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Not yet recruitingNCT07502703Updated Apr 20, 2026

Validation of Remote Photoplethysmography (rPPG)-Derived Cardiovascular Parameters Against Standard Clinical Measurements and Risk Scores in a Community

An observational study in Dyslipidemia, Angina (Stable) and Coronary Artery Disease (CAD), sponsored by Tarumanagara University. Not yet recruiting at 1 site in Indonesia. Open to participants aged 30 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-04-20.

Sponsored by Tarumanagara University · Observational

Study type
Observational
Model
Ecologic or community
Time perspective
Cross-sectional
Enrollment
300
Ages
30 Years and older
Sex
All
01

Study summary

The goal of this observational study is to evaluate whether a contactless camera-based technology, called remote photoplethysmography (rPPG), can accurately measure cardiovascular parameters and estimate cardiovascular risk in adults aged 30 years and older living in a community setting in Semanan, Jakarta. This study aims to determine if rPPG can be used as a simple and accessible tool for early cardiovascular screening.

The main questions it aims to answer are:

  1. Do cardiovascular parameters measured using rPPG (such as blood pressure, heart rate, and cardiac workload) agree with standard clinical measurements?
  2. Do cardiovascular risk estimates generated by rPPG (such as ASCVD risk and Framingham heart age) correspond to risk calculations obtained using conventional clinical and laboratory methods?

Researchers will compare results obtained from rPPG-based facial video scans with results from standard medical assessments, including blood pressure measurements, heart rate evaluation, and laboratory tests for cholesterol levels, to determine the level of agreement and accuracy.

Participants will:

  1. Undergo a short facial video scan (approximately 30-60 seconds) using an rPPG-based system
  2. Receive standard clinical assessments, including blood pressure and heart rate measurements
  3. Provide basic health information (such as age, sex, smoking status, and treatment history) Undergo simple laboratory testing for cholesterol levels

This study is expected to help determine whether rPPG can be used as a reliable, non-invasive, and scalable screening tool for cardiovascular risk in community and primary healthcare settings.

Read the detailed description

Introduction Remote photoplethysmography (rPPG) is an emerging contactless technology that enables extraction of physiological signals from facial video, allowing estimation of cardiovascular parameters such as heart rate and blood pressure. With the growing burden of atherosclerotic cardiovascular disease (ASCVD), early and accessible risk screening tools are essential, particularly in community settings with limited access to laboratory-based assessments. Although established risk models such as the ASCVD and Framingham scores are widely used, their application often requires clinical and laboratory data that may not be readily available. The integration of rPPG-based measurements with cardiovascular risk estimation offers a promising approach; however, its clinical validity and agreement with standard methods remain insufficiently explored .

Objective This study aims to evaluate the agreement and concordance between rPPG-derived cardiovascular parameters and standard clinical measurements, as well as to assess the alignment of rPPG-estimated ASCVD risk and Framingham heart age with conventional risk calculations.

Methods This study will use an analytical observational cross-sectional design conducted in Kelurahan Semanan, Jakarta. Adult participants (≥30 years) will be recruited through community-based sampling. Each participant will undergo clinical anamnesis, physical examination (blood pressure and heart rate), and laboratory testing (total cholesterol and HDL). In parallel, rPPG-based facial video scans will be performed under standardized conditions to obtain systolic and diastolic blood pressure, mean arterial pressure, pulse pressure, heart rate, cardiac workload, ASCVD risk, and Framingham heart age. Framingham risk will be calculated using sex-specific equations based on clinical and laboratory variables. Agreement between rPPG and standard measurements will be assessed using Bland-Altman analysis, while correlations will be evaluated using Pearson or Spearman tests. Concordance for categorical risk classification will be analyzed using Cohen's Kappa.

Expected Results It is expected that rPPG-derived heart rate will demonstrate good agreement with standard measurements, while blood pressure parameters will show moderate agreement. Additionally, rPPG-based ASCVD risk and Framingham heart age are anticipated to exhibit acceptable concordance with conventional risk calculations. These findings may support the potential role of rPPG as a preliminary screening and risk stratification tool in community-based and telemedicine settings.

