This paper is in the following e-collection/theme issue:

Wearable Devices and Sensors (903) Cardiac Arrhythmia, Atrial Fibrillation (87) Novel Sensors and Data Acquisition Methods in Cardiology (200) mHealth in a Clinical Setting (624) mHealth for Data Collection and Research (981) mHealth for Symptom and Disease Monitoring, Chronic Disease Management (1428)

Published on in Vol 9, No 10 (2021): October

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/29933, first published .
Automatic Mobile Health Arrhythmia Monitoring for the Detection of Atrial Fibrillation: Prospective Feasibility, Accuracy, and User Experience Study

Automatic Mobile Health Arrhythmia Monitoring for the Detection of Atrial Fibrillation: Prospective Feasibility, Accuracy, and User Experience Study

Automatic Mobile Health Arrhythmia Monitoring for the Detection of Atrial Fibrillation: Prospective Feasibility, Accuracy, and User Experience Study

Authors of this article:

Onni E Santala1, 2 Author Orcid Image ;   Jari Halonen1, 3 Author Orcid Image ;   Susanna Martikainen4 Author Orcid Image ;   Helena Jäntti5 Author Orcid Image ;   Tuomas T Rissanen6 Author Orcid Image ;   Mika P Tarvainen7, 8 Author Orcid Image ;   Tomi P Laitinen1, 8 Author Orcid Image ;   Tiina M Laitinen8 Author Orcid Image ;   Eemu Samuli Väliaho1, 2 Author Orcid Image ;   Juha E K Hartikainen1, 3 Author Orcid Image ;   Tero J Martikainen9 Author Orcid Image ;   Jukka A Lipponen7 Author Orcid Image
Article Authors Cited by (22) Tweetations (2) Metrics

Journals

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  2. Shen Y, Xu W, Liang A, Wang X, Lu X, Lu Z, Gao C. Online health management continuance and the moderating effect of service type and age difference: A meta-analysis. Health Informatics Journal 2022;28(3) View
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  4. Santala O, Lipponen J, Jäntti H, Rissanen T, Tarvainen M, Laitinen T, Laitinen T, Castrén M, Väliaho E, Rantula O, Naukkarinen N, Hartikainen J, Halonen J, Martikainen T. Continuous mHealth Patch Monitoring for the Algorithm-Based Detection of Atrial Fibrillation: Feasibility and Diagnostic Accuracy Study. JMIR Cardio 2022;6(1):e31230 View
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  6. Martini C, Di Maria B, Reverberi C, Tuttolomondo D, Gaibazzi N. Commercially Available Heart Rate Monitor Repurposed for Automatic Arrhythmia Detection with Snapshot Electrocardiographic Capability: A Pilot Validation. Diagnostics 2022;12(3):712 View
  7. Cao Y, Zhao X, Yang Y, Zhu S, Zheng L, Ying T, Sha Z, Zhu R, Wu T. Potential of electronic devices for detection of health problems in older adults at home: A systematic review and meta-analysis. Geriatric Nursing 2023;51:54 View
  8. Santala O, Lipponen J, Jäntti H, Rissanen T, Tarvainen M, Väliaho E, Rantula O, Naukkarinen N, Hartikainen J, Martikainen T, Halonen J. Novel Technologies in the Detection of Atrial Fibrillation: Review of Literature and Comparison of Different Novel Technologies for Screening of Atrial Fibrillation. Cardiology in Review 2024;32(5):440 View
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  10. Neri L, Oberdier M, van Abeelen K, Menghini L, Tumarkin E, Tripathi H, Jaipalli S, Orro A, Paolocci N, Gallelli I, Dall’Olio M, Beker A, Carrick R, Borghi C, Halperin H. Electrocardiogram Monitoring Wearable Devices and Artificial-Intelligence-Enabled Diagnostic Capabilities: A Review. Sensors 2023;23(10):4805 View
  11. Asan O, Choi E, Wang X. Artificial Intelligence–Based Consumer Health Informatics Application: Scoping Review. Journal of Medical Internet Research 2023;25:e47260 View
  12. Manetas-Stavrakakis N, Sotiropoulou I, Paraskevas T, Maneta Stavrakaki S, Bampatsias D, Xanthopoulos A, Papageorgiou N, Briasoulis A. Accuracy of Artificial Intelligence-Based Technologies for the Diagnosis of Atrial Fibrillation: A Systematic Review and Meta-Analysis. Journal of Clinical Medicine 2023;12(20):6576 View
  13. Ma C, Xiao Z, Zhao L, Biton S, Behar J, Long X, Vullings R, Aarts R, Li J, Liu C. A Review on Atrial Fibrillation Detection From Ambulatory ECG. IEEE Transactions on Biomedical Engineering 2024;71(3):876 View
  14. Moorthy P, Weinert L, Schüttler C, Svensson L, Sedlmayr B, Müller J, Nagel T. Attributes, Methods, and Frameworks Used to Evaluate Wearables and Their Companion mHealth Apps: Scoping Review. JMIR mHealth and uHealth 2024;12:e52179 View
  15. Yao Y, Jia Y, Wu M, Wang S, Song H, Fang X, Liao X, Li D, Zhao Q. Detection of atrial fibrillation using a nonlinear Lorenz Scattergram and deep learning in primary care. BMC Primary Care 2024;25(1) View
  16. Zhao P, Xu J, Han M. Patient-centric care: Unveiling the potential of wearable electronics in clinical practice. Wearable Electronics 2024;1:119 View
  17. Martín Gómez R, Allevard E, Kamstra H, Cotter J, Lamb P. Validity and Reliability of Movesense HR+ ECG Measurements for High-Intensity Running and Cycling. Sensors 2024;24(17):5713 View
  18. Suresh Kumar S, Connolly P, Maier A. Considering User Experience and Behavioral Approaches in the Design of mHealth Interventions for Atrial Fibrillation: Systematic Review. Journal of Medical Internet Research 2024;26:e54405 View
  19. Menezes Junior A, e Silva A, e Silva L, de Lima K, Oliveira H. A Scoping Review of the Use of Artificial Intelligence in the Identification and Diagnosis of Atrial Fibrillation. Journal of Personalized Medicine 2024;14(11):1069 View
  20. Kim N, Lee S, Kim S, Park S. Automated Arrhythmia Classification System: Proof-of-Concept With Lightweight Model on an Ultra-Edge Device. IEEE Access 2024;12:150546 View

Books/Policy Documents

  1. Ly B, Pop M, Cochet H, Duchateau N, O’Regan D, Sermesant M. AI and Big Data in Cardiology. View