An observational study in Metabolic Syndrome, Hypertension and Diabetes (DM), sponsored by Tarumanagara University. Not yet recruiting. Open to participants aged 18 Years and older, including healthy volunteers. Per ClinicalTrials.gov, last updated 2026-04-20.
Sponsored by Tarumanagara University · Observational
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:
Participants will take part in several simple and mostly non-invasive procedures:
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.
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
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.
2,116 studies on the registry are indexed under Metabolic Syndrome; 347 are open to participants now.
This study's planned enrollment of 1,000 is above the median of 197 across 496 observational studies indexed under Metabolic Syndrome.
Browse Metabolic Syndrome studies →Tarumanagara University is the lead sponsor of 11 studies on the registry; 4 are open to participants now.
Counted across the registry records on this site, refreshed daily.
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.
Go through the criteria one by one — yes, no or not sure — and take the list to your doctor or the study team. Kept in this browser only.
Exclusion Criteria:
Incomplete data or withdrawal of consent during the study.
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Underlined terms in the criteria open these too. Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Informed consent
Before you join, the study team explains what taking part involves — its purpose, what happens, the risks and possible benefits — and answers your questions, so you can decide freely. Consent is usually recorded on a form (the informed consent form, ICF) signed by you or, where the law allows, by someone legally authorised to decide for you.
You can keep asking questions during the study, you are told of new information that matters, and you can choose to leave at any time.
Explained by clinicaltrials.gg, not part of the registry record. The study team decides who can take part.
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.
Time frame: 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.
Time frame: 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.
Time frame: 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.
Time frame: At a single study visit during baseline assessment (cross-sectional measurement)
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.
Time frame: At a single study visit during baseline assessment (cross-sectional measurement)
No study locations are listed for this record.
Plan to share: 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.
Supporting information: Study protocol, Statistical analysis plan, Informed consent form, Clinical study report, Analytic code
Time frame: De-identified individual participant data (IPD) will be available beginning 6 months after publication of the primary study results and will remain available for a period of 5 years following publication. Supporting documents, including the study protocol and statistical analysis plan, will be made available within the same timeframe.
Access criteria: Access to IPD will be granted to qualified researchers with a methodologically sound research proposal. Requests must include a clear scientific rationale and, where applicable, evidence of ethical approval. Data will be limited to de-identified datasets, study protocol, and statistical analysis plan. No identifiable information or raw facial/video data will be shared. Access will be provided upon approval by the principal investigator and institutional authority, and may require a data use agreement to ensure compliance with data protection and confidentiality standards.
Data sharing details ↗ · lintar.untar.ac.id
This study is not yet recruiting, as verified in Apr 2026. You cannot join it, but the record below documents what was studied.
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Tarumanagara University