Diabetes MellitusNCT07502690
Validation of Remote Photoplethysmography for Non-Invasive Estimation of Blood Glucose and HbA1c
An observational study of Diabetes Mellitus and Hyperglycaemia (Diabetic), run by Tarumanagara University at 1 site in Indonesia.
Trial summary
The goal of this observational study is to evaluate whether a non-invasive facial scan technology using remote photoplethysmography (rPPG) can accurately estimate blood glucose and HbA1c levels in adults living in the community in Jakarta. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Diabetes Mellitus, Hyperglycaemia (Diabetic), Hyperglycaemia (Non Diabetic)
- How many people
- 300 participants
- Where
- 1 siteIndonesia
- Study dates
- 23 Mar 2026to 30 Dec 2026
- How long will I take part?
- 1 Day
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
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 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Study population: The study population consists of adult individuals aged 18 years and older residing in a community setting in Jakarta, Indonesia. Participants will be recruited through community-based health activities and primary care services. The population includes both individuals with and without a prior diagnosis of type 2 diabetes mellitus, representing a general adult population for glycemic screening. Eligible participants must be able to undergo both non-invasive facial scanning using remote photoplethysmography (rPPG) and standard laboratory blood testing. Individuals with facial conditions that interfere with signal detection, inability to remain still during scanning, or incomplete data will be excluded.
Who may be able to join
- Adults aged ≥18 years
- Willing to participate and provide informed consent
- Able to undergo facial scan and blood examination
- Stable clinical condition
Who cannot join
- Facial conditions interfering with rPPG signal (e.g., wounds, deformities)
- Use of facial coverings obstructing camera detection
- Inability to remain still during facial scan
- Incomplete data or withdrawal from study
Trial locations and contacts
1 site in 1 country. Contact the study team or a site near you to ask about taking part.
Ernawati Ernawati, Dr. contact
Indonesia1 site
Kelurahan Semanan
Jakarta, Jakarta Special Capital Region
Hanna Wijaya, +6281223787878, hannwijaya@yahoo.com
Wenny Sanwani, +6281585013412, wenny.sanwani@gmail.com
About this study
Official title: Validation of Remote Photoplethysmography for Non-Invasive Estimation of Blood Glucose and HbA1c in a Community-Based Population in Jakarta
The goal of this observational study is to evaluate whether a non-invasive facial scan technology using remote photoplethysmography (rPPG) can accurately estimate blood glucose and HbA1c levels in adults living in the community in Jakarta. The study focuses on adults aged 18 years and older, including individuals with or without diabetes.
The main questions it aims to answer are:
Read more
- Can rPPG-based facial scan estimates of blood glucose and HbA1c match results from standard laboratory blood tests?
- How well can rPPG identify individuals with high blood sugar or diabetes risk based on established clinical cut-off values?
Researchers will compare results from the rPPG facial scan with standard laboratory measurements of fasting blood glucose and HbA1c to determine how accurate and reliable the technology is for screening purposes.
Participants will:
- Provide basic information such as age, sex, and medical history
- Undergo a non-invasive facial scan using a smartphone-based system
- Have a blood sample taken to measure fasting blood glucose and HbA1c
- Complete all assessments during a single study visit
This study aims to determine whether rPPG can serve as a simple, non-invasive, and accessible tool for early detection and monitoring of diabetes in community settings.
Read the detailed description
Introduction Type 2 diabetes mellitus (T2DM) represents a major global health burden characterized by chronic hyperglycemia and associated complications. Standard monitoring methods, such as fasting blood glucose and glycated hemoglobin (HbA1c), rely on invasive blood sampling and access to laboratory facilities, which may reduce patient adherence and limit early detection. Remote photoplethysmography (rPPG), a non-contact optical technique using facial video analysis, has emerged as a promising alternative for estimating physiological and metabolic parameters. However, evidence regarding its validity in assessing glycemic markers remains limited .
