Ignite Creation Date:
2025-12-24 @ 7:18 PM
Ignite Modification Date:
2025-12-25 @ 4:57 PM
Study NCT ID:
NCT07154303
Status:
NOT_YET_RECRUITING
Last Update Posted:
2025-09-04
First Post:
2025-08-26
Is NOT Gene Therapy:
False
Has Adverse Events:
False
Brief Title:
Testing the Performance of Smartphones and Their Accessories in Detecting Irregularly Irregular Heart Rhythm
Sponsor:
Organization:
Raw JSON
{'hasResults': False, 'derivedSection': {'miscInfoModule': {'versionHolder': '2025-12-24'}, 'conditionBrowseModule': {'meshes': [{'id': 'D001281', 'term': 'Atrial Fibrillation'}], 'ancestors': [{'id': 'D001145', 'term': 'Arrhythmias, Cardiac'}, {'id': 'D006331', 'term': 'Heart Diseases'}, {'id': 'D002318', 'term': 'Cardiovascular Diseases'}, {'id': 'D010335', 'term': 'Pathologic Processes'}, {'id': 'D013568', 'term': 'Pathological Conditions, Signs and Symptoms'}]}}, 'protocolSection': {'designModule': {'studyType': 'OBSERVATIONAL', 'designInfo': {'timePerspective': 'CROSS_SECTIONAL', 'observationalModel': 'CASE_CONTROL'}, 'enrollmentInfo': {'type': 'ESTIMATED', 'count': 209}, 'patientRegistry': False}, 'statusModule': {'overallStatus': 'NOT_YET_RECRUITING', 'startDateStruct': {'date': '2025-09', 'type': 'ESTIMATED'}, 'expandedAccessInfo': {'hasExpandedAccess': False}, 'statusVerifiedDate': '2025-08', 'completionDateStruct': {'date': '2026-03', 'type': 'ESTIMATED'}, 'lastUpdateSubmitDate': '2025-08-26', 'studyFirstSubmitDate': '2025-08-26', 'studyFirstSubmitQcDate': '2025-08-26', 'lastUpdatePostDateStruct': {'date': '2025-09-04', 'type': 'ESTIMATED'}, 'studyFirstPostDateStruct': {'date': '2025-09-04', 'type': 'ESTIMATED'}, 'primaryCompletionDateStruct': {'date': '2025-12', 'type': 'ESTIMATED'}}, 'outcomesModule': {'primaryOutcomes': [{'measure': 'Differentiation of Atrial Fibrillation from Sinus Rhythm in Heart Sound Recordings', 'timeFrame': 'Day 0', 'description': 'AUSC-AF: Identification of atrial fibrillation from sinus rhythm in recorded heart sounds (phonocardiogram \\[PCG\\]) as verified by the gold standard 12-lead ECG interpretation, measured in the form of sensitivity and specificity.'}], 'secondaryOutcomes': [{'measure': 'Differentiation of Atrial Fibrillation from Sinus Rhythm in Heart Sound Recordings', 'timeFrame': 'Day 0', 'description': 'AUSC-AF: Identification of atrial fibrillation from sinus rhythm in recorded heart sounds (phonocardiogram \\[PCG\\]) as verified by the gold standard 12-lead ECG interpretation, measured in the form of positive and negative predictive values, and accuracy.'}, {'measure': 'Differentiation of Atrial Fibrillation from Sinus Rhythm in 1-Lead ECG Signals', 'timeFrame': 'Day 0', 'description': 'ECG-AF: Identification of atrial fibrillation from sinus rhythm in recorded 1-lead ECG signals as verified by the gold standard 12-lead ECG interpretation, measured in the form of sensitivity, specificity, positive and negative predictive values, and accuracy.'}, {'measure': 'Differentiation of Atrial Fibrillation from Sinus Rhythm in PCG and 1-Lead ECG Signals', 'timeFrame': 'Day 0', 'description': 'AUSC+ECG-AF: Identification of atrial fibrillation from sinus rhythm in PCG and 1-lead ECG signals as verified by the gold standard 12-lead ECG interpretation, measured in the form of sensitivity, specificity, positive and negative predictive values, and accuracy.'}, {'measure': 'Differentiation of Atrial Fibrillation from Sinus Rhythm in Facial Photoplethysmography Signals', 'timeFrame': 'Day 0', 'description': 'rPPG-AF: Identification of atrial fibrillation from sinus rhythm in facial photoplethysmography signals (also known as remote photoplethysmography \\[rPPG\\]) as verified by the gold standard 12-lead ECG interpretation, measured in the form of sensitivity, specificity, positive and negative predictive values, and accuracy.'}]}, 'oversightModule': {'isUsExport': False, 'oversightHasDmc': False, 'isFdaRegulatedDrug': False, 'isFdaRegulatedDevice': False}, 'conditionsModule': {'keywords': ['Smartphone', 'Persistent Atrial Fibrillation', 'Mobile Health', 'mHealth', 'Telemedicine', 'Artificial Intelligence', 'Heart Sounds', 'Phonocardiography', 'Photoplethysmography', 'Electrocardiography', '12-Lead ECG', '12-Lead EKG', '12-Lead Electrocardiography', 'ECG', 'EKG', 'Electrocardiogram', 'Electrocardiograph'], 'conditions': ['Atrial Fibrillation (AF)']}, 'descriptionModule': {'briefSummary': "The purpose of this 4-in-1 observational study is to test the performance of artificial intelligences (AIs) in distinguishing irregularly irregular heart rhythm called atrial fibrillation (AF) from normal heart rhythm using physiological signals collected by smartphones' built-in hardware and/or external accessories.