A Year in Diabetes Data

As we close out 2013, I’ve taken one final look back at 2012. I conducted a yearlong quantified self project where I tracked every blood sugar readings, every insulin dose, every meal and all my activity data.  My diabetes control improved considerably as a result, making 2012 the healthiest year of my life.

We’ve visualized this project as a poster (pdf here). With so much data to make sense of, we printed it big. Never before in my 27 years as a patient have I been able to step back and reflect on my health through an entire year’s worth of diabetes readings.


The main image shows 91,251 blood sugar readings from my Dexcom continuous glucose monitor (CGM). Each colored line shows one day. January 1 is at the top, with days progressing clockwise around the circle. Lines grow longer with more readings for that day. Blood sugars are color-coded and grouped based on the reading. I represented the in-range readings as white to make it easier to spot the days where my blood sugar control was not as good. High blood sugars are a colder colors, low blood sugars are warmer.

Along the outside of the circle are dark gray lines showing the amount I ran each day. You can see my marathon training start over the summer and end in late October, followed by the longest line on race day November 18.

I also listed notes for significant days from throughout the year. This is a key idea behind Databetes, giving patients a way to merge medical data and information from “the rest of our life.” Food, exercise, sleep, stress, jet lag, all these things and more have great potential to influence our blood sugars. It’s important to include this information because it helps us contextualize our medical readings. Improving our health is all about moments where we connect the dots, see the cause and effect of these various factors and adjust our treatment accordingly.


Lessons Learned

Self-tracking helped me achieve the best diabetes control of my life, as evident in my A1c results. This blood test is the standard metric for measuring diabetes control. The lower the reading the better. For context, I’ve included 10 years worth of my A1c readings.

On the back of the poster, I looked more closely at my nutrition and exercise data. For me, most interesting was the change in my insulin levels relative to my exercise. Generally speaking, I need less insulin the more I run. The marathon training showed that this is true up to a certain point. My lowest medication levels were lowest during weeks with about 30 miles a week or more of running. Even if I ran more than that, my insulin rates remained about the same. It’s also interesting to watch my insulin rates spike after the marathon when I stopped running for a few weeks.


Databetes Development

This 2012 project started as a personal experiment for me. It was also the basis for my graduate school thesis at NYU’s ITP.

Yet the spirit of this project matches our goals with Databetes as a company. There is tremendous value in aggregating all of a patient’s medical and lifestyle data. For me, it paid off with a big improvement in my health. Yet it was a very tedious effort. I was extremely motivated to do this for a year. We need to make this process much easier if we want to see wider adoption of self-tracking. But as my blood sugars show, it’s worth it.

It’s also important to remember that aggregating data is just a beginning. Improvements in diabetes treatment will also require new methods for analyzing all this data, as well as systems to help facilitate behavior change in patients. We’ll also need systems that help patients get the emotional support they need to manage their condition and make the necessary lifestyle changes.

At Databetes, we’re excited to be working on improvements in diabetes care that start with better use of data and patient-centric design.





I used 3 medical devices, 3 activity monitors and a smartphone to record data. These included:

  • Medtronic MiniMed 522 insulin pump
  • Dexcom SEVEN continuous glucose monitor
  • OneTouch UltraMini blood glucose meter
  • FitBit Ultra activity tracker
  • Nike FuelBand activity tracker
  • Garmin Forerunnner 910XT heart rate monitor with GPS
  • Apple iPhone 4s



I used 10 different software programs, apps or websites to manage my data, feeding that information into custom software we built.

  • Medtronic CareLink
  • Dexcom Studio
  • Google Docs
  • Flickr
  • RunKeeper
  • MapMyRun
  • Milermeter
  • Foursquare
  • OpenPaths
  • Garmin Connect



I tagged 2,733 photos as diabetes-related and uploaded them to Flickr. I used a Google Doc spreadsheet to record all my food and drink, topping out at 3,517 entries. I ate 47% of my meals at home (87% of the time for breakfast, 40% for lunch and 54% for dinner). I ate at 264 different restaurants.



I completed 138 runs, recording 112 in RunKeeper and 26 in MapMyRun. These apps say the mileage totaled 1,128 miles. But MilerMeter (which uses Google Maps) said the milage was actually 993, which I believe is more accurate.



I had 229 Foursquare restaurant check-ins. I also used OpenPaths to record my mobile phone’s GPS data.


The Finish

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