02

Conditions studied

  • Dyslipidemia
  • Angina (Stable)
  • Coronary Artery Disease (CAD)
  • Heart Disease
  • Hypertension
  • Diabetes (DM)

Keywords

  • remote photoplethysmography
  • rPPG
  • cardiovascular risk
  • ASCVD
  • Framingham score
  • digital health
  • screening tool
03

Who can participate

Ages eligible
30 Years and older
Sexes eligible
All
Accepts healthy volunteers
Yes
Sampling method
Non-probability sample

Study population

The study population consists of community-dwelling adults aged 30 years and older residing in Semanan, Jakarta, Indonesia. Participants will be recruited through community-based consecutive sampling, including local residents, visitors to primary healthcare facilities, and individuals participating in community health programs. Eligible participants are those who are able to provide informed consent and undergo facial video scanning, clinical examination, and basic laboratory testing. Individuals with conditions that may interfere with rPPG signal acquisition (e.g., significant facial abnormalities), inability to remain still during measurement, severe clinical instability, or incomplete key data will be excluded. This population represents a general adult community suitable for evaluating cardiovascular risk screening tools in real-world, primary care and community settings.

Inclusion criteria

  1. Adults aged ≥30 years
  2. Willing to participate and provide informed consent
  3. Able to undergo face scan, clinical examination, and laboratory testing

Exclusion criteria

Exclusion Criteria:

  1. Facial abnormalities interfering with rPPG signal acquisition
  2. Inability to remain still during measurement
  3. Severe clinical instability
  4. Incomplete key variables
04

Study design

Observational model
Ecologic or community
Time perspective
Cross-sectional
Enrollment
300 participants (estimated)
Patient registry
No
Biospecimen retention
Samples without dna

Groups and cohorts

  • Community Adults Undergoing rPPG and Standard Cardiovascular Assessment

    This cohort includes adults aged ≥30 years residing in Semanan, Jakarta, recruited through community-based sampling. Participants will undergo both index testing using remote photoplethysmography (rPPG) via facial video scan and reference standard assessments, including blood pressure measurement, heart rate evaluation, and laboratory testing (total cholesterol and HDL). Additional data such as age, sex, smoking status, and antihypertensive treatment will be collected. There is no intervention applied; all procedures are non-invasive and observational. The study aims to compare rPPG-derived cardiovascular parameters and risk estimates (ASCVD risk and Framingham heart age) with standard clinical measurements to assess agreement and validity.

05

What researchers measure

Primary outcomes

  1. Agreement of rPPG-Derived Blood Pressure with Standard Measurements

    Assessment of agreement between systolic blood pressure (SBP), diastolic blood pressure (DBP), mean arterial pressure (MAP), and pulse pressure obtained from rPPG-based facial video analysis and standard measurements using aneroid or digital sphygmomanometers. Agreement will be evaluated using Bland-Altman analysis (mean difference and limits of agreement).

    Time frame: Day 1

  2. Agreement of rPPG-Derived Heart Rate and Cardiac Workload

    Evaluation of agreement between heart rate and cardiac workload obtained from rPPG and those measured using standard methods (palpation and pulse oximetry). Agreement will be analyzed using Bland-Altman and correlation analysis (Pearson/Spearman).

    Time frame: Day 1

  3. Concordance of rPPG-Based ASCVD Risk with Standard Risk Calculation

    Assessment of agreement and concordance between ASCVD risk (%) and risk categories (low, intermediate, high) estimated using rPPG and those calculated using conventional clinical and laboratory data. Concordance will be evaluated using Cohen's Kappa and correlation analysis.

    Time frame: Day 1

  4. Concordance of rPPG-Derived Framingham Heart Age

    Evaluation of agreement between Framingham heart age estimated using rPPG-derived parameters and heart age calculated using standard Framingham risk equations based on clinical and laboratory variables. Agreement will be assessed using correlation and Bland-Altman analysis.

