Metabolic SyndromeNCT07491978
Development and Multicenter Validation of an AI-Based Remote Photoplethysmography (rPPG) Facial Scan for Multimodal Health Assessment
An observational study of Metabolic Syndrome and Hypertension, run by Tarumanagara University.
Trial summary
The goal of this observational study is to learn if a non-contact facial scan using artificial intelligence (AI) can be used to check health status in adults living in urban areas such as Jakarta. Read more about this study
- Who is it for?
- Ages 18 years and olderHealthy volunteers can take part
- Conditions the study is for
- Metabolic Syndrome, Hypertension, Diabetes (DM)
- How many people
- 1,000 participants
- Where
- No sites listed yet
- Study dates
- 24 Apr 2026to 30 Mar 2027
- 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 above residing in urban areas of Jakarta and the greater metropolitan region (Jabodetabek). Participants will be recruited through a multicenter community-based approach, including primary care facilities, community health posts, campuses, and workplace settings. The population is designed to reflect real-world heterogeneity, including variations in age, sex, ethnicity, and skin tone, as well as a spectrum of cardiometabolic conditions such as hypertension, diabetes mellitus, and dyslipidemia. Both healthy individuals and those with existing metabolic risk factors will be included to ensure broad applicability of findings in an urban population setting.
Who may be able to join
- Adults aged ≥18 years.
- Able and willing to provide written informed consent.
- Able to comply with study procedures, including face scan, physical examination, blood sampling, and questionnaire completion.
- Clinically stable at the time of assessment.
Who cannot join
- Facial conditions affecting the region of interest (ROI), such as injury, deformity, or impaired circulation, that may interfere with rPPG signal acquisition.
- Presence of facial tattoos or coverings that obstruct optical signal detection.
- Inability to remain still or comply with measurement procedures during data acquisition.
- Severe medical conditions that preclude safe participation, as judged by the investigator.
- Incomplete data or withdrawal of consent during the study.
- -
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
Official title: Development and Multicenter Validation of an AI-Based Remote Photoplethysmography (rPPG) Facial Scan for Multimodal Health Assessment: Agreement With Clinical, Laboratory, and Psychological Parameters in an Urban Population
The goal of this observational study is to learn if a non-contact facial scan using artificial intelligence (AI) can be used to check health status in adults living in urban areas such as Jakarta. The facial scan uses a method called remote photoplethysmography (rPPG), which measures small changes in blood flow from the face using a camera.
The main questions this study aims to answer are:
Read more
- How close are the results from the facial scan to standard medical measurements, such as heart rate, breathing rate, blood pressure, and oxygen levels?
- Can the facial scan estimate other health indicators, such as blood sugar, lipid profile, HbA1c, and hemoglobin levels?
- Is there a relationship between the facial scan results and mental health, such as stress, anxiety, and depression?
Participants will take part in several simple and mostly non-invasive procedures:
- Answer questionnaires about their mental health and daily habits
- Have basic health checks, such as blood pressure, heart rate, and body measurements
- Provide a blood sample for laboratory testing
- Complete a facial scan using a camera for about 1 to 3 minutes
Researchers will compare the results from the facial scan with standard clinical and laboratory tests to see how well the technology works.
This study may help develop a simple and accessible screening tool that can be used for early detection of health risks. It may also support the use of digital health and telemedicine in community and clinical settings.
Read the detailed description
Remote photoplethysmography (rPPG) is an emerging non-contact optical technology that enables extraction of physiological signals from facial video using standard cameras. This approach has gained increasing attention in telemedicine due to its scalability, cost-effectiveness, and ability to perform remote health screening. Recent advancements in artificial intelligence (AI) have further expanded the potential of rPPG beyond basic vital sign monitoring to include estimation of cardiometabolic biomarkers and health risk indices. However, comprehensive validation of rPPG-based systems against standardized clinical measurements, laboratory biomarkers, and psychological parameters remains limited, particularly in low- and middle-income settings such as Indonesia. Given the high burden of cardiometabolic diseases in urban populations like Jakarta, evaluating the accuracy and feasibility of AI-based facial scanning technologies is essential to support early detection and digital health integration.
