The DOI matches its Crossref record; 12 of the 12 references it cites were found in a registry or an index.
Large-Scale Assessment of a Smartwatch to Identify Atrial Fibrillation
Open accessAbstract
BACKGROUND: Optical sensors on wearable devices can detect irregular pulses. The ability of a smartwatch application (app) to identify atrial fibrillation during typical use is unknown. METHODS: Participants without atrial fibrillation (as reported by the participants themselves) used a smartphone (Apple iPhone) app to consent to monitoring. If a smartwatch-based irregular pulse notification algorithm identified possible atrial fibrillation, a telemedicine visit was initiated and an electrocardiography (ECG) patch was mailed to the participant, to be worn for up to 7 days. Surveys were administered 90 days after notification of the irregular pulse and at the end of the study. The main objectives were to estimate the proportion of notified participants with atrial fibrillation shown on an ECG patch and the positive predictive value of irregular pulse intervals with a targeted confidence interval width of 0.10. RESULTS: We recruited 419,297 participants over 8 months. Over a median of 117 days of monitoring, 2161 participants (0.52%) received notifications of irregular pulse. Among the 450 participants who returned ECG patches containing data that could be analyzed - which had been applied, on average, 13 days after notification - atrial fibrillation was present in 34% (97.5% confidence interval [CI], 29 to 39) overall and in 35% (97.5% CI, 27 to 43) of participants 65 years of age or older. Among participants who were notified of an irregular pulse, the positive predictive value was 0.84 (95% CI, 0.76 to 0.92) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular pulse notification and 0.71 (97.5% CI, 0.69 to 0.74) for observing atrial fibrillation on the ECG simultaneously with a subsequent irregular tachogram. Of 1376 notified participants who returned a 90-day survey, 57% contacted health care providers outside the study. There were no reports of serious app-related adverse events. CONCLUSIONS: The probability of receiving an irregular pulse notification was low. Among participants who received notification of an irregular pulse, 34% had atrial fibrillation on subsequent ECG patch readings and 84% of notifications were concordant with atrial fibrillation. This siteless (no on-site visits were required for the participants), pragmatic study design provides a foundation for large-scale pragmatic studies in which outcomes or adherence can be reliably assessed with user-owned devices. (Funded by Apple; Apple Heart Study ClinicalTrials.gov number, NCT03335800.).
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
“Importantly, arrhythmia detection algorithms such as the irregular pulse notification in the Apple Heart Study (Perez et al., 2019) remained robust, with sensitivity for atrial fibrillation unaffected by skin tone across a large-scale population [15].”
From Photoplethysmography in Diverse Skin Tones: Evaluating Bias in Smartwatch Health Monitoring (2025) · cites this paper as [15]
Experimental — accuracy not yet measuredIn the citing paper
“While recent large-scale longitudinal studies have shown promise in leveraging wearable sensor data for health monitoring and prediction, they also introduce new challenges for data modelling.”
From Human-centred artificial intelligence for mobile health sensing: challenges and opportunities (2023) · cites this paper as [18]
In the citing paper
“PPG tachograms, especially when augmented by single-lead ECG, can also identify arrhythmias19.”
From Smart wearable devices in cardiovascular care: Where we are and how to move forward (2021) · cites this paper as [19]
In the citing paper
“AF screening through PPG has shown variable accuracy depending on the algorithms and devices used19,51,52”
From Smart wearable devices in cardiovascular care: Where we are and how to move forward (2021) · cites this paper as [19]
In the citing paper
“The positive predictive value for AF detection was 84% and 71% for the irregular notification algorithm and individual tachograms, respectively19.”
From Smart wearable devices in cardiovascular care: Where we are and how to move forward (2021) · cites this paper as [19]
In the citing paper
“a common practice is to involve an accelerometer for artifact identification [11][12][13][27][28][29][30].”
From Modeling of artifacts in the wrist photoplethysmogram: Application to the detection of life-threatening arrhythmias (2021) · cites this paper as [11]
In the citing paper
“The positive predictive value (PPV) of notifications was found to be 0.84, indicating that wearables may have utility for identifying possible AF.”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [29]
In the citing paper
“In [29], an irregular pulse notification was generated if an irregular pulse was identified in at least five out of six PPG recordings in a 48-h period [29].”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [29]
In the citing paper
“Initial work indicates that PPG signal quality is highest during sleep when movement and ambient light are minimal, supporting the approach of only acquiring PPG signals during periods of low activity [29].”
