Clinical trial NCT06642467: BGEM Use as Blood Glucose Prediction Model in T2DM Population of Indonesia
Official title: BGEM Use as Blood Glucose Prediction Model in T2DM Population of Indonesia
Study type: OBSERVATIONAL
Overall status: COMPLETED
Conditions: Diabete Type 2
Lead sponsor: Krida Wacana Christian University
Sponsor class: OTHER
Collaborators: name: Actxa
name: Lif
First posted: 2024-10-15
Enrollment: 885
Sex: ALL
Minimum age (years): 18
Maximum age (years): 59
Standard ages: ADULT
Accepts healthy volunteers: no
Observational model: CASE_CROSSOVER
Time perspective: CROSS_SECTIONAL
Sampling method: NON_PROBABILITY_SAMPLE
Study population: 500 people of diabetic subjects and 400 people of non diabetic subjects

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.

Eligibility criteria: 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

Primary outcomes:
measure: Prediction value of BGEM
timeFrame: July-December 2024
measure: Prediction value of BGEM
timeFrame: July-December 2024
Secondary outcomes:
measure: Variables influencing BGEM
timeFrame: July-December 2024
Other outcomes:
Locations:
city: Jakarta
country: Indonesia
facility: Ukrida Hospital
state: Jakarta Raya
Study officials:
References: