Precision Detection and Prediction of Atrial Arrhythmias Using Artificial Intelligence and Consumer Wearable Devices

Precision Detection and Prediction of Atrial Arrhythmias Using Artificial Intelligence and Consumer Wearable Devices (REMOTE-AF2)

Status

Active, not recruiting

Phases

Unknown

Study type

Observational

Source

ClinicalTrials.gov

Registry ID

NCT07291570

Acronym

REMOTE-AF2

Enrollment

Registered

2025-12-18

Start date

2026-01-13

Completion date

2027-05-01

Last updated

2026-07-15

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Atrial Fibrillation (AF)

Keywords

atrial fibrillation, remote monitoring, artificial intelligence, wearables, photophlethysmography (PPG)

Brief summary

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia affecting over one million people in the UK. It is associated with increased cardiovascular morbidity and mortality and costs the NHS between £1.4 billion and 2.5 billion annually. Current methods to detect AF include opportunistic pulse palpation, single time point 12-lead electrocardiograms (ECGs), ambulatory Holter monitoring, and implantable loop recorders (ILRs). The more widely used intermittent monitoring methods, such as ECGs and Holter monitoring, are limited in terms of duration and have lower detection yields of atrial arrhythmias. At the other end of the spectrum, the ILR can give continuous and accurate arrhythmia detection but is invasive and requires specialist expertise to implant, monitor, and analyse. In recent years, the use of wearable mobile health (mHealth) devices has emerged as a direct-to-consumer option for monitoring parameters such as heart rate and activity levels. From a clinical perspective they potentially offer a less invasive and cost-effective investigative approach, with remote monitoring solutions to possibly predict and detect AF. This technology has significant potential in terms of passive, non-invasive and continuous monitoring to aid the early diagnosis and management of AF. The original REMOTE-AF study (NCT05037136) developed novel methodology to detect AF using PPG-dervived data from a wearable. This study will further enhance this foundational work by recruiting patients to develop a AI-enabled, multi-parametric algorithm using PPG-derived data to detect AF.

Interventions

None listed

Study design

Observational model

COHORT

Time perspective

PROSPECTIVE

Eligibility

Sex/Gender

ALL

Age

18 Years to No maximum

Healthy volunteers

Inclusion criteria

1. Adults aged 18 and above with a confirmed diagnosis of paroxysmal AF or those who have undergone treatment for paroxysmal, or persistent AF and had sinus rhythm restored. 2. Capability to provide informed consent, coupled with self-reported sufficiency of digital literacy. 3. Regular access to a Wi-Fi connection (at least weekly). 4. Own a smartphone (released after 2017).

Exclusion criteria

1. Individuals with permanent or persistent AF that remains uncontrolled despite receiving treatment. 2. Conditions or disabilities that preclude adherence to study instructions or proper use of the devices. 3. A known severe allergy to any of the materials in the wearable or ECG device poses a risk to participant safety.

Design outcomes

Primary

Measure	Time frame
To evaluate the accuracy of an AI algorithm based on PPG-derived metrics in predicting and detecting AF against intermittent rhythm monitoring.	6 Months

Countries

United Kingdom

Outcome results