Objective This study aims to evaluate the validity and diagnostic performance of rPPG-based facial scan technology in estimating blood glucose and HbA1c levels compared with standard laboratory measurements.
Methods This study employs an analytical observational design with a cross-sectional diagnostic validation approach conducted in Kelurahan Semanan, Jakarta. A total of 150-300 adult participants will be recruited using a community-based sampling method. Each participant will undergo venous blood sampling for laboratory measurement of fasting blood glucose and HbA1c, alongside a non-contact rPPG facial scan using a smartphone-based system. Agreement between methods will be assessed using Bland-Altman analysis, while correlation analysis (Pearson/Spearman) will evaluate the strength of association. Diagnostic performance, including sensitivity and specificity, will be calculated using clinical cut-offs (≥126 mg/dL for glucose and ≥6.5% for HbA1c).
Expected Results It is expected that rPPG-derived estimates will demonstrate moderate to good correlation with laboratory measurements, with acceptable agreement for screening purposes. The technology is anticipated to show reasonable diagnostic performance in identifying individuals with high glycemic risk. These findings may support the feasibility of rPPG as a non-invasive, accessible screening tool for diabetes monitoring in community settings.
- Conditions
- Diabetes Mellitus, Hyperglycaemia (Diabetic), Hyperglycaemia (Non Diabetic), Hypoglycaemia
- Keywords
- remote photoplethysmography, rppg, blood glucose, HbA1c, diabetes mellitus, non-invasive monitoring, digital health screening
- Medical subject headings
- Diabetes Mellitus, Hyperglycemia, Hypoglycemia
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Community Adults Undergoing rPPG and Laboratory Glycemic Assessment | This cohort includes adults aged ≥18 years from a community-based population in Jakarta who undergo both non-invasive remote photoplethysmography (rPPG) facial scanning and standard laboratory testing. Participants will receive a smartphone-based facial scan to estimate blood glucose and HbA1c levels, followed by venous blood sampling for fasting blood glucose and HbA1c measurement using standard laboratory methods. No therapeutic intervention is administered, as this is a diagnostic validation study comparing rPPG-derived estimates with laboratory reference values. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Agreement Between rPPG-Derived and Laboratory Blood Glucose
Assessment of agreement between blood glucose values obtained from remote photoplethysmography (rPPG) facial scan and standard laboratory fasting blood glucose measurements using Bland-Altman analysis, including mean bias and limits of agreement.
Measured: Single assessment at baseline (during study visit)
Agreement Between rPPG-Derived and Laboratory HbA1c
Evaluation of agreement between HbA1c values estimated using rPPG facial scan and laboratory HbA1c measurements using Bland-Altman analysis, including bias and limits of agreement.
Measured: Single assessment at baseline (during study visit)
Correlation and Validation of rPPG Estimates with Laboratory Blood Glucose and HbA1c
Measurement of the strength of association between rPPG-derived and laboratory-measured blood glucose and HbA1c values using Pearson or Spearman correlation coefficients (Bland Altman)
Measured: Single assessment at baseline (during study visit)
Diagnostic Performance of rPPG for Detecting Hyperglycemia and Diabetes Risk
Evaluation of sensitivity, specificity, and accuracy of rPPG-derived blood glucose (≥126 mg/dL) and HbA1c (≥6.5%) in identifying individuals with elevated glycemic levels compared to laboratory reference standards.
Measured: Single assessment at baseline (during study visit)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted31 Mar 2026
- Study start23 Mar 2026 (estimated)
- Primary completion30 Jun 2026 (estimated)
- Study completion30 Dec 2026 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (8)Papers the sponsor has linked to this study.