\n\nParticipants will:\n\n* Have their weight, height, resting heart rate and blood pressures measured\n* Have 12-lead electrocardiogram (ECG) of their heart electrical activities recorded\n* Have their heart sounds and 1-lead ECG recorded from their chest, and optical-based blood flow data and 1-lead ECG recorded from their fingers using smartphones' built-in microphone, camera, and/or external accessories\n* Optionally have their optical-based blood flow data recorded from their face using smartphones' built-in camera.\n\nThe researchers will also create a database containing the physiological signals collected in this study along with the participants' medically relevant information to help train and test future AIs for medical applications.", 'detailedDescription': '4 observational studies have been combined into 1 observational study to share the same pool of participants. These 4 studies are designated as AUSC-AF, ECG-AF, AUSC+ECG-AF, and rPPG-AF corresponding to the signal modality/modalities used for AF detection (see outcome measures) and designated as AUSC/ECG/rPPG-AF when combined as 1 study.'}, 'eligibilityModule': {'sex': 'ALL', 'stdAges': ['ADULT', 'OLDER_ADULT'], 'minimumAge': '22 Years', 'samplingMethod': 'NON_PROBABILITY_SAMPLE', 'studyPopulation': 'Hospital cardiology patients', 'eligibilityCriteria': 'Inclusion Criteria:\n\n* Age: ≥22 years (adult)\n* Patients who have one of the following:\n* Permanent atrial fibrillation, or\n* Long-standing persistent atrial fibrillation (12 months or longer), or\n* Confirmed 12-lead ECG diagnosis for persistent atrial fibrillation (\\> 7 days) or sinus rhythm within 12 months at the time of their normal attendance at the hospital\n\nExclusion Criteria:\n\nAny of the following:\n\n* Implanted active medical devices in the torso, such as pacemakers and defibrillators\n* Patients without atrial fibrillation who have another arrhythmia\n* Completely missing one or more limbs, or missing any hand\n* Disability in using their hands or arms\n* Lack of both index fingers, or all fingers in any hand\n* Both index fingers with any of the following characteristics:\n* Tattooed/inked\n* Reduced blood flow in the fingertip (e.g. perniosis or callus formation)'}, 'identificationModule': {'nctId': 'NCT07154303', 'briefTitle': 'Testing the Performance of Smartphones and Their Accessories in Detecting Irregularly Irregular Heart Rhythm', 'organization': {'class': 'OTHER', 'fullName': 'The University of Hong Kong'}, 'officialTitle': 'Smartphone-Based Use of Phonocardiography, Electrocardiography Accessory, and/or Facial Photoplethysmography to Detect Atrial Fibrillation: Diagnostic Performance Study', 'orgStudyIdInfo': {'id': 'AUSC/ECG/rPPG-AF'}, 'secondaryIdInfos': [{'id': 'UW 24-763', 'type': 'OTHER', 'domain': 'Institutional Review Board of The University of Hong Kong/Hospital Authority Hong Kong West Cluster'}]}, 'armsInterventionsModule': {'armGroups': [{'label': 'Atrial Fibrillation', 'interventionNames': ['Diagnostic Test: Computer algorithms']}, {'label': 'Sinus Rhythm', 'interventionNames': ['Diagnostic Test: Computer algorithms']}], 'interventions': [{'name': 'Computer algorithms', 'type': 'DIAGNOSTIC_TEST', 'otherNames': ['ausculto®', 'Vitogram®', 'FacialAI'], 'description': "Computer algorithms that are designed to perform heart sound, ECG, and/or facial photoplethysmography analysis on data collected from smartphone's internal hardware and/or external accessories.", 'armGroupLabels': ['Atrial Fibrillation', 'Sinus Rhythm']}]}, 'contactsLocationsModule': {'locations': [{'city': 'Hong Kong', 'country': 'China', 'contacts': [{'name': 'Chun Ka Wong', 'role': 'CONTACT', 'email': 'wongeck@hku.hk', 'phone': '+852 22556233'}, {'name': 'Chun Ka Wong', 'role': 'PRINCIPAL_INVESTIGATOR'}, {'name': 'Joshua Wing-Kei Ho', 'role': 'SUB_INVESTIGATOR'}], 'facility': 'Queen Mary Hospital', 'geoPoint': {'lat': 22.27832, 'lon': 114.17469}}], 'centralContacts': [{'name': 'Joshua Wing-Kei Ho', 'role': 'CONTACT', 'email': 'jwkho@hku.hk', 'phone': '+852 39179512'}]}, 'ipdSharingStatementModule': {'ipdSharing': 'NO'}, 'sponsorCollaboratorsModule': {'leadSponsor': {'name': 'The University of Hong Kong', 'class': 'OTHER'}, 'collaborators': [{'name': 'Laboratory of Data Discovery for Health', 'class': 'UNKNOWN'}], 'responsibleParty': {'type': 'PRINCIPAL_INVESTIGATOR', 'investigatorTitle': 'Clinical Assistant Professor', 'investigatorFullName': 'Wong Chun Ka', 'investigatorAffiliation': 'The University of Hong Kong'}}}}