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Masterson Creber R, Turchioe MR. Returning Cardiac Rhythm Data to Patients: Opportunities and Challenges. Card Electrophysiol Clin 2021; 13:555-567. [PMID: 34330381 PMCID: PMC8328196 DOI: 10.1016/j.ccep.2021.05.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
Abstract
Spurred by federal legislation, professional organizations, and patients themselves, patient access to data from electronic cardiac devices is increasingly transparent. Patients can collect data through consumer devices and access data traditionally shared only with health care providers. These data may improve screening, self-management, and shared decision-making for cardiac arrhythmias, but challenges remain, including patient comprehension, communication with providers, and sustained engagement. Ways to address these challenges include leveraging visualizations that support comprehension, involving patients in designing and developing patient-facing digital tools, and establishing clear practices and goals for data exchange with health care providers.
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Affiliation(s)
- Ruth Masterson Creber
- Division of Health Informatics, Weill Cornell Medicine, 425 E 61st St, Floor 3, New York, NY 10065, USA.
| | - Meghan Reading Turchioe
- Division of Health Informatics, Weill Cornell Medicine, 425 E 61st St, Floor 3, New York, NY 10065, USA
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Masterson Creber RM, Reading Turchioe M, Biviano A, Caceres B, Garan H, Goldenthal I, Koleck T, Mitha S, Hickey K, Bakken S. Cardiac symptom burden and arrhythmia recurrence drives digital health use: results from the iHEART randomized controlled trial. Eur J Cardiovasc Nurs 2021; 21:107-115. [PMID: 34009326 PMCID: PMC8560656 DOI: 10.1093/eurjcn/zvab009] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/08/2020] [Revised: 11/05/2020] [Accepted: 01/27/2021] [Indexed: 12/26/2022]
Abstract
Aims Digital health can transform the management of atrial fibrillation (AF) and enable patients to take a central role in detecting symptoms and self-managing AF. There is a gap in understanding factors that support sustained use of digital health tools for patients with AF. This study identified predictors of Alivecor® KardiaMobile ECG monitor usage among patients with AF enrolled in the iPhone®Helping Evaluate Atrial fibrillation Rhythm through Technology (iHEART) randomized controlled trial. Methods and results We analysed data from 105 English and Spanish-speaking adults with AF enrolled in the intervention arm of the iHEART trial. The iHEART intervention included smartphone-based electrocardiogram self-monitoring with Alivecor® KardiaMobile and triweekly text messages for 6 months. The primary outcome was use of Alivecor® categorized as: infrequent (≤5 times/week), moderate (>5 times and ≤11 times/week), and frequent (>11 times/week). We applied multinomial logistic regression modelling to characterize frequency and predictors of use. Of the 105 participants, 25% were female, 75% were White, and 45% were ≥65 years of age. Premature atrial contractions (PACs) [adjusted odds ratio (OR): 1.23, 1.08–1.40, P = 0.002] predicted frequent as compared to infrequent use. PACs (adjusted OR: 1.17, 95% confidence interval 1.06–1.30, P = 0.003), lower symptom burden (adjusted OR: 1.06, 1.01–1.11, P = 0.02), and less treatment concern (adjusted OR: 0.96, 0.93–0.99, P = 0.02) predicted moderate as compared to infrequent use. Conclusions Frequent use of AliveCor® is associated with AF symptoms and potentially symptomatic cardiac events. Symptom burden and frequency should be measured and incorporated into analyses of future digital health trials for AF management.
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Affiliation(s)
- Ruth M Masterson Creber
- Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medical College, 425 East 61st Street, Suite 301, New York, NY 10065, USA
| | - Meghan Reading Turchioe
- Division of Health Informatics, Department of Population Health Sciences, Weill Cornell Medical College, 425 East 61st Street, Suite 301, New York, NY 10065, USA
| | - Angelo Biviano
- Department of Medicine-Cardiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Billy Caceres
- School of Nursing, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Hasan Garan
- Department of Medicine-Cardiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Isaac Goldenthal
- Department of Medicine-Cardiology, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Theresa Koleck
- School of Nursing, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Shazia Mitha
- School of Nursing, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Kathleen Hickey
- Department of Medicine-Cardiology, Columbia University Irving Medical Center, New York, NY 10032, USA.,School of Nursing, Columbia University Irving Medical Center, New York, NY 10032, USA
| | - Suzanne Bakken
- School of Nursing, Columbia University Irving Medical Center, New York, NY 10032, USA.,Department of Biomedical Informatics, Columbia University Irving Medical Center, New York, NY 10032, USA
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Chen E, Jiang J, Su R, Gao M, Zhu S, Zhou J, Huo Y. A new smart wristband equipped with an artificial intelligence algorithm to detect atrial fibrillation. Heart Rhythm 2020; 17:847-853. [DOI: 10.1016/j.hrthm.2020.01.034] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/28/2019] [Accepted: 01/23/2020] [Indexed: 10/24/2022]
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