Literature DB >> 33865810

Rationale and design of a large population study to validate software for the assessment of atrial fibrillation from data acquired by a consumer tracker or smartwatch: The Fitbit heart study.

Steven A Lubitz1, Anthony Z Faranesh2, Steven J Atlas3, David D McManus4, Daniel E Singer3, Sherry Pagoto5, Alexandros Pantelopoulos2, Andrea S Foulkes6.   

Abstract

BACKGROUND: Early detection of atrial fibrillation or flutter (AF) may enable prevention of downstream morbidity. Consumer wrist-worn wearable technology is capable of detecting AF by identifying irregular pulse waveforms using photoplethysmography (PPG). The validity of PPG-based software algorithms for AF detection requires prospective assessment.
METHODS: The Fitbit Heart Study (NCT04380415) is a single-arm remote clinical trial examining the validity of a novel PPG-based software algorithm for detecting AF. The proprietary Fitbit algorithm examines pulse waveform intervals during analyzable periods in which participants are sufficiently stationary. Fitbit consumers with compatible wrist-worn trackers or smartwatches were invited to participate. Enrollment began May 6, 2020 and as of October 1, 2020, 455,699 participants enrolled. Participants in whom an irregular heart rhythm was detected were invited to attend a telehealth visit and eligible participants were then mailed a one-week single lead electrocardiographic (ECG) patch monitor. The primary study objective is to assess the positive predictive value of an irregular heart rhythm detection for AF during the ECG patch monitor period. Additional objectives will examine the validity of irregular pulse tachograms during subsequent heart rhythm detections, self-reported AF diagnoses and treatments, and relations between irregular heart rhythm detections and AF episode duration and time spent in AF.
CONCLUSIONS: The Fitbit Heart Study is a large-scale remote clinical trial comprising a unique software algorithm for detection of AF. The study results will provide critical insights into the use of consumer wearable technology for AF detection, and for characterizing the nature of AF episodes detected using consumer-based PPG technology.
Copyright © 2021 Elsevier Inc. All rights reserved.

Entities:  

Year:  2021        PMID: 33865810     DOI: 10.1016/j.ahj.2021.04.003

Source DB:  PubMed          Journal:  Am Heart J        ISSN: 0002-8703            Impact factor:   4.749


  12 in total

Review 1.  Stroke Prevention in Atrial Fibrillation.

Authors:  Xu Gao; Rod Passman
Journal:  Curr Cardiol Rep       Date:  2022-09-22       Impact factor: 3.955

2.  Lessons learned in the Apple Heart Study and implications for the data management of future digital clinical trials.

Authors:  Ariadna Garcia; Vidhya Balasubramanian; Justin Lee; Rebecca Gardner; Santosh Gummidipundi; Grace Hung; Todd Ferris; Lauren Cheung; Christopher Granger; Peter Kowey; John Rumsfeld; Andrea Russo; Mellianie Ture Hills; Nisha Talati; Divya Nag; Jeffrey Stein; David Tsay; Sumbul Desai; Kenneth Mahaffey; Mintu Turakhia; Marco Perez; Haley Hedlin; Manisha Desai
Journal:  J Biopharm Stat       Date:  2022-06-12       Impact factor: 1.503

3.  Pandemic-proof recruitment and engagement in a fully decentralized trial in atrial fibrillation patients (DeTAP).

Authors:  Ashish Sarraju; Clark Seninger; Vijaya Parameswaran; Christina Petlura; Tamara Bazouzi; Kiranbir Josan; Upinder Grewal; Thomas Viethen; Hardi Mundl; Joachim Luithle; Leonard Basobas; Alexis Touros; Michael J T Senior; Koen De Lombaert; Kenneth W Mahaffey; Mintu P Turakhia; Rajesh Dash
Journal:  NPJ Digit Med       Date:  2022-06-28

Review 4.  Remote Cardiac Rhythm Monitoring in the Era of Smart Wearables: Present Assets and Future Perspectives.

Authors:  Anastasia Xintarakou; Vasileios Sousonis; Dimitrios Asvestas; Panos E Vardas; Stylianos Tzeis
Journal:  Front Cardiovasc Med       Date:  2022-03-01

5.  Remote Design of a Smartphone and Wearable Detected Atrial Arrhythmia in Older Adults Case Finding Study: Smart in OAC - AFNET 9.

Authors:  Larissa Fabritz; D Connolly; E Czarnecki; D Dudek; A Zlahoda-Huzior; E Guasch; D Haase; T Huebner; K Jolly; P Kirchhof; Ulrich Schotten; Antonia Zapf; Renate B Schnabel
Journal:  Front Cardiovasc Med       Date:  2022-03-21

Review 6.  Digital Technologies to Support Better Outcome and Experience of Care in Patients with Heart Failure.

Authors:  K C C McBeath; C E Angermann; M R Cowie
Journal:  Curr Heart Fail Rep       Date:  2022-04-29

Review 7.  Telemedicine: Benefits for Cardiovascular Patients in the COVID-19 Era.

Authors:  Liviu-Nicolae Ghilencea; Maria-Roxana Chiru; Miroslava Stolcova; Gabriel Spiridon; Laura-Maria Manea; Ana-Maria Alexandra Stănescu; Awais Bokhari; Ismail Dogu Kilic; Gioel Gabriel Secco; Nicolas Foin; Carlo Di Mario
Journal:  Front Cardiovasc Med       Date:  2022-07-20

Review 8.  How should I treat patients with subclinical atrial fibrillation and atrial high-rate episodes? Current evidence and clinical importance.

Authors:  Fabienne Kreimer; Andreas Mügge; Michael Gotzmann
Journal:  Clin Res Cardiol       Date:  2022-03-15       Impact factor: 6.138

9.  High-Resolution Digital Phenotypes From Consumer Wearables and Their Applications in Machine Learning of Cardiometabolic Risk Markers: Cohort Study.

Authors:  Weizhuang Zhou; Yu En Chan; Chuan Sheng Foo; Jingxian Zhang; Jing Xian Teo; Sonia Davila; Weiting Huang; Jonathan Yap; Stuart Cook; Patrick Tan; Calvin Woon-Loong Chin; Khung Keong Yeo; Weng Khong Lim; Pavitra Krishnaswamy
Journal:  J Med Internet Res       Date:  2022-07-29       Impact factor: 7.076

Review 10.  Current Advancement in Diagnosing Atrial Fibrillation by Utilizing Wearable Devices and Artificial Intelligence: A Review Study.

Authors:  Yu-Chiang Wang; Xiaobo Xu; Adrija Hajra; Samuel Apple; Amrin Kharawala; Gustavo Duarte; Wasla Liaqat; Yiwen Fu; Weijia Li; Yiyun Chen; Robert T Faillace
Journal:  Diagnostics (Basel)       Date:  2022-03-11
View more

北京卡尤迪生物科技股份有限公司 © 2022-2023.