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The code for this project is on my GitHub (here).
While newer blood glucose monitors (BGM) have Bluetooth capability (BLE), they are often designed only to sync data to their own clouds. Many applications call for mixing glucose measurements with data like weight, activity, carb consumption from other apps to enable consumers themselves, coaching, or care teams to use the information directly.
This code pulls glucose measurements from a BLE-enabled BGM and stores in:
- HealthKit. Many apps and EHRs have HealthKit integration and can use HK as a channel to import glucose data.
- Open format. Developer can integrate into other database systems.
The code was developed using a Contour Next One BGM since it was found in a study published by the Diabetes Technology Society to be the most accurate of the set of 18 BGMs tested and one of the few to meet the FDA accuracy standard for glucometers.
The Bluetooth Forum developed a standard “GATT” profile for BGMs. Details can be found here.
Reads from the BGM are performed via its “Record Access Control Point (RACP)” characteristic. The code writes to RACP requesting data from the Bluetooth Glucose Measurement and Glucose
A recent study by the Diabetes Technology on a cohort of 1000 people found that of 18 popular glucometers in the US, only 6 met the FDA standard for accuracy:
Investigation of the Accuracy of 18 Marketed Blood Glucose Monitors. David C. Klonoff, Joan Lee Parkes, Boris P. Kovatchev, David Kerr, Wendy C. Bevier, Ronald L. Brazg, Mark Christiansen, Timothy S. Bailey, James H. Nichols and Michael A. Kohn. Diabetes Care 2018 Aug; 41(8): 1681-1688.
and another good study on accuracy here: Performance Evaluation of Three Blood Glucose Monitoring Systems Using ISO 15197
I was surprised by 2 things:
- Of course that only 6 BGMs met the standard….
- The FDA standard itself which specifies accuracy of only +- 15mg/dL at or below 100mg/dl and +-15% above that number. If your fasting blood glucose was measured at say 95mg/dl, that could mean to a 95% confidence level you are anywhere from 80mg/dl (relatively close to hypoglycemia) to 110mg/dl (pre-diabetic).
I decided to take a look inside the best scoring BGM: Contour Next One. Basically it looks like this:
Top side features a simple connector housing for the strip insert, a Toshiba custom analogue front end and an MCU.
A decap of the MCU reveals that it is from Renesas and is likely a R5F51135ADLJ (128KB ROM flash, 32KB RAM, 8KB data flash). This is based on Renesas’ proprietary RX CPU architecture
As you sleep, the motion of the heart beat and resulting surges of blood around the body cause the body and bed to shake. Just a bit, but enough to detect with a MEMs accelerometer like the orientation sensor in your phone as well as in step counting fitness trackers. A good paper on the subject is here: NIH paper
You can use this effect to detect heart rate, respiration rate, heart rate variance (a good measure of stress) as well as sleep/wake times; all while you sleep without needing to wear a device. This is key since to collect sleep data over months/years, it is important that the process be transparent to the user; just tape the sensor to the edge of the mattress and forget about it.
This approach has been studied for 100 years and has been made practical by emergence of cheap, sensitive technologies like MEMs. Joonas Paalasmaa’s (co-founder of Beddit – acquired by Apple) paper on the subject “unobtrusive online monitoring of sleep at home” is here on Pubmed
Withings Nokia have a set of connected health devices which allow you to track your activity, blood pressure, weight, fat mass, temperature, sleep etc.