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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.
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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]
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“[24] provided evidence on the impact of a selfapplied wearable ECG patch in AF detection.”
BackgroundFabritz, L., Connolly, D. L., Czarnecki, E., Dudek, D., Guasch, E., Haase, D., Huebner, T. A., Złahoda-Huzior, A., Jolly, K. B., Kirchhof, P. F., et al. (2022) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztac067 · Cited by 23
“Both age and self-reported estimated CHA 2 DS 2 -VASc-Score were comparable to or even lower than in similar studies 29–31, 33 Limitations.”
BackgroundResultInfluentialLeveraging electronic health records to streamline the conduct of cardiovascular clinical trials
Khan, M. S., Usman, M., Talha, K. M., Spall, H. G. C. V., Greene, S. J., Vaduganathan, M., Khan, S. S., Mills, N. L., Ali, Z. A., Mentz, R. J., et al. (2023) · European Heart Journal · doi:10.1093/eurheartj/ehad171 · Cited by 21
Kim, Y.-H., Lee, M., Yoon, J., Kim, Y., Min, H., Cho, H., Park, J., & Shin, T. (2023) · Diagnostics · doi:10.3390/diagnostics13172849 · Cited by 22
“By refining the current diagnostic paradigms, we believe we can bring a robust solution that aids in the proactive management of atrial fibrillation and other arrhythmias [10,11].”
Gruwez, H., Verbrugge, F. H., Proesmans, T., Evens, S., Vanacker, P., Rutgers, M. P., Vanhooren, G., Bertrand, P. B., Pison, L., Haemers, P., et al. (2023) · European Heart Journal - Digital Health · doi:10.1093/ehjdh/ztad054 · Cited by 23
Nadarajah, R., Wahab, A., Reynolds, C., Raveendra, K., Askham, D., Dawson, R. L., Keene, J., Shanghavi, S., Lip, G. Y. H., Hogg, D., et al. (2023) · Open Heart · doi:10.1136/openhrt-2023-002447 · Cited by 22
Evaluating Health Technology Through Pragmatic Trials
Peterson, E. D., & Harrington, R. A. (2018) · JAMA · doi:10.1001/jama.2018.8512 · Cited by 21
Enhanced Detection and Prompt Diagnosis of Atrial Fibrillation Using Apple Watch
van Steijn, N. J., Blommestijn, I. S., Blok, S., Pepplinkhuizen, S., Somsen, G. A., Knops, R. E., Breukel, L., Tijssen, J. G. P., Tulevski, I. I., Croon, P. M., et al. (2026) · JACC · doi:10.1016/j.jacc.2025.11.032 · Cited by 21
HRS White Paper on Clinical Utilization of Digital Health Technology
Wan, E. Y., Ghanbari, H., AKOUM, N. W., Attia, Z. I., Asirvatham, S. J., Chung, E. H., Dagher, L., Al‐Khatib, S. M., Mendenhall, G. S., McManus, D. D., et al. (2021) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2021.07.001 · Cited by 21
Steinhubl, S. R., Waalen, J., Sanyal, A., Edwards, A. M., Ariniello, L. M., Ebner, G. S., Baca-Motes, K., Zambon, R. A., Sarich, T. C., & Topol, E. J. (2021) · PLoS ONE · doi:10.1371/journal.pone.0258276 · Cited by 21
Self-citationFully digital self-screening for atrial fibrillation with patch electrocardiogram
Sandberg, E. L., Halvorsen, S., Berge, T., Grimsmo, J., Atar, D., Fensli, R. W., Grenne, B. L., & Jortveit, J. (2023) · EP Europace · doi:10.1093/europace/euad075 · Cited by 23
Usability of a Wrist-Worn Smartwatch in a Direct-to-Participant Randomized Pragmatic Clinical Trial
Galarnyk, M., Quer, G., McLaughlin, K., Ariniello, L. M., & Steinhubl, S. R. (2019) · Digital Biomarkers · doi:10.1159/000504838 · Cited by 21
Self-citationMannina, C. M., Jin, Z., Matsumoto, K., Ito, K., BIVIANO, A. B., Elkind, M. S. V., Rundek, T., Homma, S., Sacco, R. L., & Tullio, M. R. D. (2021) · International Journal of Cardiology · doi:10.1016/j.ijcard.2021.05.006 · Cited by 21
Yan, B., Tu, H. T. H., Lam, C., Swift, C., Ho, M. S., Mok, V., Sui, Y., Sharpe, D., Ghia, D., Jannes, J., et al. (2020) · Journal of Stroke · doi:10.5853/jos.2020.00689 · Cited by 20
Should we screen for atrial fibrillation?
