WERABLE TECHNOLOGIES IN ATRIAL FIBRILLATION SCREENING: A NARRATIVE REVIEW
DOI:
https://doi.org/10.31435/ijitss.2(50).2026.5542Keywords:
Atrial Fibrillation, Wearable Technologies, Screening, Mobile ECG, Artificial IntelligenceAbstract
Background: Atrial fibrillation (AF) is a major public health challenge and a primary independent risk factor for ischemic stroke. The diagnosis of AF is often hindered by its frequently asymptomatic or paroxysmal nature, which leads to a significant diagnostic gap in traditional monitoring.
Purpose: This review evaluates the clinical reliability of wearable technologies for AF screening and analyzes their impact on modern cardiovascular prevention and healthcare systems.
Methods: A comprehensive literature search was conducted across major medical databases, focusing on landmark clinical trials, meta-analyses, and the latest European Society of Cardiology guidelines.
Results: Contemporary wearable devices utilizing photoplethysmography and mobile ECG have demonstrated high positive predictive values for AF detection in large-scale studies such as the Apple Heart Study and Fitbit Heart Study. Advanced artificial intelligence algorithms further enhance diagnostic precision by reducing motion artifacts. However, successful clinical integration remains limited by socio-ethical challenges, including the digital divide and data privacy concerns.
Conclusion: Wearable technologies represent a breakthrough in population-based screening, yet their clinical efficacy depends on a hybrid care model that integrates digital data with physician judgment.
References
2023 ACC/AHA/ACCP/HRS guideline for the diagnosis and management of atrial fibrillation: A report of the American College of Cardiology/American Heart Association Joint Committee on Clinical Practice Guidelines. (2024). Circulation, 149(1), e1–e156. https://doi.org/10.1161/CIR.0000000000001193
Allen, J. (2007). Photoplethysmography and its application in clinical physiological measurement. Physiological Measurement, 28(3), R1–R39. https://doi.org/10.1088/0967-3334/28/3/R01
Asgari, E., Kaur, J., Nuredini, G., Balloch, J., Taylor, A. M., Sebire, N., Robinson, R., Peters, C., Sridharan, S., & Pimenta, D. (2024). Impact of electronic health record use on cognitive load and burnout among clinicians: Narrative review. JMIR Medical Informatics, 12, e55499. https://doi.org/10.2196/55499
Barrera, N., Solorzano, M., Jimenez, Y., Kushnir, Y., Gallegos-Koyner, F., & Dagostin de Carvalho, G. (2025). Accuracy of smartwatches in the detection of atrial fibrillation. JACC: Advances, 4(11), 102133. https://doi.org/10.1016/j.jacadv.2025.102133
Blackshear, J. L., & Odell, J. A. (1996). Appendage obliteration to reduce stroke in cardiac surgical patients with atrial fibrillation. The Annals of Thoracic Surgery, 61(2), 755–759. https://doi.org/10.1016/0003-4975(95)00887-X
Bray, J. J. H., Lloyd, E. F., Adenwalla, F., Kelly, S., Wareham, K., & Halcox, J. P. J. (2021). Single-lead ECGs (AliveCor) are a feasible, cost-effective and safer alternative to 12-lead ECGs in community diagnosis and monitoring of atrial fibrillation. BMJ Open Quality, 10(1), e001270. https://doi.org/10.1136/bmjoq-2020-001270