Specific Objectives
- To assess the agreement between rPPG derived vital signs (heart rate, respiratory rate, blood pressure, SpO₂) and corresponding measurements obtained from standardized physical examination by trained personnel and validated medical devices
- To determine the degree of concordance between rPPG based estimates and laboratory values of hemoglobin, blood glucose, HbA1c, LDL, HDL, triglycerides, and total cholesterol.
- To analyze the association between rPPG derived physiological parameters and levels of depression, anxiety, and stress as measured by the DASS 21 questionnaire.
- To calculate mean arterial pressure (MAP), ASCVD risk scores, and heart age from rPPG outputs and to compare these indices with those derived from standard clinical and laboratory data.
- To develop and preliminarily evaluate exploratory algorithms using rPPG video data to estimate kidney function, liver function, muscle mass, visceral fat, body weight, body height, body mass index, and subcutaneous fat as potential screening parameters.
Methods This study will employ a multicenter observational design conducted across selected subdistricts in Jakarta and expanded to the Jabodetabek region. Adult participants will undergo comprehensive assessment including psychological questionnaires (DASS, PHQ, GAD), anthropometric measurements, body composition analysis, spirometry, muscle strength testing, and venous blood sampling. Blood samples will be analyzed using POCT (≤30 minutes) and ISO-standardized clinical laboratory methods. In parallel, participants will undergo a non-contact facial scan, generating rPPG-based outputs including vital signs, hemodynamic indices, and AI-estimated biomarkers. Statistical analysis will include Bland-Altman agreement analysis, Cohen's kappa for categorical variables, correlation analysis, and machine learning performance metrics (MAE, MSE, RMSE, R²).
Expected Results It is expected that rPPG-based measurements will demonstrate good agreement with standard clinical measurements for core vital signs (heart rate, respiratory rate, SpO₂), with moderate agreement for blood pressure and selected biomarkers. AI-based models are anticipated to show acceptable predictive performance for certain metabolic parameters and exploratory variables, supporting the feasibility of rPPG as a screening tool. The study is also expected to identify key confounding factors, such as skin tone and demographic variability, influencing signal accuracy.
- Conditions
- Metabolic Syndrome, Hypertension, Diabetes (DM), Tachycardia, ASCVD, Depression Disorder
- Keywords
- remote photoplethysmography, rPPG, artificial intelligence, digital biomarker, telemedicine, vital signs, cardiometabolic risk, machine learning, facial scan, screening tool
- Medical subject headings
- Metabolic Syndrome, Hypertension, Diabetes Mellitus, Tachycardia, Anxiety Disorders, Obesity, Overweight
What is being measured
The primary outcome is the main question the study is designed to answer.
Agreement of rPPG-Derived Vital Signs With Standardized Clinical Measurements
The primary outcome is the level of agreement between vital signs obtained from the artificial intelligence-based remote photoplethysmography (rPPG) facial scan and corresponding reference measurements obtained through standardized physical examination and validated medical devices. The vital signs assessed include heart rate, respiratory rate, blood pressure, and oxygen saturation (SpO₂). Agreement will be evaluated using paired comparisons between index and reference methods, primarily through Bland-Altman analysis, including mean difference (bias) and limits of agreement. This outcome is intended to determine the clinical validity of AI as a non-contact screening tool for core physiological parameters in adults.
Measured: At a single study visit during baseline assessment (cross-sectional measurement)
Concordance Between rPPG-Derived Biomarker Estimates and Standard Laboratory Measurements
The outcome measures the degree of concordance between biomarker estimates derived from remote photoplethysmography (rPPG)-based analysis and corresponding reference values obtained from standardized point-of-care testing and clinical laboratory methods. Biomarkers assessed include hemoglobin, blood glucose, glycated hemoglobin (HbA1c), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides, and total cholesterol. Concordance will be evaluated using correlation analysis and agreement statistics, including Bland-Altman analysis and appropriate regression-based performance metrics.
