Topic Archives: Personal Projects

Matthew Ames: One Small Step on a Scale

In this video from the Boston Quantified Self Show&Tell, Matthew Ames describes the self-tracking project that dramatically changed his weight and fitness. Beginning with simply measuring his weight daily using a Withings scale, he added together a number of common QS tools, including Weight WatchersRunkeeper, MyFitnessPalGarmin Forerunner watch, and the Nike+ system, to support his self-transformation.

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Quick and Reliable Mood Measurement

Sami Inkinen, triathalete, self-quantifier, and founder of Trulia, measures his mood on a five point scale every morning, within five minutes of waking up. This method fascinates me. I do something similar (though I use only a three point scale). Sami has found that this quick and easy measurement reliably correlates with his athletic performance, suggesting that it indeed measures something significant about his overall well being in the day ahead.

Read Sami’s full post here: What the first 2 minutes after waking up can tell you about the day ahead?

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Living Without Numbers

Here at Quantified Self we talk about living with numbers. Look a few inches above these words and it’s right there in our four-word tagline: self knowledge through numbers. Information, increasingly numerical information, is becoming a driving force in the world. We’re surrounded by numerical representations of ourselves in almost all aspects of our lives. Probably no more so than in our digital interactions with each other.

Those meaningful and mundane comments, retweets, posts, likes, scores, friends, and followers all have raw or algorithmically defined numerical value. What you think of those value isn’t as necessary as understanding that a value exists. This is the place where you would probably expect me to go on  long diatribe about the nature of scoring our social lives, but I’ll leave that for others to handle. It’s a hot topic and I’m sure you’ll able to find well-written arguments with a quick google search. Instead I want to use this space to show you something interesting and thought provoking.

Facebook Demetricator

Ben Grosser is an artist and a composer, and is currently completing an MFA in New Media at the University of Illinois at Urbana-Champaign. He’s also the creator of a nifty little piece of software called Facebook Demetricator. Simply put, the Facebook Demetricator is a  browser extension that hides the numerous instances of social metrics that live in the Facebook interaction experience:

The Facebook interface is filled with numbers. These numbers, or metrics, measure and present our social value and activity, enumerating friends, likes, comments, and more. Facebook Demetricator is a web browser addon that hides these metrics. No longer is the focus on how many friends you have or on how much they like your status, but on who they are and what they said. Friend counts disappear. ’16 people like this’ becomes ‘people like this’. Through changes like these, Demetricator invites Facebook’s users to try the system without the numbers, to see how their experience is changed by their absence.

How does it work? Take a close look at the short gif below or watch this short explanatory video.

Facebook Demetricator Prototype Removing Metrics on the Friends Page

Ben did a great interview with Matthew Fuller that I highly suggest you read if you’re interested in learning more him and the reasoning behind this project. I preparation for this short post I emailed Ben and asked him his thoughts on how this fits into the broader conversation around Quantified Self:

I suppose a key difference between the metrics on Facebook and a more typical QS approach is that in QS one has substantial control over what they collect and how they interpret it.  On Facebook, the what, how, and even in some cases, the why is largely left to the system to manage.  In response, Demetricator removes the numbers and thus makes the familiar unfamiliar, focusing us on the ways those numbers are functioning as drivers of interaction. It’s not that the numbers themselves are bad, but they’re certainly worth questioning—especially when their accumulation and presentation are handled by others.

I’ve leave you with a few thoughts I’ve had on this as I’ve been contemplating this work and Ben’s response.

Numbers are powerful. Ben mentions in his announcement and again in the interview that one of reasons for creating this software was to combat the “(capitalism-inspired) innate desire for more.” Numbers have this seemingly magical evolutionary trait in that they seek to increase and we as creators and consumers of those numbers tend to oblige (I understand this isn’t true for all numbers). This isn’t a judgement statement, just something we need to be aware of as we build and interact with social and behavioral systems that are becoming increasingly quantifiable.