Castaneda, D., Esparza, A., Ghamari, M., Soltanpur, C., & Nazeran, H. (2018). A review on wearable photoplethysmography sensors and their potential future applications in health care. International Journal of Biosensors & Bioelectronics, 4(4), 195–202. https://doi.org/10.15406/ijbsbe.2018.04.00125
Cheng, S., He, J., Han, Y., Han, S., Li, P., Liao, H., & Guo, J. (2024). Global burden of atrial fibrillation/atrial flutter and its attributable risk factors from 1990 to 2021. Europace, 26(7), euae195. https://doi.org/10.1093/europace/euae195
Chuang, B. B.-S., & Yang, A. C. (2024). Optimization of using multiple machine learning approaches in atrial fibrillation detection based on a large-scale data set of 12-lead electrocardiograms: Cross-sectional study. JMIR Formative Research, 8, e47803. https://doi.org/10.2196/47803
Dai, H., Younis, A., Kong, J. D., Puce, L., Jabbour, G., Yuan, H., & Bragazzi, N. L. (2022). Big data in cardiology: State-of-art and future prospects. Frontiers in Cardiovascular Medicine, 9, 844296. https://doi.org/10.3389/fcvm.2022.844296
Duarte, R., Stainthorpe, A., Greenhalgh, J., Richardson, M., Nevitt, S., Mahon, J., Kotas, E., Boland, A., Thom, H., Marshall, T., Hall, M., & Takwoingi, Y. (2020). Lead-I ECG for detecting atrial fibrillation in patients with an irregular pulse using single time point testing: A systematic review and economic evaluation. Health Technology Assessment, 24(3), 1–164. https://doi.org/10.3310/hta24030
Francisco-Pascual, J., Cantalapiedra-Romero, J., Pérez-Rodon, J., Benito, B., Santos-Ortega, A., Maldonado, J., Ferreira-Gonzalez, I., & Rivas-Gándara, N. (2021). Cardiac monitoring for patients with palpitations. World Journal of Cardiology, 13(11), 608–627. https://doi.org/10.4330/wjc.v13.i11.608
Frederix, I., Caiani, E. G., Dendale, P., Anker, S., Bax, J., Böhm, A., Cowie, M., Crawford, J., de Groot, N., Dilaveris, P., Hansen, T., Koehler, F., Krstačić, G., Lambrinou, E., Lancellotti, P., Meier, P., Neubeck, L., Parati, G., Piotrowicz, E., . . . van der Velde, E. (2019). ESC e-Cardiology Working Group position paper: Overcoming challenges in digital health implementation in cardiovascular medicine. European Journal of Preventive Cardiology, 26(11), 1166–1177. https://doi.org/10.1177/2047487319832394
Ghamari, M., Nazeran, H., & Soltanpur, C. (2018). A review on wearable photoplethysmography sensors and their potential future applications in health care. International Journal of Biosensors & Bioelectronics, 4, 195–202. https://doi.org/10.15406/ijbsbe.2018.04.00125
Gladstone, D. J., Wachter, R., Schmalstieg-Bahr, K., Quinn, F. R., Hummers, E., Ivers, N., Marsden, T., Thornton, A., Djuric, A., Suerbaum, J., von Grünhagen, D., McIntyre, W. F., Benz, A. P., Wong, J. A., Merali, F., Henein, S., Nichol, C., Connolly, S. J., & Healey, J. S. (2021). Screening for atrial fibrillation in the older population. JAMA Cardiology, 6(5), 1–10. https://doi.org/10.1001/jamacardio.2021.0038
Grond, M., Jauss, M., Hamann, G., Stark, E., Veltkamp, R., Nabavi, D., Horn, M., Weimar, C., Köhrmann, M., Wachter, R., Rosin, L., & Kirchhof, P. (2013). Improved detection of silent atrial fibrillation using 72-hour Holter ECG in patients with ischemic stroke. Stroke, 44(12), 3357–3364. https://doi.org/10.1161/STROKEAHA.113.001884