Measured: At a single study visit during baseline assessment (cross-sectional measurement)
Association Between rPPG-Derived Physiological Parameters and Psychological Status
The outcome measures the association between physiological parameters derived from remote photoplethysmography (rPPG) and psychological status assessed using the DASS-21, PHQ and GAD. Physiological parameters include heart rate, respiratory rate, heart rate variability, and other autonomic-related indices. Psychological outcomes include depression, anxiety, and stress scores. The relationship will be analyzed using correlation and regression analyses to evaluate the extent to which rPPG-derived signals reflect mental health status.
Measured: At a single study visit during baseline assessment (cross-sectional measurement)
Agreement of rPPG-Derived Cardiovascular Risk Indices With Standard Clinical Calculations
The outcome measures the level of agreement between cardiovascular risk indices derived from remote photoplethysmography (rPPG)-based parameters and those calculated using standard clinical and laboratory data. The indices include mean arterial pressure (MAP), atherosclerotic cardiovascular disease (ASCVD) risk score, and heart age. Agreement will be evaluated using Bland-Altman analysis, correlation coefficients, and classification concordance where applicable, to determine the reliability of rPPG-based estimations in reflecting established cardiovascular risk assessments.
Measured: At a single study visit during baseline assessment (cross-sectional measurement)
Secondary outcomes (1)
Predictive Performance of rPPG-Based Models for Estimation of Organ Function and Body Composition
The outcome measures the predictive performance of models derived from remote photoplethysmography (rPPG)-based data in estimating physiological and body composition parameters. These include kidney function, liver function, muscle mass, visceral fat, subcutaneous fat, body weight, body height, and body mass index (BMI). Model performance will be evaluated against reference standards obtained from clinical laboratory measurements and validated assessment tools using regression-based metrics such as mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), and coefficient of determination (R²). This outcome is exploratory and aims to assess the feasibility of rPPG as a screening approach for broader health parameters.
Measured: At a single study visit during baseline assessment (cross-sectional measurement)
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted25 Mar 2026
- Study start24 Apr 2026 (estimated)
- Primary completion31 Dec 2026 (estimated)
- Study completion30 Mar 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (9)Papers the sponsor has linked to this study.
- Other trials for Metabolic SyndromeStudies 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
- 1,000 participants (estimated)
- Target duration
- 1 Day
Sponsor and investigators
- Lead sponsor
- Tarumanagara University
- Sponsor type
- University, hospital or other
- Responsible party
- Principal investigator, Yohanes Firmansyah, dr, MH, MM, Tarumanagara University
- Investigators
- David Wongso, Study director, DexWellnessPutu Tommy Yudha Sumatera Suyasa, Study chair, Faculty of Psychology, Universitas TarumanagaraMeiske Yunithree Suparman, Study director, Faculty of Psychology, Universitas TarumanagaraErnawati Ernawati, Principal investigator, Faculty of Medicine, Universitas TarumanagaraSri Tiatri, Study chair, Faculty of Psychology, Universitas TarumanagaraYohanes Firmansyah, Study director, Faculty of Medicine, Universitas TarumanagaraAlexander Halim Santoso, Study director, Faculty of Medicine, Universitas Tarumanagara
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07491978
- Sponsor's protocol ID
- 20260319
- Status last verified
- Apr 2026
- First submitted
- 19 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) underlying the results reported in this study will be made available to qualified researchers upon reasonable request. Shared data will include key variables derived from clinical assessments, laboratory results, and processed outputs from non-contact physiological measurements. All data will be fully anonymized to ensure participant confidentiality, and no identifiable information, including facial images or raw video data, will be shared. Access will be granted after approval of a methodologically sound research proposal and, where applicable, ethical clearance. Data sharing will be conducted in accordance with institutional policies and applicable data protection regulations.
Publications
Tan SYL, Chai JX, Choi M, Javaid U, Tan BPY, Chow BSY, Abdullah HR. Remote Photoplethysmography Technology for Blood Pressure and Hemoglobin Level Assessment in the Preoperative Assessment Setting: Algorithm Development Study. JMIR Form Res. 2025 Jun 6;9:e60455. doi: 10.2196/60455.