From Wearable Photoplethysmography for Cardiovascular Monitoring (2022) · cites this paper as [29]
In the citing paper
“The Apple Heart Study [15] is another example that was a breakthrough for showing that wearable devices may detect atrial fibrillation (AF) and foster a discussion of potentials and limitations with regard to health care providers, researchers, and members of the media and economy [16,17].”
From The impact of wearable technologies in health research: scoping review (2022) · cites this paper as [15]
In the citing paper
“For example, Fitbit alone has approximately 476 published studies and 449 studies registered on ClinicalTrials.gov [4] and the Apple Watch Heart Study recruited 419 927 people to track irregular heart rhythms [20].”
From Limiting racial disparities and bias for wearable devices in health science research (2020) · cites this paper as [20]
In the citing paper
“Researchers are increasingly using consumer wearables or wrist-worn smartwatches and fitness monitors for the digital measurement of cardiovascular health and psychophysiological stress as these devices allow for continuous, scalable, and unobtrusive, “big data” collection of overall cardiac activity in real-world conditions with large samples25–30.”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [27]
In the citing paper
“However, if a study were interested in HR arrythmias using wearable assessments (such as atrial fibrillation), then these extreme values may be of vital interest and should not be removed or altered27.”
From Guidelines for wrist-worn consumer wearable assessment of heart rate in biobehavioral research (2020) · cites this paper as [27]
In the citing paper
“Deep neural networks (DNNs) have emerged as a powerful tool for learning features and predictions from raw data in cardiology.12”
From Unlocking Tomorrow’s Health Care: Expanding the Clinical Scope of Wearables by Applying Artificial Intelligence (2024) · cites this paper as [12]
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
Cryptogenic stroke and underlying atrial fibrillation
Sanna, T., Diener, H.-C., Passman, R. S., Di Lazzaro, V., Bernstein, R. A., Morillo, C. A., Rymer, M. M., Thijs, V., Rogers, T., Beckers, F., et al. (2014) · New England Journal of Medicine · doi:10.1056/nejmoa1313600 · Cited by 2,161
Subclinical atrial fibrillation and the risk of stroke
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., et al. (2012) · New England Journal of Medicine · doi:10.1056/nejmoa1105575 · Cited by 2,100
Colilla, S., Crow, A., Petkun, W., Singer, D. E., Simon, T. A., & Liu, X. (2013) · The American Journal of Cardiology · doi:10.1016/j.amjcard.2013.05.063 · Cited by 1,323
Passive Detection of Atrial Fibrillation Using a Commercially Available Smartwatch
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., et al. (2018) · JAMA Cardiology · doi:10.1001/jamacardio.2018.0136 · Cited by 566
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., et al. (2018) · JAMA · doi:10.1001/jama.2018.8102 · Cited by 539
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
Self-citationGenetic Predisposition, Clinical Risk Factor Burden, and Lifetime Risk of Atrial Fibrillation
Weng, L.-C. C., Preis, S. R., Hulme, O. L., Larson, M. G., Choi, S. H., Wang, B., Trinquart, L., McManus, D. D., Stærk, L., Lin, H., et al. (2017) · Circulation · doi:10.1161/circulationaha.117.031431 · Cited by 276
Estimated prevalence of undiagnosed atrial fibrillation in the United States
Turakhia, M. P., Shafrin, J. T., Bognar, K., Trocio, J. N., Abdulsattar, Y., Wiederkehr, D., & Goldman, D. P. (2018) · PLoS ONE · doi:10.1371/journal.pone.0195088 · Cited by 180
Self-citationStrain, T., Wijndaele, K., & Brage, S. (2018) · JMIR mhealth and uhealth · doi:10.2196/11898 · Cited by 66
Cited by
Sorted by citations
Items 101–200 of 1,002
Leclercq, C., Witt, H., Hindricks, G., Katra, R. P., Albert, D., Belliger, A., Cowie, M. R., Deneke, T., Friedman, P., Haschemi, M., et al. (2022) · EP Europace · doi:10.1093/europace/euac052 · Cited by 109
