Search Results for: sleep

Hot Stuff: Body Temperature Tracking and Ovulatory Cycles

For the past eight months I’ve been tracking my temperature every minute using small, wireless sensors.

I work in a lab that recently showed minute-by-minute body temperature can tell you fascinating things about female physiology, at least in mice. Using temperature, we can tell what day a mouse will ovulate, whether or not it will become pregnant within hours of pairing with a male, and in the same time, whether or not its pregnancy will be successful. Just as interesting, the temperature reveals that some mice have stable ovulatory cycles and some don’t. We wanted to see if any of this holds up in humans (read: lab mates, a sporting family member and myself). I’ll show you what we did, what we found, and how to get started if you’d like to start tracking your temperature too.

Why Temperature?
Think of metabolism as a continuous symphony and body temperature as the din that carries through the concert hall walls.

Many of the metabolic reactions taking place throughout our bodies generate small amounts of heat and are actually coordinated in a similar way to musical chords. For example, during the luteal phase of the menstrual cycle progesterone levels will pulse in concert with estradiol, often following a luteinizing hormone pulse occurring 15 minutes prior. These fluctuations, as well as other things that affect metabolism (ovulating, eating a meal, etc.), translate into small temperature ripples which register on the surface of the body.

Temperature has long been used as a predictor of ovulation. But most temperature based techniques rely on a single measurement per day. Limiting data collection to one time point per day is the equivalent of listening to the symphony only at what we hope is the crescendo of each piece: with training, we might identify the chord, but we’ll still miss most of the show.

What are we doing and what have we seen already?
To see if we could use high temporal resolution temperature to recapitulate any of our previous findings, we began monitoring distal (wrist), axial (arm-pit) and core (ahem, core) temperatures every minute, using small devices called iButtons. We’ve seen some interesting things so far. I’ve shown the temperature data as a heat map, because it allows you to see many measurements while giving a clear picture of the overall pattern of rising and falling average temperatures over the course of 28 days.

HeatMap_LabMate

Temperature Can Predict the Start of Menstruation.
In the graph above, which uses my lab mate’s data, you’ll see that the range of temperatures she passes through in a day shifts a little higher every day leading up to the start of spotting/ menstruation. This timing is clear in her data, but it isn’t identical for everyone, though. My cycles are irregular and the chart below shows that menstruation starts when my average temperature reaches its highest level of the month. Note that this can be more than once per 28 days, as in the month graphed below.

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In my mom’s case, the heat map below clearly shows the shift between follicular (cooler) and luteal (warmer) phases. I’ve outlined the profiles of the Progesterone (P) and Estrogen (E) that my mom takes each day as part of hormone replacement therapy. In the valley where both hormones are low, she transitions from follicular to luteal phase. This corresponds to a temperature increase, and a few days later she gets her period.

HeatMap_Mom

These findings keep us coming back for more: more subjects and more longitudinal data for each of us. Perhaps the differences we have observed between us support that there are different ‘types’ of cyclers in the population, just as there are different body types. And maybe the temperature features we have in common will apply to other women.

So how do we gather the data (and how might you)?

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iButtons are about the size of the button on your jeans, and one side has a sensor which is worn pressed to the skin. A sweat band is enough to secure one button to your wrist, and the axial button can be tucked into a bra strap or secured with a non-irritating skin tape (here is my favorite so far). Body temperature shouldn’t ever fluctuate more than a couple of degrees C, so devices with high precision are key. This model is accurate up to .0625 C. Both the resolution and the sampling rate can be user-specified, meaning you can take very precise measurements very frequently. I find that anywhere between one and three-minute resolution works well to capture changes throughout the day.

iButtons don’t ever need to charge, but the data needs to be read once the memory fills up. Depending on the sampling rate, that’s every 3-7 days. At the end of a recording period, the ibutton is touched to its reader, and a simple interface allows the user to view the data and export it as a csv. iButton will plot the data, but it won’t do any further analysis. We’ve taken these csv outputs into Matlab and Python for our analyses, and because they are widely used formats, anyone could make graphs and start to play with their data. I’m not associated with the company, but I’m excited to share what we’re finding and want others to know how to jump in. An ibutton and a reader together cost about $100.

