Photo-a-Day: Advocate

Photo-a-Day-AdvocateI use each of these items every day to help manage my diabetes.  Each one tracks a necessary variable.  In order to optimize my treatment, I need to analyze the data I collect, and yet none of these devices talk to one another or have a common analysis platform.  I advocate for simplifying diabetes data collection and analysis, while empowering patients.  (See my proposal, Pixels: Your Personal Diabetes Big Picture).


This post is one in a series for the National Diabetes Month of November.  Kerri from sixuntilme.com initiated the Photo-a-Day idea and prompts and lots of other diabetes bloggers have chosen to follow suite.

Pixels: Your Personal Diabetes Big Picture

Over the last several weeks my husband and I spent our evenings working on a proposal (below) we submitted to the Target Simplicity Challenge.  The whole point of our idea is to simplify and customize diabetes analysis and management for patients and doctors.  Check it out!  Let us know if you’d use a site like this, or what features you’d like to see to manage your diabetes.

Pixels

This is an initial mock-up to help convey the idea. Click for a larger pdf version.

 

PROBLEM:  Diabetes management requires continuous collection, tracking, and analysis of vast amounts of data, however current digital interfaces for diabetes data have four crucial faults.

1)   Web-based programs that pull data directly off of diabetes devices such as glucometers, insulin pumps, and continuous glucose monitors are often designed by the device manufactures and are largely proprietary.  Therefore data is siloed, increasing both access and analysis difficulty.  For healthcare professionals, this means patients come in with data either manually entered on paper, printed from the multitude of proprietary web-based platforms, or still stored on the diabetes device and requiring download assistance.

2)   They usually track only two or three variables, medication (e.g. insulin), blood glucose (BG) levels, and sometimes carbohydrate intake.  This limited number of variables is grossly lacking in context for treating individual patients, thus lending itself only to very generalized treatment recommendations…a one-size-fits-all approach.  In reality many other factors play a major role in optimizing control such as monthly cycle, fat and protein content, glycemic index, physical activity, remembering medication, stress level, etc.

3)   They simply display the data but offer very little or nothing in the way of analysis assistance.  While graphs are helpful, they are insufficient to continuously analyze long-term patterns across multiple variables.

4)   They lack any means of patient engagement and empowerment.  Data entry is time consuming and often complicated.  Analysis is focused on patient error rather than rewards.

PROPOSAL:  In order to optimize diabetes management, we need to simplify and unify diabetes data analysis, while empowering and engaging patients.  Start by creating a single platform for integration of data from multiple diabetes devices. Then leverage the current Quantified Self, and Lifelogging trends to incorporate important variables via smartphone apps and wearable fitness devices that patients already use (many of which utilize an open API), allowing patients to create a custom diabetes management mashup of apps displayed alongside diabetes device data.  The individually tailored scope will have broad patient and doctor appeal, e.g., one patient may focus on testing BG 4 times a day, need a medication reminder, and passively track walking with a pedometer, while another may test BG 10-15 times a day, use a CGM, avidly track running, nutritional information, geolocation, mood, sleep patterns, and monthly cycle.

Enable doctor and patient data mining for potential meaningful and actionable correlations by applying prevailing 3D data visualization and comparative analysis techniques and algorithms (already in practice to mine big genomics data) e.g., compare several data sets to come up with key talking points for limited doctor-patient interface time such as, “75% of your nighttime lows occur during the week following your period in your menstrual cycle” or “On days when you walk after lunch, your BG is an average of 20% lower than on days when you don’t” or use a passive geolocation app to see what places you frequent when your sugars are higher/lower.

Finally, engage users in multiple ways.  The mashup format provides individual context for the data and will enable patients to set small, attainable goals that make sense for their lifestyle.  Incentivize both with rewards and positive feedback, e.g provide coupons and create a gamification aspect where you receive Target rewards as positive reinforcement, or earn badges for meeting your goals, or receive a text saying “Great job checking your BG 5 times today!”.  Integrate the site with social media, so users can share positive feedback and receive/provide support.   Integrate healthy meal suggestions with Target shopping lists.  Provide a Target kiosk to assist patients with uploading device data or setting up an account, etc.


Check out these related (less professional and more personal) posts:

My Fitbit Flex: A Delightful Surprise

Big Diabetes Data Requires Big Analysis

My Fitbit Flex: A Delightful Surprise

Just got a new Fitbit Flex.  So far, I’ve only used it for two days, although two days was enough to have me feeling a bit like Wonder Woman with her indestructible wrist cuffs.