Self-citationStroke Prevention in Atrial Fibrillation
Katsanos, A. H., Kamel, H., Healey, J. S., & Hart, R. G. (2020) · Circulation · doi:10.1161/circulationaha.120.049768 · Cited by 108
Doherty, C., Baldwin, M., Keogh, A., Caulfield, B., & Argent, R. (2024) · Sports Medicine · doi:10.1007/s40279-024-02077-2 · Cited by 133
Wearables in cardiology: Here to stay
Dagher, L., Shi, H., Zhao, Y., & Marrouche, N. F. (2020) · Heart Rhythm · doi:10.1016/j.hrthm.2020.02.023 · Cited by 107
Green, T., McNair, N. D., Hinkle, J. L., Middleton, S., Miller, E., Perrin, S. M., Power, M., Southerland, A. M., & Summers, D. (2021) · Stroke · doi:10.1161/str.0000000000000357 · Cited by 112
Optical coherence tomography in the 2020s—outside the eye clinic
Chopra, R., Wagner, S. K., & Keane, P. A. (2020) · Eye · doi:10.1038/s41433-020-01263-6 · Cited by 103
Machine Learning for Real-Time Heart Disease Prediction
Bertsimas, D., Mingardi, L., & Stellato, B. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3066347 · Cited by 116
Chronic disease management in heart failure: focus on telemedicine and remote monitoring
Alvarez, P. A., Sianis, A., Brown, J., Ali, A., & Briasoulis, Α. (2021) · Reviews in Cardiovascular Medicine · doi:10.31083/j.rcm2202046 · Cited by 106
Kim, K. H., Oh, J., Yang, G., Lee, J., Kim, J., Gwak, S., Cho, I., Lee, S., Byun, H. K., Choi, H., et al. (2022) · JAMA Oncology · doi:10.1001/jamaoncol.2022.4202 · Cited by 103
Self-citationArtificial Intelligence (AI) and Cardiovascular Diseases: An Unexpected Alliance
Romiti, S., Vinciguerra, M., Saade, W., Cortajarena, I. A., & Greco, E. (2020) · Cardiology Research and Practice · doi:10.1155/2020/4972346 · Cited by 109
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
Nielsen, J. C., Lin, Y., Figueiredo, M. J. D. O., Shamloo, A. S., Alfie, A., Bovéda, S., Dagres, N., Toro, D. D., Eckhardt, L. L., Ellenbogen, K. A., et al. (2020) · Journal of Arrhythmia · doi:10.1002/joa3.12338 · Cited by 106
Artificial Intelligence-Based Clinical Decision Support Systems in Cardiovascular Diseases
Bozyel, S. (2024) · The Anatolian Journal of Cardiology · doi:10.14744/anatoljcardiol.2023.3685 · Cited by 106
Nielsen, J. C., Lin, Y.-J., de Oliveira Figueiredo, M. J., Sepehri Shamloo, A., Alfie, A., Boveda, S., Dagres, N., Di Toro, D., Eckhardt, L. L., Ellenbogen, K., et al. (2020) · EP Europace · doi:10.1093/europace/euaa065 · Cited by 98
Baek, Y., Lee, S., Choi, W., & Kim, D. (2021) · Scientific Reports · doi:10.1038/s41598-021-92172-5 · Cited by 99
Accuracy of Smartphone Camera Applications for Detecting Atrial Fibrillation
O’Sullivan, J. W., Grigg, S. E., Crawford, W., Turakhia, M. P., Perez, M. V., Ingelsson, E., Wheeler, M. T., Ioannidis, J. P. A., & Ashley, E. A. (2020) · JAMA Network Open · doi:10.1001/jamanetworkopen.2020.2064 · Cited by 100
Self-citationCosoli, G., Spinsante, S., Scardulla, F., D’Acquisto, L., & Scalise, L. (2021) · Measurement · doi:10.1016/j.measurement.2021.109243 · Cited by 102
Detecting beats in the photoplethysmogram: benchmarking open-source algorithms
Charlton, P. H., Kotzen, K., Mejía‐Mejía, E., Aston, P. J., Budidha, K., Mant, J., Pettit, C., Behar, J. A., & Kyriacou, P. A. (2022) · Physiological Measurement · doi:10.1088/1361-6579/ac826d · Cited by 116
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
Chen, E., Jiang, J., Su, R., Gao, M., Zhu, S., Zhou, J., & Huo, Y. (2020) · Heart Rhythm · doi:10.1016/j.hrthm.2020.01.034 · Cited by 90
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
Atrial fibrillation: stroke prevention
Chao, T., Potpara, T., & Lip, G. Y. H. (2024) · The Lancet Regional Health - Europe · doi:10.1016/j.lanepe.2023.100797 · Cited by 103
The Use of Wearable ECG Devices in the Clinical Setting: A Review
Kamga, P., Mostafa, R., & Zafar, S. (2022) · Current Emergency and Hospital Medicine Reports · doi:10.1007/s40138-022-00248-x · Cited by 94