Interested?
Temperature tracking is a scavenger hunt: we don’t know precisely what we’re looking for, but clues keep turning up that lead us in interesting and verifiable directions. Multiple hormonal systems in our bodies (the stress axis, the digestive system, the thyroid axis) affect body temperature, and the reproductive system is just one of those. This raises the question: could we see predictable changes in temperature associated with a long run, a large meal, or a bad night of sleep? Probably. Mapping the personal, research, and clinical applications of high temporal resolution body temperature tracking will take time and user participation. Luckily, it gives interesting and useful personal feedback along the way.

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Getting to Know the Gut: A QS Dublin Report

Earlier this month, the Quantified Self Dublin group got together for an engaging evening of talks on gut health by members of the local medical community.

CDSA Explained

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Francesco Polito, a nutritional therapist, talked about the markers that are found in a Comprehensive Digestive Stool Analysis (CDSA). This is a test that he has his clients get to understand the current state of their gut. Francisco walked through the test results, explaining what each marker represented and what it could mean if it is out of range. It’s an incredibly fascinating talk and I will be writing more about it in-depth next week. In the meantime, you can watch a video of the talk and review his slides, which contain an actual CDSA report from one of his clients.

Video of Francesco’s talk
Francesco’s slides

A Gut Hormone Primer

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Natasha Kapoor, a researcher at University College Dublin, gave a primer on hormones in the gut. She explained the relationship that ghrelin has with appetite.  Higher ghrelin levels correspond with increased hunger. This is concerning, since lack of sleep can cause ghrelin to rise, meaning that carrying a sleep debt could induce you to eat more than you otherwise would. It may follow, then, to try and manipulate ghrelin levels to help control appetite. However, clinical attempts to lower ghrelin levels are not advised since it is a complex hormone involved in more than just hunger, such as cardiovascular function, sleep and memory.

Still, there are other hormones that play a role in appetite. Natasha described three hormones that have the opposite effect as ghrelin, making you feel full while eating a meal: cholecystokinin, peptide YY and glucagon-like peptide-1. She is currently recruiting subjects for a study on whether these hormones could be manipulated to control appetite through a “gut hormone infusion” method. As Natasha explains in the video below, there are more mundane ways of taking advantage of these hormones to reach satiety quicker, such as eating your food in a certain order (hint: start with the protein portion).

Video of Natasha’s Talk
Natasha’s slides.

If you live in the Dublin area, you can join their meetup group and be notified about upcoming events (like the next Tuesday!). You can also keep up with QS Dublin on twitter.

If you are interested in exploring more about the microbiome, we’ve had a number of interesting Show&Tell talks on gut health:

QS17

You can meet Justin and other members of QS Dublin at our next conference on June 17-18 in lovely Amsterdam. It’s the perfect event to see the latest self-experiments, discuss the most interesting topics in personal data, and meet 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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Meetups This Week in Washington D.C. and Copenhagen

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photo by Erica Tanamachi

Copenhagen has an amazing slate of presentations lined up for their Quantified Self meetup this week. Katarzyna Wac will speak about what she’s learned from using a continuous glucose monitor (there’s an interesting discussion on this topic going on in the QS forum).  Thomas Blomseth Christiansen will talk about what he tracked while training for a half-marathon. Jakob Eg Larsen will look at sleep and resting heart rate over a long time period. And finally, Frederik Ackermann will give pointers for designing N=1 experiments.

Wednesday
, March 22
Washington, D.C.

Thursday, March 23
Copenhagen, Denmark

Join us at QS17

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

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What We Are Reading

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Articles

His Doctors Were Stumped. Then He Took Over by Katie Thomas. David Fajgenbaum, a medical student, discovered that he had Castleman disease, a hard-to-classify condition that sits between cancer and immune disorder which kills a third of patients within five years of diagnosis. Like most rare diseases, he found very little about it in the literature. It’s hard to fund research for a disease that affects so few (though legislation like the Orphan Drug Act helps). Fajgenbaum was tenacious in doing his own research and was willing to experiment on himself. By keeping copious records of his condition, including the T cell and VEGF levels in his weekly blood work, he may have made a breakthrough. Again, this breakthrough may help a small number of people, but here’s the thing about rare disease: there is a lot of them. According to the article, 10 percent of the population are afflicted with one of 7,000 rare diseases. With a group that large, we need alternate methods for doing research that does not rely on large sample sizes. -Steven (Thanks to Gwern)

