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Testing the Performance of Smartphones and Their Accessories in Detecting Irregularly Irregular Heart Rhythm

NCT ID: NCT07154303

Last Updated: 2025-12-31

Study Results

Results pending

The study team has not published outcome measurements, participant flow, or safety data for this trial yet. Check back later for updates.

Basic Information

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Recruitment Status

ENROLLING_BY_INVITATION

Total Enrollment

209 participants

Study Classification

OBSERVATIONAL

Study Start Date

2025-08-27

Study Completion Date

2026-10-31

Brief Summary

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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.

Participants will:

* Have their weight, height, resting heart rate and blood pressures measured
* Have 12-lead electrocardiogram (ECG) of their heart electrical activities recorded
* Have their heart sounds and 1-lead ECG recorded from their chest, and optical-based blood flow data (photoplethysmography or PPG) and 1-lead ECG recorded from their fingers using smartphones' built-in microphone, camera, and/or external accessories
* Optionally have their optical-based blood flow data recorded from their face using smartphones' built-in camera (remote PPG or rPPG).

The 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.

Detailed Description

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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.

Conditions

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Atrial Fibrillation (AF)

Keywords

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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

Study Design

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Observational Model Type

CASE_CONTROL

Study Time Perspective

CROSS_SECTIONAL

Study Groups

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Atrial Fibrillation

Computer algorithms

Intervention Type DIAGNOSTIC_TEST

Computer algorithms that are designed to perform heart sound, electrocardiography (ECG), and/or facial photoplethysmography (rPPG) analysis on data collected from smartphone's internal hardware and/or external accessories.

Sinus Rhythm

Computer algorithms

Intervention Type DIAGNOSTIC_TEST

Computer algorithms that are designed to perform heart sound, electrocardiography (ECG), and/or facial photoplethysmography (rPPG) analysis on data collected from smartphone's internal hardware and/or external accessories.

Interventions

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Computer algorithms

Computer algorithms that are designed to perform heart sound, electrocardiography (ECG), and/or facial photoplethysmography (rPPG) analysis on data collected from smartphone's internal hardware and/or external accessories.

Intervention Type DIAGNOSTIC_TEST

Other Intervention Names

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ausculto® Vitogram® FacialAI

Eligibility Criteria

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Inclusion Criteria

* Age: ≥22 years (adult)
* Patients who have one of the following:
* Permanent atrial fibrillation, or
* Long-standing persistent atrial fibrillation (12 months or longer), or
* 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

Exclusion Criteria

Any of the following:

* Implanted active medical devices in the torso, such as pacemakers and defibrillators
* Patients without atrial fibrillation who have another arrhythmia
* Completely missing one or more limbs, or missing any hand
* Disability in using their hands or arms
* Lack of both index fingers, or all fingers in any hand
* Both index fingers with any of the following characteristics:
* Tattooed/inked
* Reduced blood flow in the fingertip (e.g. perniosis or callus formation)
Minimum Eligible Age

22 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

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Laboratory of Data Discovery for Health

UNKNOWN

Sponsor Role collaborator

The University of Hong Kong

OTHER

Sponsor Role lead

Responsible Party

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Wong Chun Ka

Clinical Assistant Professor

Responsibility Role PRINCIPAL_INVESTIGATOR

Locations

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Queen Mary Hospital

Hong Kong, , China

Site Status

Countries

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China

Other Identifiers

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UW 24-763

Identifier Type: OTHER

Identifier Source: secondary_id

AUSC/ECG/rPPG-AF

Identifier Type: -

Identifier Source: org_study_id