Guo, Y., Chen, Y., Lane, D. A., Liu, L., Wang, Y., & Lip, G. Y. H. (2017). Mobile health technology for atrial fibrillation management integrating decision support, education, and patient involvement: mAF app trial. The American Journal of Medicine, 130(12), 1388–1396.e6. https://doi.org/10.1016/j.amjmed.2017.07.003
Guo, Y., Wang, H., Zhang, H., Liu, T., Liang, Z., Xia, Y., Yan, L., Xing, Y., Shi, H., Li, S., Liu, Y., Liu, F., Feng, M., Chen, Y., Lip, G. Y. H., & MAFA II Investigators. (2019). Mobile photoplethysmographic technology to detect atrial fibrillation. Journal of the American College of Cardiology, 74(19), 2365–2375. https://doi.org/10.1016/j.jacc.2019.08.019
Haïssaguerre, M., Jaïs, P., Shah, D. C., Takahashi, A., Hocini, M., Quiniou, G., Garrigue, S., Le Mouroux, A., Le Métayer, P., & Clémenty, J. (1998). Spontaneous initiation of atrial fibrillation by ectopic beats originating in the pulmonary veins. The New England Journal of Medicine, 339(10), 659–666. https://doi.org/10.1056/NEJM199809033391003
Hannun, A. Y., Rajpurkar, P., Haghpanahi, M., Tison, G. H., Bourn, C., Turakhia, M. P., & Ng, A. Y. (2019). Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network. Nature Medicine, 25(1), 65–69. https://doi.org/10.1038/s41591-018-0268-3
Healey, J. S., Connolly, S. J., Gold, M. R., Israel, C. W., Van Gelder, I. C., Capucci, A., Lau, C. P., Fain, E., Yang, S., Bailleul, C., Morillo, C. A., Carlson, M., Themeles, E., Kaufman, E. S., Hohnloser, S. H., & ASSERT Investigators. (2012). Subclinical atrial fibrillation and the risk of stroke. The New England Journal of Medicine, 366(2), 120–129. https://doi.org/10.1056/NEJMoa1105575
Ilyas, M. H., Sharih, A. M., Tukur Jido, J., Ali, A. R. Z., Khoshnaghsh, A., Nadeem, M., & Zahra, S. A. (2024). Screening for atrial fibrillation: Risks, benefits, and implications on future clinical practice. Current Treatment Options in Cardiovascular Medicine, 26(8), 233–242. https://doi.org/10.1007/s11936-024-01047-1
Isakadze, N., & Martin, S. S. (2020). How useful is the smartwatch ECG? Trends in Cardiovascular Medicine, 30(7), 442–448. https://doi.org/10.1016/j.tcm.2019.10.010
Kirchhof, P., Toennis, T., Goette, A., Camm, A. J., Diener, H. C., Becher, N., Bertaglia, E., Lundqvist, C. B., Borlich, M., Brandes, A., Cabanelas, N., Calvert, M., Chlouverakis, G., Dan, G.-A., de Groot, J. R., Dichtl, W., Kravchuk, B., Lubiński, A., Marijon, E., . . . Zapf, A. (2023). Anticoagulation with edoxaban in patients with atrial high-rate episodes. The New England Journal of Medicine, 389(13), 1167–1179. https://doi.org/10.1056/NEJMoa2303062
Ko, D., Chung, M. K., Evans, P. T., Benjamin, E. J., & Helm, R. H. (2025). Atrial fibrillation. JAMA, 333(4), 329–342. https://doi.org/10.1001/jama.2024.22451
Koerber, D., Khan, S., Shamsheri, T., Kirubarajan, A., & Mehta, S. (2023). Accuracy of heart rate measurement with wrist-worn wearable devices in various skin tones: A systematic review. Journal of Racial and Ethnic Health Disparities, 10(6), 2676–2684. https://doi.org/10.1007/s40615-022-01446-9
