The DOI matches its Crossref record; 49 of the 51 references it cites were found in a registry or an index.
Effect of a Home-Based Wearable Continuous ECG Monitoring Patch on Detection of Undiagnosed Atrial Fibrillation: The mSToPS Randomized Clinical Trial
Open accessAbstract
Importance: Opportunistic screening for atrial fibrillation (AF) is recommended, and improved methods of early identification could allow for the initiation of appropriate therapies to prevent the adverse health outcomes associated with AF. Objective: To determine the effect of a self-applied wearable electrocardiogram (ECG) patch in detecting AF and the clinical consequences associated with such a detection strategy. Design, Setting, and Participants: A direct-to-participant randomized clinical trial and prospective matched observational cohort study were conducted among members of a large national health plan. Recruitment began November 17, 2015, and was completed on October 4, 2016, and 1-year claims-based follow-up concluded in January 2018. For the clinical trial, 2659 individuals were randomized to active home-based monitoring to start immediately or delayed by 4 months. For the observational study, 2 deidentified age-, sex- and CHA2DS2-VASc-matched controls were selected for each actively monitored individual. Interventions: The actively monitored cohort wore a self-applied continuous ECG monitoring patch at home during routine activities for up to 4 weeks, initiated either immediately after enrolling (n = 1364) or delayed for 4 months after enrollment (n = 1291). Main Outcomes and Measures: The primary end point was the incidence of a new diagnosis of AF at 4 months among those randomized to immediate monitoring vs delayed monitoring. A secondary end point was new AF diagnosis at 1 year in the combined actively monitored groups vs matched observational controls. Other outcomes included new prescriptions for anticoagulants and health care utilization (outpatient cardiology visits, primary care visits, or AF-related emergency department visits and hospitalizations) at 1 year. Results: The randomized groups included 2659 participants (mean [SD] age, 72.4 [7.3] years; 38.6% women), of whom 1738 (65.4%) completed active monitoring. The observational study comprised 5214 (mean [SD] age, 73.7 [7.0] years; 40.5% women; median CHA2DS2-VASc score, 3.0), including 1738 actively monitored individuals from the randomized trial and 3476 matched controls. In the randomized study, new AF was identified by 4 months in 3.9% (53/1366) of the immediate group vs 0.9% (12/1293) in the delayed group (absolute difference, 3.0% [95% CI, 1.8%-4.1%]). At 1 year, AF was newly diagnosed in 109 monitored (6.7 per 100 person-years) and 81 unmonitored (2.6 per 100 person-years; difference, 4.1 [95% CI, 3.9-4.2]) individuals. Active monitoring was associated with increased initiation of anticoagulants (5.7 vs 3.7 per 100 person-years; difference, 2.0 [95% CI, 1.9-2.2]), outpatient cardiology visits (33.5 vs 26.0 per 100 person-years; difference, 7.5 [95% CI, 7.2-7.9), and primary care visits (83.5 vs 82.6 per 100 person-years; difference, 0.9 [95% CI, 0.4-1.5]). There was no difference in AF-related emergency department visits and hospitalizations (1.3 vs 1.4 per 100 person-years; difference, 0.1 [95% CI, -0.1 to 0]). Conclusions and Relevance: Among individuals at high risk for AF, immediate monitoring with a home-based wearable ECG sensor patch, compared with delayed monitoring, resulted in a higher rate of AF diagnosis after 4 months. Monitored individuals, compared with nonmonitored controls, had higher rates of AF diagnosis, greater initiation of anticoagulants, but also increased health care resource utilization at 1 year. Trial Registration: ClinicalTrials.gov Identifier: NCT02506244.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“One pioneering trial used a 'band-aid' patch sensor for detecting atrial fibrillation; the sensor was mailed to participants who were enrolled remotely, without the use of any clinical sites, and set the foundation for digitized clinical trials 37 .”
From Multimodal biomedical AI (2022) · cites this paper as [37]
Experimental — accuracy not yet measuredIn the citing paper
“AF screening via ECG patches in selected high-risk individuals is feasible and clinically valuable50”
From Smart wearable devices in cardiovascular care: Where we are and how to move forward (2021) · cites this paper as [50]
In the citing paper
“The study showed that ECG monitoring led to a higher rate of new AF diagnosis at 4 months and 1 year and was associated with the increased initiation of anticoagulation therapy and outpatient cardiology and primary care visits in patients without previously known AF50.”
From Smart wearable devices in cardiovascular care: Where we are and how to move forward (2021) · cites this paper as [50]
In the citing paper
“For example, iRhythm's single-use Zio patch monitors electrical pulses from the heart for 14 days, and is more effective than intermittent hospital check-ups at detecting abnormal rhythms 4 .”
From Skin sensors are the future of health care (2019) · cites this paper as [4]
In the citing paper
“For example, iRhythm's single-use Zio patch monitors electrical pulses from the heart for 14 days, and is more effective than intermittent hospital check-ups at detecting abnormal rhythms 4 .”
From Skin sensors are the future of health care (2019) · cites this paper as [4]
In the citing paper
“For instance, in the mSTOP trial, the information from the device was communicated to the patient and their primary physician, which led to higher rates of cardiology visits and follow-up investigations.”
From Unlocking Tomorrow’s Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence (2024) · cites this paper as [27]
In the citing paper
“This study showed that ECG monitoring analyzed by a deep learning model led to a higher rate of new AF diagnosis than standard methods, and was associated with increased initiation of anticoagulation therapy and cardiology referral.”