- Other trials for Diabetes MellitusStudies 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
- Ecologic or community
- Time perspective
- Cross-sectional
- Enrolment
- 300 participants (estimated)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- Tarumanagara University
- Sponsor type
- University, hospital or other
- Collaborators
- Universitas Tarumanagara
- Responsible party
- Principal investigator, Yohanes Firmansyah, dr, MH, MM, Tarumanagara University
- Investigators
- Yohanes Firmansyah, MD, Principal investigator, Klinik Citra SemananErnawati Ernawati, Principal investigator, Universitas TarumanagaraAlexander Halim Santoso, Study director, Universitas TarumanagaraSri Tiarti, Study chair, Universitas TarumanagaraNoer Saelan Tadjudin, Study chair, Universitas TarumanagaraPutu Tommy Yudha Sumatera Suyasa, Study chair, Universitas TarumanagaraDavid Wongso, Study director, DexWellnessRatheesh Nair, Study director, Watch Your Health
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07502690
- Sponsor's protocol ID
- 20260324
- Status last verified
- Apr 2026
- First submitted
- 24 Mar 2026
- Last updated
- 20 Apr 2026
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: De-identified individual participant data (IPD) will be shared, including demographic characteristics (age, sex), clinical variables (medical history, fasting status), rPPG-derived measurements (estimated blood glucose and HbA1c), and corresponding laboratory results (fasting blood glucose and HbA1c). Derived variables used in the analysis, such as glycemic risk classifications and diagnostic performance indicators, may also be included. All shared data will be fully anonymized, with no direct identifiers to ensure participant confidentiality.
Publications
Zeynali M, Alipour K, Tarvirdizadeh B, Ghamari M. Non-invasive blood glucose monitoring using PPG signals with various deep learning models and implementation using TinyML. Sci Rep. 2025 Jan 2;15(1):581. doi: 10.1038/s41598-024-84265-8.
PubMed 39753714Zanelli S, Ammi M, Hallab M, El Yacoubi MA. Diabetes Detection and Management through Photoplethysmographic and Electrocardiographic Signals Analysis: A Systematic Review. Sensors (Basel). 2022 Jun 29;22(13):4890. doi: 10.3390/s22134890.
PubMed 35808386Shi B, Dhaliwal SS, Soo M, Chan C, Wong J, Lam NWC, Zhou E, Paitimusa V, Loke KY, Chin J, Chua MT, Liaw KCS, Lim AWH, Insyirah FF, Yen SC, Tay A, Ang SB. Assessing Elevated Blood Glucose Levels Through Blood Glucose Evaluation and Monitoring Using Machine Learning and Wearable Photoplethysmography Sensors: Algorithm Development and Validation. JMIR AI. 2023 Oct 27;2:e48340. doi: 10.2196/48340.
PubMed 38875549Santillan A, Travez Proano EI, Jaramillo Encalada IN, Abril Lopez PA, Tricallotis J, Acosta-Espana JD. Structured telemonitoring reduces HbA1c and emergency visits in insulin-treated type 2 diabetes: a controlled cohort study in Ecuador's public hospital. Front Clin Diabetes Healthc. 2026 Feb 9;7:1734589. doi: 10.3389/fcdhc.2026.1734589. eCollection 2026.
PubMed 41737571Qawqzeh YK, Bajahzar AS, Jemmali M, Otoom MM, Thaljaoui A. Classification of Diabetes Using Photoplethysmogram (PPG) Waveform Analysis: Logistic Regression Modeling. Biomed Res Int. 2020 Aug 11;2020:3764653. doi: 10.1155/2020/3764653. eCollection 2020.
PubMed 32851065Kwon TH, Kim KD. Machine-Learning-Based Noninvasive In Vivo Estimation of HbA1c Using Photoplethysmography Signals. Sensors (Basel). 2022 Apr 12;22(8):2963. doi: 10.3390/s22082963.
PubMed 35458947Farenden E, Kelly J, Russell A, Menon A. Remote Monitoring for Type 2 Diabetes: What Do Patients, Healthcare Professionals, and Executives Think? Stud Health Technol Inform. 2024 Jan 25;310:1526-1527. doi: 10.3233/SHTI231276.
