Meal Memory for iOS now available!

Meal Memory for iOS

We’re excited to announce that Meal Memory for iOS is now available in the App Store! You can download it here.

Meal Memory makes it as easy to log a meal and understand its effects on your blood sugar. Those familiar with this app will notice a few small changes. We’ve added a Notes field, giving patients a way to document the details of a meal and leave messages to themselves.

We’re also using a new palette for color-coding blood sugar readings as high, in range or low. These colors make it easier to spot meals that proved challenging to manage. These changes are based on our experience designing the poster of my year of self-tracking. The logic of having high blood sugars as cold colors and low blood sugars as warm colors was effective. Red effectively highlights low blood sugars as the most pressing concern for patients.

Looking past this week’s launch, we’re already planning the next version of Meal Memory that will auto-integrate medical  device data. We know self-tracking is helpful to patients managing their diabetes. We’re always looking for opportunities to make the process easier, faster and more rewarding. Stay tuned for these new features! We’re hopeful that 2015 will be an exciting year.

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Databetes at CES

Hello from Las Vegas! Databetes is excited to be exhibiting at this year’s Consumer Electronics Show. You can find us at the DexCom continuous glucose monitor (CGM) booth in the Health & Wellness section.


We’re showing off our newly launched Meal Memory iOS app, as well as a coming update that auto-integrates CGM data. DexCom is working to upload patient blood glucose data to the cloud via its Share software. Once CGM data is available, patients using Meal Memory can link their accounts and will no longer need to manually enter blood glucose data. The readings will simply auto-populate in your meal entries. Plus, we’ll be able to provide a more detailed view using dozens of blood sugar readings instead of the current system of 2 readings from before and after eating. Seeing the effects of a meal on your blood sugar will literally be as easy as taking a picture of your food.

These are exciting times for the patient community! Great things are possible when patients have open access to their medical data. We’re glad to see DexCom responding to the wishes of their customers to store their readings in the cloud. Data access means we can deliver better, easier to use tools to help you manage your diabetes. Stay tuned for further updates.







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MedX Recap

This was my first year attending Stanford’s MedicineX. After years of following the conference, it was great to take part as one of this year’s ePatients. MedX is one of the most patient-centric medical conferences around and puts a strong emphasis on making the patient experience a central part of discussions about health care reform. It was an inspiring few days, with many like-minded participants in attendance. I left there energized and excited by the talks. Here are some of my highlights:

IDEO Design Challenge

Before the conference officially kicked off, I was one of five ePatient participating in this daylong design challenge. The session was led by IDEO’s Dennis Boyle, who leads the agency’s Health and Wellness practice. The other ~30 participants were from a variety of backgrounds in health care, ranging from doctors to NGO leaders, academic researchers to policy makers.

We were split into 5 teams, each with its own ePatient. My group asked lots of questions about my daily diabetes habits, specified a problem they were looking to address, created the outlines of a solution, then made a presentation video.

Dennis Boyle kicks things off

Dennis Boyle kicks things off


We were advised to define the problem we were looking to solve with a “How Might We…” statement. Building on our discussions throughout the morning, our group asked how might we use data to help humanize communication between patients and doctors during clinic visits. This is a problem I highlighted based on my own experience. Patients get on average 7-12 minutes with their doctor every 3-4 months. We wanted to design an approach that makes better use of this limited time, one that allows both patient and doctor to feel that their biggest concerns are being addressed. We named the product Ovrlap and produced a demo video in under an hour.



Big thanks to everyone on the team: Brian Pitts, Jodi Sperber, Claudia Williams, Anthony Back and our group leader Nick Dawson.

Nick & Claudia in design mode

Nick & Claudia in design mode

The core idea behind Ovrlap as a Post-It note, showing the focus on where patient and doctor goals meet.

The core idea behind Ovrlap as a Post-It note, showing the focus on where patient and doctor goals meet.

