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

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:

1. Can rPPG-based facial scan estimates of blood glucose and HbA1c match results from standard laboratory blood tests?
2. 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:

1. Provide basic information such as age, sex, and medical history
2. Undergo a non-invasive facial scan using a smartphone-based system
3. Have a blood sample taken to measure fasting blood glucose and HbA1c
4. 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.

Study Overview

• Status: Not yet recruiting
• Conditions: Diabetes Mellitus; Hypoglycaemia; Hyperglycaemia (Diabetic); Hyperglycaemia (Non Diabetic)

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 Type: Observational
• Enrollment (Estimated): 300

Contacts and Locations

• Study Contact: Name: Ernawati Ernawati, Dr.; Email: ernawati@fk.untar.ac.id
• Study Contact Backup: Name: Alexander Halim Santoso; Phone Number: +6281381606869; Email: alexanders@fk.untar.ac.id

Study Locations

• Indonesia
  • Jakarta Special Capital Region
    • Jakarta, Jakarta Special Capital Region, Indonesia
      • Kelurahan Semanan
      • Contact: Wenny Sanwani; Phone Number: +6281585013412; Email: wenny.sanwani@gmail.com
      • Contact: Hanna Wijaya; Phone Number: +6281223787878; Email: hannwijaya@yahoo.com
      • Principal Investigator: Ernawati Ernawati
      • Sub-Investigator: Enny Irawaty
      • Sub-Investigator: Zita Atzmardina
      • Principal Investigator: Alexander Halim Santoso
      • Sub-Investigator: Wikrama Lokapradhana
      • Sub-Investigator: Amita Pradhani
      • Sub-Investigator: William Kuswandi
      • Sub-Investigator: Diana Dinali
      • Sub-Investigator: Muhammad Fikri Dzakwan
      • Sub-Investigator: Clement Drew
      • Sub-Investigator: Silviana Tirtasari
      • Sub-Investigator: Triyana Sari
      • Principal Investigator: Yohanes Firmansyah
      • Principal Investigator: David Wongso
      • Sub-Investigator: Steve Geraldo Bustam
      • Sub-Investigator: Bryan Anna Wijaya

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: Yes

• 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.

Description

Inclusion Criteria:

1. Adults aged ≥18 years
2. Willing to participate and provide informed consent
3. Able to undergo facial scan and blood examination
4. Stable clinical condition

Exclusion Criteria:

1. Facial conditions interfering with rPPG signal (e.g., wounds, deformities)
2. Use of facial coverings obstructing camera detection
3. Inability to remain still during facial scan
4. Incomplete data or withdrawal from study

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort

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 the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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)

Collaborators and Investigators

• Sponsor: Tarumanagara University
• Collaborators: Universitas Tarumanagara
• Investigators: Principal Investigator: Yohanes Firmansyah, MD, Klinik Citra Semanan; Study Director: David Wongso, DexWellness; Principal Investigator: Ernawati Ernawati, Universitas Tarumanagara; Study Director: Alexander Halim Santoso, Universitas Tarumanagara; Study Director: Ratheesh Nair, Watch Your Health; Study Chair: Sri Tiarti, Universitas Tarumanagara; Study Chair: Noer Saelan Tadjudin, Universitas Tarumanagara; Study Chair: Putu Tommy Yudha Sumatera Suyasa, Universitas Tarumanagara; Study Director: Kieren Nathan Wong, Monash University; Study Director: Jaydee Kirani Wong, Melbourne University; Study Chair: Meiske Yunithree Suparman, Universitas Tarumanagara

Publications and helpful links

General 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.
• 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.

Study record dates

Study Major Dates

• Study Start (Estimated): March 23, 2026
• Primary Completion (Estimated): June 30, 2026
• Study Completion (Estimated): December 30, 2026

Study Registration Dates

• First Submitted: March 24, 2026
• First Submitted That Met QC Criteria: March 24, 2026
• First Posted (Actual): March 31, 2026

Study Record Updates

• Last Update Posted (Actual): April 20, 2026
• Last Update Submitted That Met QC Criteria: April 15, 2026
• Last Verified: April 1, 2026

More Information

Terms related to this study

• Keywords: HbA1c; blood glucose; diabetes mellitus; remote photoplethysmography; non-invasive monitoring; rppg; digital health screening
• Additional Relevant MeSH Terms: Endocrine System Diseases; Metabolic Diseases; Glucose Metabolism Disorders; Nutritional and Metabolic Diseases; Hypoglycemia; Diabetes Mellitus; Hyperglycemia
• Other Study ID Numbers: 20260324

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: YES
• IPD Plan Description: 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.
• IPD Sharing 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.
• IPD Sharing 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.
• IPD Sharing Supporting Information Type: STUDY_PROTOCOL; SAP; ICF; ANALYTIC_CODE; CSR

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No