PubMed 38269728Chu J, Yang WT, Lu WR, Chang YT, Hsieh TH, Yang FL. 90% Accuracy for Photoplethysmography-Based Non-Invasive Blood Glucose Prediction by Deep Learning with Cohort Arrangement and Quarterly Measured HbA1c. Sensors (Basel). 2021 Nov 24;21(23):7815. doi: 10.3390/s21237815.
PubMed 34883817
Study at a glance
From the registry record, last updated by the sponsor on 20 Apr 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Validation of Remote Photoplethysmography for Non-Invasive Estimation of Blood Glucose and HbA1c in a Community-Based Population in Jakarta
- ClinicalTrials.gov ID
- NCT07502690
- Acronym / sponsor ID
- 20260324
- Conditions
- Diabetes Mellitus, Hyperglycaemia (Diabetic), Hyperglycaemia (Non Diabetic), Hypoglycaemia
- Keywords
- remote photoplethysmography, rppg, blood glucose, HbA1c, diabetes mellitus, non-invasive monitoring, digital health screening
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 300 participants (estimated)
- Lead sponsor
- Tarumanagara UniversityUniversity, hospital or other
- Collaborators
- Universitas Tarumanagara
- Study start
- 23 Mar 2026 (estimated)
- Primary completion
- 30 Jun 2026 (estimated)
- Study completion
- 30 Dec 2026 (estimated)
- First posted
- 31 Mar 2026 (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.
The goal of this observational study is to evaluate whether a non-invasive facial scan technology using remote photoplethysmography (rPPG) can accurately estimate blood glucose and HbA1c levels in adults living in the community in Jakarta. The study focuses on adults aged 18 years and older, including individuals with or without diabetes.
The main questions it aims to answer are:
- Can rPPG-based facial scan estimates of blood glucose and HbA1c match results from standard laboratory blood tests?
- How well can rPPG identify individuals with high blood sugar or diabetes risk based on established clinical cut-off values?
Researchers will compare results from the rPPG facial scan with standard laboratory measurements of fasting blood glucose and HbA1c to determine how accurate and reliable the technology is for screening purposes.
Participants will:
- Provide basic information such as age, sex, and medical history
- Undergo a non-invasive facial scan using a smartphone-based system
- Have a blood sample taken to measure fasting blood glucose and HbA1c
- Complete all assessments during a single study visit
This study aims to determine whether rPPG can serve as a simple, non-invasive, and accessible tool for early detection and monitoring of diabetes in community settings.
Detailed description
Introduction Type 2 diabetes mellitus (T2DM) represents a major global health burden characterized by chronic hyperglycemia and associated complications. Standard monitoring methods, such as fasting blood glucose and glycated hemoglobin (HbA1c), rely on invasive blood sampling and access to laboratory facilities, which may reduce patient adherence and limit early detection. Remote photoplethysmography (rPPG), a non-contact optical technique using facial video analysis, has emerged as a promising alternative for estimating physiological and metabolic parameters. However, evidence regarding its validity in assessing glycemic markers remains limited .
Objective This study aims to evaluate the validity and diagnostic performance of rPPG-based facial scan technology in estimating blood glucose and HbA1c levels compared with standard laboratory measurements.
Methods This study employs an analytical observational design with a cross-sectional diagnostic validation approach conducted in Kelurahan Semanan, Jakarta. A total of 150-300 adult participants will be recruited using a community-based sampling method. Each participant will undergo venous blood sampling for laboratory measurement of fasting blood glucose and HbA1c, alongside a non-contact rPPG facial scan using a smartphone-based system. Agreement between methods will be assessed using Bland-Altman analysis, while correlation analysis (Pearson/Spearman) will evaluate the strength of association. Diagnostic performance, including sensitivity and specificity, will be calculated using clinical cut-offs (≥126 mg/dL for glucose and ≥6.5% for HbA1c).
