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| ID | Type | Description | Link |
|---|---|---|---|
| R01DK085623 | U.S. NIH Grant/Contract | View source | |
| DP3DK094331 | U.S. NIH Grant/Contract | View source |
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study team will review the collected data and make any necessary changes to the educational tool.
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| Name | Class |
|---|---|
| National Institutes of Health (NIH) | NIH |
| National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK) | NIH |
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This study combines data collection and simulation-based education, and it will enroll up to 36 adults with T1DM who already have experience with insulin pump therapy and some experience with a continuous glucose monitor (CGM). The study will first track participants for of a 3-day CGM-training period. The training period will be followed by a 1-week CGM monitoring and CGM data collection period. Following the 1-week CGM monitoring-only portion of the study, participants will begin the education intervention component of the study, referred to as Insight-Based Online Learning Using Simulation and Education for Diabetes (iBOLUSED). The education intervention component is 4 weeks in length. The intervention will involve daily diary data upload and daily simulation-based feedback based on the collected diary data. During the first week (and for up to 2 weeks) of the intervention, participants will view glycemic outcome data that represents the participant's hypo- and hyperglycemic risk throughout the day, based on the CGM data collected during the CGM monitoring period. In the next 2 weeks of the intervention, participants will have an opportunity to interact with the internet-based system using a simulation-based tool designed to provide insight to the participant regarding the different effects of modifications to insulin therapy. Throughout the educational intervention, participants will record diary data every day through the use of a diary component in the internet-based system. Diary entries include data on meals, physical activity, history of the insulin basal rate and insulin boluses given that day, self-reported stress level, hypo- and hyperglycemic fear levels, and, if applicable, menstrual cycle and any physical illness. Participants will also upload data from the CGM via the Dexcom Data Manager 3 (DM3) software or the Dexcom Studio Software. Two assessments, one prior to the intervention period and one following the intervention, will be administered to gather relevant psychobehavioral information. Focus group sessions will be conducted at the end of the study, which will allow for the collection of information regarding the effectiveness of the Internet intervention and will provide insight for the design of future studies. Parallel recording of CGM, insulin, and behavioral data, as well as psychometric instruments, will produce a rich synchronized data set for each person that will facilitate the development of personalized behavioral profiles that will be employed to provide individualized feedback to educate participants. In particular, this study tests the use of collected diary data to educate participants by describing glucose profile information and presenting relevant data regarding: (1) hypo- and hyperglycemia risk zones throughout the day, (2) insulin meal bolus information and associated glycemic outcome indices, and (3) basal rate information with associated glycemic outcome indices.
Development of an insulin delivery educational tool in T1DM requires fundamental behavioral and physiology studies, as well as translational modeling and engineering development. In order to be successful, Closed Loop Control (CLC) in T1DM must adapt to individual physiologic characteristics and to the behavioral profile of each person. Here, we have laid the foundation by testing an educational tool that aims at providing feedback to patients with the goal of optimizing patient's glucose control under conventional insulin therapy. This educational tool is based on collecting data from patients that are processed by algorithms giving insight into the relationship between glucose control and psychobehavioral characteristics of the individual. This information will eventually inform a CLC system, which will be initialized for each individual based on relevant behavioral and physiologic characteristics, and will include algorithmic observers of patients' behavior and metabolic state.
