[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"health-studies-list:{\"overallStatus\":[\"RECRUITING\",\"AVAILABLE\",\"NOT_YET_RECRUITING\"],\"leadSponsorName\":\"Tarumanagara University\",\"orderBy\":\"LastUpdateSubmitDate:desc\",\"size\":25,\"offset\":0}":3,"health-study-condition:":147},{"pageToken":4,"total":5,"offset":6,"count":5,"results":7},null,4,0,[8,51,85,112],{"id":9,"slug":10,"hasResults":11,"nctId":12,"briefTitle":13,"officialTitle":14,"acronym":15,"eligibilityCriteria":16,"healthyVolunteers":17,"sex":18,"minAge":19,"maxAge":4,"enrollmentInfo":20,"targetDuration":4,"studyType":23,"phases":4,"briefSummary":24,"conditions":25,"keywords":27,"overallStatus":38,"whyStopped":4,"lastUpdateSubmitDate":39,"lastUpdatePostDateStruct":40,"startDateStruct":43,"completionDateStruct":45,"leadSponsor":47,"locationsCount":50},"100645702","validation-of-non-invasive-hemoglobin-measurement-using-optical-method-for-anemia-screening-among-adults-in-jakarta-100645702",false,"NCT07701499","Validation of Non-Invasive Hemoglobin Measurement Using Optical Method for Anemia Screening Among Adults in Jakarta","Validation of Non-Invasive Hemoglobin Measurement (Using Optical Method) Compared With Point-of-Care Testing and Hematology Analyzer Among Adult in Jakarta","SPECTRO-Hb","Inclusion Criteria:\n\n* Adults aged 18 years and older.\n* Residents or community members participating in community health screening activities in Kelurahan Kota Bambu, Jakarta.\n* Willing to undergo non-invasive hemoglobin measurement.\n* Willing to undergo capillary blood hemoglobin testing using point-of-care testing (POCT).\n* Willing to undergo venous blood collection for hematology analyzer examination.\n* Able to provide written informed consent.\n\nExclusion Criteria:\n\n* Active bleeding at the time of examination.\n* Severe acute illness requiring urgent medical attention.\n* Refusal to undergo finger-prick or venous blood sampling.\n* History of severe anxiety, syncope, or adverse reactions related to blood collection procedures.\n* Visible infection, wound, or skin lesion at the measurement site.\n* Severe hand deformity preventing proper non-invasive measurement.\n* Nail polish or artificial nails that cannot be removed before examination.\n* Markedly cold hands or poor peripheral perfusion that may interfere with optical measurements.\n* Incomplete hemoglobin measurement data from any of the study methods.",true,"ALL","18 Years",{"count":21,"type":22},200,"ESTIMATED","OBSERVATIONAL","Anemia remains a major public health concern and is commonly diagnosed through hemoglobin measurement using capillary or venous blood samples. Although laboratory-based and point-of-care testing methods are widely used, they require blood collection, trained personnel, and may cause discomfort to participants. Non-invasive spectrophotometric technology has emerged as a potential alternative for rapid hemoglobin assessment without blood sampling.\n\nThis study aims to evaluate the validity and diagnostic performance of a non-invasive hemoglobin using optical method compared with capillary point-of-care testing (POCT) and venous blood hemoglobin measured using an automated hematology analyzer as the reference standard. A total of 150 adults from Kelurahan Kota Bambu, Jakarta, will undergo non-invasive hemoglobin measurement, capillary blood testing, and venous blood testing during a single study visit.\n\nThe study will assess agreement between methods, diagnostic accuracy for anemia detection, and factors that may influence measurement performance. Findings from this study are expected to support the development of convenient, community-based anemia screening strategies and provide evidence for future implementation of non-invasive hemoglobin assessment technologies.",[26],"Anemia",[26,28,29,30,31,32,33,34,35,36,37],"Hemoglobin","Point-of-Care Testing","Hematology Analyzer","Diagnostic Validation'","Diagnostic Accuracy","Anemia Screening","Community Screening","Optical Hemoglobin Measurement","Non-Invasive Hemoglobin Measurement","optical