Expected Results It is expected that rPPG-derived estimates will demonstrate moderate to good correlation with laboratory measurements, with acceptable agreement for screening purposes. The technology is anticipated to show reasonable diagnostic performance in identifying individuals with high glycemic risk. These findings may support the feasibility of rPPG as a non-invasive, accessible screening tool for diabetes monitoring in community settings.
Study design
- Observational model
- Ecologic or community
- Time perspective
- Cross-sectional
- Target follow-up
- 1 Day
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Community Adults Undergoing rPPG and Laboratory Glycemic Assessment | – | –This cohort includes adults aged ≥18 years from a community-based population in Jakarta who undergo both non-invasive remote photoplethysmography (rPPG) facial scanning and standard laboratory testing. Participants will receive a smartphone-based facial scan to estimate blood glucose and HbA1c levels, followed by venous blood sampling for fasting blood glucose and HbA1c measurement using standard laboratory methods. No therapeutic intervention is administered, as this is a diagnostic validation study comparing rPPG-derived estimates with laboratory reference values. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
- Study population
- The study population consists of adult individuals aged 18 years and older residing in a community setting in Jakarta, Indonesia. Participants will be recruited through community-based health activities and primary care services. The population includes both individuals with and without a prior diagnosis of type 2 diabetes mellitus, representing a general adult population for glycemic screening. Eligible participants must be able to undergo both non-invasive facial scanning using remote photoplethysmography (rPPG) and standard laboratory blood testing. Individuals with facial conditions that interfere with signal detection, inability to remain still during scanning, or incomplete data will be excluded.
Key inclusion criteria (4 listed)
- Adults aged ≥18 years
- Willing to participate and provide informed consent
- Able to undergo facial scan and blood examination
- Stable clinical condition
Key exclusion criteria (4 listed)
- Facial conditions interfering with rPPG signal (e.g., wounds, deformities)
- Use of facial coverings obstructing camera detection
- Inability to remain still during facial scan
- Incomplete data or withdrawal from study
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
Agreement Between rPPG-Derived and Laboratory Blood Glucose
Time frame: Single assessment at baseline (during study visit)
Assessment of agreement between blood glucose values obtained from remote photoplethysmography (rPPG) facial scan and standard laboratory fasting blood glucose measurements using Bland-Altman analysis, including mean bias and limits of agreement.
Agreement Between rPPG-Derived and Laboratory HbA1c
Time frame: Single assessment at baseline (during study visit)
Evaluation of agreement between HbA1c values estimated using rPPG facial scan and laboratory HbA1c measurements using Bland-Altman analysis, including bias and limits of agreement.
Correlation and Validation of rPPG Estimates with Laboratory Blood Glucose and HbA1c
Time frame: Single assessment at baseline (during study visit)
Measurement of the strength of association between rPPG-derived and laboratory-measured blood glucose and HbA1c values using Pearson or Spearman correlation coefficients (Bland Altman)
Diagnostic Performance of rPPG for Detecting Hyperglycemia and Diabetes Risk
Time frame: Single assessment at baseline (during study visit)
Evaluation of sensitivity, specificity, and accuracy of rPPG-derived blood glucose (≥126 mg/dL) and HbA1c (≥6.5%) in identifying individuals with elevated glycemic levels compared to laboratory reference standards.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 20 Apr 2026.
- Background: PubMed 39753714Zeynali M, Alipour K, Tarvirdizadeh B, Ghamari M. Non-invasive blood glucose monitoring using PPG signals with various deep learning models and implementation using TinyML. Sci Rep. 2025 Jan 2;15(1):581. doi: 10.1038/s41598-024-84265-8.
- Background: PubMed 35808386Zanelli S, Ammi M, Hallab M, El Yacoubi MA. Diabetes Detection and Management through Photoplethysmographic and Electrocardiographic Signals Analysis: A Systematic Review. Sensors (Basel). 2022 Jun 29;22(13):4890. doi: 10.3390/s22134890.