Lown, M. T., & Moran, P. S. (2019) · BMJ · doi:10.1136/bmj.l43 · Cited by 20
New Avenues for Optimal Treatment of Atrial Fibrillation and Stroke Prevention
Marchis, G. M. D., Sposato, L. A., Kühne, M., Dittrich, T. D., Bonati, L. H., Fischer, U., & Chaturvedi, S. (2021) · Stroke · doi:10.1161/strokeaha.120.032060 · Cited by 20
Bias and Loss to Follow‐Up in Cardiovascular Randomized Trials: A Systematic Review
Fong, L. C. W., Ford, T. J., da Costa, B. R., Jüni, P., & Berry, C. (2020) · Journal of the American Heart Association · doi:10.1161/jaha.119.015361 · Cited by 19
Corica, B., Bonini, N., Imberti, J. F., Romiti, G. F., Vitolo, M., Attanasio, L., Basili, S., Freedman, B., Potpara, T., Boriani, G., et al. (2023) · European Heart Journal Open · doi:10.1093/ehjopen/oead031 · Cited by 19
Deep learning evaluation of echocardiograms to identify occult atrial fibrillation
Yuan, N., Stein, N. R., Duffy, G., Sandhu, R. K., Chugh, S. S., Chen, P., Rosenberg, C., Albert, C. M., Cheng, S. C., Siegel, R. J., et al. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01090-z · Cited by 22
Fuse, Y., Murphy, S. N., Ikari, H., Takahashi, A., Fuse, K., & Kawakami, E. (2025) · Allergology International · doi:10.1016/j.alit.2025.06.005 · Cited by 25
Principles for evaluating the clinical implementation of novel digital healthcare devices
Park, S. H., Do, K., Choi, J., Sim, J. S., Yang, D. M., Eo, H., Woo, H., Lee, J. M., Jung, S. E., & Oh, J. H. (2018) · Journal of Korean Medical Association · doi:10.5124/jkma.2018.61.12.765 · Cited by 18
“또한, 2017년에는 싱 가폴 의료진이 주축이 되어 수행한 다국적 다기관 연구가 JAMA에 발표되었는데[36], 이 연구는 7만 건이 넘는 영상 으로 학습한 딥러닝기반 인공지능 소프트웨어를 중국, 싱가 폴, 미국, 오스트레일리아, 멕시코, 홍콩에 있는 10개의 다 양한 상황의 외부 의료기관의 자료를 이용하여 진단능을 검 증하였고 인공지능 소프트웨어가 당뇨망막병증 진단에 있 어 높은 정확도를 낼 수 있음을 재차 확인하였다.”
InfluentialStroke Prevention in Atrial Fibrillation
Chiang, C., Chao, T., Choi, E., Lim, T. W., Krittayaphong, R., Li, M., Chen, M., Guo, Y., Okumura, K., & Lip, G. Y. H. (2022) · JACC Asia · doi:10.1016/j.jacasi.2022.06.004 · Cited by 18
“Novel Anticoagulation Agents in Atrial Fibrillation) trial showed that the Amulet or Watchman device was noninferior to NOACs for major AF-related events in patients with nonvalvular AF.72 However, the total patient number was only 402, and the study was underpowered to prove its effect on stroke prevention.72 The Amu…”
MethodInfluentialNanosensor Technologies and the Digital Transformation of Healthcare
Udoh, E., Hermel, M. H., Bharmal, M., Nayak, A., Patel, S., Butlin, M., & Bhavnani, S. P. (2023) · Personalized Medicine · doi:10.2217/pme-2022-0065 · Cited by 18
Robust in-vehicle heartbeat detection using multimodal signal fusion
Warnecke, J. M., Lasenby, J., & Deserno, T. M. (2023) · Scientific Reports · doi:10.1038/s41598-023-47484-z · Cited by 18
Smartwatch for the Detection of Atrial Fibrillation
Tajrishi, F. Z., Chitsazan, M., Chitsazan, M., Shojaei, F., Gunnam, V., & Chi, G. (2019) · Critical Pathways in Cardiology A Journal of Evidence-Based Medicine · doi:10.1097/hpc.0000000000000192 · Cited by 18
Reliable Detection of Atrial Fibrillation with a Medical Wearable during Inpatient Conditions
Jacobsen, M., Dembek, T. A., Ziakos, A. P., Gholamipoor, R., Kobbe, G., Kollmann, M., Blum, C. F., Müller‐Wieland, D., Napp, A., Heinemann, L., et al. (2020) · Sensors · doi:10.3390/s20195517 · Cited by 18
Andrikopoulou, E. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-15980-0.00011-9 · Cited by 18
A Review on Atrial Fibrillation Detection From Ambulatory ECG
Ma, C., Xiao, Z., Zhao, L., Biton, S. B., Behar, J. A., Long, X., Vullings, R., Aarts, R. M., Li, J., & Liu, C. (2023) · IEEE Transactions on Biomedical Engineering · doi:10.1109/tbme.2023.3321792 · Cited by 20
Overview of Wearable Healthcare Devices for Clinical Decision Support in the Prehospital Setting
Gathright, R., Mejia, I., Gonzalez, J. M., Torres, S. I. H., Berard, D., & Snider, E. J. (2024) · Sensors · doi:10.3390/s24248204 · Cited by 18
Current Use and Future Needs of Noninvasive Ambulatory Electrocardiogram Monitoring
Ikeda, T. (2020) · Internal Medicine · doi:10.2169/internalmedicine.5691-20 · Cited by 17
Chronic kidney disease and undiagnosed atrial fibrillation in individuals with diabetes
Heo, N. J., Rhee, S. Y., Waalen, J., & Steinhubl, S. R. (2020) · Cardiovascular Diabetology · doi:10.1186/s12933-020-01128-y · Cited by 17
Self-citationSensor Fusion for Robust Heartbeat Detection during Driving
Warnecke, J. M., Boeker, N., Spicher, N., Wang, J., Flormann, M., & Deserno, T. M. (2021) · Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC) · doi:10.1109/embc46164.2021.9630935 · Cited by 17
“Early detection of symptoms improves therapeutic outcomes and reduces mortality [2], but requires continuous health monitoring or regular medical check-ups [3].”