From Unlocking Tomorrow’s Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence (2024) · cites this paper as [27]
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Atrial fibrillation as an independent risk factor for stroke: the Framingham study
Wolf, P. A., Abbott, R. D., & Kannel, W. B. (1991) · Stroke · doi:10.1161/01.str.22.8.983 · Cited by 7,388
2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS
Kirchhof, P. F., Benussi, S., Kotecha, D., Ahlsson, A., Atar, D., Casadei, B., Castellà, M., Diener, H., Heidbüchel, H., Hendriks, J., et al. (2016) · EP Europace · doi:10.1093/europace/euw295 · Cited by 6,438
2016 ESC Guidelines for the management of atrial fibrillation developed in collaboration with EACTS
Kirchhof, P., Benussi, S., Kotecha, D., Ahlsson, A., Atar, D., Casadei, B., Castella, M., Diener, H.-C., Heidbuchel, H., Hendriks, J., et al. (2016) · European Heart Journal · doi:10.1093/eurheartj/ehw210 · Cited by 6,418
Hart, R. G., Pearce, L. A., & Aguilar, M. I. (2007) · Annals of Internal Medicine · doi:10.7326/0003-4819-146-12-200706190-00007 · Cited by 4,793
Meschia, J. F., Bushnell, C., Boden-Albala, B., Braun, L. T., Bravata, D. M., Chaturvedi, S., Creager, M. A., Eckel, R. H., Elkind, M. S. V., Fornage, M., et al. (2014) · Stroke · doi:10.1161/str.0000000000000046 · Cited by 2,650
Benjamin, E. J. (1994) · JAMA · doi:10.1001/jama.1994.03510350050036 · Cited by 2,613
Goldstein, L. B., Bushnell, C. D., Adams, R. J., Appel, L. J., Braun, L. T., Chaturvedi, S., Creager, M. A., Culebras, A., Eckel, R. H., Hart, R. G., et al. (2011) · Stroke · doi:10.1161/str.0b013e3181fcb238 · Cited by 1,306
Atrial Fibrillation: A Major Contributor to Stroke in the Elderly: The Framingham Study
Wolf, P. A. (1987) · Archives of Internal Medicine · doi:10.1001/archinte.1987.00370090041008 · Cited by 1,108
Antithrombotic Therapy for Atrial Fibrillation
You, J. J., Singer, D. E., Howard, P. A., Lane, D. A., Eckman, M. H., Fang, M. C., Hylek, E. M., Schulman, S., Go, A. S., Hughes, M., et al. (2012) · CHEST Journal · doi:10.1378/chest.11-2304 · Cited by 933
Goldstein, L. B., Bushnell, C., Adams, R. J., Appel, L. J., Braun, L. T., Chaturvedi, S., Creager, M. A., Culebras, A., Eckel, R. H., Hart, R. G., et al. (2011) · Stroke · Cited by 745
Research works printed but not linked: 1
- Proietti, 2016, A population screening programme for atrial fibrillation., Europace, 18, 1779
Cited by
Sorted by citations
Hindricks, G., Potpara, T., Dagres, N., Arbelo, E., Bax, J. J., Blomström‐Lundqvist, C., Boriani, G., Castella, M., Dan, G.-A., Dilaveris, P., et al. (2020) · European Heart Journal · doi:10.1093/eurheartj/ehaa612 · Cited by 10,550
Gelder, I. C. V., Rienstra, M., Bunting, K. V., Casado-Arroyo, R., Caso, V., Crijns, H. J. G. M., Potter, T. J. R. D., Dwight, J., Guasti, L., Hanke, T., et al. (2024) · European Heart Journal · doi:10.1093/eurheartj/ehae176 · Cited by 3,594
“102 There have been several studies investigating the cost-effectiveness of screening for AF.”
BackgroundLarge-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation
Perez, M. V., Mahaffey, K. W., Hedlin, H., Rumsfeld, J. S., Garcia, A., Ferris, T., Balasubramanian, V., Russo, A. M., Rajmane, A., Cheung, L., et al. (2019) · New England Journal of Medicine · doi:10.1056/nejmoa1901183 · Cited by 1,917
Acosta, J. N., Falcone, G. J., Rajpurkar, P., & Topol, E. J. (2022) · Nature Medicine · doi:10.1038/s41591-022-01981-2 · Cited by 1,365
“One pioneering trial used a ‘band-aid’ patch sensor for detecting atrial fibrillation; the sensor was mailed to participants who were enrolled remotely, without the use of any clinical sites, and set the foundation for digitized clinical trials 37 .”