The Usefulness of Useless Knowledge by Abraham Flexner. Princeton University Press has just reissued this classic essay by the founder of the Institute for Advanced Study, with a new companion essay by the physicist Robbert Dijkgraaf. I’m going to buy the book, but you don’t have to do that to read the original essay, with it’s terribly relevant opening paragraph: “Is it not a curious fact that in a world steeped in irrational hatreds which threaten civilization itself, men and women – old and young – detach themselves wholly or partly from the angry current of daily life to devote themselves to the cultivation of beauty, to the extension of knowledge, to the cure of disease, to the amelioration of suffering, just as though fanatics were not simultaneously engaged in spreading pain, ugliness, and suffering?” -Gary

Total recall: the people who never forget by Linda Rodriguez McRobbie. This article looks at a small group of people with an ability called highly superior autobiographical memory. These are people who can recall, with amazing clarity, specific details from every day of their life. It’s a fascinating read, but there are two details that I want to make special mention of. One is that this doesn’t a special talent people are born with, but, rather, it was a conscious decision they made early in life. The second is that this group has certain mental habits that I recognized from my attempt to memorize days from my daybook. Unlike most people, they actively organize their memories, tying them to the calendar. For instance, they can cycle through memories to figure out their favorite Tuesday. Memories exist in relation to one another in time. Another is that they keep the memories and their relationship between one another alive through constant repetition throughout the day. This suggests to me that with spaced repetition systems, one could train themselves to have a similar ability to remember their life. -Steven

What Happens When You Mix Java with a 1960 IBM Mainframe by David Cassel – This short, amusing piece about the improvisational talent of government fix-it artists, focused on an engineer for the US Digital Service named Marianne Bellotti, describes how they manage to connect ancient databases to modern web services, using methods that will mostly remain undocumented for the protection and safety of all concerned. As Bellotti says: ““The systems that I love are really the systems that other engineers hate,” Bellotti told the audience — “the messy, archaic, half chewing gum and duct tape systems that are sort of patched together. Fortunately, I work for the federal government, so there’s really no shortage of things like that for me to play with.” -Gary

When Things Go Missing by Kathryn Schultz. There are two types of loss that I hear described by people in the Quantified Self community. The first is the loss of existing data from, say, a corrupted hard drive, or a service that folds without notifying it’s users and allowing them to download their data. The other type of loss is the missed opportunity to track something that is now in the past. Not just the steps missed by a dead fitbit, but sleep before consumer sleep trackers existed. It’s a bit odd that the same emotion is invoked by two very different situations. In this essay, Schultz explores the concept of loss, from the trivial to the life altering. It seems to be our nature to hold on to things. Life logging could be seen as a desperate pencil rubbing of experiences before they pass. As Schultz puts it, “When we are experiencing it, loss often feels like an anomaly, a disruption in the usual order of things. In fact, though, it is the usual order of things. Entropy, mortality, extinction: the entire plan of the universe consists of losing, and life amounts to a reverse savings account in which we are eventually robbed of everything. Our dreams and plans and jobs and knees and backs and memories, the childhood friend, the husband of fifty years, the father of forever, the keys to the house, the keys to the car, the keys to the kingdom, the kingdom itself: sooner or later, all of it drifts into the Valley of Lost Things.” -Steven

Design Beyond the Numbers by Elisabeth van Dijk & Wijnand IJsselsteijn. This paper from the Eindhoven University of Technology is an astute examination of the motivations of self-trackers when they share their data over social media. Despite a reputation of oversharing, most people are careful of what, how and to whom they share their personal data. We are considerate about wasting other people’s time with low-value content and look to find “true peers” for whom the information has greater relevance and less likely to be rebuked. This is an important read for any QS toolmaker for building tools that help people get more from their data by sharing it effectively. -Steven

Show & Tell

This Is What Happens to Your Body on a Thru-Hike by Kyle Boelte. Kyle was already in good shape, but then he hiked over 486 miles on the high-altitude Colorado Trail in just under a month and compared his blood work before and after. Looking at body composition, resting heart rate, blood sugar, cortisol, and testosterone, the results moved in the expected direction, but the degree of change is still astounding. One metric that was new to me is the crossover point for heart rate where fat and carbohydrates are burned equally (As one’s heart rate increases, a greater percentage of carbs is burned). Kyle’s crossover point went from 153 beats per minute to 168. As Kyle joked to his wife, “I should start a business called 8-Hour Abs. Really, just eight hours a day is all it takes!” -Steven (Thanks to Richard Sprague)

Train for Strength or Endurance? by Laila Zemraini. Laila wanted to see if she benefited more from endurance or strength-based exercises. She alternated focusing on each category and looked at the rate of progress. In finding that she responded better to strength-based exercises, she found evidence for why that would be in her 23andMe data. -Steven

Finding out more about me, for free! by Matt Macdonald-Wallace. Matt was uncomfortable with entrusting his QS data to a “corporate organisation who could potentially profit from it”. He shows how he set up his own personal data server with a dashboard by using Connector DB, which bills itself as a “open-source platform for Quantified Self and IoT.” -Steven

College Performance by Tiffany Qi. When Tiffany started college, she assiduously tracked how she spent her time in Google Calendar. Now that she’s graduated, she looks at how the way she spent her time changed over the course of four years and the impact it had on her grades.

