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

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

Status
Completed
Phases
Unknown
Study type
Observational
Source
ClinicalTrials.gov
Registry ID
NCT06642467
Enrollment
885
Registered
2024-10-15
Start date
2024-07-30
Completion date
2024-10-05
Last updated
2024-10-15

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Diabete Type 2

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.

Interventions

DEVICEBGEM

BGEM is an ai driven model to predict blood glucose using ppg sensor

Sponsors

Actxa
CollaboratorUNKNOWN
Lif
CollaboratorUNKNOWN
Krida Wacana Christian University
Lead SponsorOTHER

Study design

Observational model
CASE_CROSSOVER
Time perspective
CROSS_SECTIONAL

Eligibility

Sex/Gender
ALL
Age
18 Years to 59 Years
Healthy volunteers
No

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

Design outcomes

Primary

MeasureTime frameDescription
Prediction value of BGEMJuly-December 2024Result of predictive model will be compared with blood glucose analysis

Secondary

MeasureTime frameDescription
Variables influencing BGEMJuly-December 2024Analysis to determine any variables from subjects that influence BGEM

Countries

Indonesia

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: Feb 4, 2026