Koltowski, L., Balsam, P., Glowczynska, R., Rokicki, J. K., Peller, M., Maksym, J., Blicharz, L., Maciejewski, K., Niedziela, M., Opolski, G., & Grabowski, M. (2021). Kardia Mobile applicability in clinical practice: A comparison of Kardia Mobile and standard 12-lead electrocardiogram records in 100 consecutive patients of a tertiary cardiovascular care center. Cardiology Journal, 28(4), 543–548. https://doi.org/10.5603/CJ.a2019.0001
Krijthe, B. P., Kunst, A., Benjamin, E. J., Lip, G. Y. H., Franco, O. H., Hofman, A., Witteman, J. C. M., Stricker, B. H., & Heeringa, J. (2013). Projections on the number of individuals with atrial fibrillation in the European Union, from 2000 to 2060. European Heart Journal, 34(35), 2746–2751. https://doi.org/10.1093/eurheartj/eht280
Lin, H. J., Wolf, P. A., Kelly-Hayes, M., Beiser, A. S., Kase, C. S., Benjamin, E. J., & D’Agostino, R. B. (1996). Stroke severity in atrial fibrillation: The Framingham Study. Stroke, 27(10), 1760–1764. https://doi.org/10.1161/01.str.27.10.1760
Lopez-Jimenez, F., Attia, Z., Arruda-Olson, A. M., Carter, R., Chareonthaitawee, P., Jouni, H., Kapa, S., Lerman, A., Luong, C., Medina-Inojosa, J. R., Noseworthy, P. A., Pellikka, P. A., Redfield, M. M., Roger, V. L., Sandhu, G. S., Senecal, C., & Friedman, P. A. (2020). Artificial intelligence in cardiology: Present and future. Mayo Clinic Proceedings, 95(5), 1015–1039. https://doi.org/10.1016/j.mayocp.2020.01.038
Lubitz, S. A., Faranesh, A. Z., Selvaggi, C., Atlas, S. J., McManus, D. D., Singer, D. E., Pagoto, S., McConnell, M. V., Pantelopoulos, A., & Foulkes, A. S. (2022). Detection of atrial fibrillation in a large population using wearable devices: The Fitbit Heart Study. Circulation, 146(19), 1415–1424. https://doi.org/10.1161/CIRCULATIONAHA.122.060291
Lyon, A., Mincholé, A., Martínez, J. P., Laguna, P., & Rodriguez, B. (2018). Computational techniques for ECG analysis and interpretation in light of their contribution to medical advances. Journal of the Royal Society Interface, 15(138), 20170821. https://doi.org/10.1098/rsif.2017.0821
Lyth, J., Svennberg, E., Bernfort, L., Aronsson, M., Frykman, V., Al-Khalili, F., Friberg, L., Rosenqvist, M., Engdahl, J., & Levin, L.-Å. (2022). Cost-effectiveness of population screening for atrial fibrillation: The STROKESTOP study. European Heart Journal, 44(3), 196–204. https://doi.org/10.1093/eurheartj/ehac547
Malmqvist, J., Engdahl, J., Sjölund, G., & Doliwa, P. (2024). Sensitivity and specificity of handheld one-lead ECG detecting atrial fibrillation in an outpatient clinic setting. Journal of Electrocardiology, 83, 106–110. https://doi.org/10.1016/j.jelectrocard.2024.02.001
Martin, S. S., Aday, A. W., Almarzooq, Z. I., Anderson, C. A. M., Arora, P., Avery, C. L., Baker-Smith, C. M., Barone Gibbs, B., Beaton, A. Z., Boehme, A. K., Commodore-Mensah, Y., Currie, M. E., Elkind, M. S. V., Evenson, K. R., Generoso, G., Heard, D. G., Hiremath, S., Johansen, M. C., Kalani, R., . . . Palaniappan, L. P. (2024). 2024 heart disease and stroke statistics: A report of US and global data from the American Heart Association. Circulation, 149(8), e347–e913. https://doi.org/10.1161/CIR.0000000000001209
Medical Device Coordination Group. (2019). MDCG 2019-11: Guidance on qualification and classification of software in Regulation (EU) 2017/745–MDR and Regulation (EU) 2017/746–IVDR.
