Diabete Type 2NCT06642467
BGEM Use as Blood Glucose Prediction Model in T2DM Population of Indonesia
An observational study of Diabete Type 2, run by Krida Wacana Christian University at 1 site in Indonesia.
Trial 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… Read more about this study
- Who is it for?
- Ages 18 to 59 years
- Conditions the study is for
- Diabete Type 2
- What is being tested?
- BGEM
- Type of treatment
- Medical device
- How many people
- 885 participants
- Where
- 1 siteIndonesia
- Study dates
- 30 Jul 2024to 5 Oct 2024
- Phase
- Observational
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 to 59 years
- All sexes
- No healthy volunteers
- Adult
Study population: 500 people of diabetic subjects and 400 people of non diabetic subjects
Who may be able to join
- age between 18-59 yo
- diabetic or non diabetic
- healthy enough to undergoes normal daily activity
Who cannot join
- o Wears a pacemaker
- Is currently pregnant
- Has an infection
- Has a fever
Trial locations and contacts
1 site in 1 country.
Indonesia1 site
Ukrida Hospital
Jakarta, Jakarta Special Capital Region, 11510
About this study
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.
Read the 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.
- Conditions
- Diabete Type 2
- Being tested
- BGEM
- Medical subject headings
- Diabetes Mellitus, Type 2
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
BGEMDevice
BGEM is an ai driven model to predict blood glucose using ppg sensor
| Study group | Role | What they receive |
|---|---|---|
| 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 Receives: Device: BGEM | |
| Non diabetic Group | Subjects age 18-59 years old who never diagnosed to have diabetes mellitus or pre DM Receives: Device: BGEM |
What is being measured
The primary outcome is the main question the study is designed to answer.
Prediction value of BGEM
Result of predictive model will be compared with blood glucose analysis
Measured: July-December 2024
Prediction value of BGEM
Result of predictive model will be compared with Hba1c
Measured: July-December 2024
Secondary outcomes (1)
Variables influencing BGEM
Analysis to determine any variables from subjects that influence BGEM
Measured: July-December 2024
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Oct 2024
- Study start30 Jul 2024
- Primary completion5 Oct 2024
- Study completion5 Oct 2024
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Diabete Type 2Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Case crossover
- Time perspective
- Cross-sectional
- Enrolment
- 885 participants (actual)
Sponsor and investigators
- Lead sponsor
- Krida Wacana Christian University
- Sponsor type
- University, hospital or other
- Collaborators
- Actxa, Lif
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT06642467
- Sponsor's protocol ID
- KridaWacanaCU
- Status last verified
- Oct 2024
- First submitted
- 11 Oct 2024
- Last updated
- 15 Oct 2024
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Publications
Suradji EW, Dhaliwal SS, Li-Feng Z, Zhou E, Lim A, Wong HH, Du Y, Fredicia F, Dianky A, Pasaribu P, Masayoshi Takashi EG, Tan JYA, Santoso M, Ang SB. Assessment of Blood Glucose Measurement Using New Noninvasive Technology: Protocol and Methodology. JMIR Res Protoc. 2026 Jan 8;15:e76558. doi: 10.2196/76558.
PubMed 41505712
Study at a glance
From the registry record, last updated by the sponsor on 15 Oct 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- BGEM Use as Blood Glucose Prediction Model in T2DM Population of Indonesia
- ClinicalTrials.gov ID
- NCT06642467
- Acronym / sponsor ID
- KridaWacanaCU
- Conditions
- Diabete Type 2
- Study type
- Observational
- Recruitment status
- Completed
- Enrolment
- 885 participants (actual)
- Lead sponsor
- Krida Wacana Christian UniversityUniversity, hospital or other
- Collaborators
- Actxa, Lif
- Study start
- 30 Jul 2024 (actual)
- Primary completion
- 5 Oct 2024 (actual)
- Study completion
- 5 Oct 2024 (actual)
- First posted
- 15 Oct 2024 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Observational model
- Case crossover
- Time perspective
- Cross-sectional
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| Diabetic Group | – | Device: BGEMSubjects 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 |
| Non diabetic Group | – | Device: BGEMSubjects age 18-59 years old who never diagnosed to have diabetes mellitus or pre DM |
Who may qualify
- Ages
- 18 to 59 years
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult
- Study population
- 500 people of diabetic subjects and 400 people of non diabetic subjects
Key inclusion criteria (3 listed)
- age between 18-59 yo
- diabetic or non diabetic
- healthy enough to undergoes normal daily activity
Key exclusion criteria (4 listed)
- o Wears a pacemaker
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| BGEMBGEM is an ai driven model to predict blood glucose using ppg sensor | Device | Diabetic Group, Non diabetic Group |
Primary outcome
Prediction value of BGEM
Time frame: July-December 2024
Result of predictive model will be compared with blood glucose analysis
Prediction value of BGEM
Time frame: July-December 2024
Result of predictive model will be compared with Hba1c
1 secondary outcome
Variables influencing BGEM
Time frame: July-December 2024
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Oct 2024.
- Derived from this study: PubMed 41505712Suradji EW, Dhaliwal SS, Li-Feng Z, Zhou E, Lim A, Wong HH, Du Y, Fredicia F, Dianky A, Pasaribu P, Masayoshi Takashi EG, Tan JYA, Santoso M, Ang SB. Assessment of Blood Glucose Measurement Using New Noninvasive Technology: Protocol and Methodology. JMIR Res Protoc. 2026 Jan 8;15:e76558. doi: 10.2196/76558.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Diabete Type 2, run by Krida Wacana Christian University at 1 site in Indonesia.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 3 inclusion and 4 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 15 Oct 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in Indonesia. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Prediction value of BGEM (time frame: July-December 2024). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT06642467, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Oct 2024.