Ignite Creation Date:
2024-10-26 @ 3:42 PM
Last Modification Date:
2024-10-26 @ 3:42 PM
Study NCT ID:
NCT06642467
Status:
COMPLETED
Last Update Posted:
None
First Post:
2024-10-11
Brief Title:
BGEM Use as Blood Glucose Prediction Model in T2DM Population of Indonesia
Sponsor:
None
Organization:
None
Study Overview
Official Title:
BGEM Use as Blood Glucose Prediction Model in T2DM Population of Indonesia
Status:
COMPLETED
Status Verified Date:
2024-10
Last Known Status:
None
Delayed Posting:
No
If Stopped, Why?:
Not Stopped
Has Expanded Access:
No
If Expanded Access, NCT#:
N/A
Has Expanded Access, NCT# Status:
N/A
Acronym:
None
Brief Summary:
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 models 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 prediabetesdiabetes individuals
Detailed Description:
Background Powered by our AI-driven algorithm the Actxas 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
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 Our previous study has shown the potential of using PPG sensors to detect elevated blood glucose levels among a non-diabetic population1
Objective Ukrida in collaboration with Actxa Lif to enhance the current models 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 prediabetesdiabetes individuals as part of Actxas collaboration with UKRIDA Hospital
With 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
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 Our previous study has shown the potential of using PPG sensors to detect elevated blood glucose levels among a non-diabetic population1
Objective Ukrida in collaboration with Actxa Lif to enhance the current models 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 prediabetesdiabetes individuals as part of Actxas collaboration with UKRIDA Hospital
With 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
Study Oversight
Has Oversight DMC:
None
Is a FDA Regulated Drug?:
None
Is a FDA Regulated Device?:
None
Is an Unapproved Device?:
None
Is a PPSD?:
None
Is a US Export?:
None
Is an FDA AA801 Violation?:
None