Meyerowitz-Katz, G., Ravi, S., Arnolda, L., Feng, X., Maberly, G., & Astell-Burt, T. (2020). Rates of attrition and dropout in app-based interventions for chronic disease: Systematic review and meta-analysis. Journal of Medical Internet Research, 22(9), e20283. https://doi.org/10.2196/20283
Mitchell, U. A., Chebli, P. G., Ruggiero, L., & Muramatsu, N. (2019). The digital divide in health-related technology use: The significance of race/ethnicity. The Gerontologist, 59(1), 6–14. https://doi.org/10.1093/geront/gny138
Nijor, S., Rallis, G., Lad, N., & Gokcen, E. (2022). Patient safety issues from information overload in electronic medical records. Journal of Patient Safety, 18(6), e999–e1003. https://doi.org/10.1097/PTS.0000000000001002
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., . . . Apple Heart Study Investigators. (2019). Large-scale assessment of a smartwatch to identify atrial fibrillation. The New England Journal of Medicine, 381(20), 1909–1917. https://doi.org/10.1056/NEJMoa1901183
Pergolizzi, J., Jr., LeQuang, J. A. K., Vasiliu-Feltes, I., Breve, F., & Varrassi, G. (2023). Brave new healthcare: A narrative review of digital healthcare in American medicine. Cureus, 15(10), e46489. https://doi.org/10.7759/cureus.46489
Price, W. N., & Cohen, I. G. (2019). Privacy in the age of medical big data. Nature Medicine. https://doi.org/10.1038/s41591-018-0272-7
Rizas, K. D., Freyer, L., Sappler, N., von Stülpnagel, L., Spielbichler, P., Krasniqi, A., Schreinlechner, M., Wenner, F. N., Theurl, F., Behroz, A., Eiffener, E., Klemm, M. P., Schneidewind, A., Zens, M., Dolejsi, T., Mansmann, U., Massberg, S., & Bauer, A. (2022). Smartphone-based screening for atrial fibrillation: A pragmatic randomized clinical trial. Nature Medicine, 28(9), 1823–1830. https://doi.org/10.1038/s41591-022-01979-w
Romiti, G. F., Proietti, M., Bonini, N., Ding, W. Y., Boriani, G., Huisman, M. V., & Lip, G. Y. H. (2023). Adherence to the atrial fibrillation better care (ABC) pathway and the risk of major outcomes in patients with atrial fibrillation: A post hoc analysis from the prospective GLORIA-AF Registry. eClinicalMedicine, 55, 101757. https://doi.org/10.1016/j.eclinm.2022.101757
Rosman, L., Lampert, R., Zhuo, S., Li, Q., Varma, N., Burg, M., Gaffey, A. E., Armbruster, T., & Gehi, A. (2024). Wearable devices, health care use, and psychological well-being in patients with atrial fibrillation. Journal of the American Heart Association, 13(15), e033750. https://doi.org/10.1161/JAHA.123.033750
Steinhubl, S. R., Waalen, J., Edwards, A. M., Ariniello, L. M., Mehta, R. R., Ebner, G. S., Carter, C., Baca-Motes, K., Felicione, E., Sarich, T., & Topol, E. J. (2018). Effect of a home-based wearable continuous ECG monitoring patch on detection of undiagnosed atrial fibrillation: The mSToPS randomized clinical trial. JAMA, 320(2), 146–155. https://doi.org/10.1001/jama.2018.8102
Svendsen, J. H., Diederichsen, S. Z., Højberg, S., Krieger, D. W., Graff, C., Kronborg, C., Olesen, M. S., Nielsen, J. B., Holst, A. G., Brandes, A., Haugan, K. J., & Køber, L. (2021). Implantable loop recorder detection of atrial fibrillation to prevent stroke (The LOOP Study): A randomised controlled trial. The Lancet, 398(10310), 1507–1516. https://doi.org/10.1016/S0140-6736(21)01698-6