PubMed 40479628Ahmad Hatib NA, Lee JH, Chong SL, Sng QW, Tan VSR, Ong GY, Lim AM, Quek BH, How MS, Chan JMF, Saffari SE, Ng KC. A two-phased study on the use of remote photoplethysmography (rPPG) in paediatric care. Ann Transl Med. 2024 Jun 10;12(3):46. doi: 10.21037/atm-23-1896. Epub 2024 May 27.
PubMed 38911566Allado E, Poussel M, Renno J, Moussu A, Hily O, Temperelli M, Albuisson E, Chenuel B. Remote Photoplethysmography Is an Accurate Method to Remotely Measure Respiratory Rate: A Hospital-Based Trial. J Clin Med. 2022 Jun 24;11(13):3647. doi: 10.3390/jcm11133647.
PubMed 35806932Padaki AS, Zarzour AL, Keene KR, Canepa CA, Levin DR, Antonsen EL. Clinical validation of non-contact vital signs in an emergency department setting. Front Med Technol. 2026 Jan 20;7:1728913. doi: 10.3389/fmedt.2025.1728913. eCollection 2025.
PubMed 41640807Brown A, Tulkens J, Mattelin M, Sanglet T, Dhuyvetters B. Remote photoplethysmography for health assessment: a review informed by IntelliProve technology. Front Digit Health. 2026 Jan 5;7:1667423. doi: 10.3389/fdgth.2025.1667423. eCollection 2025.
PubMed 41561164Heiden E, Jones T, Brogaard Maczka A, Kapoor M, Chauhan M, Wiffen L, Barham H, Holland J, Saxena M, Wegerif S, Brown T, Lomax M, Massey H, Rostami S, Pearce L, Chauhan A. Measurement of Vital Signs Using Lifelight Remote Photoplethysmography: Results of the VISION-D and VISION-V Observational Studies. JMIR Form Res. 2022 Nov 14;6(11):e36340. doi: 10.2196/36340.
PubMed 36374541Debnath 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 40542336Wiffen L, Brown T, Brogaard Maczka A, Kapoor M, Pearce L, Chauhan M, Chauhan AJ, Saxena M; Lifelight Trials Group. Measurement of Vital Signs by Lifelight Software in Comparison to Standard of Care Multisite Development (VISION-MD): Protocol for an Observational Study. JMIR Res Protoc. 2023 Jan 11;12:e41533. doi: 10.2196/41533.
PubMed 36630158Misra G, Wegerif S, Fairlie L, Kapoor M, Fok J, Salt G, Halbert J, Maconochie I, Mullen N. The Measurement of Vital Signs in Pediatric Patients by Lifelight Software in Comparison to the Standard of Care: Protocol for the VISION-Junior Observational Study. JMIR Res Protoc. 2025 Mar 14;14:e58334. doi: 10.2196/58334.
PubMed 40085833
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
- Development and Multicenter Validation of an AI-Based Remote Photoplethysmography (rPPG) Facial Scan for Multimodal Health Assessment: Agreement With Clinical, Laboratory, and Psychological Parameters in an Urban Population
- ClinicalTrials.gov ID
- NCT07491978
- Acronym / sponsor ID
- 20260319
- Conditions
- Metabolic Syndrome, Hypertension, Diabetes (DM), Tachycardia, ASCVD, Depression Disorder
- Keywords
- remote photoplethysmography, rPPG, artificial intelligence, digital biomarker, telemedicine, vital signs, cardiometabolic risk, machine learning
- Study type
- Observational
- Recruitment status
- Not yet recruiting
- Enrolment
- 1,000 participants (estimated)
- Lead sponsor
- Tarumanagara UniversityUniversity, hospital or other
- Study start
- 24 Apr 2026 (estimated)
- Primary completion
- 31 Dec 2026 (estimated)
- Study completion
- 30 Mar 2027 (estimated)
- First posted
- 25 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 learn if a non-contact facial scan using artificial intelligence (AI) can be used to check health status in adults living in urban areas such as Jakarta. The facial scan uses a method called remote photoplethysmography (rPPG), which measures small changes in blood flow from the face using a camera.
The main questions this study aims to answer are:
- How close are the results from the facial scan to standard medical measurements, such as heart rate, breathing rate, blood pressure, and oxygen levels?