MethodSelf-citationSmart wearable devices in cardiovascular care: Where we are and how to move forward
Bayoumy, K., Gaber, M., Elshafeey, A., Mhaimeed, O., Dineen, E. H., Marvel, F. A., Martin, S. S., Muse, E. D., Turakhia, M. P., Tarakji, K. G., et al. (2021) · Nature Reviews Cardiology · doi:10.1038/s41569-021-00522-7 · Cited by 829
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., et al. (2021) · The Lancet · doi:10.1016/s0140-6736(21)01698-6 · Cited by 550
Mobile Photoplethysmographic Technology to Detect Atrial Fibrillation
Guo, Y., Wang, H., Zhang, H., Liu, T., Liang, Z., Xia, Y., Yan, L., Xing, Y., Shi, H., Li, S., et al. (2019) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2019.08.019 · Cited by 489
Turakhia, M. P., Desai, M., Hedlin, H., Rajmane, A., Talati, N., Ferris, T., Desai, S., Nag, D., Patel, M., Kowey, P., et al. (2019) · American Heart Journal · doi:10.1016/j.ahj.2018.09.002 · Cited by 474
Thompson, A., Fleischmann, K. E., Smilowitz, N. R., Fuentes, L. D. L., Mukherjee, D., Aggarwal, N. R., Ahmad, F. S., Allen, R. B., Altin, S. E., Auerbach, A. D., et al. (2024) · Circulation · doi:10.1161/cir.0000000000001285 · Cited by 435
The impact of wearable technologies in health research: scoping review
Huhn, S., Axt, M., Gunga, H.-C., Maggioni, M. A., Munga, S., Obor, D., Sié, A., Boudo, V., Bunker, A., Sauerborn, R., et al. (2022) · JMIR mHealth and uHealth · doi:10.2196/34384 · Cited by 379
Lubitz, S. A., Faranesh, A. Z., Selvaggi, C. A., Atlas, S. J., McManus, D. D., Singer, D. E., Pagoto, S., McConnell, M. V., Pantelopoulos, A., & Foulkes, A. S. (2022) · Circulation · doi:10.1161/circulationaha.122.060291 · Cited by 397
ECG Monitoring Systems: Review, Architecture, Processes, and Key Challenges
Serhani, M. A., Kassabi, H. T. E., Ismail, H., & Navaz, A. N. (2020) · Sensors · doi:10.3390/s20061796 · Cited by 373
“While according to the authors of [18], the effect of home-based wearable continuous ECG monitoring covers the discovery of undiagnosed AF in a randomized clinical trial, in [20] a real-time wearable ECG monitoring system for cardiac arrhythmia classification is introduced in a living environment.”
BackgroundSvennberg, E., Friberg, L., Frykman, V., Al‐Khalili, F., Engdahl, J., & Rosenqvist, M. (2021) · The Lancet · doi:10.1016/s0140-6736(21)01637-8 · Cited by 366
Raghunath, S., Pfeifer, J. M., Ulloa-Cerna, A. E., Nemani, A., Carbonati, T., Jing, L., vanMaanen, D. P., Hartzel, D. N., Ruhl, J. A., Lagerman, B. F., et al. (2021) · Circulation · doi:10.1161/circulationaha.120.047829 · Cited by 312
Wearable Devices in Cardiovascular Medicine
Hughes, A. N., Shandhi, M. M. H., Master, H., Dunn, J., & Brittain, E. L. (2023) · Circulation Research · doi:10.1161/circresaha.122.322389 · Cited by 341
Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic
Ding, X., Clifton, D., Ji, N., Lovell, N. H., Bonato, P., Chen, W., Yu, X., Xue, Z., Xiang, T., Long, X., et al. (2021) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.2992838 · Cited by 281
“times higher compared with regular ECG check [80].”
BackgroundHow to use digital devices to detect and manage arrhythmias: an EHRA practical guide
Svennberg, E., Tjong, F., Goette, A., Akoum, N., Di Biase, L., Bordachar, P., Boriani, G., Burri, H., Conte, G., Deharo, J. C., et al. (2022) · Europace · doi:10.1093/europace/euac038 · Cited by 261
Wearable Devices for Physical Monitoring of Heart: A Review
Prieto-Avalos, G., Cruz-Ramos, N. A., Alor‐Hernández, G., Sánchez-Cervantes, J. L., Rodríguez-Mazahua, L., & Guarneros-Nolasco, L. R. (2022) · Biosensors · doi:10.3390/bios12050292 · Cited by 275
Krittanawong, C., Rogers, A. J., Johnson, K. W., Wang, Z., Turakhia, M. P., Halperin, J. L., & Narayan, S. M. (2020) · Nature Reviews Cardiology · doi:10.1038/s41569-020-00445-9 · Cited by 248
Thompson, A., Fleischmann, K. E., Smilowitz, N. R., Fuentes, L. D. L., Mukherjee, D., Aggarwal, N. R., Ahmad, F. S., Allen, R. B., Altin, S. E., Auerbach, A. D., et al. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.06.013 · Cited by 257
Skin sensors are the future of health care
Xu, S., Jayaraman, A., & Rogers, J. A. (2019) · Nature · doi:10.1038/d41586-019-02143-0 · Cited by 214
Screening for Atrial Fibrillation in the Older Population
Gladstone, D. J., Wachter, R., Schmalstieg-Bahr, K., Quinn, F. R., Hummers, E., Ivers, N. M., Marsden, T., Thornton, A., Djuric, A., Suerbaum, J., et al. (2021) · JAMA Cardiology · doi:10.1001/jamacardio.2021.0038 · Cited by 201
Huang, J.-D., Wang, J., Ramsey, E., Leavey, G., Chico, T., & Condell, J. V. (2022) · Sensors · doi:10.3390/s22208002 · Cited by 203
“The continuous monitoring of physiological parameters then offers a potential solution to more timely access to CVD-based healthcare [9,10,14,47,50,52,53,59,66].”