High Points

I’m always amazed when diabetes patients get together and start talking about the specifics of their condition. Type-1 diabetics including Chris SniderKim Vlasnik and Dana Lewis were all in attendance. On the second day, Chris, Kim and I each pulled out our CGMs and realized that all our blood sugars were running about 50 mg/dL higher than normal, something we all attributed it to the excitement around the conference. I personally find these types of shared diabetes reactions fascinating.

Ignite Talk

I made a 5 minute presentation at the start of a panel discussion on “Patients with Chronic Illness: The New Self-Tracker?” and showed the progress of my work on Databetes. The panel was led by Ernesto Ramirez of Quantified Self and also featured fellow patients Britt Johnson and Sara Riggare.



Ernesto focused on the idea that the panel was split between patients like me, who are very much in support of self-tracking, and patients like Britt, who are not. Each patients talked about wanting to feel better and solve the problems inherent in having a chronic condition.  I felt that our discussion highlighted the similarities in thinking as patients. It showed that self-tracking’s value really rests on the specifics of managing each different  condition, not on any inherent potential value of self-tracking itself. For me, the self-tracking allows me to spot trends in my readings and make specific changes in my disease management. For Britt, it seems to highlight factors beyond her control and leaves her more frustrated. In this way, both of our approaches to self-tracking seem logical.

Photo by MedicineX (

Photo by MedicineX (

E-nabling the Future

One of my favorite presentations of the conference was by Jon Schull. He is a, “biological psychologist, inventor, entrepreneur, and human-computer interaction researcher, Jon Schull is the creator of e-NABLE, an online community that designs, customizes and fabricates affordable 3D-printed prosthetic hands for children and adults with missing fingers and hands.” Joining him on stage was Eva. She has received for free one of the crowdsourced, 3D-printed hands that the group makes possible. It was truly amazing to see how this global community’s work can help brighten up her life.


photo courtesy of MedX

Leaving Inspired

Flying out of San Francisco, I was certainly glad I participated in MedX. So much of the time I spend at conferences is focused on diabetes care. It was interesting for me to see issues that run through the entire medical community, as well as meet the patients doing their best to manage a range of conditions.

It seems like patient-centric design thinking is gaining traction and becoming more widespread. Many of the presentations by hospitals doing innovative work were from leading institutions in the U.S.  Hopefully the success they are having will serve as an example to other facilities around the country looking to adjust their existing treatment methods and do more to both improve patient engagement and health outcomes.

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MedX on the Horizon

It’s just under a month now until the start of the Stanford MedicineX conference. I’m excited to be participating this year as an ePatient. I’ll also be giving an Ignite talk on the first day, September 5. Registration is now open for the Global Access Program for those of you who want to watch it live online.

I’ve been watching MedX grow for years and think it’s a really unique event. Their focus on including so many patient voices is  impressive. Most of the conferences I attend are focused solely on diabetes, so this is a great opportunity for me to see what issues resonate across multiple conditions.

The fact that MedX is held at Stanford is also very important because it pulls in so many different forces from the tech community. It’s been interesting to watch the announcements coming out of the Bay Area in recent months, from  health IT startups to wearable companies to industry giants like Apple (HealthKit) and Google (Fit). It’s encouraging to see these additions to the discussion about ways to improve patient access to device data and the potential for innovation built upon these data platforms. It’s very much in line with the diabetes community’s #WeAreNotWaiting movement.

Part of my role as an ePatient is to share news and updates about the conference, so this won’t be the last time you hear from me about MedX. In the meantime, sign up for the live stream!

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Databetes in the news

We’re lucky to have received some great coverage recently, beginning with a feature by Leo Brown on the diabetes blog A Sweet Life. Leo is also very interested in nutrition, so it made for a lively discussion of our goals in this area (including our Meal Memory app). I always learn something new by talking with other diabetes patients and learning about their approach to food. Many of the diabetics I’ve met with the best control are very focused on nutrition and making the adjustments needed to maintain good blood sugar control. It takes a lot of will power, but certainly pays dividends.