Data Visualizations

image (6)Gerrymandering in NC, or, A Tale of Two States by Jeb Stuart. A well-employed data visualization or metric can bring a topic into sharp relief. So it is here with an analysis of gerrymandering in North Carolina. Stuart looks at the number of wasted votes in elections for North Carolina representation in the U.S. House of Representatives. What is a wasted vote? When a candidate wins, all of the votes for that candidate above 50% are considered wasted (those votes could have been used in neighboring districts if the lines were drawn differently). The resulting graph shows how extreme this effort to sequester voters really was. -Steven

P-BirdsNA_10292015Birds of North America. This came out a couple years ago, but I just stumbled across it and spent a good ten minutes looking at all these birds. I loved looking at the variation in body and bill shapes, as well as the similarities that warranted grouping certain species together. This is a product page for a poster, but thankfully, you are able to zoom in and explore. -Steven

tumblr_ol3hg3dcUQ1qf3gj0o1_1280 (1)Seeing Me seeing by Simon Flühmann. I came across this visualization of a Swiss person’s Moves data on Tumblr. Unfortunately, there’s not a lot of context, but I love this type of location data visualization. -Steven

Come to QS17

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 only 13 discounted tickets left. We can’t wait to see you there.

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QS Access App

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FAQ - Privacy Policy

Access your data from HealthKit in a table so you can explore it using Numbers, Excel, R, or any other CSV compatible tool. Download it here.

QS Access is designed to give you a simply-formatted table so that you can make health, fitness, and other discoveries by exploring your own data. When you open QS Access you will see a list of all the types of data potentially collected by HealthKit. You can choose as many items as you like from this list.

IMG_9857Select “Create Table” from the bottom right corner of your screen.

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The first time you select any data type for review using QS Access, you will be asked by iOS to give permission for QS Access to read your data. After you grant permission, QS Access will produce a table:

QSaccessTableGWYou can view the table directly on your iOS device, print it, send it to yourself via email or text message.

Hourly/Daily

Each time you create a table, you can chose to work with either hourly or daily values. QS Access does some processing to HealthKit data so that it can be accessed in convenient “chunks” for analysis. These won’t be appropriate for all uses, but many interesting questions can be asked of data that is presented as a time series using hourly and/or daily values.

QS Labs

The mission of QS Labs is to inspire meaningful discoveries about ourselves and our communities, grounded in accurate observation, and enlivened by a spirit of friendship.

Since 2008, we’ve been organizing “QS Show&Tell” events where people share their self-tracking projects, working with a loosely affiliated network of over 100 independent Quantified Self groups around the world, and maintaing a Quantified Self website and QS forum.

If you use QS Access to learn something about yourself, we hope you’ll consider sharing your discoveries by coming to a QS event. Some ways to participate include:

Join a QS Meetup (scroll down the linked page to find your city)
Start a QS Meetup
Come to the QS15 Global Conference and Exposition

Examples

When your data is in tabular format, there are lots of things you can try. Some are simple, and some are quite complex. For inspiration, here are a few examples from talks given at Quantified Self events of self-tracking projects featuring the analysis of tabular data using common tools.

Kouris Kalligas: Analyzing My Weight and Sleep

“You’ll find it surprising that to decrease my weight I had to slightly increase my calories during the day.”

http://quantifiedself.com/2014/09/kouris-kalligas-analyzing-weight-sleep/

Adrienne Andrew Slaughter on Tracking Carbs and Exercise

“I recently experienced extreme fatigue while riding my bike in downtown Seattle. A few days before I had changed my diet, eating all vegetables, lean chicken, seafood, and nuts, on the guidance of my doctor. Three days into this I was feeling terrible.”

http://quantifiedself.com/2014/06/adrienne-andrew-slaughter-going-carbless-seattle/

Paul La Fontaine: ‘We Never Fight On Wednesdays’

“In 1991 I was a soldier in combat, and I was very alert but felt quite calm. In 2006 I was in Istanbul in a negotiation; one comment was made and I felt physical panic. So I was interested in how I reacted to situations because there didn’t seem to be any logic to it at all. So I devised a study to see how I reacted… What I found was the the vast amount of my stress came from me anticipating a disaster scenario in my future, which was not at all what I expected.”

