Development of a Model-Based Noninvasive Continuous-Time Glucose Monitoring Device for Non-Insulin Dependent People

Wednesday, October 19, 2011
Exhibit Hall B (Minneapolis Convention Center)
Derrick K. Rollins Sr.1, Lucas Beverlin2, Kaylee Kotz1, Nisarg Vyas3 and Dave Andre4, (1)Chemical and Biological Engineering, Iowa State University, Ame, IA, (2)Department of Statistics, Iowa State University, Ames, IA, (3)BodyMedia Inc., Pittsburgh, PA, (4)BodyMedia, Inc.

Continuous-time glucose monitoring (CGM) effectively improves glucose control by providing frequently sampled information that allows the user to associate changes in their glucose levels with changes in their behavior. For example, a user is able to see immediately how the size of a meal affects how high their glucose level becomes as well as the duration of elevated glucose levels. Currently, the most widely used and effective CGM devices rely on a sensor that is inserted invasively under the skin. Sensors cost from $35 to $60 and last 3 days to a week. Due to invasiveness and cost, the primary users of current CGM devices are insulin dependent people (type 1 and some type 2 diabetics). Thus, the primary goal of this research is the development of a non-invasive CGM device that would be used by health conscious non-insulin dependent people (including non-diabetics) that would help reduce obesity, the onset of type 2 diabetes, as well as the progression of type 2 diabetes. 

To accomplish this objective, this work is focusing on the development of a device that: 1. has a simple or no reliance for on food entry; 2. has a relatively short calibration period; 3. requires few to no lancet measurements for calibration and; 4. has an accuracy that is comparable to lancet meters. The approach of this research is to use a novel modeling method technique to infer glucose concentration using non-invasive input measurements from variables representing food, activity, circadian rhythm, and stress variations. The main component of this system will be a the BodyMedia® armband that will automatically collect the activity and stress data. The food information will be entered manually by the user via the time stamp button on the armband. The model and model development algorithm will reside in the armband and will use a Wiener approach based on the work of Rollins et al. (2010). Data from a lancet glucose  meter will be entered automatically or manually  and will be used to develop a subject specific model for the person wearing the device. After the model is completely developed, lancet measurements will no longer be needed for calibration. An interface device will be connected to the armband to display the glucose concentration in every five minutes.

Using about 20 test subjects with 4 weeks of data collection each, results have been obtained to support the modeling viability necessary to build an armband monitoring device. Before using these data sets the food quantities were converted to food indices to mimic time stamping and a lancet sampling rate of only four values per day. Several improvements to the Wiener modeling method developed by Rollins et al. were made to address the infrequent nature of the data (i.e., four glucose measurements/day), frequent start- ups (i.e., taking the armband on and off and restarting it frequently), and the lack of steady state data. The accomplishments of this work include the ability to develop subject specific models under these restrictions. Results are presented for models developed after three days, 2 weeks and four 4 weeks that support an initial calibration period of three days with accuracy improving over time and no need for lancet measurements after 3 to 4 weeks. Thus, since the model does not drift, this device would not appear to need glucose measurements for calibration once it is fully calibrated to the user wearing the device.

Rollins, D. K., N. Bhandari, J. Kleinedler, K. Kotz, A. Strohbehn, L. Boland, M. Murphy, D. Andre, N. Vyas, G. Welk and W. Franke, "Free-living inferential modeling of blood glucose level using only noninvasive inputs," Journal of Process Control 20 95-107 (2010).

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