method","NOT_YET_RECRUITING","2026-07-13",{"date":41,"type":42},"2026-07-15","ACTUAL",{"date":44,"type":22},"2026-07-11",{"date":46,"type":22},"2028-07-30",{"name":48,"class":49},"Tarumanagara University","OTHER",1,{"id":52,"slug":53,"hasResults":11,"nctId":54,"briefTitle":55,"officialTitle":56,"acronym":4,"eligibilityCriteria":57,"healthyVolunteers":17,"sex":18,"minAge":58,"maxAge":4,"enrollmentInfo":59,"targetDuration":4,"studyType":23,"phases":4,"briefSummary":61,"conditions":62,"keywords":69,"overallStatus":38,"whyStopped":4,"lastUpdateSubmitDate":77,"lastUpdatePostDateStruct":78,"startDateStruct":80,"completionDateStruct":82,"leadSponsor":84,"locationsCount":50},"100631593","validation-of-remote-photoplethysmography-rppg-derived-cardiovascular-parameters-against-standard-clinical-measurements-and-risk-scores-in-a-community-100631593","NCT07502703","Validation of Remote Photoplethysmography (rPPG)-Derived Cardiovascular Parameters Against Standard Clinical Measurements and Risk Scores in a Community","Validation of Remote Photoplethysmography (rPPG)-Derived Cardiovascular Parameters Against Standard Clinical Measurements and Risk Scores in a Community-Based Population in Semanan, Jakarta","Inclusion Criteria:\n\n1. Adults aged ≥30 years\n2. Willing to participate and provide informed consent\n3. Able to undergo face scan, clinical examination, and laboratory testing\n\nExclusion Criteria:\n\n1. Facial abnormalities interfering with rPPG signal acquisition\n2. Inability to remain still during measurement\n3. Severe clinical instability\n4. Incomplete key variables","30 Years",{"count":60,"type":22},300,"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.\n\nThe main questions it aims to answer are:\n\n1. Do cardiovascular parameters measured using rPPG (such as blood pressure, heart rate, and cardiac workload) agree with standard clinical measurements?\n2. 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?\n\nResearchers 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.\n\nParticipants will:\n\n1. Undergo a short facial video scan (approximately 30-60 seconds) using an rPPG-based system\n2. Receive standard clinical assessments, including blood pressure and heart rate measurements\n3. Provide basic health information (such as age, sex, smoking status, and treatment history) Undergo simple laboratory testing for cholesterol levels\n\nThis 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.",[63,64,65,66,67,68],"Dyslipidemia","Angina (Stable)","Coronary Artery Disease (CAD)","Heart Disease","Hypertension","Diabetes (DM)",[70,71,72,73,74,75,76],"remote photoplethysmography","rPPG","cardiovascular risk","ASCVD","Framingham score","digital health","screening tool","2026-04-15",{"date":79,"type":42},"2026-04-20",{"date":81,"type":22},"2026-04-23",{"date":83,"type":22},"2026-12-30",{"name":48,"class":49},{"id":86,"slug":87,"hasResults":11,"nctId":88,"briefTitle":89,"officialTitle":90,"acronym":4,"eligibilityCriteria":91,"healthyVolunteers":17,"sex":18,"minAge":19,"maxAge":4,"enrollmentInfo":92,"targetDuration":93,"studyType":23,"phases":4,"briefSummary":94,"conditions":95,"keywords":100,"overallStatus":38,"whyStopped":4,"lastUpdateSubmitDate":77,"lastUpdatePostDateStruct":107,"startDateStruct":108,"completionDateStruct":110,"leadSponsor":111,"locationsCount":50},"100631592","validation-of-remote-photoplethysmography-for-non-invasive-estimation-of-blood-glucose-and-hba1c-100631592","NCT07502690","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","Inclusion Criteria:\n\n1. Adults aged ≥18 years\n2. Willing to participate and provide informed consent\n3. Able to undergo facial scan and blood examination\n4. Stable clinical condition\n\nExclusion Criteria:\n\n1. Facial conditions interfering with rPPG signal (e.g., wounds, deformities)\n2. Use of facial coverings obstructing camera detection\n3. Inability to remain still during facial scan\n4. Incomplete data or withdrawal from study",{"count":60,"type":22},"1 Day","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.\n\nThe main questions it aims to answer are:\n\n1. Can rPPG-based facial scan estimates of blood glucose and HbA1c match results from standard laboratory blood tests?