I also spoke with Christopher Snider for an episode of his Just Talking podcast. Chris writes the wonderful ToBeSugarfree blog and is also a member of the Stanford MedicineX ePatient Advisory Board. I’m looking forward to meeting him in person this Fall. He’s been a great advocate for the diabetes patient community.

Finally, thanks to the folks at for featuring me as a Superhuman on their blog. Human is a great app with a simple, powerful goal of inspiring patients to move a minimum of 30 minutes a day. All the data is tracked passively and requires no additional tracker. Check it out!

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Quantified Self Public Health Symposium

image by joyce lee

image by joyce lee

I was thrilled to speak last week at the Quantified Self Public Health Symposium. The event featured 100 attendees including Bryan Sivak, CTO of the US Department of Health and Human Services. It focused on “improving access to personal data for individual and public health benefit.” Topics included ways to integrate the individual self-tracking projects into central databases available to researchers and clinicians and ways to make medical data more accessible to patients.

Gary Wolf and Ernesto Ramirez of QS always do a great job organizing their events and leading thoughtful discussions. Susannah Fox of Pew Research Center’s Internet & American Life Project talked about “Secret questions, naked truths” and the power of presenting personal data as a way to expose insights in our own lives. Larry Smarr of Calit2 did an amazing presentation on his own self-tracking and his vision for creating systems to integrate quantified self projects into mainstream scientific research, moving from N=1 to N=1,000 and beyond.

I spoke about data visualization and the design choices I made in making the poster of a year’s worth of my diabetes data. The audience was composed of academics, clinicians and industry representatives. From their feedback, it seems that design is still a challenge for a wide range of professionals. Their frustration started with the choice of the visualization tools available to non-specialists.  Additionally, they spoke of the difficulty in scaling solutions across a patient population. While one report could be insightful for a few patients, the same report often provided minimal benefit for others with the same medical condition. I expect that there will be a great deal of progress in designing solutions for a range of conditions in the coming years. I expect that a carefully crafted group of designs will be beneficial in analyzing not only  raw data, but the relationship between various data types for each condition. I hope my work can help with this for the diabetes sector.

During the conference, there was an interesting comment by someone from Kaiser Permanente. He believed that regulation is required to force device manufacturers to open patient data streams of patients. He believes the current solution of letting the market decide has failed because these companies have no incentive to go this extra step. Often times it’s the opposite, with manufacturers mistakenly believing this data is an asset that should be guarded. I do agree with this idea of regulation and believe freeing the data would make it much easier for innovation of new data services to occur.

There was also a very thoughtful discussion about integrating privacy protections within systems that aggregate data across conditions. While I am very open to sharing my own data, it was interesting to hear about the potential unintended consequences for patients. Dr. Joyce Lee talked about issues she’s encountered within the type-1 diabetes community, including adolescents and parents who have overshared.

I was also glad to see Open mHealth get a chance to present some of their work on data standardization and interoperability. Because they’re based in NY, I’ve gotten to know them and their work. Looking forward to the new features they’re planning to release soon.

Overall, it was exciting to be a part of such a great event attended by people I’ve been following for years. It’s encouraging that HHS is thinking about the topic of self-tracking and ways of integrating it into the greater health care system. The QS community is a unique collection of early adopters thinking through the challenges and potential of patient-generated data. I think their work will continue to positively influential the  the health care system.

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Joining the MedX ePatient Community


I’ve been named an ePatient for the Stanford MedicineX conference happening this September. The conference describes itself as “a catalyst for new ideas about the future of medicine and health care.” For years I’ve been following Dr. Larry Chu’s work and am quite excited to take part. It’s great to see their focus on including patient voices in discussions about “how emerging technologies will advance the practice of medicine, improve health, and empower patients to be active participants in their own care.”

I’ve been named to the design track, which includes the IDEO Design Challenge led by Dennis Boyle. I’m interested to see how my thinking about design can be applied beyond my own experience living with diabetes. As part of participation, ePatients are asked to tweet and blog about the event. So expect plenty of commentary from me in the Fall.

