Diabete Type 2
Conditions
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
BGEM is an ai driven model to predict blood glucose using ppg sensor
Sponsors
Study design
Eligibility
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
| Measure | Time frame | Description |
|---|---|---|
| Prediction value of BGEM | July-December 2024 | Result of predictive model will be compared with blood glucose analysis |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Variables influencing BGEM | July-December 2024 | Analysis to determine any variables from subjects that influence BGEM |
Countries
Indonesia