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

Krida Wacana Christian University

Status: Completed

Conditions: Diabete Type 2

Treatments: Device: BGEM

Study type: Observational

Funder types: Other

Identifiers

NCT06642467

KridaWacanaCU

Details and patient eligibility

About

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.

Full 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.

Enrollment: 885 patients

Sex: All

Ages: 18 to 59 years old

Volunteers: No Healthy Volunteers

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

Trial design

885 participants in 2 patient groups

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

Treatment:

Device: BGEM

Non diabetic Group

Description:

Subjects age 18-59 years old who never diagnosed to have diabetes mellitus or pre DM

Treatment:

Device: BGEM