Individualized Studies of Triggers of Paroxysmal Atrial Fibrillation
Marcus, G. M., Modrow, M. F., Schmid, C. H., Sigona, K., Nah, G., Yang, J., Chu, T.-C., Joyce, S., Getabecha, S., Ogomori, K., et al. (2021) · JAMA Cardiology · doi:10.1001/jamacardio.2021.5010 · Cited by 94
Self-citationA 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
Consumer Wearable Health and Fitness Technology in Cardiovascular Medicine
Petek, B. J., Al‐Alusi, M. A., Moulson, N., Grant, A. J., Besson, C., Guseh, J. S., Wasfy, M. M., Grémeaux, V., Churchill, T. W., & Baggish, A. L. (2023) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2023.04.054 · Cited by 113
Nazarian, S., Lam, K., Darzi, A. W., & Ashrafian, H. (2021) · Journal of Medical Internet Research · doi:10.2196/28974 · Cited by 101
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
The role of wearables in heart failure
Singhal, A., & Cowie, M. R. (2020) · Current Heart Failure Reports · doi:10.1007/s11897-020-00467-x · Cited by 85
Digital approaches to enhancing community engagement in clinical trials
Tan, R. K. J., Wu, D., Day, S., Zhao, Y., Larson, H. J., Sylvia, S., Tang, W., & Tucker, J. D. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00581-1 · Cited by 93
Cardiovascular Disease Screening in Women: Leveraging Artificial Intelligence and Digital Tools
Adedinsewo, D. A., Pollak, A. W., Phillips, S. D., Smith, T. L., Svatikova, A., Hayes, S. N., Mulvagh, S. L., Norris, C., Roger, V. L., Noseworthy, P. A., et al. (2022) · Circulation Research · doi:10.1161/circresaha.121.319876 · Cited by 97
Uittenbogaart, 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
Avram, R., Ramsis, M., Cristal, A. D., Nathan, V., Zhu, L., Kim, J., Kuang, J., Gao, A. J., Vittinghoff, E., Rohdin-Bibby, L., et al. (2021) · Heart Rhythm · doi:10.1016/j.hrthm.2021.03.044 · Cited by 85
Chung, S., Sofat, R., Acosta, D., Taylor, J., Lambiase, P. D., Casas, J. P., & Providencia, R. A. (2021) · The Lancet Regional Health - Europe · doi:10.1016/j.lanepe.2021.100157 · Cited by 87
Assessing the Economic Value of Clinical Artificial Intelligence: Challenges and Opportunities
Hendrix, N., Veenstra, D. L., Cheng, M. M., Anderson, N. C., & Verguet, S. (2021) · Value in Health · doi:10.1016/j.jval.2021.08.015 · Cited by 89
Ethnicity and Metabolic Syndrome: Implications for Assessment, Management and Prevention
Lear, S. A., & Gašević, D. (2019) · Nutrients · doi:10.3390/nu12010015 · Cited by 93
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
Svennberg, E., Han, J. K., Caiani, E. G., Engelhardt, S., Ernst, S., Friedman, P. A., Garcia, R., Ghanbari, H., Hindricks, G., Man, S. H., et al. (2025) · EP Europace · doi:10.1093/europace/euaf071 · Cited by 98
Brandes, A., Stavrakis, S., Freedman, B., Antoniou, S., Boriani, G., Camm, A. J., Chow, C. K., Ding, E., Engdahl, J., Gibson, C. M., et al. (2022) · Circulation · doi:10.1161/circulationaha.121.058911 · Cited by 88
Self-citationThe present and future of cough counting tools
Hall, J., Lozano, M., Estrada, L., Birring, S. S., & Turner, R. D. (2020) · Journal of Thoracic Disease · doi:10.21037/jtd-2020-icc-003 · Cited by 82
Mobile health applications for the detection of atrial fibrillation: a systematic review
Lopez Perales, C. R., Van Spall, H. G. C., Maeda, S., Jimenez, A., Laţcu, D. G., Milman, A., Kirakoya-Samadoulougou, F., Mamas, M. A., Muser, D., & Casado Arroyo, R. (2021) · EP Europace · doi:10.1093/europace/euaa139 · Cited by 77
Health Disparities, Clinical Trials, and the Digital Divide
Adedinsewo, D. A., Eberly, L. A., Sokumbi, O., Rodriguez, J. A., Patten, C. A., & Brewer, L. C. (2023) · Mayo Clinic Proceedings · doi:10.1016/j.mayocp.2023.05.003 · Cited by 91
Mizuno, A., Changolkar, S., & Patel, M. S. (2020) · Annual Review of Medicine · doi:10.1146/annurev-med-050919-031534 · Cited by 82
Is there a role for statistics in artificial intelligence?