\n2. How well can rPPG identify individuals with high blood sugar or diabetes risk based on established clinical cut-off values?\n\nResearchers 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.\n\nParticipants will:\n\n1. Provide basic information such as age, sex, and medical history\n2. Undergo a non-invasive facial scan using a smartphone-based system\n3. Have a blood sample taken to measure fasting blood glucose and HbA1c\n4. Complete all assessments during a single study visit\n\nThis 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.",[96,97,98,99],"Diabetes Mellitus","Hyperglycaemia (Diabetic)","Hyperglycaemia (Non Diabetic)","Hypoglycaemia",[70,101,102,103,104,105,106],"rppg","blood glucose","HbA1c","diabetes mellitus","non-invasive monitoring","digital health screening",{"date":79,"type":42},{"date":109,"type":22},"2026-03-23",{"date":83,"type":22},{"name":48,"class":49},{"id":113,"slug":114,"hasResults":11,"nctId":115,"briefTitle":116,"officialTitle":117,"acronym":4,"eligibilityCriteria":118,"healthyVolunteers":17,"sex":18,"minAge":19,"maxAge":4,"enrollmentInfo":119,"targetDuration":93,"studyType":23,"phases":4,"briefSummary":121,"conditions":122,"keywords":133,"overallStatus":38,"whyStopped":4,"lastUpdateSubmitDate":77,"lastUpdatePostDateStruct":141,"startDateStruct":142,"completionDateStruct":144,"leadSponsor":146,"locationsCount":4},"100630769","development-and-multicenter-validation-of-an-ai-based-remote-photoplethysmography-rppg-facial-scan-for-multimodal-health-assessment-100630769","NCT07491978","Development and Multicenter Validation of an AI-Based Remote Photoplethysmography (rPPG) Facial Scan for Multimodal Health Assessment","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","Inclusion Criteria:\n\n1. Adults aged ≥18 years.\n2. Able and willing to provide written informed consent.\n3. Able to comply with study procedures, including face scan, physical examination, blood sampling, and questionnaire completion.\n4. Clinically stable at the time of assessment.\n\nExclusion Criteria:\n\n1. Facial conditions affecting the region of interest (ROI), such as injury, deformity, or impaired circulation, that may interfere with rPPG signal acquisition.\n2. Presence of facial tattoos or coverings that obstruct optical signal detection.\n3. Inability to remain still or comply with measurement procedures during data acquisition.\n4. Severe medical conditions that preclude safe participation, as judged by the investigator.\n5. Incomplete data or withdrawal of consent during the study.\n\n   \\-",{"count":120,"type":22},1000,"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.\n\nThe main questions this study aims to answer are:\n\n1. How close are the results from the facial scan to standard medical measurements, such as heart rate, breathing rate, blood pressure, and oxygen levels?\n2. Can the facial scan estimate other health indicators, such as blood sugar, lipid profile, HbA1c, and hemoglobin levels?\n3. Is there a relationship between the facial scan results and mental health, such as stress, anxiety, and depression?\n\nParticipants will take part in several simple and mostly non-invasive procedures:\n\n1. Answer questionnaires about their mental health and daily habits\n2. Have basic health checks, such as blood pressure, heart rate, and body measurements\n3. Provide a blood sample for laboratory testing\n4. Complete a facial scan using a camera for about 1 to 3 minutes\n\nResearchers will compare the results from the facial scan with standard clinical and laboratory tests to see how well the technology works.\n\nThis 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.",[123,67,68,124,73,125,126,127,128,129,130,131,132],"Metabolic Syndrome","Tachycardia","Depression Disorder","Anxiety","Stress (Psychology)","Obesity & Overweight","Cardiometabolic Risk Factors","Cardiometabolic Health Indicators","Sleep","Wellness",[70,71,134,135,136,137,138,139,140,76],"artificial intelligence","digital biomarker","telemedicine","vital signs","cardiometabolic risk","machine learning","facial scan",{"date":79,"type":42},{"date":143,"type":22},"2026-04-24",{"date":145,"type":22},"2027-03-30",{"name":48,"class":49},""]