BackgroundScreening for Atrial Fibrillation
US Preventive Services Task Force, Davidson, K. W., Barry, M. J., Mangione, C. M., Cabana, M. D., Caughey, A. B., Davis, E. M., Donahue, K. E., Doubeni, C. A., Epling, J. W., et al. (2022) · JAMA · doi:10.1001/jama.2021.23732 · Cited by 169
Windows into human health through wearables data analytics
Witt, D. R., Kellogg, R., Snyder, M. P., & Dunn, J. (2019) · Current Opinion in Biomedical Engineering · doi:10.1016/j.cobme.2019.01.001 · Cited by 165
Remote smartphone monitoring of Parkinson’s disease and individual response to therapy
Omberg, L., Neto, E. C., Perumal, T. M., Pratap, A., Tediarjo, A., Adams, J., Bloem, B. R., Bot, B. M., Elson, M. J., Goldman, S. M., et al. (2021) · Nature Biotechnology · doi:10.1038/s41587-021-00974-9 · Cited by 141
Digital Technologies for Emotion-Regulation Assessment and Intervention: A Conceptual Review
Bettis, A. H., Burke, T. A., Nesi, J. L., & Liu, R. T. (2021) · Clinical Psychological Science · doi:10.1177/21677026211011982 · Cited by 174
“Promising wireless adhesive devices for real-time ECG monitoring include the NUVANT Mobile Cardiac Telemetry Monitor (Engel et al., 2012) and the Zio patch (Steinhubl et al., 2018); all of these have been approved by the U.S. Food and Drug Administration.”
Lubitz, S. A., Atlas, S. J., Ashburner, J. M., Lipsanopoulos, A. T. T., Borowsky, L. H., Guan, W., Khurshid, S. S., Ellinor, P. T., Chang, Y., McManus, D. D., et al. (2022) · Circulation · doi:10.1161/circulationaha.121.057014 · Cited by 128
Sanchis, K., Cariou, E., Colombat, M., Ribes, D., Huart, A., Cintas, P., Fournier, P., Rollin, A., Carrié, D., Galinier, M., et al. (2019) · Amyloid · doi:10.1080/13506129.2019.1620724 · Cited by 125
Lowres, N., Olivier, J., Chao, T., Chen, S.-A., Chen, Y., Diederichsen, A. C. P., Fitzmaurice, D. A., Gómez‐Doblas, J. J., Harbison, J. A., Healey, J. S., et al. (2019) · PLoS Medicine · doi:10.1371/journal.pmed.1002903 · Cited by 124
Gupta, P., Moghimi, M. J., Jeong, Y., Gupta, D., Inan, O. T., & Ayazi, F. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0225-7 · Cited by 125
Smartphone-based screening for atrial fibrillation: a pragmatic randomized clinical trial
Rizas, K. D., Freyer, L., Sappler, N., von Stülpnagel, L., Spielbichler, P., Krasniqi, A., Schreinlechner, M., Wenner, F. N., Theurl, F., Behroz, A., et al. (2022) · Nature Medicine · doi:10.1038/s41591-022-01979-w · Cited by 125
Kalarus, Z. F., Mairesse, G. H., Sokal, A., Boriani, G., Średniawa, B., Casado-Arroyo, R., Wachter, R., Frommeyer, G., TRAYKOV, V. B., Dagres, N., et al. (2022) · EP Europace · doi:10.1093/europace/euac144 · Cited by 127
Atrial Fibrillation and Ischemic Stroke: A Clinical Review
Migdady, I., Russman, A., & Buletko, A. B. (2021) · Seminars in Neurology · doi:10.1055/s-0041-1726332 · Cited by 130
Mobile Health Technologies in Cardiopulmonary Disease
MacKinnon, G. E., & Brittain, E. L. (2019) · CHEST Journal · doi:10.1016/j.chest.2019.10.015 · Cited by 114
Population-Based Screening for Atrial Fibrillation
Khurshid, S. S., Healey, J. S., McIntyre, W. F., & Lubitz, S. A. (2020) · Circulation Research · doi:10.1161/circresaha.120.316341 · Cited by 104
Wearable sensors to improve detection of patient deterioration
Joshi, M., Ashrafian, H., Aufegger, L., Khan, S. N., Arora, S., Cooke, G., & Darzi, A. W. (2018) · Expert Review of Medical Devices · doi:10.1080/17434440.2019.1563480 · Cited by 97
Accuracy of mHealth Devices for Atrial Fibrillation Screening: Systematic Review
Giebel, G. D., & Gissel, C. (2019) · JMIR mHealth and uHealth · doi:10.2196/13641 · Cited by 99
Mobile health and cardiac rehabilitation in older adults
Bostrom, J., Sweeney, G., Whiteson, J. H., & Dodson, J. A. (2019) · Clinical Cardiology · doi:10.1002/clc.23306 · Cited by 102
Hulme, O. L., Khurshid, S. S., Weng, L.-C. C., Anderson, C. D., Wang, E. Y., Ashburner, J. M., Ko, D., McManus, D. D., Benjamin, E. J., Ellinor, P. T., et al. (2019) · JACC. Clinical electrophysiology · doi:10.1016/j.jacep.2019.07.016 · Cited by 96
Jensen, M. T., Treskes, R. W., Caiani, E. G., Casado-Arroyo, R., Cowie, M. R., Dilaveris, P., Duncker, D., Di Rienzo, M., Frederix, I., De Groot, N., et al. (2021) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztab011 · Cited by 93
Lubitz, S. A., Faranesh, A. Z., Atlas, S. J., McManus, D. D., Singer, D. E., Pagoto, S., Pantelopoulos, A., & Foulkes, A. S. (2021) · American Heart Journal · doi:10.1016/j.ahj.2021.04.003 · Cited by 94
Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography
Kwon, J., Kim, K., Jeon, K., Lee, S. Y., Park, J., & Oh, B. (2020) · Scandinavian Journal of Trauma Resuscitation and Emergency Medicine · doi:10.1186/s13049-020-00791-0 · Cited by 95
A systematic review of methods used to conduct decentralised clinical trials
Rogers, A., Paoli, G. D., Subbarayan, S., Copland, R. R., Harwood, K. E., Coyle, J., Mitchell, L., MacDonald, T. M., & Mackenzie, I. S. (2021) · British Journal of Clinical Pharmacology · doi:10.1111/bcp.15205 · Cited by 96
“Eleven trials presented some bias concerns, and 8 were assessed as at low risk of bias.(29,34,36,49,51,57,59,63) Two trials reported methodological comparisons, 1 assessed as low risk,(39) and 1 high.”
