Topic Archives: QS Resource
Update: Want to make your own Sparktweet? We made a simple tool that you can use. Check it out here!
I was stumbling around Twitter the other day when I was confronted with something new and different:
— Steve Cavendish (@scavendish) April 5, 2013
Apparently that little data representation is not all that new and different. Way back in 2010 Alex Kerin figured out that Twitter was accepting unicode and decide to play around and see if it could represent data. Lo and behold it could and a SparkTweet was born:
▁▁▂▂▃▄▄█▁▁▂ ▃▄▄▅▆▁▁▂▂▃▄▄▅▆▁▁▂▂▃▄▄▅▆ Can you guess what I’m coding in Excel? Eh? Eh?
— Alex Kerin (@AlexKerin) June 9, 2010
Before we get into how you too can start populating your Twitter feed and Facebook (I checked and it worked there as well) with representations of your own Quantified Self data let’s dive into some history.
a small intense, simple, word-sized graphic with typographic resolution. Sparklines mean that graphics are no longer cartoonish special occasions with captions and boxes, but rather sparkline graphic can be everywhere a word or number can be: embedded in a sentence, table, headline, map, spreadsheet, graphic.
In another wonderful book, The Visual Display of Quantitative Information, Tufte describes sparklines as “datawords: data-intense, design-simple, word-sized graphics.“ Of course, those of us in the QS community are deeply interested not only in data, but also in how data operates in society, what is means as a cultural artifact that is discussed and exchanged in language both written and verbal. This interest iswhat initially piqued my curiosity. The movement of data and a dataword distributed among text and publicly expressed in a tweet. I can’t help but wonder, what does this mean for how we think about and express data about our world?*
If you want display quantitative data in your Twitter stream it shouldn’t take you all that long to get started. Lucky for us Alex Kerin has provided a nifty little Excel workbook that will generate the unicode that can be pasted into your tweet. Just download this workbook and follow the simple instructions! Soon you’ll be able to send out tweets just like this:
My 30-day step history: ▄ ▄ ▄ ▅ ▅ ▅ ▄ ▆ ▄ █ █ ▅ ▁ ▃ ▆ ▅ ▁ ▄ ▇ ▃ ▅ ▆ ▂ ▂ ▅ ▃ ▄ ▄ ▅ ▄ #QuantifiedSelf
— Ernesto Ramirez (@eramirez) April 11, 2013
Now you’re ready and able to go forth and tweet your data! If you use a sparktweet to express your Quantified Self data be sure to let us know in the comments or tweet at us with #sparktweet and/or #quantifiedself.
*Of course the use of sparktweets is not without controversy in the world of data visualization. For more discussion on sparktweets and their utility I suggest you start here.
If you’re like me, then you’re always looking for new ways to learn about yourself through the data you collect. As a long time Fitbit user I’m always drawn back to my data in order to understand my own physical activity patterns. Last year we showed you how to access your Fitbit data in a Google spreadsheet. This was by far the easiest method for people who want to use the Fitbit API, but don’t have the programming skills to write their own code. As luck would have it one of our very own QS Meetup Organizers, Mark Leavitt from QS Portland, decided to make some modifications to that script to make it even easier to get your data. In this video below I walk you through the steps necessary to setup your very own Fitbit data Google spreadsheet.
Step-by-step instructions after the jump. Continue reading
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 or more randomized controlled trials
- 1 or more cohort studies
- 1 or more case-control studies
- 1 or more case-series
- 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.
Many people think the Quantified Self mostly involves physical metrics: heart rate, sleep, diet, etc. but what about what goes on in our brains? Can we quantify that? There have been several inspiring Quantified Self talks about tracking learning and memory. This post will collect all them into one place, along with good resources for further exploration.
Memorization is only a small part of learning, but it in many circumstances it is unavoidable. There is an ideal moment to practice what you want to memorize. Practice too soon and you waste your time. Practice too late and you’ve forgotten the material and have to relearn it. The right time to practice is just at the moment you’re about to forget. If you are using a computer to practice, a spaced repetition program can predict when you are likely to forget an item, and schedule it on the right day.
In this graph, you can see how successive reminders change the shape of the forgetting curve, a pattern in our mental life that was first discovered by one of the great modern self-trackers, Hermann Ebbinghaus. With each well-timed practice, you extend the time before your next practice. Spaced repetition software tracks your practice history, and schedules each review at the right time.
Convenient tools to take advantage of fast memorization techniques have been around since Piotr Wozniak began developing his Supermemo software in the early 1980s. (I wrote a profile of Wozniak for Wired in 2008, which is cited in some of these talks.) Many of us in the Quantified Self use spaced repetition. We’ve put together this page to list resources, share experiences, and invite comments and questions. We hope you find it useful. If you do, please contribute some knowledge or questions to the comments.