Evaluation of a Simulation-Based Educational Tool for Personalized Feedback in Insulin Therapy Management

University of Virginia15 enrolled

Overview

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.

Interventions

iBOLUSEDBEHAVIORAL

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.

Eligibility

Sex: ALLMin age: 21 YearsMax age: 65 Years

Medical Language ↔ Plain English

Inclusion Criteria: For this pilot trial, recruitment will ONLY occur through UVa's IRB-HSR #14263 Recruitment Database. Inclusion criteria for subject to enroll: 1. T1DM (as defined by the American Diabetes Association criteria or judgment of a physician) for at least 2 years prior to the enrollment in the study 2. Age 21-65. 3. Use of an insulin pump to treat their diabetes for at least 6 months. 4. Use of a CGM for at least a 6 week period in the past 2 years. 5. Has the ability to use a computer that is capable of running the Dexcom Data Manager 3 (DM3) software daily. 6. Has regular access to the Internet. 7. Has an identified healthcare provider who can provide advice about diabetes care. 8. Actively using a bolus calculator function with the current insulin pump with pre-defined parameters for glucose goal, carbohydrate ratio, and insulin sensitivity factor. 9. Willingness to participate in the study for 11 weeks wearing a DexCom CGM for the duration of the study and perform daily journaling of BG, pump activity, meals, physical activity, stress level and menstrual cycle for 3 weeks during the study intervention period. 10. Willingness to do extra fingersticks when CGM alarms at low or high end (\<70 mg/dl or \>300 mg/dl), 11. Willingness to come to Center for Diabetes Technology on at least two occasions to obtain and return supplies. 12. Willingness to avoid consumption of acetaminophen-containing products for the duration of the study. 13. Demonstration of proper mental status and cognition for completion of the study. 14. Has the ability to contact their own personal diabetes care team to have clinical questions answered about changes to diabetes treatment. Exclusion Criteria: 1. Individuals with cognitive impairment that prevents understanding either consent form or intervention content 2. Active renal dialysis 3. Psychiatric disorders that would interfere with study tasks (e.g., substance abuse) 4. History of a systemic deep tissue infection with methicillin-resistant staph aureus or Candida albicans 5. Known bleeding diathesis or dyscrasia 6. Pregnant or intending to get pregnant during study 7. Active enrollment in another clinical trial 8. Medical requirement for acetaminophen-containing products during the study period for more than 1 week 9. Medical condition that would make operating a CGM or insulin pump difficult (e.g. blindness, severe arthritis, extensive scar tissue at sites where devices are inserted) 10. Individuals who do not have access to their diabetes care team to have clinical questions answered about changes in their diabetes treatment. 11. Need for magnetic resonance imaging (MRI)/magnetic resonance angiogram (MRA)during the study 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.

Outcomes

Primary Outcomes

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.

Time frame: 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.

Time frame: within 6 months of study conclusion

Secondary Outcomes

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.

Time frame: 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.

Time frame: within 2 years from study conclusion