that everyone wear size 9 shoes to the office.

So how does this connect with Quantified Self? Stefano and his colleagues have embarked on a series of studies to better understand people’s individual preferences for their environments and they are doing it with QS tools. The first study was fairly simple. They tracked the ambient temperature and air quality of the person’s surroundings and used an app for feedback on whether the environment was “acceptable” or not. Carbon dioxide and temperature measurements were taken throughout the day, while the person was in the car, at work, the restaurant, etc.

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Stefano and his colleagues noticed a couple things. One is that there was higher exposure to CO2 in air conditioned rooms as opposed to naturally ventilated rooms. While Stefano says that this CO2 level is not a concern in of itself, it correlates with other pollutants, such as, airborne transmitted diseases (e.g., influenza).

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Despite these data, Stefano and his colleagues found that just recording the environment gave him a limited ability to predict a person’s comfort. He is hoping, and the focus of his next study, is that by getting a person’s QS data (heart rate and body temperature), this predication ability will improve, making it easier to personalize a space’s comfort for each individual.

4_Stefano Schiavon Personal Comfort Quantified Self MeetUp.010For Stefano, all of this is in support of a larger cause, climate change. He was saddened to discover that nearly 40% of greenhouse emissions come from buildings. He hopes that by building better models for personal comfort by using QS tools, he can help people enjoy their environments more, while minimizing the environmental impact.

You can see the entire video of his talk at his QS project page.

Here are links to some of Stefano’s papers:

Get your tickets for QS17

Our next conference is June 17-18 in lovely Amsterdam. It’s a perfect event for seeing the latest self-experiments, debating the most interesting topics in personal data, and meeting the most fascinating people in the Quantified Self community. There are only a few early-bird discount tickets left. We can’t wait to see you there.

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Ahnjili Zhuparris: Menstrual Cycles, 50 Cent, and Right Swipes

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“I love reading random papers about the human body.”

Ahnjili Zhuparris came across a study on the menstrual cycle’s influence on cognition and emotion and was curious to see how hormonal changes may affect her day-to-day behavior. She figured her internet use may be a convenient and easy data set to assemble and examine for this effect. Using a few chrome plugins, Ahnjili was able to see not only where she spent her time online, but how she interacted with sites like Facebook and Youtube.

Her analysis yielded some interesting patterns. She found the most distinctive behaviors occurred during the fertile window, a span of about six days in the menstrual cycle when the body is most ready for conception. Looking at her shopping data from a clothing website:

 ”I found that there was no change in the amount of money I spent or the amount of time I shopped online… but while I was most fertile, I bought more red items. In fact, it was the only time I bought red items.”

In this talk, Ahnjili shows the differences in how she browsed Facebook, swiped in Tinder, and listened to music on YouTube.

Here are a few of the tools and papers that Ahnjili cites in her talk:

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Randy Sargent: Unlocking Patterns with Spectrograms

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In this talk, Randy Sargent shows how he used a spectrogram, a tool mostly used for audio, to better understand his own biometric data. A spectrogram was preferable to a line graph for its ability to visualize a large number of data points. As Randy points out, an eeg sensor can produce 100 million data points per day. It is unusual for a person to wear an eeg  sensor for that long, but Randy used the spectrogram on his heart rate variability data that was captured during a night of sleep. In the video, you’ll see an interesting pattern that he discovered that occurs during his REM sleep.

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Richard Sprague: Microbiome Gut Cleanse

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Richard Sprague has been closely tracking his microbiome and sharing his findings for the last couple of years. He’s even joined our friends at uBiome as their citizen-scientist-in-residence.

In this talk, Richard shares his attempt to improve his sleep quality by increasing the amount of bifidobacterium in his gut through eating potato starch. You’ll learn why he took the extreme step of flushing his digestive tract and rebuilding it from scratch.