Feedback and Support

You can download the QS Access App on iTunes.

Please share your feedback with us by emailing qsaccess@quantifiedself.com.

We’ve created a QS Access Questions thread on the QS Forum. We’ll keep an eye out for your questions there.

Leave a comment

What We Are Reading

It’s a new year, and we are starting it off with a collection of articles that we’ve been collecting for the last couple of months. I hope you find them as interesting as we did. -Steven

Articles

Making Statistics Matter: Using Self-data to Improve Statistics Learning by Jeffrey L. Thayne. Can Quantified Self projects solve an ongoing problem in teaching statistics? This doctoral dissertation supervised by Victor Lee, a long time participant in our Quantified Self Public Health symposia, argues that it can. The reason QS can help is simple: in QS practices, statistics become personally relevant. As Thayne writes:

[A]n essential feature of effective statistics instruction [is] a relevant, immediately available context of application, wherein learners feel that they are taking part in an ongoing inquiry process in which statistics is being used as a tool for illuminating something new and important about their world.

What I found especially interesting about this research, which used qualitative methods to explore student’s interest and involvement in their statistics learning, was that the use of self-collected data was not powerful because it appealed to the student’s vanity, but because it was familiar and had contextual meaning. Just as professionals who use statistical methods benefit from understanding where the data comes from and what it is for, students who can situate their practice in a rich context find it easier to master new methods. -Gary

On Progress and Historical Change by Ada Palmer. Historian and science fiction author Ada Palmer’s lucid essay on the idea of historical progress is great to read in light of the never-dying hope among the makers of self-tracking tools that there can be a formula for positive change. I sometimes tire out my colleagues opposing this idea, and I know it seems odd that here at Quantified Self we spend every day supporting people trying to figure out how to use technology for change while at the same time not believing that definite techniques for inducing such change can exist. Isn’t that a contradiction? In contrast to my usual philosophical abstractions and pedantic references to the history of behavioral psychology, Palmer tells the story of where our idea of progress comes from, and offers a fascinating account of how events can be simultaneously free and determined, based on the DIY historical simulation machine she builds every year with her students. -Gary

How a Guy From A Montana Trailer Park Overturned 150 Years of Biology by Ed Yong. One of my favorite stories in citizen science is how Beatrix Potter (of Peter Rabbit fame) was an early and ridiculed proponent of the idea that lichen was a symbiotic fusion of a fungi and an algae. The need for the term “symbiosis” arose from this discovery (credited to Swiss botanist Simon Schwendener). This article follows the humble beginnings of Toby Spribille and the process for how he determined that the theory that lichen is composed of two organisms is wrong. It’s actually three. -Steven

How To Do What You Want: Akrasia and Self-Binding by Daniel Reeves. I’ve been going back and reading some of Daniel Reeves’ excellent posts on the Beeminder Blog about the cluster of concepts and techniques associated with self-control, including also Smoking Sticks and Carrots and What is Willpower? -Gary

How Language Helps Erase the Tragedy of Millions of Road Deaths by Julie Sedivy. What is the difference between the words “accident” and “collision”? The word “accident” implies a lack of blame. This article explores the effects that these connotations have on our subconscious interpretation of the world. -Steven

Faster, Not Smarter: Does Caffeine Really Make You More Productive? by Alex Senemar. Alex surveys what is known about the effects of caffeine on productivity. How do you keep caffeine a boon and not a crutch. What I love about this article is that Alex finishes it with suggestions on how to run your own experiment to see caffeine’s effect on your productivity. -Steven

In Defense of Tracking Our Poop by Adam Butler. Adam makes the argument that one of the best ways to understand the health of the microbiome is to track and pay attention to your poop. How do you turn that into data? Luckily, there is a time-tested classification system that your physician should recognize called the Bristol Stool Scale. Which will help the next time you need to talk to your doctor. -Steven (courtesy of Ernesto Ramirez)

