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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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.
“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]
Split into 2 atoms — 1 confirmed, 1 partially supported, 0 disputed, 0 misattributed, 0 unverifiable. Expand the claim to see per-atom verdicts.
Experimental — accuracy not yet measuredAtrial fibrillation screening via ECG patches in selected high-risk individuals is feasible and clinically valuable.
“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 mSToPS study, ECG monitoring led to a higher rate of new AF diagnosis at 4 months and 1 year and was associated with increased initiation of anticoagulation therapy and outpatient cardiology and primary care visits in patients without previously known AF.
“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]
iRhythm's single-use Zio patch monitors electrical pulses from the heart for 14 days.
“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]
The Zio patch is more effective than intermittent hospital check-ups at detecting abnormal rhythms.
“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 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.
“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 mSToPS study, 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.
“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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Wearable Devices in Cardiovascular Medicine
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Wearable Sensing and Telehealth Technology with Potential Applications in the Coronavirus Pandemic
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“times higher compared with regular ECG check [80].”
BackgroundHow to use digital devices to detect and manage arrhythmias: an EHRA practical guide
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Skin sensors are the future of health care
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“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 · see citing works
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Remote smartphone monitoring of Parkinson’s disease and individual response to therapy
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“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.”
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Artificial intelligence algorithm for predicting cardiac arrest using electrocardiography
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A systematic review of methods used to conduct decentralised clinical trials
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“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 93 · see citing works
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“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
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World Heart Federation Roadmap on Atrial Fibrillation – A 2020 Update
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Technology-Enabled Clinical Trials
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A polygenic risk score predicts atrial fibrillation in cardiovascular disease
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World Heart Federation Roadmap for Digital Health in Cardiology
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The virtual physical exam in the 21st century
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“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.”
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The Auto-eFACE: Machine Learning–Enhanced Program Yields Automated Facial Palsy Assessment Tool
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Dependence of Skin-Electrode Contact Impedance on Material and Skin Hydration
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“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 · see citing works
Research Priorities in Atrial Fibrillation Screening
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Deep Learning of Electrocardiograms in Sinus Rhythm From US Veterans to Predict Atrial Fibrillation
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Wearable Devices for Cardiac Rhythm Diagnosis and Management
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Prediction of atrial fibrillation from at-home single-lead ECG signals without arrhythmias
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How will machine learning inform the clinical care of atrial fibrillation?
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A Real-Time Wearable Physiological Monitoring System for Home-Based Healthcare Applications
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“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 59 · see citing works
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 64 · see citing works
“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 · see citing works
A Wearable Electrocardiogram Telemonitoring System for Atrial Fibrillation Detection
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“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 · see citing works
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“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
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Machine learning guided electrochemical sensor for passive sweat cortisol detection
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Effect of Screening for Undiagnosed Atrial Fibrillation on Stroke Prevention
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Connected Health Technology for Cardiovascular Disease Prevention and Management
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Self-citationAtrial Fibrillation and Stroke
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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 · see citing works
“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 50 · see citing works
Present and Future of Digital Health in Diabetes and Metabolic Disease
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“Results showed that, the diagnostic rate of atrial fibrillation was significantly higher in users who used the ECG patch early [53].”
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Data management and wearables in older adults: A systematic review
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Noninvasive Continuous Monitoring of Vital Signs With Wearables: Fit for Medical Use?
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Home Monitoring of Cardiac Devices in the Era of COVID-19
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Digital health in the era of personalized healthcare
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Wearables research for continuous monitoring of patient outcomes: A scoping review
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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 · see citing works
“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 56 · see citing works
“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 · see citing works
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 · see citing works
“[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 · see citing works
“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 · see citing works
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 · see citing works
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 · see citing works
“However, there are also examples that show an increase of health care resource utilization with wearables [20].”
Background
- 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.
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