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

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

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.

Study Overview

• Status: Not yet recruiting
• Conditions: Atrial Fibrillation (AF)

• Study Type: Observational
• Enrollment (Estimated): 40

Contacts and Locations

• Study Contact: Name: Gamith S Adasuriya, MBBS, BSc (Hons); Phone Number: 01895823737; Email: gamith.adasuriya@nhs.net

Study Locations

• United Kingdom
  • London
    • London, London, United Kingdom, UB9 6JH
      • Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust
      • Contact: Gamith S Adasuriya, MBBS, BSc (Hons); Phone Number: 01895823737; Email: gamith.adasuriya@nhs.net
      • Principal Investigator: Shouvik K Haldar, MBBS, MD (Res)

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult; Older Adult
• Accepts Healthy Volunteers: No

• 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

Description

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.

Study Plan

How is the study designed?

Number of groups / cohorts

1

Cohorts and Interventions

Group / Cohort
Wearable

What is the study measuring?

Primary Outcome Measures

Outcome 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

Collaborators and Investigators

• Sponsor: Royal Brompton & Harefield NHS Foundation Trust

Publications and helpful links

General Publications

• 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.

Study record dates

Study Major Dates

• Study Start (Estimated): December 1, 2025
• Primary Completion (Estimated): June 1, 2026
• Study Completion (Estimated): August 1, 2026

Study Registration Dates

• First Submitted: December 5, 2025
• First Submitted That Met QC Criteria: December 5, 2025
• First Posted (Actual): December 18, 2025

Study Record Updates

• Last Update Posted (Actual): December 18, 2025
• Last Update Submitted That Met QC Criteria: December 5, 2025
• Last Verified: October 1, 2025

More Information

Terms related to this study

• Keywords: atrial fibrillation; artificial intelligence; wearables; remote monitoring; photophlethysmography (PPG)
• Additional Relevant MeSH Terms: Cardiovascular Diseases; Pathologic Processes; Heart Diseases; Arrhythmias, Cardiac; Pathological Conditions, Signs and Symptoms; Atrial Fibrillation
• Other Study ID Numbers: 354811; C0118: RFSG-26/2 (Other Grant/Funding Number: Royal Brompton and Harefield Hospitals Charity)

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No