Mind the data. There are these great little inlaid signs in the London Underground that remind passengers to “mind the gap.” It serves as a simple warning to make people aware of the distance between the train and the platform. Being mindful or ourselves through the lens of the data we produce and use has been a recurring theme lately for some of us in the QS community. So much so that we opened up the QS Conference with a wonderful presentation by Nancy Dougherty on Mindfulness in QS. With the increasing scorification of our social interactions in various online mediums the Facebook Demetricator reminds us to be mindful of the role numbers play in our lives and how we choose to use them.

What do you think? Take a look at Ben’s project and if you use it let us know your thoughts. I’ve created a post on the Quantified Self Forum for discussion, but feel free to comment here as well.

Thanks for Ben Grosser and Alex Carmichael for providing feedback on this post. Also thanks to everyone (especially @xarodai) who uses the #quantifiedself hashtag on Twitter. Without you this unique tool would have never surfaced for me. 

 

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A Visualized Self

Typically when the Quantified Self-er talks about using photography and image capture for self-tracking they’re talking about taking pictures of their food. Pictures are a very powerful way to capture information for better understanding, you know, they are worth a thousand words. On the blog here we’ve also highlighted a few really interesting projects that take the idea of using visual images for tracking and decided to turn the lens around such as Jeff Harris and his 13 years of self portraits.

One of the projects that I found super interesting was LifeSlice by Stan James.

For those of you who want to try LifeSlice Stan has put the code online for you to use and possibly tinker with. As a new user I can say that it is pretty interesting to see how my facial characteristics map to what I’m doing on the computer. For examples here’s me looking at a new statistical software package for mac (Wizard).

 

 

And here’s me writing this post while listening at a conference on health data.

The last project I want to highlight here is the self-portrait project of Noah Kalina. Noah is a photographer who has been taking self portraits every day for 12.5 years (January 11, 2000 – June 20, 2012). A few months ago he put all 4514 images together into one amazingly insightful video.

Than Tibbetts was so intrigued by this project he decided to work some fancy image processing magic to find out what “Average Noah” looked like and found this:

I’m sure there are more projects out there that involve individuals turning the camera on themselves. We all have cameras with us in our pockets and on our computers. How are you using those image capture technologies to better understand yourself? If you’re working on something interesting let us know!

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An Experiment with Polyphasic Sleep by Emi Gal

A great talk on a Polyphasic Sleep experiment by Emi Gal, the CEO of the interactive advertising platform Brainient.

“The main takeaway was that it is fun, but not sustainable. None of the polyphasic sleepers have succeeded in doing it more than six months. You always act tired…”

If you’ve experimented with polyphasic sleep, we’re interested in your stories.

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Why Self-Track? The Possibility of Hard-to-Explain Change

My personal science introduced me to a research method I have never seen used in research articles or described in discussions of scientific method. It might be called wait and see. You measure something repeatedly, day after day, with the hope that at some point it will change dramatically and you will be able to determine why. In other words: 1. Measure something repeatedly, day after day. 2. When you notice an outlier, test possible explanations. In most science, random (= unplanned) variation is bad. In an experiment, for example, it makes the effects of the treatment harder to see. Here it is good.

Here are examples where wait and see paid off for me:

1. Acne and benzoyl peroxide. When I was a graduate student, I started counting the number of pimples on my face every morning. One day the count improved. It was two days after I started using benzoyl peroxide more regularly. Until then, I did not think benzoyl peroxide worked well — I started using it more regularly because I had run out of tetracycline (which turned out not to work).

2. Sleep and breakfastI changed my breakfast from oatmeal to fruit because a student told me he had lost weight eating foods with high water content (such as fruit). I did not lose weight but my sleep suddenly got worse. I started waking up early every morning instead of half the time. From this I figured out that any breakfast, if eaten early, disturbed my sleep.