BackgroundPassive detection of COVID-19 with wearable sensors and explainable machine learning algorithms
Gadaleta, M., Radin, J. M., Baca-Motes, K., Ramos, E., Kheterpal, V., Topol, E. J., Steinhubl, S. R., & Quer, G. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00533-1 · Cited by 94
Self-citationNazarian, S., Lam, K., Darzi, A. W., & Ashrafian, H. (2021) · Journal of Medical Internet Research · doi:10.2196/28974 · Cited by 101
“of AF detection are criticized for their periodic investigative approach, during which an irregular pulse may be absent [34-36].”
BackgroundSynthetic data in biomedicine via generative artificial intelligence
van Breugel, B., Liu, T., Oglić, D., & van der Schaar, M. (2024) · Nature Reviews Bioengineering · doi:10.1038/s44222-024-00245-7 · Cited by 111
World Heart Federation Roadmap on Atrial Fibrillation – A 2020 Update
Freedman, B., Hindricks, G., Banerjee, A., Baranchuk, A. M., Ching, C. K., Du, X., Fitzsimons, D., Healey, J. S., Ikeda, T., Lobban, T., et al. (2021) · Global Heart · doi:10.5334/gh.1023 · Cited by 95
Technology-Enabled Clinical Trials
Marquis‐Gravel, G., Roe, M. T., Turakhia, M. P., Boden, W. E., Temple, R., Sharma, A., Hirshberg, B., Slater, P., Craft, N. A., Stockbridge, N., et al. (2019) · Circulation · doi:10.1161/circulationaha.119.040798 · Cited by 86
Self-citationUittenbogaart, S. B., Gurp, N. V.-V., Lucassen, W. A. M., Winkens, B., Nielen, M. M. J., Erkens, P. M. G., Knottnerus, J. A., van Weert, H. C. P. M., & Stoffers, J. (2020) · BMJ · doi:10.1136/bmj.m3208 · Cited by 85
A polygenic risk score predicts atrial fibrillation in cardiovascular disease
Marston, N., Garfinkel, A. C., Kamanu, F., Melloni, G. E. M., Roselli, C., Jarolím, P., Berg, D. D., Bhatt, D. L., Bonaca, M. P., Cannon, C. P., et al. (2022) · European Heart Journal · doi:10.1093/eurheartj/ehac460 · Cited by 87
World Heart Federation Roadmap for Digital Health in Cardiology
Tromp, J., Jindal, D., Redfern, J., Bhatt, A. B., Séverin, T., Banerjee, A., Ge, J., Itchhaporia, D., Jaarsma, T., Lanas, F. T., et al. (2022) · Global Heart · doi:10.5334/gh.1141 · Cited by 88
The virtual physical exam in the 21st century
Ansary, A. M., Martinez, J. N., & Scott, J. D. (2019) · Journal of Telemedicine and Telecare · doi:10.1177/1357633x19878330 · Cited by 83
“Finally, in a direct-to-participant randomised clinical trial and prospective matched observational cohort study, patients were selected to wear a self-applied continuous ECG monitor patch.(38) These patients had higher rate of atrial fibrillation diagnosis and greater initiation of anticoagulants.”
MethodMizuno, A., Changolkar, S., & Patel, M. S. (2020) · Annual Review of Medicine · doi:10.1146/annurev-med-050919-031534 · Cited by 82
Kubíček, J., Fiedorova, K., Vilímek, D., Černý, M., Penhaker, M., Janura, M., & Rosický, J. (2020) · IEEE Reviews in Biomedical Engineering · doi:10.1109/rbme.2020.3043623 · Cited by 80
The Auto-eFACE: Machine Learning–Enhanced Program Yields Automated Facial Palsy Assessment Tool
Miller, M. Q., Hadlock, T. A., Fortier, E., & Guarín, D. L. (2020) · Plastic & Reconstructive Surgery · doi:10.1097/prs.0000000000007572 · Cited by 75
Dependence of Skin-Electrode Contact Impedance on Material and Skin Hydration
Goyal, K., Borkholder, D. A., & Day, S. W. (2022) · Sensors · doi:10.3390/s22218510 · Cited by 74
“Accurate and reliable in-home monitoring of biopotential signals such as electrocardiogram (ECG) can provide early diagnosis of cardiac health conditions [1,2].”
