BGEM Use as Blood Glucose Prediction Model in T2DM Population of Indonesia

Completed

• Conditions: Diabete Type 2

• Registration Number: NCT06642467
• Lead Sponsor: Krida Wacana Christian University

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 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.

Detailed Description

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.

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 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.

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 Timeline

• Study Start: 2024-07-30
• Status Verified: 2024-10
• Primary Completion: 2024-10-05
• Study Completion: 2024-10-05
• Study First Submitted: 2024-10-11
• Study First Submitted QC: 2024-10-11
• Last Update Submitted: 2024-10-11
• Study First Posted: 2024-10-15
• Last Update Posted: 2024-10-15

Recruitment & Eligibility

• Status: COMPLETED
• Sex: All
• Target Recruitment: 885

Inclusion Criteria

• age between 18-59 yo
• diabetic or non diabetic
• healthy enough to undergoes normal daily activity

Exclusion Criteria

• o Wears a pacemaker

  • Is currently pregnant
  • Has an infection
  • Has a fever

Study & Design

• Study Type: OBSERVATIONAL
• Study Design: Not specified

Primary Outcome Measures

Name	Time	Method
Prediction value of BGEM	July-December 2024	Result of predictive model will be compared with Hba1c

Secondary Outcome Measures

Name	Time	Method
Variables influencing BGEM	July-December 2024	Analysis to determine any variables from subjects that influence BGEM

Trial Locations

Locations (1)

Ukrida Hospital; 🇮🇩 Jakarta, Jakarta Raya, Indonesia