3. Sleep and standing (twice). I started to stand a lot to see if it would cause weight loss. It didn’t, but I started to sleep better. Later, I discovered by accident that standing on one leg to exhaustion made me sleep better.

4. Brain function and butter. For years I measured how fast I did arithmetic. One day I was a lot faster than usual. It turned out to be due to butter.

5. Brain function and dental amalgam. My brain function, measured by an arithmetic test, improved over several months. I eventually decided that removal of two mercury-containing fillings was the likely cause.

6. Blood sugar and walking. My fasting blood sugar used to be higher than I would like — in the 90s. (Optimal is low 80s.) Even worse, it seemed to be increasing. (Above 100 is “pre-diabetic.”) One day I discovered it was much lower than expected (in the 80s). The previous day I had walked for an hour, which was unusual. I determined it was indeed cause and effect. If I walked an hour per day, my fasting blood sugar was much better.

This method and examples emphasize the point that different scientific methods are good at different things and we need all of them (in contrast to evidence-based medicine advocates who say some types of evidence are “better” than other types — implying one-dimensional evaluation). One thing we want to do is test cause-effect ideas (X causes Y). This method doesn’t do that at all. Experiments do that well, surveys are better than nothing. Another thing we want to do is assess the generality of our cause-effect ideas. This method doesn’t do that at all. Surveys do that well (it is much easier to survey a wide range of people than do an experiment with a wide range of people), multi-person experiments are better than nothing. A third thing we want to do is come up with cause-effect ideas worth testing. Most experiments are a poor way to do this, surveys are better than nothing. This method is especially good for that.

The possibility of such discoveries is a good reason to self-track. Professional scientists almost never use this method. But you can.

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QS Primer: Case-Crossover Design

We’ve already published this QS Show&Tell talk by Mark Drangsholt about using self-tracking to identify the triggers of his heart problems, lessen their frequency, and make good decisions about treatment. I’m re-posting it here to focus on attention on the interesting and powerful method Mark used, the case-crossover design, and invite you to think about whether this has promise for your own self-tracking projects.


Mark is a professor and chair of oral medicine at the University of Washington School of Dentistry. He’s a triathlete and long time self-tracker. He is in good physical condition, but suffers from heart ailments that are frightening and dangerous. For instance, he has tachycardia (sudden acceleration of heart rate). At times his heart goes from 60 to 220 beats per minute. It feels like his heart is going to jump out of his chest. He also has atrial fibrillation, with palpitations, a feeling of immanent doom, and a sense that he is choking.

“The first time it happened in 2003 I really thought I was dying,” Mark says in his talk. He had always assumed that if he ever had a heart attack he, of all people, would know to pick up the phone and call 911, but the opposite happened. He just thought to himself “this is it,” and slumped down in his chair. Fortunately, he survived, and when he recovered he asked himself whether he could identify the triggers of these unpleasant events and avoid them. He created a simple Excel table of all episodes for one year, on which he recorded information about his attacks.

Mark is an expert on evidence based medicine, so he was naturally curious about what kind of evidence his self-tracking data contained. In standard reference material on medical evidence, students learn about a hierarchy that goes something like this:

  1. 1 or more randomized controlled trials
  2. 1 or more cohort studies
  3. 1 or more case-control studies
  4. 1 or more case-series
  5. expert opinion without above evidence

Mark’s self-tracking data didn’t naturally fit with any of these approaches. To understand whether these triggers actually had an effect on his arrhythmias, he used a special technique originally proposed by the epidemiologists Murray Mittleman and K. Malcolm Maclure. A case-crossover design is a scientific way to answer the question: “Was the patient doing anything unusual just before the onset of the disease?” It is a design that compares the exposure to a certain agent during the interval when the event does not occur to the exposure during the interval when the event occurs.