Friedrich, S., Antes, G., Behr, S., Binder, H., Brannath, W., Dumpert, F., Ickstadt, K., Kestler, H. A., Lederer, J., Leitgöb, H., et al. (2021) · Advances in Data Analysis and Classification · doi:10.1007/s11634-021-00455-6 · Cited by 99
Healthsheet: Development of a Transparency Artifact for Health Datasets
Rostamzadeh, N., Mincu, D., Roy, S., Smart, A., Wilcox, L. G., Pushkarna, M., Schrouff, J., Amironesei, R., Moorosi, N., & Heller, K. (2022) · ACM Conference on Fairness, Accountability, and Transparency (FAccT) · doi:10.1145/3531146.3533239 · Cited by 87
Postoperative atrial fibrillation (POAF) after cardiac surgery: clinical practice review
Suero, O. R., Ali, A. K., Barron, L. M., Segar, M. W., Moon, M. R., & Chatterjee, S. (2024) · Journal of Thoracic Disease · doi:10.21037/jtd-23-1626 · Cited by 78
Emerging Technologies for Identifying Atrial Fibrillation
Ding, E., Marcus, G. M., & McManus, D. D. (2020) · Circulation Research · doi:10.1161/circresaha.119.316342 · Cited by 77
The 2020 ESC Guidelines on the Diagnosis and Management of Atrial Fibrillation
Kotalczyk, A., Lip, G. Y. H., & Calkins, H. G. (2021) · Arrhythmia & Electrophysiology Review · doi:10.15420/aer.2021.07 · Cited by 78
Artificial Intelligence in Cardiovascular Care—Part 2: Applications
Jain, S. S., Elias, P., Poterucha, T., Randazzo, M., Lopez Jimenez, F., Khera, R., Perez, M., Ouyang, D., Pirruccello, J., Salerno, M., et al. (2024) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2024.03.401 · Cited by 74
Self-citationVarma, 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
Self-citationAnker, S. D., Butler, J., Khan, M. S., Abraham, W. T., Bauersachs, J. K., Bocchi, E. A., Bozkurt, B., D, E. B. M., Chopra, V., Cleland, J. G. F., et al. (2020) · European Heart Journal · doi:10.1093/eurheartj/ehaa461 · Cited by 73
Fürstenau, D., Gersch, M., & Schreiter, S. (2023) · Business & Information Systems Engineering · doi:10.1007/s12599-023-00804-z · Cited by 83
Omboni, S., Panzeri, E., & Campolo, L. (2020) · Current Hypertension Reports · doi:10.1007/s11906-020-01056-y · Cited by 74
Digital Health Innovations to Improve Cardiovascular Disease Care
Santo, K., & Redfern, J. (2020) · Current Atherosclerosis Reports · doi:10.1007/s11883-020-00889-x · Cited by 72
Wu, C., & Lim, G. G. (2024) · Frontiers in Public Health · doi:10.3389/fpubh.2024.1449594 · Cited by 71
Smart-watch-programmed green-light-operated percutaneous control of therapeutic transgenes
Mansouri, M., Hussherr, M., Strittmatter, T., Buchmann, P., Xue, S., Camenisch, G., & Fussenegger, M. (2021) · Nature Communications · doi:10.1038/s41467-021-23572-4 · Cited by 71
The potential for large language models to transform cardiovascular medicine
Quer, G., & Topol, E. J. (2024) · The Lancet Digital Health · doi:10.1016/s2589-7500(24)00151-1 · Cited by 76
Machine learning in the detection and management of atrial fibrillation
Wegner, F. K., Plagwitz, L., Doldi, F., Ellermann, C., Willy, K., Wolfes, J., Sandmann, S., Varghese, J., & Eckardt, L. (2022) · Clinical Research in Cardiology · doi:10.1007/s00392-022-02012-3 · Cited by 69
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
Erweiterte lebensrettende Maßnahmen für Erwachsene