BackgroundVarma, N., Cygankiewicz, I., Turakhia, M. P., Heidbuchel, H., Hu, Y.-F., Chen, L. Y., Couderc, J.-P., Cronin, E. M., Estep, J. D., Grieten, L., et al. (2021) · Circulation: Arrhythmia and Electrophysiology · doi:10.1161/circep.120.009204 · Cited by 74
Research Priorities in Atrial Fibrillation Screening
Benjamin, E. J., Go, A. S., Desvigne-Nickens, P. M., Anderson, C. D., Casadei, B., Chen, L. Y., Crijns, H. J. G. M., Freedman, B., Hills, M. T., Healey, J. S., et al. (2021) · Circulation · doi:10.1161/circulationaha.120.047633 · Cited by 71
van Eijk, R. P. A., Bakers, J. N. E., Bunte, T. M., de Fockert, A. J., Eijkemans, M. J. C., & van den Berg, L. H. (2019) · Journal of Neurology · doi:10.1007/s00415-019-09427-5 · Cited by 68
Rooney, M. R., Soliman, E. Z., Lutsey, P. L., Norby, F. L., Loehr, L. R., Mosley, T. H., Zhang, M. J., Gottesman, R. F., Coresh, J., Folsom, A. R., et al. (2019) · Circulation Arrhythmia and Electrophysiology · doi:10.1161/circep.119.007390 · Cited by 67
Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation
Yuan, N., Duffy, G., Dhruva, S. S., Oesterle, A., Pellegrini, C. N., Theurer, J., Vali, M., Heidenreich, P. A., Keyhani, S., & Ouyang, D. (2023) · JAMA Cardiology · doi:10.1001/jamacardio.2023.3701 · Cited by 74
Wearable Devices for Cardiac Rhythm Diagnosis and Management
Ip, J. E. (2019) · JAMA · doi:10.1001/jama.2018.20437 · Cited by 67
Prediction of atrial fibrillation from at-home single-lead ECG signals without arrhythmias
Gadaleta, M., Harrington, P., Barnhill, E., Hytopoulos, E., Turakhia, M. P., Steinhubl, S. R., & Quer, G. (2023) · npj Digital Medicine · doi:10.1038/s41746-023-00966-w · Cited by 75
Self-citationDarmawahyuni, A., Nurmaini, S., Sukemi, S., Caesarendra, W., Bhayyu, V., Rachmatullah, M. N., & Firdaus, F. (2019) · Algorithms · doi:10.3390/a12060118 · Cited by 67
How will machine learning inform the clinical care of atrial fibrillation?
Siontis, K. C., Yao, X., Pirruccello, J. P., Philippakis, A. A., & Noseworthy, P. A. (2020) · Circulation Research · doi:10.1161/circresaha.120.316401 · Cited by 61
A Real-Time Wearable Physiological Monitoring System for Home-Based Healthcare Applications
Jeong, J.-W., Lee, W., & Kim, Y. (2021) · Sensors · doi:10.3390/s22010104 · Cited by 64
“Several ECG studies have been reported the quantitative ECG measurements in clinical applications to evaluate the relationship between cardiac, neurological, and functional outcomes of ischemic stroke [2,3].”
BackgroundCost-effectiveness of Screening for Atrial Fibrillation Using Wearable Devices
Chen, W., Khurshid, S. S., Singer, D. E., Atlas, S. J., Ashburner, J. M., Ellinor, P. T., McManus, D. D., Lubitz, S. A., & Chhatwal, J. (2022) · JAMA Health Forum · doi:10.1001/jamahealthforum.2022.2419 · Cited by 61
An Evaluation of Biometric Monitoring Technologies for Vital Signs in the Era of COVID‐19
Manta, C. J., Jain, S. S., Coravos, A., Mendelsohn, D., & Izmailova, E. S. (2020) · Clinical and Translational Science · doi:10.1111/cts.12874 · Cited by 65
“For example, some ECG patches, such as iRhythm ZioXT, have demonstrated improved ability to detect hidden arrhythmias compared with Holter monitors.(64,65) A large-scale Apple Watch study investigating the ability of PPGs to identify atrial fibrillation found that, among participants who received notification of an ir…”
BackgroundYenikomshian, M. A., Jarvis, J. L., Patton, C., Yee, C. W., Mortimer, R., Birnbaum, H. G., & Topash, M. (2019) · Current Medical Research and Opinion · doi:10.1080/03007995.2019.1610370 · Cited by 61
A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection
Shao, M., Zhou, Z., Bin, G., Bai, Y., & Wu, S. (2020) · Sensors · doi:10.3390/s20030606 · Cited by 61
“Wearable long-term ECG monitoring systems using noninvasive sensors, including hand-held devices [5,6], skin patch recorders [7,8] and smart clothing [9], have been developed for the detection of undiagnosed AF.”
BackgroundMethodScreening for Atrial Fibrillation
Kahwati, L. C., Asher, G. N., Kadro, Z. O., Keen, S., Ali, R., Coker‐Schwimmer, E., & Jonas, D. E. (2022) · JAMA · doi:10.1001/jama.2021.21811 · Cited by 57
Wang, Y.-C., Xu, X., Hajra, A., Apple, S. J., Kharawala, A., Duarte, G., Liaqat, W., Fu, Y., Li, W., Chen, Y., et al. (2022) · Diagnostics · doi:10.3390/diagnostics12030689 · Cited by 55
“We have witnessed the extensive use of consumer wearables during the COVID-19 pandemic for monitoring heart rate and oxygen saturation at home [61].”
