Tag Archives: data visualization

Doug Kanter on Data, Diabetes, and Marathon Training

Posted on May 23, 2013 by Ernesto Ramirez

Doug Kanter has been a Type 1 diabetic for 26 years. Through this time he’s come to learn more about his disease by using many data-gathering tools and his own work in visual analysis at the NYU ITP program. We’ve featured Doug’s compelling work here on the blog before and we were excited to hear him talk at the NY QS Meetup about his new project to understand how marathon training and running effect his blood sugar and insulin treatment.

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Fun With Sparktweets

Posted on May 3, 2013 by Ernesto Ramirez

It’s no secret we love data here at Quantified Self, but we also love seeing how people interact with data. We’ve explored many of those interactions here and we’re always on the lookout for new and different ways people communicate their data and the insights therein.  A few weeks ago we wrote up a short “how to” post describing a recent phenomenon on Twitter – sparktweets. It didn’t take too long before we started seeing the Quantified Self community using these new “data words.”

We couldn’t stop thinking about sparktweets. What kind of data could you communicate in 140 characters? What would people do if it was easier to make a sparktweet? So we asked out friend Stan James to help us out and our Sparktweet Tool was born. Since then we’ve seen some great tweets roll though our feed, and we would love to see more. Need some inspiration? Here’s a few we really enjoyed:

What kind of conversation can you start with you data? Head on over to our Sparktweet Tool then make sure to add a link to your tweet in the comments or add to our conversation on the forum.

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How To Make A Sparktweet

Posted on April 13, 2013 by Ernesto Ramirez

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:

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:

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.

The data visualization theorist and pioneer, Edward Tufte, is primarily responsible for the widespread use of sparklines. In his wonderful his book, Beautiful Evidence, Tufte describes sparklines as

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?*

How To

Update:Thanks to our QS friend, Stan James, you can now make Sparktweets right here on Quantifiedself.com. Just head over to our Sparktweet Tool page and start making your own “data words.”

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:

For those of you with a bit more technical skill Zack Holman has made a very neat command line tool that will quickly generate the unicode for sparklines.

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.

 

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Jana Beck on Learning from over 100,000 Blood Glucose Readings

Posted on July 6, 2012 by Ernesto Ramirez

Jana Beck was diagnosed with Type 1 diabetes when she was 19 years old and has been interested in tracking her health ever since. Last year when she received a continuous blood glucose monitor she decided to take a more active role in understand what was effecting her blood glucose levels and insulin dosing. Spurred by reading about carbohydrate restricted diets, she decided to see if she could see changes in her blood glucose readings and as a result of changing her diet. In this talk at the New York QS Meetup she describes exactly what she found and shares some really neat visualizations that help tell her story.

Jana Beck – Quantifying Diabetes: Lessons learned from 100,000+ blood glucose readings from Steven Dean on Vimeo.

You can read more about the last New York QS Meetup here. If you’re interested in using theses data visualizations with your own blood glucose data be sure to check our Jana’s iPancreas project on GitHub.

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Numbers From Around the Web: Round 8

Posted on May 24, 2012 by Ernesto Ramirez

If you have diabetes, or know someone who does, you’ve probably encountered a blood glucose monitor. Like many medical devices, design and data visualization are usually an afterthought. While there are many new exciting products coming to market like the iBGStar designed by Agamatrix, there are individuals who want to learn more than just their current blood glucose values. Diabetes care is also moving towards an automated and coordinated process driven by continuous blood glucose monitoring and implantable insulin pumps. These devices live on data, huge amounts of data, but what do their users know? More specifically, what do their users understand about their data, their condition, and themselves?

Doug Kanter is a designer, photographer and a student in the Interactive Telecommunications Program (ITP) at NYU. He’s also a Type-1 diabetic who has a keen interest in applying actionable design and interaction schemes to the data he gathers from his monitoring systems.

It is time to re-imagine the entire user experience of being a patient with diabetes. There is tremendous potential in applying information technology, creative design and research into behavior change into a comprehensive product for patients. Technology-based solutions are increasingly important resources in these times of skyrocketing treatment costs and lmited doctor availability.

Doug has been using his skills to better visualize and understand his own data, particularly his continuous blood glucose monitor. His first project, 7729, explored one month of his continuous blood glucose monitoring – the 7729 readings to be exact.

His second project expanded on the 7729 project to include not only his blood glucose monitoring, but also the insulin he was receiving. Insulin on Board, is based on 100 days of data collection and includes 820 insulin pump reading and 25,012 blood glucose reading. By coordinating these two data sets he was able to look for patterns and identify the efficacy of his insulin dosing.

The goal of Insulin on Board was to better understand the relationship between the insulin I take and the resulting blood sugar readings. It visualizes not simply when I take a dose of insulin, but when that insulin “kicks in.” Because insulin has a latency, it is helpful to see it actually has an effect on blood sugar. Often times I’ll take two or more doses of insulin within a few hours. Insulin on Board calculates the sum overlapping effect of these dosages.