Fitbit_Wonder_Woman_cuffsWith logging diabetes data on my OmniPod, CGM, and BG tracking app, why on earth would I want yet another data entry device to worry about?!  Turns out one of the most attractive things about the Fitbit is that it isn’t subject to any of the proprietary B.S. that all my other devices fall victim to when it comes time to actually view the data.  It’s so simple to use that I can see my data with no more hassle than looking at a mobile app or website, it syncs automatically via WiFi, and works across all operating systems and platforms!  It’s open API and integrates with lots of mobile apps, giving it even more functionality.  Bonus: I don’t have to pierce my body to get it on, deal with calibrations, site changes, alarms, or discomfort.  I just wear it like a bracelet and essentially forget about it.  It’s providing me with a sense of freedom in patient generated data (PGD) that I’ve longed for with my diabetes devices.  Because it’s so much easier than what I’m used to, I’m actually motivated to set activity goals and complete them.  What a treat!

Fitbit_gollum_and_ringI know a lot of Fitbit users like to keep in touch via social media and share their data, but something else I really love about the Fitbit is that I don’t have to share.  This is my data and mine alone!  I’m not required to whip it out and share it with physicians at every turn in the road.  I don’t have to scramble to have all my data-entry ducks in a row, only to have my Endo pour over it (in 5 minutes flat!) offering the inevitable praise here and reprimand there.  It’s my Fitbit…my secret…my precious…


You might also like Big Diabetes Data Requires Big Analyses

Big Diabetes Data Requires Big Analyses

If you’re anything like me, you’re currently clocking data on your CGM, your insulin pump, your BG meter, and any other number of devices including mobile apps for diabetes, fitness, or menstrual cycle, and wearable fitness devices like Fitbit.

It’s easy to look at one post-meal high and make a judgment call.  But it’s really hard to look at months worth of data and try to pull out patterns to really improve overall BGs and health.  Websites that integrate with my OmniPod and CGM (and are Mac compatible…don’t get me started on this…Gah!) only have the capability to really track BG and carb counts well.  But we all know it’s the type of carbs, not the number of carbs that really matters.  Also, was I especially active on a particular day?  Stressed from a big meeting at work?  Having PMS?  So many variables to consider!

big diabetes dataSince I already have all the BG and insulin data on my pump, meter, and CGM, (that I’ve laboriously collected!) I literally fantasize about just uploading those items to a single program online and then using apps of my choice to input details about my other “life variables,” such as food, exercise and activities, moods, monthly cycle, etc…..and finally (here’s the kicker) have the apps sync their data with the existing pump/meter/CGM data online in the same program!  Perfect!  Easy!  Right?  No way!

As you’re probably well aware, most of our diabetes meters, pumps, and CGMs have proprietary software and/or limited relationships with other diabetes-device companies.  So, based on who manufactured our devices, we’re all pretty much limited to one or two platforms for viewing the data…and those options sadly don’t integrate with apps we’re using to track our food, fitness, etc.

My current work-a-round solution for viewing OmniPod and CGM data (on a Mac) is Diasend, however even here you need a Clinic ID# (or to register as a non-US citizen) or your CGM tab will be grayed out.  I also use the mySugr app for logging (btw…I love mySugar), Google Cal for my monthly cycle and HRT, and just started tracking activity and sleep with a Fitbit Flex.  I make it work but it’s still me piecing together data from four different locations.

fitbit open apiFortunately (and just in the nick of time if you ask me) the US is at the beginning of a wave of personalized, data-centric healthcare, sometimes called the Quantified Self.  A lot of new data collection platforms designed for non-PWDs (like Fitbit and Lose It!) are using open APIs, which means they share and can integrate data.  After years of finding work-a-rounds and “making do” I feel like the current big data trend in healthcare is finally going to make my fantasy a realty…in the very near future.  So, everyone put down your proprietary diabetes devices for a second and raise your glass!  Here’s to hoping!


Super interested in the Quantified-Self movement like me?  Here are a few really cool recent articles:

Will An App A Day Keep The Doctor Away? The Coming Health Revolution (Forbes)

Solving America’s Big Health Care Challenges With Big Data (Huffington Post)

How Patient Generated Data Changes Healthcare (Information Week Healthcare)