Using this method, Mark discovered that events linked to his attacks included high intensity exercise, afternoon caffeine, public speaking to large groups, and inadequate sleep on the previous night. While these were not surprising discoveries, it was interesting to him to be able to rigorously analyze them, and see his intuition supported by evidence.
“A citizen scientist isn’t even on the conventional evidence pyramid,” Mark notes. “But you can structure a single subject design to raise the level of evidence and it will be more convincing.”

Please let us know if you use this method in your own projects. We’ll post more reports when we have them.

REFERENCES AND GUIDES

There are some tricks to doing a good case-crossover study on yourself. Mark’s video provides a basic introduction.  For technical details, this detailed introduction to case-crossover design by Yue-Fang Chang especially useful.

The seminal paper on case-crossover design is “The Case-Crossover Design: A Method for Studying Transient Effects on the Risk of Acute Events” by Malcom Maclure. (1991) [PDF] A search on Google Scholar for case-crossover design will get you deep into this literature. Unfortunately very little of it involves the kind of n-of-1 studies we’re usually interested in, but there are many technical details that may contain clues for dedicated experimenters.

One paper that will be of special interest is this one: “Should We Use a Case-Crossover Design?” by K. Malcolm Maclure and his collaborator Murray Mittleman. (2000) [PDF] In the midst of discussing technical details important for scientists proposing to use this method in studies funding by research grants whose reviewers may not be familiar with it, Maclure and Mittlemen describe using case-crossover analysis to retrospectively understand more about the death of Maclure’s father. I quote the relevant section below:

We did an n-of-1 case-crossover study of hypothesized triggers of repeated syncope experienced by Kenneth Maclure (MM’s father), who was diagnosed with sick sinus syndrome and died of fatal MI at age 73 during a morning swim, after several other potential triggers. The target person times wereKenneth’s 62nd–74th years (and subsequent years if he had lived longer). The study base comprised the years 1980–1981 and 1986, during which there were 33 instances of syncope. We restricted the study base to those years because his wife, Margaret, was willing to review only 3 years of her diaries because the memories rekindled her grief. We had no intention to generalize the findings to other individuals, only to other years. Our goal was to identify triggers to which Kenneth may have been susceptible and to test Margaret’s general hypothesis, “Perhaps I should have done more to help him avoid stress.” Hypothesized triggers included visitors to the home, trips out of town, eating out, unusual exertion, and so on. The 24-h period before an episode of syncope was classified as a case day. Each case day was matched with a control day, the same 24-h period 2 weeks before. Margaret was surprised by our null findings and relieved some lingering feelings of guilt.

 

 

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Temple of Self: A QS Masters Design Thesis

Aarti Vashisht has done some interesting QS-related work for her MFA at Art Center College Design.

She designed some prototype sensors that could be worn on our bodies in the future, and interviewed people to learn their thoughts on how these integrated sensors might impact their lives.

This is an image of the sensors she designed, to be worn across the shoulders and on the wrist, among other places. Take a look at her report here, called Temple of Self.

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Dan Catt On Spotting Mild Depression with QS Data

This interesting post by Dan Catt (@revdancatt) describes how he used Quantified Self ideas to get a handle on his depression:

I’d never been depressed before, or at least not that I could remember. …

Spotting the depression was interesting. Obviously I knew something was up, but when it started it kind of blinded me to itself. I didn’t really have the energy to spot what was going on.

But, because I back-up my data regularly, grabbing content of various social networks either with scripts or services that do it for you, I noticed something. The amount I was tweeting was way down, it had suddenly dropped. Not so much general tweets but conversations with people, @ messages and direct messaging was down, I could see the numbers right in front of me.

The amount of photos I was posting to Flickr had also dropped (cross posted from Instagram I’ll get to in a second).

I could see the interactions with people I was having around the internet had reduced, weeknotes had stopped, emails slowed down, I was leaving my IM client off more, blogged (or at least writing drafts) took even longer than normal.

