Ignite Creation Date:
2025-12-24 @ 6:42 PM
Ignite Modification Date:
2025-12-24 @ 6:42 PM
Study NCT ID:
NCT06676657
Status:
RECRUITING
Last Update Posted:
2025-04-11
First Post:
2024-11-04
Is Possible Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Closed Loop Context Aware AID
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D003922', 'term': 'Diabetes Mellitus, Type 1'}], 'ancestors': [{'id': 'D003920', 'term': 'Diabetes Mellitus'}, {'id': 'D044882', 'term': 'Glucose Metabolism Disorders'}, {'id': 'D008659', 'term': 'Metabolic Diseases'}, {'id': 'D009750', 'term': 'Nutritional and Metabolic Diseases'}, {'id': 'D004700', 'term': 'Endocrine System Diseases'}, {'id': 'D001327', 'term': 'Autoimmune Diseases'}, {'id': 'D007154', 'term': 'Immune System Diseases'}]}}, 'protocolSection': {'designModule': {'phases': ['NA'], 'studyType': 'INTERVENTIONAL', 'designInfo': {'allocation': 'NA', 'maskingInfo': {'masking': 'NONE'}, 'primaryPurpose': 'TREATMENT', 'interventionModel': 'SINGLE_GROUP'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 10}}, 'statusModule': {'overallStatus': 'RECRUITING', 'startDateStruct': {'date': '2024-11-22', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-04', 'completionDateStruct': {'date': '2025-08-30', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-04-08', 'studyFirstSubmitDate': '2024-11-04', 'studyFirstSubmitQcDate': '2024-11-04', 'lastUpdatePostDateStruct': {'date': '2025-04-11', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-11-06', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-07-30', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Percent of time with sensor glucose less than 70 mg/dl', 'timeFrame': 'entire 4 week study', 'description': 'Assess the percent of time that the Dexcom G6 reported sensor glucose values less than 70 mg/dl comparing first 3 weeks vs. last week'}, {'measure': 'Percent of time with sensor glucose less than 54 mg/dl', 'timeFrame': 'entire 4 week study', 'description': 'Assess the percent of time that the Dexcom G6 reported sensor glucose values less than 54 mg/dl comparing first 3 weeks vs. last week'}, {'measure': 'Percent of time the pattern evaluation windows occurred', 'timeFrame': 'entire 4 week study', 'description': 'To assess the hyperglycemia pattern detection and dosing feature by evaluating the percent time that the feature is active in detecting hyperglycemic patterns during the pattern evaluation windows calculated as: (minutes in pattern evaluation window) / (total minutes) \\* 100'}, {'measure': 'Percent of time with sensor glucose greater than 250 mg/dl', 'timeFrame': 'entire 4 week study', 'description': 'Assess the percent of time that the Dexcom G6 reported sensor glucose values more than 250 mg/dl comparing first 3 weeks vs. last week'}], 'secondaryOutcomes': [{'measure': 'Percent of time with sensor glucose 70 to 180 mg/dl', 'timeFrame': 'entire 4 week study', 'description': 'Assess the percent of time that the Dexcom G6 reported sensor glucose values between 70-180 mg/dl comparing first 3 weeks vs. last week'}, {'measure': 'Percent of time with sensor glucose 70 to 140 mg/dl', 'timeFrame': 'entire 4 week study', 'description': 'Assess the percent of time that the Dexcom G6 reported sensor glucose values between 70-140 mg/dl comparing first 3 weeks vs. last week'}, {'measure': 'Percent of time with sensor glucose greater than 180 mg/dl', 'timeFrame': 'entire 4 week study', 'description': 'Assess the percent of time that the Dexcom G6 reported sensor glucose values more than 180 mg/dl'}, {'measure': 'Coefficient of variation of glucose', 'timeFrame': 'entire 4 week study', 'description': 'Assess the coefficient of variation of sensor glucose reported from the Dexcom G6 CGM comparing first 3 weeks vs. last week'}, {'measure': 'Insulin delivered', 'timeFrame': 'entire 4 week study', 'description': 'Assess the insulin delivered in units/kg/day comparing first 3 weeks vs. last week'}, {'measure': 'Hypoglycemia treatments', 'timeFrame': 'entire 4 week study', 'description': 'Assess the number of hypoglycemia treatments (counted as 15 grams of oral carbohydrate) per day comparing first 3 weeks vs. last week'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': True}, 'conditionsModule': {'keywords': ['automated insulin delivery systems', 'glucose sensor'], 'conditions': ['Type 1 Diabetes']}, 'descriptionModule': {'briefSummary': 'An artificial pancreas (AP) is a control system for automatic insulin delivery. The investigators have implemented a high blood sugar detection and dosing algorithm for use within an AP control system. If a high