CI

At a glance

ClinicalIndex Comparison Record
N/ACompleted· 57 enrolled
Drug / intervention
Focus Groupbehavioral
Likely dose
Not stated in record
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Search/NCT01434030
NCT01434030N/ACompleted

Development of a Behavioral Observer for Type 1 Diabetes Mellitus

University of Virginia·interventional·Posted Sep 14, 2011·Updated Sep 4, 2014

In Brief

A clinical study evaluating Focus Group for Diabetes Mellitus, Type 1. Completed, enrolled 57 participants across 1 site.

Detailed Summary

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.

Study Details

Study Typeinterventional
Allocation--
Masking--
Primary Purpose--
CountriesUnited States

Timeline

N/ACompletedFinished
20102011201220132014201520162017201820192020202120222023202420252026
First PostedSep 14, 2011
Enrollment StartApr 1, 2010
Primary CompletionJun 1, 2011
TodayJul 2, 2026
Enrollment to primary: 1.2 yearsPosted 14.8 years ago

Interventions

Focus Groupbehavioral

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.