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Ellis Bartholomeus: Draw a Face a Day

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Ellis Bartholomeus has many of the standard self-tracking tools like pedometers, heart rate monitors, and eeg sensors. But she explored a different type of tool when a friend gave her a logbook with a place to record her daily mood by drawing a smiley or frowny face on a colored circle.

Although it initially felt like a silly exercise, she was surprised by how she responded to these faces over time. There was a visceral pleasure to seeing these faces. Even though they were representations of her own emotional state, they seemed to take on a life of their own.

Although Ellis had the day-to-day pleasure of rendering her mood as a cartoon, she couldn’t resist the urge to structure these images to see bigger trends. You’ll see her amusing methods in the video. How do you measure a smiley face? (hint below)

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Peter Torelli: 20 Years of Memories Tucked Away in Personal Finance Data

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Peter Torelli had $2000 saved when he entered college. He knew that it wouldn’t last long, so he had to be careful about his spending. He switched to using a credit card in order to have a record of his purchases and reconciled his accounts every month. It became a habit that he kept for a long time. A really long time.

Peter now has 20 years of financial data, and the way he’s logged his data has followed larger technological trends. Starting with manually logging transactions in Quattro Pro and storing his data on floppy disks, his data now resides on Quicken’s servers. These changes have brought better security with better backups, but also uncertainty about the ownership of his data and lack of flexibility to move his information elsewhere because of proprietary data formats.

One of the surprising findings is how many memories flooded back when he reviewed past transactions. Both memories and transactions are tied to places. A simple line item can trigger a forgotten moment with an out-of-touch friend. When Peter’s spending trends are displayed on a multi-year timeline, it’s not just a representation of his finances, but the chapters of his life as well.

There are many more great insights from Peter’s talk at the Quantified Self San Francisco meetup in April:

You can read more about Peter’s projects on his website. For more on this topic, here’s a great roundup of QS projects related to money.

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Abe Gong: Changing Sleep Habits with Unforgettable Reminders

Abe had an issue with staying up too late. The early morning hours often found him on his couch, working on his laptop.

The problem is that he simply lost track of time. To help make his bedtime unforgettable, Abe built a reminder he could not ignore. He wrote a simple app that uses colors to gently prod him to get ready for bed and installed it on an old android phone that he mounted on the wall in his living room. When the screen first lights up in the evening, the colors are blue (“bedtime is coming.”) and increasingly become red (“bedtime is here.”). When he long-presses the screen, it means that he is ready to sleep, and the phone responds by lighting up with a celebratory array of colors.

It was a simple intervention, but did it work? Abe thought so. But the skepticism of friends spurred him to dig into the data to make sure. The problem was that his simple app didn’t record any data. He had an idea, though. For the past year, a webcam connected to a Raspberry Pi had been recording his living room. Abe used the light levels of the video stream as a proxy for his bedtime. When the light levels dropped, it meant that he had gone to bed. This proved to be a reliable indicator because, as Abe says, “I’m always the last one to sleep, and the last light I turn off is always the living room light.”

Would this work for you? Possibly not, but that’s not the point. It is an excellent example of a person building a solution that is specifically designed for his personality, and also how meaning can be found in the unlikeliest of datasets. In the video, you will find out how much sleep Abe saved and learn more about how he set up his device and ran the analysis.

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Mark Leavitt: Daily HRV As a Measure of Health and Willpower

“The heartbeat is a treasure chest of information…”

Mark Leavitt has a unique perspective in that he is both an engineer and a physician. In his retirement, he is applying his wealth of knowledge to keeping himself healthy.

In this talk, Mark looks at how heart rate variability relates to his willpower. Does he lift more weight when his HRV is high? What happens to his eating habits when his HRV is low? And if the term “heart rate variability” is new to you, Mark gives a lucid explanation.

Also, you will get a glimpse of his amazing customized workstation with pedals to keep him active, a split keyboard on the armrests to keep his knees free and built-in copper strips for measuring HRV. Cue envy.

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