Soar, J., Böttiger, B. W., Carli, P. A., Couper, K., Deakin, C. D., Djärv, T., Lott, C., Olasveengen, T. M., Paal, P., Pellis, T., et al. (2021) · Notfall + Rettungsmedizin · doi:10.1007/s10049-021-00893-x · Cited by 68
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-citationAndersen, T. O., Langstrup, H., & Lomborg, S. (2020) · Journal of Medical Internet Research · doi:10.2196/15873 · Cited by 65
Response To: Investigating sources of inaccuracy in wearable optical heart rate sensors
Colvonen, P. J. (2021) · npj Digital Medicine · doi:10.1038/s41746-021-00408-5 · Cited by 65
Prasitlumkum, N., Cheungpasitporn, W., Chokesuwattanaskul, A., Thangjui, S., Thongprayoon, C., Bathini, T. J., Vallabhajosyula, S., Kanitsoraphan, C., Leesutipornchai, T., & Chokesuwattanaskul, R. (2020) · Archives of cardiovascular diseases · doi:10.1016/j.acvd.2020.05.015 · Cited by 68
Patel, V., Orchanian‐Cheff, A., & Wu, R. C. (2021) · JMIR mhealth and uhealth · doi:10.2196/17411 · Cited by 65
Guasti, L., Dilaveris, P., Mamas, M. A., Richter, D. J., Christodorescu, R., Lumens, J., Schuuring, M. J., Carugo, S., Afilalo, J., Ferrini, M., et al. (2022) · ESC Heart Failure · doi:10.1002/ehf2.14022 · Cited by 74
The Apple Watch for Monitoring Mental Health–Related Physiological Symptoms: Literature Review
Lui, G. Y., Loughnane, D. C., Polley, C., Jayarathna, T., & Breen, P. P. (2022) · JMIR Mental Health · doi:10.2196/37354 · Cited by 65
Artificial Intelligence Interpretation of the Electrocardiogram: A State-of-the-Art Review
Ose, B. M., Sattar, Z., Gupta, A., Gahona, C. C. T., Harvey, C., & Noheria, A. (2024) · Current Cardiology Reports · doi:10.1007/s11886-024-02062-1 · Cited by 73
Petersen, C. C., Smith, J. R. L., Freimuth, R. R., Goodman, K. W., Jackson, G. P., Kannry, J. L., Liu, H., Madhavan, S., Sittig, D. F., & Wright, A. T. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa319 · Cited by 66
Kwon, S., Hong, J., Choi, E.-K., Lee, B., Baik, C., Lee, E., Jeong, E.-R., Koo, B.-K., Oh, S., & Yi, Y. (2020) · Journal of Medical Internet Research · doi:10.2196/16443 · Cited by 65
Mulder, S. T., Omidvari, A., Rueten‐Budde, A. J., Huang, P., Kim, K.-H., Bais, B., Rousian, M., Hai, R., Akgün, C., van Lennep, J. E. R., et al. (2022) · Journal of Medical Internet Research · doi:10.2196/35675 · Cited by 64
Beyond neural data: Cognitive biometrics and mental privacy
Magee, P., Ienca, M., & Farahany, N. A. (2024) · Neuron · doi:10.1016/j.neuron.2024.09.004 · Cited by 64
Svennberg, E., Caiani, E. G., Bruining, N., Desteghe, L., Han, J. K., Narayan, S. M., Rademakers, F. E., Sanders, P., & Duncker, D. (2023) · EP Europace · doi:10.1093/europace/euad176 · Cited by 66
Daniore, P., Nittas, V., Haag, C., Bernard, J., Gonzenbach, R. R., & von Wyl, V. (2024) · npj Digital Medicine · doi:10.1038/s41746-024-01151-3 · Cited by 72
Wearable devices—addressing bias and inequity
Zinzuwadia, A., & Singh, J. P. (2022) · The Lancet Digital Health · doi:10.1016/s2589-7500(22)00194-7 · Cited by 58
A digital health industry cohort across the health continuum
Cohen, A. B., Dorsey, E. R., Mathews, S. C., Bates, D. W., & Safavi, K. C. (2020) · npj Digital Medicine · doi:10.1038/s41746-020-0276-9 · Cited by 66
Care Models for Acute Chest Pain That Improve Outcomes and Efficiency
