Ignite Creation Date:
2025-12-25 @ 12:36 AM
Ignite Modification Date:
2025-12-25 @ 10:44 PM
Study NCT ID:
NCT06642467
Status:
COMPLETED
Last Update Posted:
2024-10-15
First Post:
2024-10-11
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
BGEM Use as Blood Glucose Prediction Model in T2DM Population of Indonesia
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': 'CROSS_SECTIONAL', 'observationalModel': 'CASE_CROSSOVER'}, 'enrollmentInfo': {'type': 'ACTUAL', 'count': 885}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'COMPLETED', 'startDateStruct': {'date': '2024-07-30', 'type': 'ACTUAL'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2024-10', 'completionDateStruct': {'date': '2024-10-05', 'type': 'ACTUAL'}, 'lastUpdateSubmitDate': '2024-10-11', 'studyFirstSubmitDate': '2024-10-11', 'studyFirstSubmitQcDate': '2024-10-11', 'lastUpdatePostDateStruct': {'date': '2024-10-15', 'type': 'ACTUAL'}, 'studyFirstPostDateStruct': {'date': '2024-10-15', 'type': 'ACTUAL'}, 'primaryCompletionDateStruct': {'date': '2024-10-05', 'type': 'ACTUAL'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Prediction value of BGEM', 'timeFrame': 'July-December 2024', 'description': 'Result of predictive model will be compared with blood glucose analysis'}, {'measure': 'Prediction value of BGEM', 'timeFrame': 'July-December 2024', 'description': 'Result of predictive model will be compared with Hba1c'}], 'secondaryOutcomes': [{'measure': 'Variables influencing BGEM', 'timeFrame': 'July-December 2024', 'description': 'Analysis to determine any variables from subjects that influence BGEM'}]}, 'oversightModule': {'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'conditions': ['Diabete Type 2']}, 'descriptionModule': {'briefSummary': "Using signals from consumer-grade PPG sensors on wrist wearables, smart rings or hearables, BGEM® AI model computes the relevant digital biomarkers correlated with the change of blood glucose level to predict a blood glucose result for monitoring and evaluating diabetic risks Ukrida in collaboration with Actxa \\& Lif aims to enhance the current model's prediction accuracy to predict the blood glucose levels of individuals almost as accurately as a glucometer. To achieve this, Actxa aims to collect data from around 500 individuals with diabetes in this exercise and 400 healthy or undiagnosed (prediabetes/diabetes) individuals.", 'detailedDescription': "Background Powered by our AI-driven algorithm, the Actxa's Blood Glucose Evaluation and Monitoring (BGEM®) is a cloud-based technology that enables wearables with photoplethysmography (PPG) sensors to monitor and evaluate diabetic risk of individuals regularly in a non-invasive way.\n\nUsing signals from consumer-grade PPG sensors on wrist wearables, smart rings or hearables, BGEM® AI model computes the relevant digital biomarkers correlated with the change of blood glucose level to predict a blood glucose result for monitoring and evaluating diabetic risks. Our previous study has shown the potential of using PPG sensors to detect elevated blood glucose levels among a non-diabetic population1.\n\nObjective Ukrida in collaboration with Actxa \\& Lif to enhance the current model's prediction accuracy to predict the blood glucose levels of individuals almost as accurately as a glucometer. To achieve this, Actxa aims to collect data from around 500 individuals with diabetes in this exercise and 400 healthy or undiagnosed (prediabetes/diabetes) individuals, as part of Actxa's collaboration with UKRIDA Hospital.\n\nWith the data collected, our algorithm holds the potential to significantly improve the management of blood glucose levels for people with and without diabetes, ultimately enhancing their overall quality of life."}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT'], 'maximumAge': '59 Years', 'minimumAge': '18 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': '500 people of diabetic subjects and 400 people of non diabetic subjects', 'healthyVolunteers': False, 'eligibilityCriteria': 'Inclusion Criteria:\n\n* age between 18-59 yo\n* diabetic or non diabetic\n* healthy enough to undergoes normal daily activity\n\nExclusion Criteria:\n\n* o Wears a pacemaker\n\n * Is currently pregnant\n * Has an infection\n * Has a fever'}, 'identificationModule': {'nctId': 'NCT06642467', 'briefTitle': 'BGEM Use as Blood Glucose Prediction Model in T2DM Population of Indonesia', 'organization': {'class': 'OTHER', 'fullName': 'Krida Wacana Christian University'}, 'officialTitle': 'BGEM Use as Blood Glucose Prediction Model in T2DM Population of Indonesia', 'orgStudyIdInfo': {'id': 'KridaWacanaCU'}}, 'armsInterventionsModule': {'armGroups': [{'label': 'Diabetic Group', 'description': 'Subjects age 18-59 years old who was diagnosed with type 2 diabetes mellitus, or pre DM or known to have abnormal Hba1c or blood glucose results', 'interventionNames': ['Device: BGEM']}, {'label': 'Non diabetic Group', 'description': 'Subjects age 18-59 years old who never diagnosed to have diabetes mellitus or pre DM', 'interventionNames': ['Device: BGEM']}], 'interventions': [{'name': 'BGEM', 'type': 'DEVICE', 'description': 'BGEM is an ai driven model to predict blood glucose using ppg sensor', 'armGroupLabels': ['Diabetic Group', 'Non diabetic Group']}]}, 'contactsLocationsModule': {'locations': [{'zip': '11510', 'city': 'Jakarta', 'state': 'Jakarta Special Capital Region', 'country': 'Indonesia', 'facility': 'Ukrida Hospital', 'geoPoint': {'lat': -6.21462, 'lon': 106.84513}}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'Krida Wacana Christian University', 'class': 'OTHER'}, 'collaborators': [{'name': 'Actxa', 'class': 'UNKNOWN'}, {'name': 'Lif', 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'SPONSOR'}}}}