Already the MedicineX community has invited to me participate in their events, beginning with a MedX Live! video podcast. The topic at hand was “Entrepreneurship: Innovating in healthcare, human centered design and brainstorming challenges.” There was a great selection of presenters including Dr. Jason Hwang, co-author of The Innovator’s Prescription with Clayton Christensen. It gives a great overview of the health care industry and ways that innovation can succeed to help improve care and lower costs.

I look forward to getting more involved with the MedX community and talking more about the ways that health care needs to become more patient-centric.



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Meal Memory is now available for Android!

We’re pleased to announce the release of our first mobile app Meal Memory on Google Play.


Meal Memory takes a new approach to nutrition tracking. Our goal was to create not just a self-tracking app, but also an improved feedback loop for patients. With this approach, Meal Memory helps diabetes patients understand the effect that each meal has on their blood sugar.

Our system records a blood sugar reading and carb estimate when a meal is logged, then asks for a post-meal reading with an alert on their phone two hours after eating. Having these two data points makes it easier for patients to better manage that same meal when they eat it again. It also gives patients a simple way to scan through their eating history and see how often they’re managing meals accurately.

We started with a focus on nutrition after talking with lots of patients. Consistently people said that balancing their meals was their biggest self-management challenge. Nutrition is obviously a big and complicated topic. So we zeroed in a core habit that we saw in our own eating histories, that we are creatures of habit. We have our favorite meals, favorite restaurants and favorite recipes. But remembering the details of each meal, its effects on blood sugar and the best way to manage that food is a challenge. Meal Memory helps solve that problem by making it easy to both record a meal and related readings, but also easily retrieve that information when you’re eating that food again.

Over the last few years, I’ve tried a variety of different ways of self-tracking my own diabetes. Those experiences helped shape our design. First, I realized it’s important to create a system that is fast and easy to use. Meal Memory is effective because it uses a photo to record a meal instead of typing, letting you log a meal in just a few seconds.

Second, we wanted a simple way to give back insights to patients. Too many systems ask patients to enter lots of information without giving enough feedback. We help patients understand the relationship between their decisions about a meal and changes in their blood sugar later. A mobile phone alert two hours after eating does this well. In taking just a few seconds to enter a post-meal blood sugar, a patients can be mindful of their blood sugar and can focus on understanding this one specific meal. I’ve found that improving my own diabetes self-management has come through an accumulation of small wins like this. Over time, it also helped me become more aware of the bigger trends in my readings and diabetes control.

We hope that other patients have similar success with Meal Memory! It’s our first step in providing patients with improved self-management support tools to help us all stay healthy. Please email us with any comments or suggestions at

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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

Posted in Databetes, ITP, NYU, Quantified Self | 3 Comments

Two days on a closed loop system

A few weeks ago, I completed a closed loop clinical trial at Montefiore Medical Center in the Bronx. During two visits, all my insulin dosages were calculated and delivered automatically by a Medtronic system analyzing my blood sugar data. It was the first times since being diagnosed in 1986 that I didn’t have to carb count or make any other decisions about my medication levels. As exciting and encouraging as that was, the whole experience tempered my hope that this approach will replace my current treatment any time soon.

Medtronic’s visualization of their algorithm and insulin activity.

I spent my first night in the hospital in August and returned a few weeks later in September. Each visit began in the afternoon and ran through the following evening.

The trial was run by Dr. Heptulla, the Division Chief of Pediatric Endocrinology at Montefiore. Dr. Heptulla is know for her strong interest in using technology to treat diabetes. This is one of the many clinical studies she has completed, with several more in the works.

Both times, I used Novolog insulin. On the second visit I was also given a shot of Victoza, a drug normally used only on type-2 diabetics. It slows down digestion and helps reduce post-meal blood sugar spikes. The trial’s main goal was to test whether the drug could be helpful in combination with a closed loop system to better control blood sugar levels.

