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

NCT07291570 | Not Yet Recruiting

• 2 other identifiers: 354811C0118: RFSG-26/2
• Study Type: observational
• Target: 40
• Locations: 1 country
• Sites: 1

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.

Conditions

Atrial Fibrillation (AF)

Keywords

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

Outcome Measures

Primary Outcomes (1)

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

  6 Months

Study Arms (1)

Wearable

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Patients with confirmed diagnosis of paroxysmal AF or persistent AF who have undergone treatment to restore sinus rhythm

You may qualify if:

• 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.
• Capability to provide informed consent, coupled with self-reported sufficiency of digital literacy.
• Regular access to a Wi-Fi connection (at least weekly).
• Own a smartphone (released after 2017).

You may not qualify if:

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

Sponsors & Collaborators

• Royal Brompton & Harefield NHS Foundation Trust: lead

Study Sites (1)

Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust

London, London, UB9 6JH, United Kingdom

Location

Related Publications (1)

• Adasuriya G, Barsky A, Kralj-Hans I, Mohan S, Gill S, Chen Z, Jarman J, Jones D, Valli H, Gkoutos GV, Markides V, Hussain W, Wong T, Kotecha D, Haldar S. Remote monitoring of atrial fibrillation recurrence using mHealth technology (REMOTE-AF). Eur Heart J Digit Health. 2024 Feb 12;5(3):344-355. doi: 10.1093/ehjdh/ztae011. eCollection 2024 May.

  PMID: 38774381 BACKGROUND

MeSH Terms

Conditions

Atrial Fibrillation

Condition Hierarchy (Ancestors)

Arrhythmias, Cardiac; Heart Diseases; Cardiovascular Diseases; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Gamith S Adasuriya, MBBS, BSc (Hons)

CONTACT

01895823737; gamith.adasuriya@nhs.net

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: December 5, 2025
• First Posted: December 18, 2025
• Study Start: December 1, 2025
• Primary Completion (Estimated): June 1, 2026
• Study Completion (Estimated): August 1, 2026
• Last Updated: December 18, 2025

Record last verified: 2025-10

Data Sharing

• IPD Sharing: Will not share

Locations

GB (1)