BGEM Use as Blood Glucose Prediction Model in T2DM Popula... | NCT06642467

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

NCT06642467

Sponsor

Krida Wacana Christian University

Status

Completed

Last Update Posted

Oct 15, 2024Actual

Enrollment

885Actual

Phase

Not provided

Conditions

Diabete Type 2

Interventions

BGEM

Countries

Indonesia

Protocol Section

Identification Module Status Module Sponsor/Collaborators Module Oversight Module Description Module Conditions Module Design Module Arms/Interventions Module Outcomes Module Eligibility Module Contacts/Locations Module References Module IPD Sharing Statement Module

Results Section

Participant Flow Module Baseline Characteristics Module Outcome Measures Module Adverse Events Module More Info Module

Annotation Section

Annotation Module

Document Section

Large Document Module

Derived Section

Miscellaneous Info Module Condition Browse Module Intervention Browse Module

Protocol Section

Identification Module

NCT ID

Results Section

No data available

No data is available for this block.

Annotation Section

No data available

No data is available for this block.

Document Section

No data available

No data is available for this block.

Derived Section

Miscellaneous Info Module

Version Holder

Status Module

Record Verification Date

Oct 2024

Overall Recruitment Status or Expanded Access Status

Completed

Last Known Status

Not provided

Delayed Posting

Not provided

Why Stopped

Not provided

Expanded Access Info

Start Date

Jul 30, 2024Actual

Primary Completion Date

Oct 5, 2024Actual

Completion Date

Oct 5, 2024Actual

First Submitted Date

Oct 11, 2024

First Submission Date that Met QC Criteria

Oct 11, 2024

First Posted Date

Oct 15, 2024Actual

Results Waived

Not provided

Results First Submitted Date

Not provided

Results First Submitted that Met QC Criteria

Not provided

Results First Posted Date

Not provided

Certification/Extension (aka Delayed Results) First Submitted Date

Not provided

Certification/Extension First Submitted that Passed QC Review

Not provided

Certification/Extension First Posted Date

Not provided

Last Update Submitted Date

Oct 11, 2024

Last Update Posted Date

Oct 15, 2024Actual

Name	Class
Actxa	UNKNOWN
Lif	UNKNOWN

Oversight Module

Has Data Monitoring Committee (DMC)

Is FDA Regulated Drug

Is FDA Regulated Device

Is Unapproved Device

Not provided

Pediatric Postmarket Surveillance of a Device Product

Not provided

Product Exported from US

Not provided

FDAAA801 Violation

Not provided

Description Module

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.

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.

Design Module

Study Type

Observational

Number of References to an Expanded Access Study

Not provided

Expanded Access Types

Not provided

Patient Registry

Target Follow-Up Duration

Not provided

Phases

Not provided

Interventional Study Design

Allocation

Not provided

Intervention Model

Biospecimen

No data available

No data is available for this block.

Enrollment

885Actual

Arms/Interventions Module

Arm Groups

Label	Type	Description	Intervention Names
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	Device: BGEM
Non diabetic Group		Subjects age 18-59 years old who never diagnosed to have diabetes mellitus or pre DM	Device: BGEM

Interventions

Name	Type	Description	Arm Group Labels	Other Names
BGEM	Device	BGEM is an ai driven model to predict blood glucose using ppg sensor	Diabetic Group Non diabetic Group

Outcomes Module

Primary Outcomes

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

Secondary Outcomes

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

Other Outcomes

Not provided

Eligibility Module

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

Accepts Healthy Volunteers

Sex

All

Sex/Gender Based

Not provided

Sex/Gender Description

Not provided

Minimum Age

18 Years

Maximum Age

59 Years

Standard Ages

Adult

Study Population

500 people of diabetic subjects and 400 people of non diabetic subjects

Sampling Method

Non-Probability Sample

Contacts/Locations Module

Central Contacts

Not provided

Overall Officials

Not provided

Locations

Facility	Status	City	State	ZIP	Country	Contacts
Ukrida Hospital		Jakarta	Jakarta Special Capital Region	11510	Indonesia

IPD Sharing Statement Module

Plan to Share IPD

Description

Not provided

Types

Not provided

Time Frame

Not provided

Access Criteria

Not provided

URL

Not provided

Condition Browse Module

MeSH Terms

ID	Term
D003924	Diabetes Mellitus, Type 2

Ancestor Terms

ID	Term
D003920	Diabetes Mellitus
D044882	Glucose Metabolism Disorders
D008659	Metabolic Diseases
D009750	Nutritional and Metabolic Diseases

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Browse Branches

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