The study used a Medtronic insulin pump and 3 Medtronic CGMs (continuous glucose monitors). Two of the sensors were the new Medtronic Enlite (just approved by the FDA a few weeks ago) and one older model Sof-Sensor. The data was all transmitted wirelessly to a Medtronic Comlink receiver and into a PC running Medtronic’s analysis algorithm.

The pump in the study was the same generation as my current one. The Medtronic CGM transmitters haven’t changed, even though there is a new generation of sensor.

The new Enlite CGM sensors

Medtronic Comlink wireless receiver

Mission control

The pump delivered micro-boluses of insulin (usually 0.1u) when the system sensed rising blood sugars. All other basal rates were turned off. The CGMs, which usually take readings every 5 minutes, were instead generating blood sugar readings every minute. Nurses also drew blood every half hour for 28 hours for a more accurate glucose test in plasma. The first night I learned to lay in bed with my arm extended and the IV exposed, allowing me to successfully sleep through a few overnight blood draws.

0.1u microboluses every few minutes

Medtronic software screenshot. It shows readings from the 3 CGMs, the regular blood tests taken every half hour and the microbolus dosages of insulin.

During the first visit, there were big spikes in my blood sugar after every meal, peaking at about 250 mg/dL. The system then often over-corrected and my blood sugars crashed. I took glucose tablets to treat any blood sugar below 70 mg/dL.

My blood sugars for the first visit. The vertical red lines mark my meals.

Before my second visit, I had been warned that the side effects of Victoza include nausea and headaches. But most previous participants in the study hadn’t experienced any major problems (except one patient who did get sick). I was given the shot before the 8pm dinner the first night. I still had a big spike after eating (the drug hadn’t kicked in yet). Around midnight, my blood sugars went really low. I ended up needing 48g of carbs (which meant eating 12 glucose tablets) to get back in-range (normally I need only about 15g).  But for several hours after that, my blood sugars were near perfect and I needed almost no insulin.

When I woke up however, I knew something was wrong. I was able to eat breakfast (a Boost shake) at 7am. But soon after that I became nauseous and stayed that way until about 4pm. I couldn’t eat any lunch and was only able to eat about a quarter of my dinner. So much for giving the study usable data on how well the Victoza works on meals. But I’m sure there’s value in knowing how many patients can’t tolerate the drug in the first place.


Given how I felt, I wasn’t able to keep track of my blood sugars for the second visit and can’t share them here. But I remained pretty steady most of the second day in part because I wasn’t eating.

Despite my reaction to the Victoza, I’m glad I participated in this study. It’s interesting to experience first-hand the potential of this technology. The biggest problem was the post-meal high blood sugars. It seems like the cause is the double lag in both data and insulin.  The CGM readings are 20 minutes behind current blood sugar levels because it tests outside the blood stream. When insulin is factored in, which takes 20 minutes to start working and an hour to peak, the system is always operating behind the curve.

A hybrid approach that includes pre-meal dosing seems like the most promising alternative to a pure closed loop system. This method has been integrated into some of the tests of the bionic pancreas by the Massachusetts General Hospital team. Patients use an iPhone app to enter that they are eating a small, medium or large meal. The system then delivers a percentage of a standard dosage (I think it’s around 50%) right away. The algorithm takes care of the rest of the insulin delivery and is able to reduce the severity of post-meal spikes. The bionic pancreas is also highly effective because it doses glucagon to treat low blood sugars too.

Looking back on my experience, it’s encouraging that we’re finally making real progress toward an artificial pancreas. I still have my doubts that any of these systems will hit the market within the next decade, but that’s simply my guess. Another possibility is that it will take several generations of hardware, software and medications before all the issues are resolved and patients don’t have to take any action in regulating their devices.

But until we get there, I’m still excited to be working on better self-management support systems. There’s plenty more we could be doing with existing technologies and treatments to help us stay healthy until that cure arrives.

Posted in CGM, closed loop, insulin pump, Medtronic, Victoza | Leave a comment