- Can the facial scan estimate other health indicators, such as blood sugar, lipid profile, HbA1c, and hemoglobin levels?
- Is there a relationship between the facial scan results and mental health, such as stress, anxiety, and depression?
Participants will take part in several simple and mostly non-invasive procedures:
- Answer questionnaires about their mental health and daily habits
- Have basic health checks, such as blood pressure, heart rate, and body measurements
- Provide a blood sample for laboratory testing
- Complete a facial scan using a camera for about 1 to 3 minutes
Researchers will compare the results from the facial scan with standard clinical and laboratory tests to see how well the technology works.
This study may help develop a simple and accessible screening tool that can be used for early detection of health risks. It may also support the use of digital health and telemedicine in community and clinical settings.
Detailed description
Remote photoplethysmography (rPPG) is an emerging non-contact optical technology that enables extraction of physiological signals from facial video using standard cameras. This approach has gained increasing attention in telemedicine due to its scalability, cost-effectiveness, and ability to perform remote health screening. Recent advancements in artificial intelligence (AI) have further expanded the potential of rPPG beyond basic vital sign monitoring to include estimation of cardiometabolic biomarkers and health risk indices. However, comprehensive validation of rPPG-based systems against standardized clinical measurements, laboratory biomarkers, and psychological parameters remains limited, particularly in low- and middle-income settings such as Indonesia. Given the high burden of cardiometabolic diseases in urban populations like Jakarta, evaluating the accuracy and feasibility of AI-based facial scanning technologies is essential to support early detection and digital health integration.
Specific Objectives
- To assess the agreement between rPPG derived vital signs (heart rate, respiratory rate, blood pressure, SpO₂) and corresponding measurements obtained from standardized physical examination by trained personnel and validated medical devices
- To determine the degree of concordance between rPPG based estimates and laboratory values of hemoglobin, blood glucose, HbA1c, LDL, HDL, triglycerides, and total cholesterol.
- To analyze the association between rPPG derived physiological parameters and levels of depression, anxiety, and stress as measured by the DASS 21 questionnaire.
- To calculate mean arterial pressure (MAP), ASCVD risk scores, and heart age from rPPG outputs and to compare these indices with those derived from standard clinical and laboratory data.
- To develop and preliminarily evaluate exploratory algorithms using rPPG video data to estimate kidney function, liver function, muscle mass, visceral fat, body weight, body height, body mass index, and subcutaneous fat as potential screening parameters.
Methods This study will employ a multicenter observational design conducted across selected subdistricts in Jakarta and expanded to the Jabodetabek region. Adult participants will undergo comprehensive assessment including psychological questionnaires (DASS, PHQ, GAD), anthropometric measurements, body composition analysis, spirometry, muscle strength testing, and venous blood sampling. Blood samples will be analyzed using POCT (≤30 minutes) and ISO-standardized clinical laboratory methods. In parallel, participants will undergo a non-contact facial scan, generating rPPG-based outputs including vital signs, hemodynamic indices, and AI-estimated biomarkers. Statistical analysis will include Bland-Altman agreement analysis, Cohen's kappa for categorical variables, correlation analysis, and machine learning performance metrics (MAE, MSE, RMSE, R²).
Expected Results It is expected that rPPG-based measurements will demonstrate good agreement with standard clinical measurements for core vital signs (heart rate, respiratory rate, SpO₂), with moderate agreement for blood pressure and selected biomarkers. AI-based models are anticipated to show acceptable predictive performance for certain metabolic parameters and exploratory variables, supporting the feasibility of rPPG as a screening tool. The study is also expected to identify key confounding factors, such as skin tone and demographic variability, influencing signal accuracy.
Study design
- Observational model
- Ecologic or community
- Time perspective
- Cross-sectional
- Target follow-up
- 1 Day
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 above residing in urban areas of Jakarta and the greater metropolitan region (Jabodetabek). Participants will be recruited through a multicenter community-based approach, including primary care facilities, community health posts, campuses, and workplace settings. The population is designed to reflect real-world heterogeneity, including variations in age, sex, ethnicity, and skin tone, as well as a spectrum of cardiometabolic conditions such as hypertension, diabetes mellitus, and dyslipidemia. Both healthy individuals and those with existing metabolic risk factors will be included to ensure broad applicability of findings in an urban population setting.
