MedDataX Logo

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

NCT ID: NCT07291570

Last Updated: 2025-12-18

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

Get a concise snapshot of the trial, including recruitment status, study phase, enrollment targets, and key timeline milestones.

Recruitment Status

NOT_YET_RECRUITING

Total Enrollment

40 participants

Study Classification

OBSERVATIONAL

Study Start Date

2025-12-31

Study Completion Date

2026-08-31

Brief Summary

Review the sponsor-provided synopsis that highlights what the study is about and why it is being conducted.

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.

Detailed Description

Dive into the extended narrative that explains the scientific background, objectives, and procedures in greater depth.

Conditions

See the medical conditions and disease areas that this research is targeting or investigating.

Atrial Fibrillation (AF)

Keywords

Explore important study keywords that can help with search, categorization, and topic discovery.

atrial fibrillation remote monitoring artificial intelligence wearables photophlethysmography (PPG)

Study Design

Understand how the trial is structured, including allocation methods, masking strategies, primary purpose, and other design elements.

Observational Model Type

COHORT

Study Time Perspective

PROSPECTIVE

Study Groups

Review each arm or cohort in the study, along with the interventions and objectives associated with them.

Wearable

No interventions assigned to this group

Eligibility Criteria

Check the participation requirements, including inclusion and exclusion rules, age limits, and whether healthy volunteers are accepted.

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.
Minimum Eligible Age

18 Years

Eligible Sex

ALL

Accepts Healthy Volunteers

No

Sponsors

Meet the organizations funding or collaborating on the study and learn about their roles.

Royal Brompton & Harefield NHS Foundation Trust

OTHER

Sponsor Role lead

Responsible Party

Identify the individual or organization who holds primary responsibility for the study information submitted to regulators.

Responsibility Role SPONSOR

Locations

Explore where the study is taking place and check the recruitment status at each participating site.

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

London, London, United Kingdom

Site Status

Countries

Review the countries where the study has at least one active or historical site.

United Kingdom

Central Contacts

Reach out to these primary contacts for questions about participation or study logistics.

Gamith S Adasuriya, MBBS, BSc (Hons)

Role: CONTACT

Phone: 01895823737

Email: [email protected]

Facility Contacts

Find local site contact details for specific facilities participating in the trial.

Gamith S Adasuriya, MBBS, BSc (Hons)

Role: primary

References

Explore related publications, articles, or registry entries linked to this study.

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.

Reference Type BACKGROUND
PMID: 38774381 (View on PubMed)

Other Identifiers

Review additional registry numbers or institutional identifiers associated with this trial.

C0118: RFSG-26/2

Identifier Type: OTHER_GRANT

Identifier Source: secondary_id

354811

Identifier Type: -

Identifier Source: org_study_id