- Background: PubMed 38875549Shi B, Dhaliwal SS, Soo M, Chan C, Wong J, Lam NWC, Zhou E, Paitimusa V, Loke KY, Chin J, Chua MT, Liaw KCS, Lim AWH, Insyirah FF, Yen SC, Tay A, Ang SB. Assessing Elevated Blood Glucose Levels Through Blood Glucose Evaluation and Monitoring Using Machine Learning and Wearable Photoplethysmography Sensors: Algorithm Development and Validation. JMIR AI. 2023 Oct 27;2:e48340. doi: 10.2196/48340.
- Background: PubMed 41737571Santillan A, Travez Proano EI, Jaramillo Encalada IN, Abril Lopez PA, Tricallotis J, Acosta-Espana JD. Structured telemonitoring reduces HbA1c and emergency visits in insulin-treated type 2 diabetes: a controlled cohort study in Ecuador's public hospital. Front Clin Diabetes Healthc. 2026 Feb 9;7:1734589. doi: 10.3389/fcdhc.2026.1734589. eCollection 2026.
- Background: PubMed 32851065Qawqzeh YK, Bajahzar AS, Jemmali M, Otoom MM, Thaljaoui A. Classification of Diabetes Using Photoplethysmogram (PPG) Waveform Analysis: Logistic Regression Modeling. Biomed Res Int. 2020 Aug 11;2020:3764653. doi: 10.1155/2020/3764653. eCollection 2020.
- Background: PubMed 35458947Kwon TH, Kim KD. Machine-Learning-Based Noninvasive In Vivo Estimation of HbA1c Using Photoplethysmography Signals. Sensors (Basel). 2022 Apr 12;22(8):2963. doi: 10.3390/s22082963.
- Background: PubMed 38269728Farenden E, Kelly J, Russell A, Menon A. Remote Monitoring for Type 2 Diabetes: What Do Patients, Healthcare Professionals, and Executives Think? Stud Health Technol Inform. 2024 Jan 25;310:1526-1527. doi: 10.3233/SHTI231276.
- Background: PubMed 34883817Chu J, Yang WT, Lu WR, Chang YT, Hsieh TH, Yang FL. 90% Accuracy for Photoplethysmography-Based Non-Invasive Blood Glucose Prediction by Deep Learning with Cohort Arrangement and Quarterly Measured HbA1c. Sensors (Basel). 2021 Nov 24;21(23):7815. doi: 10.3390/s21237815.
- Individual participant dataSharing planned: Yes. De-identified individual participant data (IPD) will be shared, including demographic characteristics (age, sex), clinical variables (medical history, fasting status), rPPG-derived measurements (estimated blood glucose and HbA1c), and corresponding laboratory results (fasting blood glucose and HbA1c). Derived variables used in the analysis, such as glycemic risk classifications and diagnostic performance indicators, may also be included. All shared data will be fully anonymized, with no direct identifiers to ensure participant confidentiality.
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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.
Ernawati Ernawati, Dr. contact
Yohanes Firmansyah, MD principal investigator
Klinik Citra Semanan
Ernawati Ernawati principal investigator
Universitas Tarumanagara
Alexander Halim Santoso study director
Universitas Tarumanagara
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 Diabetes Mellitus and Hyperglycaemia (Diabetic), run by Tarumanagara 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 4 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: opening soon, as of the sponsor's last update on 20 Apr 2026. 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?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Alexander Halim Santoso (+6281381606869, alexanders@fk.untar.ac.id), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Agreement Between rPPG-Derived and Laboratory Blood Glucose (time frame: Single assessment at baseline (during study visit)). The study lists 4 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 NCT07502690, which also lists 8 linked publications.
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 20 Apr 2026; AzTrials last refreshed its copy on 8 Nov 2026.