Svennberg, E., Friberg, L., Frykman, V., Al-Khalili, F., Engdahl, J., & Rosenqvist, M. (2021). Clinical outcomes in systematic screening for atrial fibrillation (STROKESTOP): A multicentre, parallel-group, unmasked, randomised controlled trial. The Lancet, 398(10310), 1498–1506. https://doi.org/10.1016/S0140-6736(21)01637-8
Tieleman, R. G., Plantinga, Y., Rinkes, D., Bartels, G. L., Posma, J. L., Cator, R., Hofman, C., & Houben, R. P. (2014). Validation and clinical use of a novel diagnostic device for screening of atrial fibrillation. Europace, 16(9), 1291–1295. https://doi.org/10.1093/europace/euu057
Tison, G. H., Sanchez, J. M., Ballinger, B., Singh, A., Olgin, J. E., Pletcher, M. J., Vittinghoff, E., Lee, E. S., Fan, S. M., Gladstone, R. A., Mikell, C., Sohoni, N., Hsieh, J., & Marcus, G. M. (2018). Passive detection of atrial fibrillation using a commercially available smartwatch. JAMA Cardiology, 3(5), 409–416. https://doi.org/10.1001/jamacardio.2018.0136
Topol, E. J. (2019). High-performance medicine: The convergence of human and artificial intelligence. Nature Medicine. https://doi.org/10.1038/s41591-018-0300-7
Turakhia, M. P., Desai, M., Hedlin, H., Rajmane, A., Talati, N., Ferris, T., Desai, S., Nag, D., Patel, M., Kowey, P., Rumsfeld, J. S., Russo, A. M., Hills, M. T., Granger, C. B., Mahaffey, K. W., & Perez, M. V. (2019). Rationale and design of a large-scale, app-based study to identify cardiac arrhythmias using a smartwatch: The Apple Heart Study. American Heart Journal, 207, 66–75. https://doi.org/10.1016/j.ahj.2018.09.002
U.S. Food and Drug Administration. (2022). Policy for device software functions and mobile medical applications.
Van Gelder, I. C., Rienstra, M., Bunting, K. V., Casado-Arroyo, R., Caso, V., Crijns, H. J. G. M., De Potter, T. J. R., Dwight, J., Guasti, L., Hanke, T., Jaarsma, T., Lettino, M., Løchen, M.-L., Lumbers, R. T., Maesen, B., Mølgaard, I., Rosano, G. M. C., Sanders, P., Schnabel, R. B., . . . ESC Scientific Document Group. (2024). 2024 ESC guidelines for the management of atrial fibrillation developed in collaboration with the European Association for Cardio-Thoracic Surgery (EACTS): Developed by the task force for the management of atrial fibrillation of the European Society of Cardiology (ESC), with the special contribution of the European Heart Rhythm Association (EHRA) of the ESC; endorsed by the European Stroke Organisation (ESO). European Heart Journal, 45(36), 3314–3414. https://doi.org/10.1093/eurheartj/ehae176
Western, M. J., Smit, E. S., Gültzow, T., Neter, E., Sniehotta, F. F., Malkowski, O. S., Wright, C., Busse, H., Peuters, C., Rehackova, L., Gabriel Oteșanu, A., Ainsworth, B., Jones, C. M., Kilb, M., Rodrigues, A. M., Perski, O., Wright, A., & König, L. (2025). Bridging the digital health divide: A narrative review of the causes, implications, and solutions for digital health inequalities. Health Psychology and Behavioral Medicine, 13(1), 2493139. https://doi.org/10.1080/21642850.2025.2493139
Wijffels, M. C., Kirchhof, C. J., Dorland, R., & Allessie, M. A. (1995). Atrial fibrillation begets atrial fibrillation: A study in awake chronically instrumented goats. Circulation, 92(7), 1954–1968. https://doi.org/10.1161/01.cir.92.7.1954
Wolf, P. A., Abbott, R. D., & Kannel, W. B. (1991). Atrial fibrillation as an independent risk factor for stroke: The Framingham Study. Stroke, 22(8), 983–988. https://doi.org/10.1161/01.str.22.8.983
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