BackgroundMaervoet, J., Bossers, N., Borge, R. P., Hilpert, S. T., van Engen, A., & Smala, A. M. (2019) · Journal of Medical Economics · doi:10.1080/13696998.2019.1663355 · Cited by 17
Garikapati, K., Turnbull, S., Bennett, R. G., Campbell, T. G., Kanawati, J., Wong, M. S., Thomas, S. P., Chow, C. K., & Kumar, S. (2022) · Heart Lung and Circulation · doi:10.1016/j.hlc.2022.08.001 · Cited by 18
McIntyre, W. F., Mendoza, P. A., Belley‐Côté, E. P., Whitlock, R. P., Um, K. J., Майстренко, Н. С., Devereaux, P. J., Conen, D., Wong, J. A., Connolly, S. J., et al. (2018) · Clinical Cardiology · doi:10.1002/clc.23053 · Cited by 15
“Conservatively, the incidence of AF in the control group can be expected to be similar to the one found when screening the general population with similar intensity, that is, 11% to 21%.(22,23) For a sample size calculation, we estimated that 20% of participants in the control group will have AF and that 45% of partic…”
ResultVarma, N., Cygankiewicz, I., Turakhia, M. P., Heidbuchel, H., Hu, Y., Chen, L. Y., Couderc, J.-P., Cronin, E. M., Estep, J. D., Grieten, L., et al. (2021) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2020.11.004 · Cited by 16
“…and protection by OAC therapy (Freedman 2016,Martinez 2014,Tsivgoulis 2019), clinical trials to determine any benefit for opportunistically detected AF have not yet been completed but are underway (Gudmundsdottir 2019, Steinhubl 2018, Svennberg 2015, Heartline study https://www.heartline.com).”
BackgroundTsai, T., Lo, L., Liu, S.-H., Cheng, W., Chou, Y., Lin, W.-L., Lin, Y., Chang, S., Hu, Y., Chung, F., et al. (2018) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2018.11.012 · Cited by 15
Big Data and Atrial Fibrillation: Current Understanding and New Opportunities
Wang, Q., & Wang, Z. (2020) · Journal of Cardiovascular Translational Research · doi:10.1007/s12265-020-10008-5 · Cited by 15
Gilliam, F. R., Ciesielski, R., Shahinyan, K., Shakya, P., Cunsolo, J., Panchal, J., Król-Józaga, B., Król, M., Kierul, O. S., Bridges, C. R., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00725-3 · Cited by 17
Assessment and Management of Atrial Fibrillation in Older Adults with Frailty
Diaz, A. N. R., Troy, A. L., Kaplinskiy, V., Pritchard, A., Vani, R., Ko, D., & Orkaby, A. R. (2024) · Geriatrics · doi:10.3390/geriatrics9020050 · Cited by 15
Media Coverage of the Benefits and Harms of Testing the Healthy: a protocol for a descriptive study
O’Keeffe, M., Barratt, A. L., Maher, C. G., Zadro, J. R., Fabbri, A., Jones, M., & Moynihan, R. N. (2019) · BMJ Open · doi:10.1136/bmjopen-2019-029532 · Cited by 15
Lohrmann, G. M., Kaplan, R. M., Ziegler, P., Monteiro, J., & PASSMAN, R. S. (2020) · Journal of Cardiovascular Electrophysiology · doi:10.1111/jce.14781 · Cited by 15
Wang, C., Zhang, Y., Kong, Q.-L., Wu, Z., Yang, P., Zhu, C.-H., Chen, S., Wu, T., Wu, Q., & Chen, Q. (2021) · Chinese Medical Journal · doi:10.1097/cm9.0000000000001650 · Cited by 15
“Moreover, many wearable devices for monitoring ECGs have been developed over the past few years.([18]) Therefore, the serum potassium concentration can be dynamically detected at home, which is highly beneficial for patients prone to hypokalemia.”
BackgroundSandberg, E. L., Grenne, B. L., Berge, T., Grimsmo, J., Atar, D., Halvorsen, S., Fensli, R. W., & Jortveit, J. (2021) · Journal of Healthcare Engineering · doi:10.1155/2021/5230947 · Cited by 15
Digital Cardiology: Opportunities for Disease Prevention
Vervoort, D., Marvel, F. A., Isakadze, N., Kpodonu, J., & Martin, S. (2020) · Current Cardiovascular Risk Reports · doi:10.1007/s12170-020-00644-6 · Cited by 10
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
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