blood sugar pattern is detected, correction insulin will be calculated and delivered. The investigators will test how well the new algorithm manages glucose compared to the AP control system without high blood sugar detection and dosing. This type of algorithm may improve glucose control for high risk patient populations.', 'detailedDescription': 'Participants will be on study for approximately 4 weeks. During the study, participants will wear an Omnipod to deliver insulin. Participants will also wear a Dexcom G6 CGM. The CGM system will send sensed glucose data every 5 minutes wirelessly via Bluetooth Low Energy (BTLE) to an Android smartphone running the iPancreas app. The closed-loop system will receive activity data through an activity watch worn by the participant. Participants will complete system training on Day 1 in clinic and then spend the rest of the 4 weeks under free-living conditions. The first 3 weeks of the study will be the training period when the system will collect patterns from the glucose sensor, insulin, and fitness data that lead to high blood sugar. After the 3-week training period, participants will complete a virtual visit to train the participant on the high blood sugar detection and dosing algorithm, then continue to use for an additional week. Participants will complete meal and exercise challenges.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'maximumAge': '100 Years', 'minimumAge': '18 Years', 'healthyVolunteers': False, 'eligibilityCriteria': "Inclusion Criteria:\n\n* Diagnosis of type 1 diabetes mellitus for at least 1 year.\n* Male or female participants 18 and older.\n* HbA1c or GMI ≥ 7.0% at screening.\n* Physically willing and able to perform 30 min of exercise (as determined by the investigator after reviewing the participant's activity level).\n* Current use of an FDA-approved hybrid closed loop system for ≥3 months.\n* Lives with another person age 18 or older who will sleep in the house at night and that can attend the training on using the system.\n* Lives within 40 miles of OHSU\n* Total daily insulin requirement is less than 139 units/day.\n* Able to read, write and understand spoken English\n* Current use of a smartphone so can be contacted by study staff off-campus.\n* Willingness to follow all study procedures, including attending all clinic visits.\n* Willingness to sign informed consent and HIPAA documents.\n\nExclusion Criteria:\n\n* GMI or A1c \\<6.5% or \\>10.5%\n* Sensor glucose shows \\< 2% of time above 250 mg/dl in last 30 days.\n* Individual of childbearing potential who is pregnant or intending to become pregnant or breast-feeding, or is not using adequate contraceptive methods. Acceptable contraception includes birth control pill / patch / vaginal ring, Depo-Provera, Norplant, an IUD, the double barrier method (the woman uses a diaphragm and spermicide and the man uses a condom), or abstinence.\n* Any cardiovascular disease, defined as a clinically significant EKG abnormality at the time of screening or any history of: stroke, heart failure, myocardial infarction, angina pectoris, or coronary arterial bypass graft or angioplasty. Diagnosis of 2nd or 3rd degree heart block or any non-physiological arrhythmia judged by the investigator to be exclusionary.\n* Renal insufficiency (GFR \\< 60 ml/min, using the MDRD equation as reported by the OHSU laboratory).\n* Liver failure, cirrhosis, or any other liver disease that compromises liver function as determined by the investigator.\n* History of severe hypoglycemia during the past 6 months prior to screening visit or hypoglycemia unawareness as judged by the investigator. Participants will complete a hypoglycemia awareness questionnaire. Participants will be excluded for four or more R responses.\n* History of diabetes ketoacidosis during the prior 6 months prior to screening visit, as diagnosed on hospital admission or as judged by the investigator.\n* Adrenal insufficiency.\n* Any active infection (example skin infection requiring antibiotics)\n* Known or suspected abuse of alcohol, narcotics, or illicit drugs.\n* Seizure disorder.\n* Active foot ulceration.\n* Peripheral arterial disease.\n* Major surgical operation within 30 days prior to screening.\n* Use of an investigational drug within 30 days prior to screening.\n* Chronic usage of any immunosuppressive medication (such as cyclosporine, azathioprine, sirolimus, or tacrolimus).\n* Bleeding disorder or platelet count below 50,000.\n* Allergy to Fiasp insulin\n* Current administration of oral or parenteral corticosteroids.