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

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.

Study Overview

• Status: Completed
• Conditions: Diabete Type 2
• Intervention / Treatment: Device: BGEM

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 Type: Observational
• Enrollment (Actual): 885

Contacts and Locations

Study Locations

• Indonesia
  • Jakarta Raya
    • Jakarta, Jakarta Raya, Indonesia, 11510
      • Ukrida Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult
• Accepts Healthy Volunteers: No

• Sampling Method: Non-Probability Sample

Study Population

500 people of diabetic subjects and 400 people of non diabetic subjects

Description

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 Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Diabetic Group; 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	Device: BGEM; BGEM is an ai driven model to predict blood glucose using ppg sensor
Non diabetic Group; Subjects age 18-59 years old who never diagnosed to have diabetes mellitus or pre DM	Device: BGEM; BGEM is an ai driven model to predict blood glucose using ppg sensor

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Prediction value of BGEM	Result of predictive model will be compared with blood glucose analysis	July-December 2024
Prediction value of BGEM	Result of predictive model will be compared with Hba1c	July-December 2024

Secondary Outcome Measures

Outcome Measure	Measure Description	Time Frame
Variables influencing BGEM	Analysis to determine any variables from subjects that influence BGEM	July-December 2024

Collaborators and Investigators

• Sponsor: Krida Wacana Christian University
• Collaborators: Actxa; Lif

Study record dates

Study Major Dates

• Study Start (Actual): July 30, 2024
• Primary Completion (Actual): October 5, 2024
• Study Completion (Actual): October 5, 2024

Study Registration Dates

• First Submitted: October 11, 2024
• First Submitted That Met QC Criteria: October 11, 2024
• First Posted (Actual): October 15, 2024

Study Record Updates

• Last Update Posted (Actual): October 15, 2024
• Last Update Submitted That Met QC Criteria: October 11, 2024
• Last Verified: October 1, 2024

More Information

Terms related to this study

• Additional Relevant MeSH Terms: Glucose Metabolism Disorders; Metabolic Diseases; Endocrine System Diseases; Diabetes Mellitus; Diabetes Mellitus, Type 2
• Other Study ID Numbers: KridaWacanaCU

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No