Childhood trauma leads to lifelong chronic illness — so why isn’t the medical community helping patients? by Donna Jackson Nakazawa. “Were there any childhood traumas or stressors that might have contributed to the extreme level of inflammation you’re experiencing as an adult?” Nakazawa says that this was the most important question posed to her in her adult life. From the question, she was able to untangle how her present day health issues have ties to the traumatic death of her father when she was twelve. The article shows that childhood trauma leads to a great likelihood of autoimmune disease. However, knowing about these links, one can help reduce the number of doctor visits. -Steven

Early Modern Bookkeeping and Life-Writing Revisited: Accounting for Richard Stonley, by Jason Scott-Warren. The use of numbers as an element in personal record keeping is ancient, but the account books of early modern elites hold particular interest for historians, since they seem to hold clues to the origin of today’s autobiographical habits. The great 17th century diarist Samuel Pepys kept his entries in a ledger book, and the carefully folded pages and ruled lines of the account books of bourgeois merchants and lawyers provide a dense cultural background for more famous documents (such as Benjamin Franklin’s memoirs) commonly imagined to to lie at the root of the Quantified Self. This essay from The Social History of the Archive (a special volume of the journal Past & Present) takes a close look at the account book of an obscure functionary named Richard Stonley, and shows how mistakes, repetitions, and elisions challenge the idea of the ledger book as a crucible for the modern sense of self. -Gary

Show&Tell

How Software, Data, and a Hell of a Lot of Work Helped Me Lose 110 pounds in 25 Months by Timothy Chambers. Although he doesn’t show his data, it was interesting to read how Timothy integrated various tools into his effort to lose weight. Each tool had a role and each needed certain features to qualify. It’s a complex interaction of data sets and feedback mechanisms. I appreciated one of his points on data portability:

It was critical that my apps could speak to each other and to the cloud, not just to what companies each toolmaker had deals with. My web-based trend tools needed to talk with my scale which needed to speak to my phone. We work so hard for the health data about steps, weight, fat percentage, etc, that should be our data open to use with whatever tools we wish. Not all vendors treat it as such.

-Steven

My to-do list is now public, and it’s the most useful thing I’ve done in years by Joe Reddington. For years, Joe has kept track his number of open to-do’s. In May, he experimented with making his to-do list publicly available. Now that he knows that he’s being “watched”, he is more conscientious about making his items comprehensible, and is  more motivated. As Joe puts it: “When it was [just] a list for me, it looked great; when I decided to make it public, it instantly looked very poor.” -Steven

Analysis of a Personal Public Talk by Alex Martinelli. Alex analyzes a recent talk he gave at a QS Dublin meetup, by looking at his heart rate and speaking speed. The piece has an appropriately casual tone, but he finishes each section of the analysis with a definitive statement based on the data. After looking at how fast he was talking, Alex writes as if he was consulting someone else:

Your average speech rate is 152 Words Per Minute (WPM), but an approximately constant and significant decrease can be observed, bringing you from an initial WPM of 166 to a final value of 142. The primary cause of this is the usage of increasingly longer pauses between words, secondarily reinforced by a combination of using longer words, as well as a tendency to slow down the pronunciation of words, while the talk unfolds.”

As an engineer at IBM, he’s clearly used to this at his job, but I like the idea of bringing this structure and formality to personal data analysis. -Steven

The Somniloquist by Adam Rosenberg. Adam was told by others that he talks in his sleep, so he set up a recorder to capture his “midnight monologues”. The recordings are transcribed, and in addition to being hilarious, they are an interesting insight into what the brain is doing during sleep. -Steven

My Quantified Wardrobe 2017 by Matt Manhattan. Matt analyzes his wardrobe in an effort to define his relationship with his clothes. He looks at how much of each article of clothing he has and their associated cost. But it’s the pictures of his clothes that makes this post delightful. -Steven

Data Visualizations

History Lesson by Clive Thompson. Not a visualization, but an article about the history of data visualizations. -Steven

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The London Wind Map. A whimsical of visualization of where you would go if “you were pushed by the wind each day” in 2015. -Steven

 

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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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Meetups This Week

I haven’t been doing these for awhile, but that does not mean that Quantified Self meetups have not been occurring. Far from it. If you are in the Washington D.C. area, there is a meetup on Friday, September 9th with presentations on using data to personalize one’s fitness regimen and how to use heart rate and EMG measurements to detect one’s sleep stage.

To see when the next meetup in your area is, check the full list of the over 100 QS meetup groups in the right sidebar or you can search for “Quantified Self” on meetup.com. Don’t see one near you? Why not start your own! If you are a QS Organizer and want some ideas for your next meetup, check out the myriad of meetup formats that other QS organizers are using here.

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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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