BackgroundEHRA White Paper: knowledge gaps in arrhythmia management—status 2019
Goette, A., Auricchio, A., Boriani, G., Braunschweig, F., Brugada, J., Burri, H., Camm, A. J., Crijns, H. J. G. M., Dagres, N., Deharo, J., et al. (2019) · EP Europace · doi:10.1093/europace/euz055 · Cited by 57
Fukuma, N., Hasumi, E., Fujiu, K., Waki, K., Toyooka, T., Komuro, I., & Ohe, K. (2019) · Scientific Reports · doi:10.1038/s41598-019-48267-1 · Cited by 53
Machine learning guided electrochemical sensor for passive sweat cortisol detection
Shahub, S., Upasham, S., Ganguly, A., & Prasad, S. (2022) · Sensing and Bio-Sensing Research · doi:10.1016/j.sbsr.2022.100527 · Cited by 56
Searcy, R., Summapund, J., Estrin, D. L., Pollak, J. P., Schoenthaler, A., Troxel, A. B., & Dodson, J. A. (2019) · Current Geriatrics Reports · doi:10.1007/s13670-019-0270-8 · Cited by 52
Xintarakou, A., Sousonis, V., Asvestas, D., Vardas, P. E., & Tzeis, S. E. (2022) · Frontiers in Cardiovascular Medicine · doi:10.3389/fcvm.2022.853614 · Cited by 58
Effect of Screening for Undiagnosed Atrial Fibrillation on Stroke Prevention
Lópes, R. D., Atlas, S. J., Go, A. S., Lubitz, S. A., McManus, D. D., Dolor, R. J., Chatterjee, R., Rothberg, M. B., Rushlow, D. R., Crosson, L. A., et al. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.08.019 · Cited by 50
Connected Health Technology for Cardiovascular Disease Prevention and Management
Wongvibulsin, S., Martin, S. S., Steinhubl, S. R., & Muse, E. D. (2019) · Current Treatment Options in Cardiovascular Medicine · doi:10.1007/s11936-019-0729-0 · Cited by 52
Self-citationAtrial Fibrillation and Stroke
Essa, H., Hill, A. M., & Lip, G. Y. H. (2021) · Cardiac Electrophysiology Clinics · doi:10.1016/j.ccep.2020.11.003 · Cited by 53
Manninger, M., Zweiker, D., Svennberg, E., Chatzikyriakou, S. V., Pavlović, N., Zaman, J. A. B., Kirćanski, B., Lenarczyk, R., Vanduynhoven, P., Kosiuk, J., et al. (2021) · EP Europace · doi:10.1093/europace/euab064 · Cited by 49
“Current guideline recommendations are based on the scarce data from randomized controlled trials that confirm the health benefits from AF screening.(3,12,13) The lower screening threshold proposed by respondents might be explained by the new opportunities with widely available wearable rhythm devices that could be use…”
BackgroundVarma, N., Cygankiewicz, I., Turakhia, M. P., Heidbüchel, H., Hu, Y., Chen, L. Y., Couderc, J., Cronin, E. M., Estep, J. D., Grieten, L., et al. (2021) · Annals of Noninvasive Electrocardiology · doi:10.1111/anec.12795 · Cited by 51
Present and Future of Digital Health in Diabetes and Metabolic Disease
Rhee, S. Y., Kim, C., Shin, D. W., & Steinhubl, S. R. (2020) · Diabetes & Metabolism Journal · doi:10.4093/dmj.2020.0088 · Cited by 47
“Results showed that, the diagnostic rate of atrial fibrillation was significantly higher in users who used the ECG patch early [53].”
BackgroundSelf-citationHernandez, M. F., & Rodríguez, F. (2022) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-022-00711-0 · Cited by 52
Data management and wearables in older adults: A systematic review
Alharbi, M., Straiton, N., Smith, S. R., Neubeck, L., & Gallagher, R. D. (2019) · Maturitas · doi:10.1016/j.maturitas.2019.03.012 · Cited by 50
Ford, C., Xie, C. X., Low, A., Rajakariar, K., Koshy, A. N., Sajeev, J., Roberts, L., Pathik, B., & Teh, A. W. (2022) · JACC. Clinical electrophysiology · doi:10.1016/j.jacep.2022.02.013 · Cited by 50
Noninvasive Continuous Monitoring of Vital Signs With Wearables: Fit for Medical Use?