    Time frame: Day 1

06

Study locations

1 site
  • Kelurahan Semanan
    Jakarta, Jakarta Special Capital Region, Indonesia
    • Wenny Sanwani · Contact · wenny.sanwani@gmail.com · +6281585013412
    • Hanna Wijaya · Contact · hannwijaya@yahoo.com · +6281223787878
    • Ernawati Ernawati · Principal investigator
    • Enny Irawaty · Sub investigator
    • Zita Atzmardina · Sub investigator
    • Alexander Halim Santoso · Principal investigator
    • Wikrama Lokapradhana · Sub investigator
    • Amita Pradhani · Sub investigator
    • William Kuswandi · Sub investigator
    • Alya Dwiana · Sub investigator
    • David Wongso · Sub investigator
    • Diana Dinali · Sub investigator
    • Muhammad Fikri Dzakwan · Sub investigator
    • Clement Drew · Sub investigator
    • Silviana Tirtasari · Sub investigator
    • Triyana Sari · Sub investigator
    • Steve Geraldo Bustam · Sub investigator
    • Bryan Anna Wijaya · Sub investigator
07

References and documents

Publications

  • van Es VAA, Lopata RGP, Scilingo EP, Nardelli M. Contactless Cardiovascular Assessment by Imaging Photoplethysmography: A Comparison with Wearable Monitoring. Sensors (Basel). 2023 Jan 29;23(3):1505. doi: 10.3390/s23031505. PubMed 36772543 ↗
  • Shetty NS, Gaonkar M, Patel N, Vekariya N, Li P, Arora G, Arora P. PREVENT and Pooled Cohort Equations in Mortality Risk Prediction: National Health and Nutrition Examination Survey. JACC Adv. 2024 Dec 26;3(12):101372. doi: 10.1016/j.jacadv.2024.101372. eCollection 2024 Dec. PubMed 39817066 ↗
  • Debnath U, Kim S. A comprehensive review of heart rate measurement using remote photoplethysmography and deep learning. Biomed Eng Online. 2025 Jun 20;24(1):73. doi: 10.1186/s12938-025-01405-5. PubMed 40542336 ↗
  • Pandey A, Mehta A, Paluch A, Ning H, Carnethon MR, Allen NB, Michos ED, Berry JD, Lloyd-Jones DM, Wilkins JT. Performance of the American Heart Association/American College of Cardiology Pooled Cohort Equations to Estimate Atherosclerotic Cardiovascular Disease Risk by Self-reported Physical Activity Levels. JAMA Cardiol. 2021 Jun 1;6(6):690-696. doi: 10.1001/jamacardio.2021.0948. PubMed 33909016 ↗
  • Nguyen QD, Odden MC, Peralta CA, Kim DH. Predicting Risk of Atherosclerotic Cardiovascular Disease Using Pooled Cohort Equations in Older Adults With Frailty, Multimorbidity, and Competing Risks. J Am Heart Assoc. 2020 Sep 15;9(18):e016003. doi: 10.1161/JAHA.119.016003. Epub 2020 Sep 2. PubMed 32875939 ↗
  • Muntner P, Colantonio LD, Cushman M, Goff DC Jr, Howard G, Howard VJ, Kissela B, Levitan EB, Lloyd-Jones DM, Safford MM. Validation of the atherosclerotic cardiovascular disease Pooled Cohort risk equations. JAMA. 2014 Apr 9;311(14):1406-15. doi: 10.1001/jama.2014.2630. PubMed 24682252 ↗
  • Mora S, Wenger NK, Cook NR, Liu J, Howard BV, Limacher MC, Liu S, Margolis KL, Martin LW, Paynter NP, Ridker PM, Robinson JG, Rossouw JE, Safford MM, Manson JE. Evaluation of the Pooled Cohort Risk Equations for Cardiovascular Risk Prediction in a Multiethnic Cohort From the Women's Health Initiative. JAMA Intern Med. 2018 Sep 1;178(9):1231-1240. doi: 10.1001/jamainternmed.2018.2875. PubMed 30039172 ↗
  • Khera R, Pandey A, Ayers CR, Carnethon MR, Greenland P, Ndumele CE, Nambi V, Seliger SL, Chaves PHM, Safford MM, Cushman M, Xanthakis V, Vasan RS, Mentz RJ, Correa A, Lloyd-Jones DM, Berry JD, de Lemos JA, Neeland IJ. Performance of the Pooled Cohort Equations to Estimate Atherosclerotic Cardiovascular Disease Risk by Body Mass Index. JAMA Netw Open. 2020 Oct 1;3(10):e2023242. doi: 10.1001/jamanetworkopen.2020.23242. PubMed 33119108 ↗
  • Chin JW, Chan PHD, Chen S, Cheng CH, So RHY, Chow E, Fok BSP, Wong KL. Clinical Validation of rPPG-Enabled Contactless Pulse Rate Monitoring Software in Cardiovascular Disease Patients. Bioengineering (Basel). 2026 Feb 20;13(2):246. doi: 10.3390/bioengineering13020246. PubMed 41749785 ↗
  • Allado E, Poussel M, Moussu A, Hily O, Temperelli M, Cherifi A, Saunier V, Bernard Y, Albuisson E, Chenuel B. Accurate and Reliable Assessment of Heart Rate in Real-Life Clinical Settings Using an Imaging Photoplethysmography. J Clin Med. 2022 Oct 17;11(20):6101. doi: 10.3390/jcm11206101. PubMed 36294422 ↗