Dawson, L. P., Smith, K. L., Cullen, L., Nehme, Z., Lefkovits, J., Taylor, A. J., & Stub, D. A. (2022) · Journal of the American College of Cardiology · doi:10.1016/j.jacc.2022.03.380 · Cited by 67
JCS/JHRS 2022 Guideline on Diagnosis and Risk Assessment of Arrhythmia
Takase, B., Ikeda, T., Shimizu, W., Abe, H., Aiba, T., Chinushi, M., Koba, S., KUSANO, K. F., Niwano, S., Takahashi, N., et al. (2023) · Circulation Journal · doi:10.1253/circj.cj-22-0827 · Cited by 68
Wyatt, K. D., Poole, L. R., Mullan, A. F., Kopecky, S. L., & Heaton, H. A. (2020) · Journal of the American Medical Informatics Association · doi:10.1093/jamia/ocaa137 · Cited by 63
Cost-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
Generative adversarial networks in electrocardiogram synthesis: Recent developments and challenges
Berger, L., Haberbusch, M., & Moscato, F. (2023) · Artificial Intelligence in Medicine · doi:10.1016/j.artmed.2023.102632 · Cited by 68
Digital Health: Implications for Heart Failure Management
Singhal, A., & Cowie, M. (2021) · Cardiac failure review · doi:10.15420/cfr.2020.28 · Cited by 60
The accuracy of heartbeat detection using photoplethysmography technology in cardiac patients
Blok, S., Piek, M. A., Tulevski, I. I., Somsen, G. A., & Winter, M. M. (2021) · Journal of Electrocardiology · doi:10.1016/j.jelectrocard.2021.06.009 · Cited by 59
Daniore, P., Nittas, V., & von Wyl, V. (2022) · Journal of Medical Internet Research · doi:10.2196/39910 · Cited by 64
When smartwatches contribute to health anxiety in patients with atrial fibrillation
Rosman, L., Gehi, A., & Lampert, R. (2020) · Cardiovascular Digital Health Journal · doi:10.1016/j.cvdhj.2020.06.004 · Cited by 61
Artificial intelligence in personalized cardiovascular medicine and cardiovascular imaging
Haq, I.-U., Haq, I., & Xu, B. (2021) · Cardiovascular Diagnosis and Therapy · doi:10.21037/cdt.2020.03.09 · Cited by 62
Stroke prevention strategies in high-risk patients with atrial fibrillation
Kotalczyk, A., Mazurek, M., Kalarus, Z. F., Potpara, T., & Lip, G. Y. H. (2020) · Nature Reviews Cardiology · doi:10.1038/s41569-020-00459-3 · Cited by 60
Golbus, J. R., Pescatore, N. A., Nallamothu, B. K., Shah, N., & Kheterpal, S. (2021) · The Lancet Digital Health · doi:10.1016/s2589-7500(21)00138-2 · Cited by 57
Artificial Intelligence Technologies in Cardiology
Ledziński, Ł., & Grześk, G. (2023) · Journal of Cardiovascular Development and Disease · doi:10.3390/jcdd10050202 · Cited by 72
Stauss, M., Floyd, L., Becker, S., Ponnusamy, A., & Woywodt, A. (2020) · Clinical Kidney Journal · doi:10.1093/ckj/sfaa103 · Cited by 56
Bending the Curve in Cardiovascular Disease Mortality
Goff, D. C., Khan, S. S., Lloyd‐Jones, D. M., Arnett, D. K., Carnethon, M. R., Labarthe, D. R., Loop, M. S., Luepker, R. V., McConnell, M. V., Mensah, G. A., et al. (2021) · Circulation · doi:10.1161/circulationaha.120.046501 · Cited by 60
Köhler, C., Bartschke, A., Fürstenau, D., Schaaf, T., & Salgado, E. (2024) · Journal of Medical Internet Research · doi:10.2196/58936 · Cited by 68
Strik, M., Ploux, S., Ramirez, F. D., Abu-Alrub, S., Jaı̈s, P., Haı̈ssaguerre, M., & Bordachar, P. (2021) · Heart Rhythm · doi:10.1016/j.hrthm.2021.06.1176 · Cited by 56
Spaccarotella, C., Polimeni, A., Mancuso, C., Pelaia, G., Esposito, G., & Indolfi, C. (2022) · Journal of Clinical Medicine · doi:10.3390/jcm11061467 · Cited by 65
