Validation of Remote Photoplethysmography for Non-Invasive Estimation of Blood Glucose and HbA1c

NCT07502690 | Not Yet Recruiting DMC

• 1 other identifier: 20260324
• Study Type: observational
• Target: 300
• Locations: 1 country
• Sites: 1

Brief 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. The study focuses on adults aged 18 years and older, including individuals with or without diabetes. The main questions it aims to answer are:

1. 1.Can rPPG-based facial scan estimates of blood glucose and HbA1c match results from standard laboratory blood tests?
2. 2.How well can rPPG identify individuals with high blood sugar or diabetes risk based on established clinical cut-off values?
3. 3.Provide basic information such as age, sex, and medical history
4. 4.Undergo a non-invasive facial scan using a smartphone-based system
5. 5.Have a blood sample taken to measure fasting blood glucose and HbA1c
6. 6.Complete all assessments during a single study visit

Conditions

Diabetes Mellitus; Hyperglycaemia (Diabetic); Hyperglycaemia (Non Diabetic); Hypoglycaemia

Keywords

remote photoplethysmography; rppg; blood glucose; HbA1c; diabetes mellitus; non-invasive monitoring; digital health screening

Outcome Measures

Primary Outcomes (4)

• 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.

  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.

  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)

  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.

  Single assessment at baseline (during study visit)

Study Arms (1)

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.

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: Yes
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

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.

You may qualify if:

• Adults aged ≥18 years
• Willing to participate and provide informed consent
• Able to undergo facial scan and blood examination
• Stable clinical condition

You may not qualify if:

• 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

Sponsors & Collaborators

• Tarumanagara University: lead
• Universitas Tarumanagara: collaborator

Study Sites (1)

Kelurahan Semanan

Jakarta, Jakarta Special Capital Region, Indonesia

Location

Related Publications (8)

• 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.

  PMID: 39753714 BACKGROUND

• Zanelli 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.

  PMID: 35808386 BACKGROUND

• Shi 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.

  PMID: 38875549 BACKGROUND

• Santillan 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.

  PMID: 41737571 BACKGROUND

• Qawqzeh 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.

  PMID: 32851065 BACKGROUND

• Kwon 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.

  PMID: 35458947 BACKGROUND

• Farenden 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.

  PMID: 38269728 BACKGROUND

• Chu 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.

  PMID: 34883817 BACKGROUND

Biospecimen

Retention: SAMPLES WITHOUT DNA

Venous blood samples will be collected from participants for the measurement of fasting blood glucose and glycated hemoglobin (HbA1c) using standard laboratory methods. These samples will be used solely for biochemical analysis related to glycemic assessment. No genetic testing or DNA extraction will be performed on any collected samples. Residual samples, if retained, will be stored temporarily under appropriate laboratory conditions for quality control and verification purposes and will be discarded according to institutional biosafety protocols.

MeSH Terms

Conditions

Diabetes Mellitus; Hyperglycemia; Hypoglycemia

Condition Hierarchy (Ancestors)

Glucose Metabolism Disorders; Metabolic Diseases; Nutritional and Metabolic Diseases; Endocrine System Diseases

Study Officials

• Yohanes Firmansyah, MD

  Klinik Citra Semanan

  PRINCIPAL INVESTIGATOR

• Ernawati Ernawati

  Universitas Tarumanagara

  PRINCIPAL INVESTIGATOR

• Alexander Halim Santoso

  Universitas Tarumanagara

  STUDY DIRECTOR

• Sri Tiarti

  Universitas Tarumanagara

  STUDY CHAIR

• Noer Saelan Tadjudin

  Universitas Tarumanagara

  STUDY CHAIR

• Putu Tommy Yudha Sumatera Suyasa

  Universitas Tarumanagara

  STUDY CHAIR

• David Wongso

  DexWellness

  STUDY DIRECTOR

• Ratheesh Nair

  Watch Your Health

  STUDY DIRECTOR

• Kieren Nathan Wong

  Monash University

  STUDY DIRECTOR

• Jaydee Kirani Wong

  Melbourne University

  STUDY DIRECTOR

• Meiske Yunithree Suparman

  Universitas Tarumanagara

  STUDY CHAIR

Central Study Contacts

Alexander Halim Santoso

CONTACT

+6281381606869; alexanders@fk.untar.ac.id

Ernawati Ernawati, Dr.

CONTACT

ernawati@fk.untar.ac.id

Study Design

• Study Type: observational
• Observational Model: ECOLOGIC OR COMMUNITY
• Time Perspective: CROSS SECTIONAL
• Target Duration: 1 Day
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Principal Investigator

Study Record Dates

• First Submitted: March 24, 2026
• First Posted: March 31, 2026
• Study Start: March 23, 2026
• Primary Completion (Estimated): June 30, 2026
• Study Completion (Estimated): December 30, 2026
• Last Updated: April 14, 2026

Record last verified: 2026-04

Data Sharing

• IPD Sharing: Will share
• Shared Documents: STUDY PROTOCOL, SAP, ICF, CSR, ANALYTIC CODE
• Time Frame: De-identified individual participant data (IPD) and supporting documentation will be available beginning 6 months after publication of the primary study results and will remain accessible for a period of 5 years thereafter.
• Access Criteria: Access to the IPD will be granted to qualified researchers, academic investigators, or institutions upon reasonable request. Applicants must submit a brief research proposal outlining the intended use of the data and agree to a data use agreement that ensures confidentiality and prohibits re-identification of participants. Approved users will have access to de-identified datasets, data dictionaries, and relevant methodological documentation. Data will be shared via a secure electronic platform or institutional repository.

Locations

ID (1)