NCT06642467

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.

Trial Health

87
On Track

Trial Health Score

Automated assessment based on enrollment pace, timeline, and geographic reach

Enrollment
885

participants targeted

Target at P75+ for all trials

Timeline
Completed

Started Jul 2024

Shorter than P25 for all trials

Geographic Reach
1 country

1 active site

Status
completed

Health score is calculated from publicly available data and should be used for screening purposes only.

Trial Relationships

Click on a node to explore related trials.

Study Timeline

Key milestones and dates

Study Start

First participant enrolled

July 30, 2024

Completed
2 months until next milestone

Primary Completion

Last participant's last visit for primary outcome

October 5, 2024

Completed
Same day until next milestone

Study Completion

Last participant's last visit for all outcomes

October 5, 2024

Completed
6 days until next milestone

First Submitted

Initial submission to the registry

October 11, 2024

Completed
4 days until next milestone

First Posted

Study publicly available on registry

October 15, 2024

Completed
Last Updated

October 15, 2024

Status Verified

October 1, 2024

Enrollment Period

2 months

First QC Date

October 11, 2024

Last Update Submit

October 11, 2024

Conditions

Diabete Type 2

Outcome Measures

Primary Outcomes (2)

  • 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 (1)

  • Variables influencing BGEM

    July-December 2024

Study Arms (2)

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

BGEMDEVICE

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

Diabetic GroupNon diabetic Group

Eligibility Criteria

Age18 Years - 59 Years
Sexall
Healthy VolunteersNo
Age GroupsAdult (18-64)
Sampling MethodNon-Probability Sample
Study Population

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

You may qualify if:

  • age between 18-59 yo
  • diabetic or non diabetic
  • healthy enough to undergoes normal daily activity

You may not qualify if:

  • o Wears a pacemaker
  • Is currently pregnant
  • Has an infection
  • Has a fever

Contact the study team to confirm eligibility.

Sponsors & Collaborators

Study Sites (1)

Ukrida Hospital

Jakarta, Jakarta Special Capital Region, 11510, Indonesia

Location

MeSH Terms

Conditions

Diabetes Mellitus, Type 2

Condition Hierarchy (Ancestors)

Diabetes MellitusGlucose Metabolism DisordersMetabolic DiseasesNutritional and Metabolic DiseasesEndocrine System Diseases

Study Design

Study Type
observational
Observational Model
CASE CROSSOVER
Time Perspective
CROSS SECTIONAL
Sponsor Type
OTHER
Responsible Party
SPONSOR

Study Record Dates

First Submitted

October 11, 2024

First Posted

October 15, 2024

Study Start

July 30, 2024

Primary Completion

October 5, 2024

Study Completion

October 5, 2024

Last Updated

October 15, 2024

Record last verified: 2024-10

Data Sharing

IPD Sharing
Will not share

Locations

ID(1)