Individual participant data

Plan to share: Yes — De-identified individual participant data (IPD) will be shared, including demographic variables (age, sex), clinical data (blood pressure, heart rate, smoking status, antihypertensive treatment), laboratory results (total cholesterol, HDL), and rPPG-derived parameters (systolic and diastolic blood pressure, mean arterial pressure, pulse pressure, heart rate, cardiac workload, ASCVD risk, and Framingham heart age). Derived variables such as calculated ASCVD risk scores and Framingham heart age based on standard methods will also be included. All shared data will be anonymized to remove any personally identifiable information, ensuring participant confidentiality. Supporting documents such as the study protocol, statistical analysis plan, and data dictionary will also be made available upon request.

Supporting information: Study protocol, Icf, Csr

08

Registry details

Key details

Study ID
NCT07502703
Lead sponsor
Tarumanagara University
Responsible party
Yohanes Firmansyah, dr, MH, MM (Principal Investigator, Tarumanagara University) — Principal investigator
First posted
Mar 31, 2026
Start date
Apr 23, 2026 (estimated)
Primary completion
Jun 30, 2026 (estimated)
Completion
Dec 30, 2026 (estimated)
Last update
Apr 20, 2026

Study contacts

Alexander Halim Santoso, MD
Contact
alexanders@fk.untar.ac.id
+6281381606869
Ernawati Ernawati, Dr
Contact
ernawati@fk.untar.ac.id
+6281389048199
Ernawati Ernawati
principal investigator · Universitas Tarumanagara
Yohanes Firmansyah
study director · Universitas Tarumanagara
Alexander Halim Santoso
study director · Universitas Tarumanagara
David Wongso
study director · DexWellness
Ratheesh Nair
study director · Watch Your Health
Sri Tiarti
study chair · Universitas Tarumanagara
Noer Saelan Tadjudin
study chair · Universitas Tarumanagara
Clement Drew
principal investigator · Universitas Tarumanagara
Zita Atzmardina
study director · Universitas Tarumanagara
Andria Priyana
study director · Universitas Tarumanagara
Putu Tommy Yudha Sumatera Suyasa
study chair · Universitas Tarumanagara
Kieren Nathan Wong
study director · Monash University
Jaydee Kirani Wong
study director · Melbourne University
Meiske Yunithree Suparman
study chair · Universitas Tarumanagara

Oversight

Data monitoring committee
Yes
FDA-regulated drug
No
FDA-regulated device
No
View the source record on ClinicalTrials.gov ↗

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