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

October 11, 2024 updated by: Krida Wacana Christian University
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

Intervention / Treatment

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

This section provides the contact details for those conducting the study, and information on where this study is being conducted.

Study Locations

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

Participation Criteria

Researchers look for people who fit a certain description, called eligibility criteria. Some examples of these criteria are a person's general health condition or prior treatments.

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

This section provides details of the study plan, including how the study is designed and what the study is measuring.

How is the study designed?

Design Details

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

Secondary Outcome Measures

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

Collaborators and Investigators

This is where you will find people and organizations involved with this study.

Sponsor

Collaborators

Lif

Study record dates

These dates track the progress of study record and summary results submissions to ClinicalTrials.gov. Study records and reported results are reviewed by the National Library of Medicine (NLM) to make sure they meet specific quality control standards before being posted on the public website.

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

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

This information was retrieved directly from the website clinicaltrials.gov without any changes. If you have any requests to change, remove or update your study details, please contact register@clinicaltrials.gov. As soon as a change is implemented on clinicaltrials.gov, this will be updated automatically on our website as well.

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