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
Seizure occurrence is linked to multiday cycles in diverse physiological signals
Gregg, N. M., Attia, T. P., Nasseri, M., Joseph, B., Karoly, P. J., Cui, J., Stirling, R. E., Viana, P. F., Richner, T. J., Nurse, E. S., et al. (2023) · Epilepsia · doi:10.1111/epi.17607 · Cited by 57
Ghanim, R. W., Kaushik, A., Park, J., & Abramson, A. (2023) · Device · doi:10.1016/j.device.2023.100092 · Cited by 57
The potential role for artificial intelligence in fracture risk prediction
Hong, N., Whittier, D. E., Glüer, C.-C., & Leslie, W. D. (2024) · The Lancet Diabetes & Endocrinology · doi:10.1016/s2213-8587(24)00153-0 · Cited by 53
Atrial Fibrillation Detection and Atrial Fibrillation Burden Estimation via Wearables
Zhu, L., Nathan, V., Kuang, J., Kim, J., Avram, R., Olgin, J. E., & Gao, J. (2021) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2021.3131984 · Cited by 54
Metadata
- Authors
- 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., Granger, C. B., Desai, M., & Turakhia, M. P.
- Affiliations
- Stanford Medicine (US)
- From the Division of Cardiovascular Medicine (M.V.P.), Stanford Center for Clinical Research (K.W.M., A.R., N.T.), the Quantitative Sciences Unit (H.H., A.G., V.B., J.L., S.E.G., M.D.), Information Resources and Technology (T.F., G.H.), Department of Medicine (S.D.), and the Center for Digital Health (M.P.T.), Stanford University, Stanford, Apple, Cupertino (L.C., D.N., A.B., S.D.), and the Veterans Affairs Palo Alto Health Care System, Palo Alto (M.P.T.) — all in California; the University of...
- Cooper Medical School of Rowan University (US)
- Lankenau Heart Institute (US)
- Duke University (US)
- VA Palo Alto Health Care System (US)
- ORCID
- Venue
- New England Journal of Medicine
- Publisher
- Massachusetts Medical Society
- Published
- 2019-11-14
- Volume
- 381
- Issue
- 20
- Pages
- 1909-1917
- ISSN
- 0028-4793 (ISSN-L)
- 1533-4406
- PMC
- PMC8112605
- OpenAlex
- W2987985090
- Cited by
- 1,917
- References
- 12
- Citations by year
- 2026: 263 · 2025: 300 · 2024: 259 · 2023: 276 · 2022: 271 · 2021: 304 · 2020: 228 · 2019: 12
- FWCI
- 71.74
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- New England Journal of Medicine (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- Funders
- Stanford University
- Topics
- Physical Sciences › Engineering › Biomedical Engineering › Non-Invasive Vital Sign Monitoring
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › Atrial Fibrillation Management and Outcomes
- Health Sciences › Medicine › Cardiology and Cardiovascular Medicine › ECG Monitoring and Analysis
- Keywords
- atrial fibrillation, smartwatches, wearable devices, optical pulse detection, positive predictive value, ECG patch, telemedicine, mobile application, health care utilization, older adults, adverse events, participant satisfaction, participant burden, participant retention, participant recruitment
- MeSH terms
- Wearable Electronic Devices, Adult, Aged, Algorithms, Atrial Fibrillation, Confidentiality, Electrocardiography, Female, Humans, Male, Middle Aged, Predictive Value of Tests, Prospective Studies, Telemedicine, Mobile Applications
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