Key inclusion criteria (4 listed)
- Adults aged ≥18 years.
- Able and willing to provide written informed consent.
- Able to comply with study procedures, including face scan, physical examination, blood sampling, and questionnaire completion.
- Clinically stable at the time of assessment.
Key exclusion criteria (6 listed)
- Facial conditions affecting the region of interest (ROI), such as injury, deformity, or impaired circulation, that may interfere with rPPG signal acquisition.
- Presence of facial tattoos or coverings that obstruct optical signal detection.
- Inability to remain still or comply with measurement procedures during data acquisition.
- Severe medical conditions that preclude safe participation, as judged by the investigator.
- Incomplete data or withdrawal of consent during the 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 of rPPG-Derived Vital Signs With Standardized Clinical Measurements
Time frame: At a single study visit during baseline assessment (cross-sectional measurement)
The primary outcome is the level of agreement between vital signs obtained from the artificial intelligence-based remote photoplethysmography (rPPG) facial scan and corresponding reference measurements obtained through standardized physical examination and validated medical devices. The vital signs assessed include heart rate, respiratory rate, blood pressure, and oxygen saturation (SpO₂). Agreement will be evaluated using paired comparisons between index and reference methods, primarily through Bland-Altman analysis, including mean difference (bias) and limits of agreement. This outcome is intended to determine the clinical validity of AI as a non-contact screening tool for core physiological parameters in adults.
Concordance Between rPPG-Derived Biomarker Estimates and Standard Laboratory Measurements
Time frame: At a single study visit during baseline assessment (cross-sectional measurement)
The outcome measures the degree of concordance between biomarker estimates derived from remote photoplethysmography (rPPG)-based analysis and corresponding reference values obtained from standardized point-of-care testing and clinical laboratory methods. Biomarkers assessed include hemoglobin, blood glucose, glycated hemoglobin (HbA1c), low-density lipoprotein (LDL), high-density lipoprotein (HDL), triglycerides, and total cholesterol. Concordance will be evaluated using correlation analysis and agreement statistics, including Bland-Altman analysis and appropriate regression-based performance metrics.
Association Between rPPG-Derived Physiological Parameters and Psychological Status
Time frame: At a single study visit during baseline assessment (cross-sectional measurement)
The outcome measures the association between physiological parameters derived from remote photoplethysmography (rPPG) and psychological status assessed using the DASS-21, PHQ and GAD. Physiological parameters include heart rate, respiratory rate, heart rate variability, and other autonomic-related indices. Psychological outcomes include depression, anxiety, and stress scores. The relationship will be analyzed using correlation and regression analyses to evaluate the extent to which rPPG-derived signals reflect mental health status.
Agreement of rPPG-Derived Cardiovascular Risk Indices With Standard Clinical Calculations
Time frame: At a single study visit during baseline assessment (cross-sectional measurement)
The outcome measures the level of agreement between cardiovascular risk indices derived from remote photoplethysmography (rPPG)-based parameters and those calculated using standard clinical and laboratory data. The indices include mean arterial pressure (MAP), atherosclerotic cardiovascular disease (ASCVD) risk score, and heart age. Agreement will be evaluated using Bland-Altman analysis, correlation coefficients, and classification concordance where applicable, to determine the reliability of rPPG-based estimations in reflecting established cardiovascular risk assessments.
1 secondary outcome
Predictive Performance of rPPG-Based Models for Estimation of Organ Function and Body Composition
Time frame: At a single study visit during baseline assessment (cross-sectional measurement)
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 40479628Tan SYL, Chai JX, Choi M, Javaid U, Tan BPY, Chow BSY, Abdullah HR. Remote Photoplethysmography Technology for Blood Pressure and Hemoglobin Level Assessment in the Preoperative Assessment Setting: Algorithm Development Study. JMIR Form Res. 2025 Jun 6;9:e60455. doi: 10.2196/60455.
