My final class at ITP in the Spring was called Printing Code, a graphics design and coding class. For my final project, I took several months of CGM blood sugar data and visualized it in a new way. At that time, I was doing a fair amount of business development work on Databetes. With this project, I was interested in doing something purely for aesthetics, so I went off on this artistic tangent. As a result, I produced these two posters.
October 2012 CGM readings
July 2012 CGM readings
Each displays a month’s worth of blood sugar readings from my Dexcom 7 continuous glucose monitor, which generates a blood sugar reading every 5 minutes. The top of the circle marks midnight. Moving around the circle, time moves clockwise with the morning readings along the right. Noon is at the bottom of the circle, followed by the afternoon and evening readings.
A core element of this visualization is seeing the readings radially instead of the traditional linear approach. I also wanted to try a new approach to categorizing the readings. A normal view of blood sugars look this:
Readings are either In-Range (often categorized as 80-120 mg/dL), High or Low. This time, I had only two categories. I split the difference of In-Range readings at 100 and presented them as either above or below this threshold.
I then converted them to the radial view.
I then stacked all the day views from the month on top of each other with a light opacity. The darker the readings, the more often the readings are in that value range. Outlier days where I have an usual spike in readings can be seen faintly. The poster with the black background from October 2012 shows that I had a few more bad days than I did in July.
I coded these in Processing using the Geomerative library. Big thanks to my teacher Rude Madsen for his help getting things working.
The last few months have been rather hectic here in NY and I’ve fallen behind on posting. But I wanted to give a quick update on my work with Databetes.
In May, I wrapped up my graduate studies at NYU’s ITP. Databetes was my thesis. Video of presentation is available here. The foundation of my thesis was the yearlong Quantified Self project I did tracking every data point related to my diabetes throughout 2012. This included blood sugar readings (~100,00), insulin dosages, every meal eaten, location data and more. With all this self-tracking, 2012 turned out to be the healthiest of my life. My A1c blood tests improved nearly a full point to the 5.6-5.8 range. I also talked about my work designing and developing a meal tracking mobile app.
In the Spring, I presented at the New York Quantified Self Meetup. This event was held at the digital design firm R/GA, who has done extensive work with Nike and were heavily involved in the Nike FuelBand project. All the presentations focused on exercise. In 2012, I trained for and ran the Philadelphia Marathon. My presentation talked about the changes in insulin rates I saw throughout my training, race day and the month after. I also showed work I did in collaboration with Jochen Wendel, a Ph.D. student at the University of Colorado and fellow T1 diabetic. His focus is on cartography. We generated a Google Earth map that showed my marathon route that is color-coded based on my blood sugar readings. Jochen has done this type of work on his own blood sugar and mountain bike data. Geomedicine is an interesting and evolving are of focus, as noted in this article.
My marathon route, color-coded based on my blood sugar readings
The Google Earth map is interactive, allowing you to click on any point along my marathon route to see the data. I added a red arrow to this screenshot to point out my blood sugar readings from my Dexcom and OneTouch (listed as Medtronic) meters.
These two pieces of work highlight the fact that increased patient engagement with their data can lead to improved health outcomes. For me, this process was quite tedious, keeping track of all this information. With Databetes, we are developing simpler ways to archive, analyze and recall this data in a way that is easier for patients to integrate into their busy lives. I strongly believe that tools focused on improving diabetes outcomes need to begin with a focus on patient engagement and education.
I have just posted on my other blog about my data analysis of the volatility within a month’s worth of CGM readings. Using R, I wanted to explore ways of judging readings by methods other than just average daily blood sugar. This was completed for my Data Without Borders class at NYU’s ITP. The full post is here.
Another marathon down(my third)! And a new personal record! My time of 3:47 at the Philadelphia Marathon was a two minute improvement.
This race is an important point in my 2012 Quantified Self project. In order to best learn from the day, I carried a total of 8 devices with me: an insulin pump, 2 continuous glucose monitors (CGM) (a Dexcom 7 and my newly arrived Dexcom Gen4), a standard blood glucose monitor, a Garmin heart race monitor chest strap and GPS watch, an iPhone, Nike FuelBand and FitBit. I’ll talk more about these later.
As for my diabetes, managing my blood sugars on the day of the race turned out to be more of a challenge than I was hoping for. During my two previous marathons, I saw my blood sugars spike in the hours before the race, both from being anxious and from how i managed my meal dosages. I was determined to prevent that from happening again this year. I had a normal breakfast (an english muffin with almond butter and a banana) very early in the morning at 5am in order to get to the start line on time. I took a dose of insulin that I would normally take assuming I wasn’t about to exercise. I then had another half of an english muffin at 6am. I took a smaller dose for this, hoping that my blood sugars would start going up a bit closer to the 7am start time (they had been at about 145). I also began a temp basal rate at 6am, setting it to 20% my normal rate for 3 1/2 hours.
Waiting for the start along with Ann, my friend and running companion. We ran the Paris Marathon together back in 2010.
As part of my grad school work at ITP, I recently completed a project called “Ready to Start” for my Collective Storytelling class. “Ready to Start” tells the stories of athlete’s first long-distance race, be it a marathon or a triathlon. It focuses on the motivating factor for people to take on this challenge, dedicating both the time and energy needed to train and complete it. In total we conducted nine interviews, three of whom are patients with diabetes (including myself). For a longer description of the project, please see my ITP blog post. Or just jump to interviews with my inspiring fellow type-1 patients Rachelle Glantz and Jen Davino.
I’ve recently completed my most recent data visualization called Insulin on Board. I looked at 100 days of blood sugar and insulin data to see whether a low-carb diet was effective in keeping my blood sugars in range. To see a PDF of the final version, click here: http://bit.ly/KRTCzP
To learn more about the project, I have another blog post here detailing how and why I made it @ http://bit.ly/Jca8tX
This project was included in the show at NYU’s ITP where I recently completed my first year of grad school. It was also featured on the Flowing Data blog @ http://bit.ly/KR2Tf2
This is a data visualization I produced during the Fall 2011 term at ITP. I used 7729 readings from my Dexcom 7 CGM (continuous glucose monitor) from November, 2011. I created it using Processing, then generated a PDF showing the entire month’s readings on top and additional PDFs for every individual day of the month below. The circle spans the respective time frames starting at the top (12:00), progressing clockwise one turn. The blue represents in-range readings (80-140 mg/dL). The gray represents low blood sugar readings (79-40 mg/dL) and the outside white contains all the high blood sugars readings above 140 mg/dL.
Happy New Year, everyone! I am starting this blog to document a new, year-long project related to Databetes, the company I have founded. Throughout the year, I am recording all my diabetes-related data in an effort to improve my type-1 diabetes control. This includes every blood sugar reading, medication dosage, exercise statistic and A1c blood test. I will also record nutritional information for every meal, snack and (non-water) beverage. I’ll also be adding photographs, geolocation data and other information from my mobile phone.