Development of a bio-behavioral stochastic model-predictive controller (SMPC) for use as an artificial pancreas in T1DM requires fundamental behavioral and physiology studies, as well as translational modeling and engineering development. In order to be successful, closed-loop control in Type 1 Diabetes Mellitus (T1DM) must adapt to individual physiologic characteristics and to the behavioral profile of each person. An essential part of this adaptation is biosystem (patient) observation. The investigators propose to lay the foundation for a closed-loop control system which will include algorithmic observers of patients' behavior and metabolic state.
This intensive descriptive study will follow 60 adults with T1DM who are currently experienced with insulin pump use for a two-week training period plus a one month active study period during which the DexCom SEVEN® PLUS Continuous Glucose Monitor (CGM) will be used in tandem with the OmniPod® Insulin Management System. The OmniPod® has a built in FreeStyle glucometer that allows tagging of food and activity-related treatment behaviors with each self-monitoring blood glucose (SMBG) value. The OmniPod® personal digital assistant (PDA) also stores information about insulin delivery and meal size in relation to the carbohydrate content. Parallel recording of CGM and behavioral data, as well as psychometric instruments will produce a rich synchronized data set for each person that will ultimately lead to the development of a behavioral event generator for use in future open-loop and closed-loop control algorithms for intelligent insulin dosing.
Study Type
INTERVENTIONAL
Allocation
NA
Masking
NONE
Enrollment
57
Focus group methodology was chosen to obtain qualitative and quantitative data on participants' desire to use glucose advisory systems to manage their diabetes, their concerns about and desired features and functions of these systems, and their perceived confidence with behavioral event recording. At the outset of each interview, the personalized glucose advisory system (PGASystem) was described to participants as a system composed of a continuous glucose monitor (CGM) device and insulin pump, into which they would input daily information about their insulin, food, and physical activity. The system would then use their data to create personalized algorithms and advice about various aspects of their diabetes management, such as suggestions regarding bolus and basal rate dosing. The interview consisted of open-ended, multiple choice, and dichotomous questions.
University of Virginia - Center for Diabetes Technology
Charlottesville, Virginia, United States
Desire to Receive Advice From Personal Glucose Advisory System (PGASystem)
The categories below indicate types of information that could be received from a PGASystem and the percentage of participants who stated that they would like to receive this type of information from a PGASystem.
Time frame: 2 hour focus group
Willingness to Follow PGASystem Advice
The categories below indicate types of information that could be received from a PGASystem and the percentage of participants who stated that they would follow this type of advice from a PGASystem.
Time frame: 2 hour focus group
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