Dan Hon wrote about the Quantified Self as a way to measure his blood sugar (and more). All these services, hardware and tools we can monitor our body with, glucose levels, weight and so on. What I was seeing was a change in my behaviour, a measurable mental state. And once I’d seen the numbers it made it easier to figure out what was going on.

[Read the whole post: Leaving the Guardian, creativity vs mild depression, the quantified self and running]

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First QS Masters Thesis: Part 2!

 After a year of research and writing, I’m finally finished with what could be called the “first master’s thesis on Quantified Self.” If some of you didn’t catch my first post, you can find it here. I spent about a year conducting research on Quantified Self for an MA in Applied Anthropology at San Jose State University. Technically, I didn’t write a “thesis” but a “project report,” because I conducted an applied research project on QS Meetup groups rather than “thesis” type research. To fulfill the requirements for the MA I had to produce two reports. The first was a report containing the findings from the research on the meetups, which I presented to QS Labs (see the earlier post). The second was a project report, which is the document I submitted to my department (which I would like to present here). For the most part, the purpose of the project report is to document the research process, including methods and theory. In addition to that, I was able to fit in some general info about QS and self-tracking into my report. Section one and section four will probably be the most interesting to members of the greater QS community. The following is an excerpt from section four.

You can see the full report here.

The practice of self-tracking   

The range of self-tracking projects that people take on is diverse and tracking can focus on almost any aspect of life. Grouping by domain, such as sleep or weight, is one way to describe self-tracking practices. However through my research, I identified three axes that can be used to describe or locate self-tracking projects within the spectrum of these phenomena. Figure 1 (below) represents the three axes as a three-dimensional field, with sample self-tracking projects plotted as examples of how projects configure within this space.

            The first axis is the degree of technological involvement. Self-tracking projects can heavily rely on complex devices with advanced sensors or on sophisticated laboratory tests. In other cases, self-trackers may use only a pencil and paper. The technology axis is essentially the initial line that delimits what can or cannot be tracked. In order to monitor and record data, you need certain sensors and recording devices. In some cases, self-tracking projects are driven by the technology; because there are sensors and devices to take measurements, people are using these technologies to monitor and collect data. In other cases, the self serves as both the sensor and the recording device.


The second axis is the level of complexity in the design of the self-tracking project. On the more complex side are projects often referred to as “self-experiments.” Self-experiments usually employ the scientific method to some degree, collecting baseline data, testing hypotheses, and in some cases controlling for variables. Some self-trackers try to find correlations across data sets, for example, trying to figure out what factors affect their sleep quality, by monitoring and recording data on sleep quality, and correlating that data on their activity before bed or even ambient data such as room temperature at night. On the less complex side of project design are practices like “life-logging.” There is a range of different practices people will call life-logging, but one example of a simpler form in terms of project design, would be basic journaling. A practice such as keeping a dream journal is considered self-tracking, in that it produces knowledge about the self through recording information, even though the data are not numerical.

The third axis self-tracking projects can be plotted on is the extent to which projects are explicitly goal driven or exploratory. Some self-trackers have particular goals when starting a project, like wanting to lose weight or improve sleep. Other self-trackers collect data with the intention that they will be able to do something with the data in the future (what that something is, may or may not be known), or in some cases track things just to keep track of them. Examples of this last case are things such as tracking daily step count with a pedometer or using mobile phone applications to “check in” to places you visit each day simply to have a record of that.

            Self-tracking projects can configure onto these axes in almost any way imaginable. The categories on each of these axes are also negotiable. Different people may not agree what constitutes a high or low level of technology involvement. In some cases, someone might not even consider things like pencils and paper technologies. Similarly with practices such as journaling, some might argue the extent to which journaling would be considered life-logging or even self-tracking. There are also secondary factors that can help classify a project, such the length of a project, whether the project is an intervention or data collected to inform an intervention, and whether the data collected is primarily objective/collected by passive sensors, or subjective/based on self-assessment. 

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