Atrial Fibrillation (AF)REMOTE-AF2NCT07291570
Precision Detection and Prediction of Atrial Arrhythmias Using Artificial Intelligence and Consumer Wearable Devices
An observational study of Atrial Fibrillation (AF), run by Royal Brompton & Harefield NHS Foundation Trust at 1 site in United Kingdom.
Trial 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. Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Atrial Fibrillation (AF)
- How many people
- 40 participants
- Where
- 1 siteUnited Kingdom
- Study dates
- 13 Jan 2026to May 2027
- Phase
- Observational
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Study population: Patients with confirmed diagnosis of paroxysmal AF or persistent AF who have undergone treatment to restore sinus rhythm
Who may be able to join
- 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).
Who cannot join
- 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.
Trial locations and contacts
1 site in 1 country.
United Kingdom1 site
Royal Brompton and Harefield Hospitals, Guy's and St Thomas' NHS Foundation Trust
London, London, UB9 6JH
About this study
Official title: 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.
- Conditions
- Atrial Fibrillation (AF)
- Keywords
- atrial fibrillation, remote monitoring, artificial intelligence, wearables, photophlethysmography (PPG)
- Medical subject headings
- Atrial Fibrillation
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
| Study group | Role | What they receive |
|---|---|---|
| Wearable | Not described |
What is being measured
The primary outcome is the main question the study is designed to answer.
To evaluate the accuracy of an AI algorithm based on PPG-derived metrics in predicting and detecting AF against intermittent rhythm monitoring.
Measured: 6 Months
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted18 Dec 2025
- Study start13 Jan 2026
- Primary completionMay 2027 (estimated)
- Study completionMay 2027 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (1)Papers the sponsor has linked to this study.
- Other trials for Atrial Fibrillation (AF)Studies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Observational
- Observational model
- Cohort
- Time perspective
- Prospective
- Enrolment
- 40 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Royal Brompton & Harefield NHS Foundation Trust
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT07291570
- Sponsor's protocol ID
- 354811
- Other IDs
- C0118: RFSG-26/2
- Acronym
- REMOTE-AF2
- Status last verified
- Jul 2026
- First submitted
- 5 Dec 2025
- Last updated
- 15 Jul 2026
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
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.
PubMed 38774381
Study at a glance
From the registry record, last updated by the sponsor on 15 Jul 2026. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Precision Detection and Prediction of Atrial Arrhythmias Using Artificial Intelligence and Consumer Wearable Devices (REMOTE-AF2)
- ClinicalTrials.gov ID
- NCT07291570
- Acronym / sponsor ID
- REMOTE-AF2 / 354811
- Conditions
- Atrial Fibrillation (AF)
- Keywords
- atrial fibrillation, remote monitoring, artificial intelligence, wearables, photophlethysmography (PPG)
- Study type
- Observational
- Recruitment status
- Active, not recruiting
- Enrolment
- 40 participants (estimated)
- Lead sponsor
- Royal Brompton & Harefield NHS Foundation TrustUniversity, hospital or other
- Study start
- 13 Jan 2026 (actual)
- Primary completion
- May 2027 (estimated)
- Study completion
- May 2027 (estimated)
- First posted
- 18 Dec 2025 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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 design
- Observational model
- Cohort
- Time perspective
- Prospective
- Number of arms
- 1
| Arm | Type | Receives |
|---|---|---|
| Wearable | – | – |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
- Study population
- Patients with confirmed diagnosis of paroxysmal AF or persistent AF who have undergone treatment to restore sinus rhythm
Key inclusion criteria (4 listed)
- 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).
Key exclusion criteria (3 listed)
- 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.
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Primary outcome
To evaluate the accuracy of an AI algorithm based on PPG-derived metrics in predicting and detecting AF against intermittent rhythm monitoring.
Time frame: 6 Months
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 15 Jul 2026.
- Background: PubMed 38774381Adasuriya 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.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
The record lists no contact. Use the official record to reach the sponsor.
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
An observational study of Atrial Fibrillation (AF), run by Royal Brompton & Harefield NHS Foundation Trust at 1 site in United Kingdom.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 4 inclusion and 3 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: under way, not looking for participants, as of the sponsor's last update on 15 Jul 2026. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United Kingdom. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
To evaluate the accuracy of an AI algorithm based on PPG-derived metrics in predicting and detecting AF against intermittent rhythm monitoring. (time frame: 6 Months). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT07291570, which also lists 1 linked publication.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 15 Jul 2026.