BGEM Use as Blood Glucose Prediction Model in T2DM Population of Indonesia
试验速览
- 阶段
- 不适用
- 状态
- 已完成
- 发起方
- 入组人数
- 885
- 试验地点
- 1
- 主要终点
- Prediction value of BGEM
研究概览
简要总结
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.
详细描述
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.
研究设计
- 研究类型
- Observational
- 观察模型
- Case Crossover
- 时间视角
- Cross Sectional
入排标准
- 年龄范围
- 18 Years 至 59 Years(Adult)
- 性别
- All
- 接受健康志愿者
- 否
入选标准
- •age between 18-59 yo
- •diabetic or non diabetic
- •healthy enough to undergoes normal daily activity
排除标准
- •o Wears a pacemaker
- •Is currently pregnant
- •Has an infection
- •Has a fever
结局指标
主要结局
Prediction value of BGEM
时间窗: July-December 2024
Result of predictive model will be compared with Hba1c
次要结局
- Variables influencing BGEM(July-December 2024)
