Ignite Creation Date:
2025-12-25 @ 12:10 AM
Ignite Modification Date:
2025-12-25 @ 10:11 PM
Study NCT ID:
NCT05482958
Status:
COMPLETED
Last Update Posted:
2025-12-17
First Post:
2022-07-26
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
DECIDE-CV Using AI
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D003924', 'term': 'Diabetes Mellitus, Type 2'}], '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'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'PROSPECTIVE', 'observationalModel': 'CASE_CONTROL'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 210}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2022-07-29', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-12', 'completionDateStruct': {'date': '2025-09-29', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2025-12-09', 'studyFirstSubmitDate': '2022-07-26', 'studyFirstSubmitQcDate': '2022-07-28', 'lastUpdatePostDateStruct': {'date': '2025-12-17', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2022-08-01', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2025-09-15', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Presence or absence of T2DM', 'timeFrame': 'Cross sectional based on a single clinic visit with device worn for an estimated 24 hours', 'description': 'As defined by HbA1c \\> 6.5 %, known history of T2DM, or on antihyperglycemic therapies'}, {'measure': 'Glycemic control amongst people with established T2DM.', 'timeFrame': 'Cross sectional based on a single clinic visit with device worn for an estimated 24 hours', 'description': 'As defined by HbA1c %'}], 'secondaryOutcomes': [{'measure': 'Glycemic control.', 'timeFrame': 'Cross sectional based on a single clinic visit with device worn for an estimated 24 hours', 'description': 'Whether the digital biomarker as identified by the wrist-worn device differs between participants who have T2DM with glycemic control while using antihyperglycemic medications versus participants who do not have T2DM with baseline HbA1c \\< 6.5%. Glycemic control is defined as HbA1c \\< 6.5%'}, {'measure': 'Change in glycemic control.', 'timeFrame': 'On average the change will be evaluated over 3-6 months', 'description': 'Whether changes provided by the digital biomarker also correlate with changes in HbA1c after initiation of antihyperglycemic treatments in the same participant over time. Change in glycemic control as measured by HbA1c % with specific antihyperglycemic medication'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Type 2 diabetes', 'Artificial intelligence', 'Wearable technology', 'Cardiovascular complications', 'Digital biomarker'], 'conditions': ['Type 2 Diabetes']}, 'descriptionModule': {'briefSummary': 'The purpose of this study is to identify digital biomarkers associated with type 2 diabetes mellitus (T2DM) by combining sensor data from a wrist-worn wearable and clinical data. This will be done by recruiting patients with and without diabetes within the cardio-metabolic clinics a the MUHC. Consented patients will be provided with a HOP Technologies (HOP) watch in this project across two observation periods. The Watch-HOP platform facilitates the development of predictive algorithms built with data collected in a clinical setting or at home in a passive (sensors) and active (self-assessments) way. Data from the Watch-Hop will be analyzed using machine learning strategies to determine associations with clinical measures of T2DM.', 'detailedDescription': 'The epidemic of type 2 diabetes mellitus (T2DM) continues to increase. Sensor technologies and artificial intelligence present us with an opportunity to identify patients suffering from T2DM and to optimize their treatment.\n\nSpecifically, our primary objective is to identify digital biomarkers associated with T2DM by combining sensor data from a wrist-worn wearable and clinical data.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'The investigators will recruit participants with T2DM (case group; n=90) and without T2DM (control group; n=120), calculated as per the method for sufficient statistical classification power (Protocol v4.0)', 'healthyVolunteers': True, 'eligibilityCriteria': 'Inclusion Criteria:\n\n1. Age \\> 18 years\n2. Able to follow-up with study protocol schedule\n3. Life expectancy \\> 1 year\n4. Case group only a. HbA1c \\>= 6.5% or is diagnosed with T2DM\n\nExclusion Criteria:\n\n1. Any person who does not meet the above criteria or who refuses to participate\n2. Undergoing chemotherapy or dialysis\n3. Currently in palliative care\n4. Any person who does not have an email address\n5. Control group only a. HbA1c \\>= 6.5% or is diagnosed with T2DM'}, 'identificationModule': {'nctId': 'NCT05482958', 'briefTitle': 'DECIDE-CV Using AI', 'organization': {'class': 'OTHER', 'fullName': 'McGill University Health Centre/Research Institute of the McGill University Health Centre'}, 'officialTitle': 'Delivering Evidence Based Care Using Artificial Intelligence to Patients With Diabetes and CardioVascular Comorbidities: The DECIDE-CV Innovation Project', 'orgStudyIdInfo': {'id': '2022-8286'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Control: Participants without T2DM', 'description': 'Participants without T2DM will be recruited and consented through the Courtois Cardiovascular Signature Biorepository protocol, HbA1c % will be measured at baseline from the stored bio-samples collected as a part of the biorepository program. A wearable will be worn for the duration of the clinic appointment.\n\nThe participant will then wear the HOP watch for the designated period of time.', 'interventionNames': ['Device: HOP watch']}, {'label': 'Case: Patients with T2DM', 'description': 'Participants with T2DM, their baseline history of T2DM will be determined from chart review and patient history. In patients with T2DM, for the HbA1c % both at baseline and follow-up, the investigators will measure this value as a part of routine standard of care in the DECIDE-CV clinic.\n\nA subset of 20 participants will be given a Polar H10 chest-strap to be worn during the clinic.\n\nParticipants will be given a HOP watch to wear in the clinical environment and will be discharged from the clinic to wear the watch for the designated period.\n\nParticipants will subsequently wear the watch again, in 3-6 months for the designated period of time.', 'interventionNames': ['Device: HOP watch']}], 'interventions': [{'name': 'HOP watch', 'type': 'DEVICE', 'description': 'A multisensor smartwatch that includes neurophysiological sensors such as heart rate sensor to monitor the vitals of the participant.', 'armGroupLabels': ['Case: Patients with T2DM', 'Control: Participants without T2DM']}]}, 'contactsLocationsModule': {'locations': [{'zip': 'H4A 3J1', 'city': 'Montreal', 'state': 'Quebec', 'country': 'Canada', 'facility': 'McGill University health Center', 'geoPoint': {'lat': 45.50884, 'lon': -73.58781}}], 'overallOfficials': [{'name': 'Abhinav Sharma', 'role': 'PRINCIPAL_INVESTIGATOR', 'affiliation': 'McGill University Health Centre/Research Institute of the McGill University Health Centre'}]}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'McGill University Health Centre/Research Institute of the McGill University Health Centre', 'class': 'OTHER'}, 'collaborators': [{'name': 'HOP-Child Technologies Inc', 'class': 'UNKNOWN'}, {'name': 'MedTeq', 'class': 'INDUSTRY'}, {'name': 'Boehringer Ingelheim', 'class': 'INDUSTRY'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Assistant Professor', 'investigatorFullName': 'Abhinav Sharma', 'investigatorAffiliation': 'McGill University Health Centre/Research Institute of the McGill University Health Centre'}}}}