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| Label | Type | Description | Intervention Names |
|---|---|---|---|
| Group A: CGM monitoring (1) vs Intervention | Participants will undergo a 1 week CGM monitoring period (session 1), followed by a 4 week intervention period using the iBOLUSED intervention. The 4 week intervention period will be followed by an additional CGM monitoring period (session 2). For those in Group A, we compare the data collection in CGM monitoring session 1 to the data collection in the intervention period. |
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| Group B: CGM monitoring (2) vs Intervention | Participants will undergo a 1 week CGM monitoring period (session 1), followed by a 4 week intervention period using the iBOLUSED intervention. The 4 week intervention period will be followed by an additional CGM monitoring period (session 2). For those in Group B, we compare the data collection in CGM monitoring session 2 to the data collection in the intervention period. |
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| Name | Type | Description | Arm Group Labels | Other Names |
|---|---|---|---|---|
| iBOLUSED | Behavioral | The intervention will involve daily diary data upload and daily simulation-based feedback based on the collected diary data. Throughout the educational intervention, participants will record diary data every day through the use of a diary component in the internet-based system. Diary entries include data on meals, physical activity, history of the insulin basal rate and insulin boluses given that day, self-reported stress level, hypo- and hyperglycemic fear levels, and, if applicable, menstrual cycle and any physical illness. Participants will also upload data from the CGM via the Dexcom DM3 software or the Dexcom Studio Software. |
| Measure | Description | Time Frame |
|---|---|---|
| Accuracy of Feedback and Intervention Feasibility | We will assess the ability of our intervention to provide accurate, useful data to participants that can improve quality of life and educate patients regarding glycemic outcome based on participant feedback collected in focus groups, the post-intervention psychobehavioral assessment, and based on an analysis of the accuracy of the feedback data presented to users based on the input (diary) data. A comparison of input diary data metrics (glucose and insulin data) to output feedback (BG risk profiles, glycemic variability metrics) will allow us to quantify this accuracy. Feasibility of the intervention will be assessed through analysis of pre- and post- assessments, and through feedback provided by subjects during the focus group sessions. A statistician with a background in qualitative data analysis will analyze the psychobehavioral assessment and focus group data. | within 6 months of study conclusion |
| Ability of Behavioral Intervention to Improve Glycemic Outcome | Measure the ability of the simulation-based feedback system intervention to assist in improving the nominal glucose profile based on a pre- and post-intervention assessment of the data. We hypothesize that, compared to the use of CGM alone, the educational system will result in: A2.1: Increased time within target range, defined as 70-140mg/dl overnight and 70-180mg/dl during the day; A2.2: Reduced risk for hypoglycemia, specifically reduction in nocturnal hypoglycemic episodes; A2.3: Reduced postprandial glucose variability and avoidance of hypoglycemia 3-4 hours after a meal; and A2.4: Acceptance of the system feedback by patients. | within 6 months of study conclusion |
| Measure | Description | Time Frame |
|---|---|---|
| Psychobehavioral Data to Inform Closed Loop Control Aggressiveness | Collection of psychobehavioral data on individuals' fear of hypo- and hyperglycemia, and connecting the influence of these factors on insulin therapy self-management can facilitate the individualization of controller aggressiveness in the development of a closed-loop system. We study the ability of the psychobehavioral data to facilitate the individualization of controller aggressiveness in the development of a closed-loop system. The data that is used for this analysis are collected in through the pre- and post-intervention psychobehavioral assessments. |
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Inclusion Criteria:
For this pilot trial, recruitment will ONLY occur through UVa's IRB-HSR #14263 Recruitment Database.
Inclusion criteria for subject to enroll:
Exclusion Criteria:
List any restrictions on use of other drugs or treatments:
The CGM must be removed prior to Magnetic Resonance Imaging and use of acetaminophen-containing medications while using the CGM sensor may affect the performance of the device. Therefore, MRI and products containing acetaminophen will be restricted. If either is required out of medical necessity, the DexCom® will be removed and participant will have the option of repeating the involved study week. If the medical condition requires use of acetaminophen for longer than 1 week, the participant will be dropped from the study.
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Type 1 Diabetes Mellitus study subjects that are currently identified in the Center for Diabetes Technology recruitment database. Subject be currently wearing an insulin pump for at least 6 months and must have worn a continuous glucose monitor for at least 6 weeks during the past 2 years.
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| Name | Affiliation | Role |
|---|---|---|
| Linda Gonder-Fredrick, Ph.D. | University of Virginia Center for Diabetes Technology | Principal Investigator |
| Facility | Status | City | State | ZIP | Country | Contacts |
|---|---|---|---|---|---|---|
| University of Virginia Center for Diabetes Technology | Charlottesville | Virginia | 22904 | United States |
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| ID | Term |
|---|---|
| D003922 | Diabetes Mellitus, Type 1 |
| ID | Term |
|---|---|
| D003920 | Diabetes Mellitus |
| D044882 | Glucose Metabolism Disorders |
| D008659 | Metabolic Diseases |
| D009750 | Nutritional and Metabolic Diseases |
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| ID | Term |
|---|---|
| D004522 | Educational Status |
| ID | Term |
|---|---|
| D012959 | Socioeconomic Factors |
| D011154 | Population Characteristics |
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| within 1 year of study conclusion |
| Relationships Between Physiology and Behavior Transferred to In Silico Environment | Collection of relevant data to establish relationships between behavior and physiology will allow to iteratively transfer, in simulation, the influence of behavioral triggers through individual physiological parameters and metabolic mechanisms, to the final element of BG fluctuation. The collected diary data will be used to establish relationships, specifically stochastic models, between behavioral events, physiology, and self-treatment. The models are developed based on the collected data and will be incorporated into the simulation environment to capture realistically physiologic response to behavior and self-treatment strategies. | within 2 years from study conclusion |
| D004700 | Endocrine System Diseases |
| D001327 | Autoimmune Diseases |
| D007154 | Immune System Diseases |