Jacobsen, M., Dembek, T. A., Kobbe, G., Gaidzik, P. W., & Heinemann, L. (2021) · Journal of Diabetes Science and Technology · doi:10.1177/1932296820904947 · Cited by 49
Home Monitoring of Cardiac Devices in the Era of COVID-19
Miller, J. C., Skoll, D., & Saxon, L. A. (2020) · Current Cardiology Reports · doi:10.1007/s11886-020-01431-w · Cited by 48
Digital health in the era of personalized healthcare
Cancela, J., Charlafti, I., Colloud, S., & Wu, C. (2020) · Elsevier eBooks · doi:10.1016/b978-0-12-820077-3.00002-x · Cited by 46
Wearables research for continuous monitoring of patient outcomes: A scoping review
Lodewyk, K., Wiebe, M., Dennett, L., Larsson, J., Greenshaw, A. J., & Hayward, J. (2025) · PLOS Digital Health · doi:10.1371/journal.pdig.0000860 · Cited by 53
Gibson, C. M., Steinhubl, S. R., Lakkireddy, D., Turakhia, M. P., PASSMAN, R. S., Jones, W. S., Bunch, T. J., Curtis, A. B., Peterson, E. D., Ruskin, J. N., et al. (2023) · American Heart Journal · doi:10.1016/j.ahj.2023.01.004 · Cited by 50
Self-citationElectrocardiogram Heartbeat Classification for Arrhythmias and Myocardial Infarction
Pham, B.-T., Le, P. T., Tai, T.-C., Hsu, Y., Li, Y., & Wang, J. (2023) · Sensors · doi:10.3390/s23062993 · Cited by 46
“As a result, the ECG monitoring system is used in hospitals [16,17], smart homes [18,19], remote contexts [20], or sports [21,22].”
BackgroundA Comprehensive Survey on Wearable Computing for Mental and Physical Health Monitoring
Elfouly, T., & Alouani, A. T. (2025) · Electronics · doi:10.3390/electronics14173443 · Cited by 59
“In large-scale clinical studies, such as the mSToPS trial, wearable ECG patches demonstrated statistically significant improvements in detecting previously undiagnosed AF when compared to standard care, validating their utility in population-level screening and preventative cardiology [105].”
A Comprehensive Explanation Framework for Biomedical Time Series Classification
Ivaturi, P., Gadaleta, M., Pandey, A. C., Pazzani, M. J., Steinhubl, S. R., & Quer, G. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3060997 · Cited by 46
Self-citationAtrial fibrillation: mechanism and clinical management
Hu, Z., Ding, L., & Yao, Y. (2023) · Chinese Medical Journal · doi:10.1097/cm9.0000000000002906 · Cited by 45
“[8] Through machine learning algorithms, healthcare providers can now detect and diagnose AF at an earlier stage.”
Saghir, N., Aggarwal, A., Soneji, N., Valencia, V. E., Rodgers, G. P., & Kurian, T. K. (2020) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2020.07.002 · Cited by 45
“Given that diagnostic-quality data are available instantly for those prompted by the AW algorithm or those experiencing specific symptoms, time-sensitive events can be captured and shared with medical providers for clinical interpretation, and true arrhythmias likely can be diagnosed earlier and treated appropriately.…”
BackgroundMobile health technology in atrial fibrillation
Bonini, N., Vitolo, M., Imberti, J. F., Proietti, M., Romiti, G. F., Boriani, G., Johnsen, S. P., Guo, Y., & Lip, G. Y. H. (2022) · Expert Review of Medical Devices · doi:10.1080/17434440.2022.2070005 · Cited by 46
Zhang, H., Zhang, J., Li, H., Chen, Y., Yang, B., Guo, Y., & Chen, Y. (2019) · Journal of Medical Internet Research · doi:10.2196/14909 · Cited by 44
Wearable Bioimpedance Monitoring: Viewpoint for Application in Chronic Conditions
Groenendaal, W., Lee, S., & Hoof, C. A. V. (2021) · JMIR Biomedical Engineering · doi:10.2196/22911 · Cited by 43
“However, there are also examples that show an increase of health care resource utilization with wearables [20].”
Background
Metadata
- Authors
- 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.
- Affiliations
- Scripps Translational Science Institute, La Jolla, California
- Healthagen Outcomes, Chicago, Illinois
- Janssen Scientific Affairs (United States) (US)
- Corresponding author
- Steinhubl, Steven R.
- ORCID
- Venue
- JAMA
- Publisher
- American Medical Association (AMA)
- Published
- 2018-07-10
- Volume
- 320
- Issue
- 2
- Pages
- 146
- ISSN
- 0002-9955 (ISSN-L)
- 0098-7484
- PMC
- PMC6583518
- OpenAlex
- W2869528658
- Cited by
- 539
- References
- 51
- Citations by year
- 2026: 65 · 2025: 70 · 2024: 58 · 2023: 56 · 2022: 72 · 2021: 79 · 2020: 68 · 2019: 58 · 2018: 13
- FWCI
- 41.17
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- JAMA (publishedVersion)
- Europe PMC (PubMed Central) (publishedVersion)
- PubMed Central (publishedVersion)
- PubMed (publishedVersion)
- Funders
- National Institutes of Health (UL1TR001114)
- National Center for Advancing Translational Sciences
- Janssen Pharmaceuticals
- Topics
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Atrial Fibrillation Management and Outcomes
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Keywords
- atrial fibrillation, opportunistic screening, undiagnosed atrial fibrillation, anticoagulant prescription, health care utilization, CHA2DS2-VASc score, stroke prevention, older adults, health insurance beneficiaries, emergency department visits, hospitalization, primary care visits, cost-effectiveness
- MeSH terms
- Wearable Electronic Devices, Aged, Anticoagulants, Atrial Fibrillation, Female, Health Resources, Humans, Male, Mass Screening, Middle Aged, Risk Factors, Cohort Studies, Electrocardiography, Ambulatory, Comorbidity, Incidence, Intention to Treat Analysis
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
