REMOTE-AF2 Trial: AI-Powered Wearable Algorithm Targets Precision AF Detection — Key Findings Awaited from Royal Brompton Study
247 Health News Editorial
·3 Aug 2026
·5 min read
·clinical-trialsThe REMOTE-AF2 trial at Royal Brompton & Harefield NHS Foundation Trust is evaluating an AI algorithm that uses PPG data from consumer wearables to detect and predict atrial fibrillation, benchmarked against standard intermittent rhythm monitoring. With AF affecting over one million people in the UK and costing the NHS up to £2.5 billion annually, a validated wearable-based detection tool could transform community-level cardiac surveillance. Results are anticipated by mid-2027.
Originally reported by ClinicalTrials.gov
References
- 1.Royal Brompton & Harefield NHS Foundation Trust. (2025). Precision detection and prediction of atrial arrhythmias using artificial intelligence and consumer wearable devices (REMOTE-AF2) [Clinical trial registration NCT07291570]. ClinicalTrials.gov. https://clinicaltrials.gov/study/NCT07291570
- 2.Hindricks, G., Potpara, T., Dagres, N., Arbelo, E., Bax, J. J., Blomström-Lundqvist, C., Boriani, G., Castella, M., Dan, G.-A., Dilaveris, P. E., Fauchier, L., Filippatos, G., Kalman, J. M., La Meir, M., Lane, D. A., Lebeau, J.-P., Lettino, M., Lip, G. Y. H., Pinto, F. J., … ESC Scientific Document Group. (2021). 2020 ESC Guidelines for the diagnosis and management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS). European Heart Journal, 42(5), 373–498. https://doi.org/10.1093/eurheartj/ehaa612
- 3.Verma, N., Ziegler, P. D., Liu, S., & Passman, R. S. (2021). Insertable cardiac monitors: Current indications and devices. Journal of Cardiovascular Electrophysiology, 32(3), 597–608. https://doi.org/10.1111/jce.14903
- 4.Perez, M. V., Mahaffey, K. W., Hedlin, H., Rumsfeld, J. S., Garcia, A., Ferris, T., Balasubramanian, V., Russo, A. M., Rajmane, A., Cheung, L., Hung, G., Lee, J., Kowey, P., Talati, N., Nag, D., Gummidipundi, S. E., Beatty, A., Hills, M. T., Desai, S., … Turakhia, M. P. (2019). Large-scale assessment of a smartwatch to identify atrial fibrillation. New England Journal of Medicine, 381(20), 1909–1917. https://doi.org/10.1056/NEJMoa1901183
- 5.Lip, G. Y. H., Tran, G., Haguenoer, K., & Fauchier, L. (2023). Atrial fibrillation: Epidemiology, pathophysiology, and clinical outcomes. Circulation Research, 132(12), 1501–1517. https://doi.org/10.1161/CIRCRESAHA.122.321779
- 6.Bumgarner, J. M., Lambert, C. T., Hussein, A. A., Cantillon, D. J., Baranowski, B., Wolski, K., Lindsay, B. D., Wazni, O. M., & Tarakji, K. G. (2018). Smartwatch algorithm for automated detection of atrial fibrillation. Journal of the American College of Cardiology, 71(21), 2381–2388. https://doi.org/10.1016/j.jacc.2018.03.003
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247 Health News Editorial
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