\n* Any life threatening disease, including malignant neoplasms and medical history of malignant neoplasms within the past 5 years prior to screening (except basal and squamous cell skin cancer).\n* Use of beta blockers or non-dihydropyridine calcium channel blockers.\n* Current use of any medication that can lower glucose other than insulin (ex. Wegovy, Jardiance) with the exception of metformin if dose has been stable for ≥3 months and patient willing to not change dose during study.\n* Gastroparesis\n* Diet consisting of less than 50 grams of carbohydrates per day.\n* A positive response to any of the questions from the Physical Activity Readiness Questionnaire with one exception: participant will not be excluded if he/she takes a single blood pressure medication that doesn't impact heart rate and blood pressure is controlled on the medication (blood pressure is less than 140/90 mmHg).\n* Any chest discomfort with physical activity, including pain or pressure, or other types of discomfort.\n* Any clinically significant disease or disorder which in the opinion of the Investigator may jeopardize the participant's safety or compliance with the protocol."}, 'identificationModule': {'nctId': 'NCT06676657', 'briefTitle': 'Closed Loop Context Aware AID', 'organization': {'class': 'OTHER', 'fullName': 'Oregon Health and Science University'}, 'officialTitle': 'Assessment of a Fully-closed Loop AID System With a Context-aware Hyperglycemia Pattern Detection and Dosing Algorithm in People With Type 1 Diabetes', 'orgStudyIdInfo': {'id': '26005'}, 'secondaryIdInfos': [{'id': 'R01DK122583-01', 'link': 'https://reporter.nih.gov/quickSearch/R01DK122583-01', 'type': 'NIH'}]}, 'armsInterventionsModule': {'armGroups': [{'type': 'EXPERIMENTAL', 'label': 'iPancreas automated insulin delivery system', 'description': 'Participants will use the iPancreas automated insulin delivery system for 4 weeks at home. After 3 weeks of collecting high blood sugar pattern data, the high blood sugar pattern detection and dosing feature will be turned on.', 'interventionNames': ['Device: iPancreas automated insulin delivery system']}], 'interventions': [{'name': 'iPancreas automated insulin delivery system', 'type': 'DEVICE', 'description': "The Model Predictive Control (MPC) insulin infusion algorithm contains a model within the controller that takes as an input the aerobic metabolic expenditure in addition to the CGM and meal in puts. The algorithm uses heart rate and accelerometer data collected on the patient's body to calculate metabolic expenditure (METs). The METs then acts on the model for the insulin dynamics, whereby more energy expenditure and longer duration exercise can lead to a more substantial effect of insulin on the CGM. The MPC also has missed meal insulin bolus detection where the system will calculate the amount of insulin that was missed for a meal. The missed meal boluses can be delivered automatically without any input from the user. This feature can also be disabled. The MPC has a new feature called hyperglycemia pattern detection and dosing algorithm that will analyze problem patterns associated with high blood sugar and automatically calculate and deliver a correction dose.", 'armGroupLabels': ['iPancreas automated insulin delivery system']}]}, 'contactsLocationsModule': {'locations': [{'zip': '97239', 'city': 'Portland', 'state': 'Oregon', 'status': 'RECRUITING', 'country': 'United States', 'contacts': [{'name': 'Leah Wilson, MD', 'role': 'CONTACT', 'email': 'wilsolea@ohsu.edu', 'phone': '503-494-3273'}, {'name': 'Deborah Branigan', 'role': 'CONTACT', 'email': 'branigad@ohsu.edu', 'phone': '503-418-9070'}, {'name': 'Peter Jacobs, PhD', 'role': 'PRINCIPAL_INVESTIGATOR'}], 'facility': 'Oregon Health and Science University', 'geoPoint': {'lat': 45.52345, 'lon': -122.67621}}], 'centralContacts': [{'name': 'Leah Wilson, MD', 'role': 'CONTACT', 'email': 'wilsolea@ohsu.edu', 'phone': '503-494-3273'}, {'name': 'Deborah Branigan', 'role': 'CONTACT', 'email': 'branigad@ohsu.edu', 'phone': '5034189070'}], 'overallOfficials': [{'name': 'Leah Wilson, MD', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'Oregon Health and Science University'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'UNDECIDED'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Oregon Health and Science University', 'class': 'OTHER'}, 'collaborators': [{'name': 'National Institute of Diabetes and Digestive and Kidney Diseases (NIDDK)', 'class': 'NIH'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Assistant Professor', 'investigatorFullName': 'Leah Wilson', 'investigatorAffiliation': 'Oregon Health and Science University'}}}}