I think patients like me could benefit massively from having improved visualizations that give you both a solid overview of how you are doing but also allow you to dial down into the details if you want.

Being a student and designer, Doug has done a great job explaining the process he takes for developing these visualizations. If you’re interesting in learning more about how he created these visualizations, what he learned, and future work you can follow along at Databetic and his blog.

Every few weeks be on the lookout for new posts profiling interesting individuals and their data. If you have an interesting story or link to share leave a comment or contact the author here.

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FitBit + Google Spreadsheets = Awesome

Posted on July 12, 2011 by Ernesto Ramirez

This post and instructions are no longer up to date. For a current how-to please visit the updated post. 

On February 11th FitBit released their API into the wild and let developers get to work. Since then there have been some very neat integrations. One of the best uses of the API it the open source script that enables users to download their data into google spreadsheets. Developed by John McLaughlin, this script gives everyone the ability to get their historical data from FitBit and play with visualizations and analytics. Even someone without any programming experience can start creating very neat dynamic charts and graphs in under 30 minutes. For example I created the the following charts in just a few minutes (click images for interactive versions):

Daily Step Count:

If you already have a FitBit you might be wondering how to actually implement John’s script to grab your own data and start making fun charts and graphs. It takes about 15 minutes from start to finish to set up your FitBit developer account and then set up the script in Google Docs. The step-by-step process is outlined after the jump.

Continue reading →

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Contests for Data Gurus

Posted on June 10, 2011 by Alexandra Carmichael

I recently came across three contests relevant to the QS community, and wanted to pass them along.

1. data in sight: making the transparent visual
This is a hands-on data visualization competition held June 25th and 26th, 2011, at the Adobe Systems, Inc. offices in San Francisco’s SoMa District. Open to coders, programmers, developers, designers, scientists, members of the media—anyone who believes that data is divine and has ideas for bringing it to life. Data sets will be provided, or bring your own. (Thanks to Indhira Rojas for sending this in!)

2. Health 2.0 Developer Challenge: Washington, DC Code-a-thon
On June 11, 2011, developers, designers and other stakeholders will be given an overview of health care issues, tools and data sets, and asked to creatively design new tools for the health care space. Developers are encouraged to use OpenGov data sets as well as private data sets to create their application. At the end of the day, developers present their application to the group, and the best solution is awarded.

3. CureTogether Health Data Discovery Contest
Over the past 3 years, CureTogether has gathered millions of patient-reported data points on symptoms and treatments for over 500 conditions. But on a larger scale, how well does CureTogether data represent the general population? In this contest, stats-minded people are asked to challenge the dataset and see whether or not it holds up to existing research studies. There are cash prizes, and the deadline for joining the contest is June 29, 2011.

4. sanofi-aventis U.S. Innovation Challenge: Data, Design, Diabetes
Starting July 1, 2011, innovators can submit their best data-inspired and human centered concepts for people living with diabetes. 5 semi-finalists will receive $20,000 and professional mentoring to develop a working prototype. Following a demo day, 2 finalists will be selected to receive an additional $10,000 to test their solution in a real life diabetes community. The final winner will receive $100,000 and a month stay at the RockHealth incubator in San Francisco to turn their prototype in to a scalable solution for people living with diabetes. (Thanks to Steve Dean for sending this in!)

Good luck! If you know of any other QS-related contests, please leave a comment.

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Personal Data Visualization

Posted on March 15, 2011 by Steven Dean

In 2007, while training for an Ironman triathlon, one of the many daily QS rituals I did included waking up in the morning and strapping on my heart rate monitor before I got out of bed to measure my resting heart rate (HR). My coach had made it one of the mandatory data points I had to capture during the 10-month training period. If my morning resting heart rate was just 2-3 beats higher than the previous days, then that most likely indicated my body was fighting an infection and I needed to pull back on my training volume no matter how good I felt. I didn’t always follow the advice and in the graph above you can see 3 times when I did not heed the advice of my coach, kept training and then within a few days I got sick (resting HR spikes). I also like seeing how, over time, my resting heart rate decreased to around 50 beats per minute and was a reflection of my improved fitness level.

In the Reflection stage of Ian Li’s stage-based model of personal informatics, he makes a distinction between reflecting in the short-term (right now) and the long-term (later on). In the morning when I read my HR, I could act upon it that day and then over time I could review the data and look for trends and patterns with my coach and modify my training as needed. Visualizing a single variable is pretty straightforward, but add multiple variables and we see how giving visual form to all this data gets tricky. What are the methods and tools that help us visualize our data so that, in turn, we can create actionable knowledge?

At our upcoming first Quantified Self Conference we have created a breakout session specifically focused on how members of the QS community are using visualization tools and methods to make meaning out of their personal data. This is going to be a hands-on session and we want you to bring your data and visualizations and share what has worked for you and the kinds of challenges you face in interpreting the data. I’ll be joined by visual artist, Laurie Frick, who has used QS data of her own and data from Ben Lipkowitz to build really beautiful analog work. Also helping out will be fellow NY QS member, Paul Marcum, who runs the New York Data Visualization and Infographics meetup.

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