- Background: PubMed 38911566Ahmad Hatib NA, Lee JH, Chong SL, Sng QW, Tan VSR, Ong GY, Lim AM, Quek BH, How MS, Chan JMF, Saffari SE, Ng KC. A two-phased study on the use of remote photoplethysmography (rPPG) in paediatric care. Ann Transl Med. 2024 Jun 10;12(3):46. doi: 10.21037/atm-23-1896. Epub 2024 May 27.
- Background: PubMed 35806932Allado E, Poussel M, Renno J, Moussu A, Hily O, Temperelli M, Albuisson E, Chenuel B. Remote Photoplethysmography Is an Accurate Method to Remotely Measure Respiratory Rate: A Hospital-Based Trial. J Clin Med. 2022 Jun 24;11(13):3647. doi: 10.3390/jcm11133647.
- Background: PubMed 41640807Padaki AS, Zarzour AL, Keene KR, Canepa CA, Levin DR, Antonsen EL. Clinical validation of non-contact vital signs in an emergency department setting. Front Med Technol. 2026 Jan 20;7:1728913. doi: 10.3389/fmedt.2025.1728913. eCollection 2025.
- Background: PubMed 41561164Brown A, Tulkens J, Mattelin M, Sanglet T, Dhuyvetters B. Remote photoplethysmography for health assessment: a review informed by IntelliProve technology. Front Digit Health. 2026 Jan 5;7:1667423. doi: 10.3389/fdgth.2025.1667423. eCollection 2025.
- Background: PubMed 36374541Heiden E, Jones T, Brogaard Maczka A, Kapoor M, Chauhan M, Wiffen L, Barham H, Holland J, Saxena M, Wegerif S, Brown T, Lomax M, Massey H, Rostami S, Pearce L, Chauhan A. Measurement of Vital Signs Using Lifelight Remote Photoplethysmography: Results of the VISION-D and VISION-V Observational Studies. JMIR Form Res. 2022 Nov 14;6(11):e36340. doi: 10.2196/36340.
- Background: PubMed 40542336Debnath 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.
- Background: PubMed 36630158Wiffen L, Brown T, Brogaard Maczka A, Kapoor M, Pearce L, Chauhan M, Chauhan AJ, Saxena M; Lifelight Trials Group. Measurement of Vital Signs by Lifelight Software in Comparison to Standard of Care Multisite Development (VISION-MD): Protocol for an Observational Study. JMIR Res Protoc. 2023 Jan 11;12:e41533. doi: 10.2196/41533.
- Background: PubMed 40085833Misra G, Wegerif S, Fairlie L, Kapoor M, Fok J, Salt G, Halbert J, Maconochie I, Mullen N. The Measurement of Vital Signs in Pediatric Patients by Lifelight Software in Comparison to the Standard of Care: Protocol for the VISION-Junior Observational Study. JMIR Res Protoc. 2025 Mar 14;14:e58334. doi: 10.2196/58334.
- Individual participant dataSharing planned: Yes. De-identified individual participant data (IPD) underlying the results reported in this study will be made available to qualified researchers upon reasonable request. Shared data will include key variables derived from clinical assessments, laboratory results, and processed outputs from non-contact physiological measurements. All data will be fully anonymized to ensure participant confidentiality, and no identifiable information, including facial images or raw video data, will be shared. Access will be granted after approval of a methodologically sound research proposal and, where applicable, ethical clearance. Data sharing will be conducted in accordance with institutional policies and applicable data protection regulations.
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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.
David Wongso study director
DexWellness
Putu Tommy Yudha Sumatera Suyasa study chair
Faculty of Psychology, Universitas Tarumanagara
Meiske Yunithree Suparman study director
Faculty of Psychology, Universitas Tarumanagara
Questions about this trial
What is this clinical trial studying?
An observational study of Metabolic Syndrome and Hypertension, run by Tarumanagara University.
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 6 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.
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 Ernawati Ernawati, Dr (+6281219308742, ernawati@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 of rPPG-Derived Vital Signs With Standardized Clinical Measurements (time frame: At a single study visit during baseline assessment (cross-sectional measurement)). 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 NCT07491978, which also lists 9 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.