ArticleHeart rhythm O22020
Review of mobile applications for the detection and management of atrial fibrillation.
Article in Heart rhythm O2, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 10 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
10 citing papers in PubMed.
- Mobile applications for atrial fibrillation self-management: A systematic search and evaluation.Heart rhythm O2 · 2026Article
- External validation of a machine learning-based classification algorithm for ambulatory heart rhythm diagnostics in pericardioversion atrial fibrillation patients using smartphone photoplethysmography: the SMARTBEATS-ALGO study.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2025Article
- Validation of a novel smartphone-based photoplethysmographic method for ambulatory heart rhythm diagnostics: the SMARTBEATS study.Europace : European pacing, arrhythmias, and cardiac electrophysiology : journal of the working groups on cardiac pacing, arrhythmias, and cardiac cellular electrophysiology of the European Society of Cardiology · 2024Article
- Patient-reported outcomes following ablation for atrial fibrillation in the era of digital health.Journal of interventional cardiac electrophysiology : an international journal of arrhythmias and pacing · 2024Article
- A Structured Review of Commercially Available Cardiac Rehabilitation mHealth Applications Using the Mobile Application Rating Scale.Journal of cardiopulmonary rehabilitation and prevention · 2022Article
- mHealth Apps for Self-Management of Cardiovascular Diseases: A Scoping Review.Healthcare (Basel, Switzerland) · 2022Article
- Patient Perspectives on Performance of a Smartphone App for Atrial FibrillationSelf-Management.Patient preference and adherence · 2022Article
- Returning Cardiac Rhythm Data to Patients: Opportunities and Challenges.Cardiac electrophysiology clinics · 2021Review
- Effect of a virtual self-management intervention for atrial fibrillation during the outbreak of COVID-19.Pacing and clinical electrophysiology : PACE · 2021Article
- Digital cardiovascular care in COVID-19 pandemic: A potential alternative?Journal of cardiac surgery · 2020Review
Corrections and comments
- Erratum issuedErratum.2021
Authors and funding
6 authors.
Funding
Abstract
backgroundFree mobile applications (apps) that use photoplethysmography (PPG) waveforms may extend atrial fibrillation (AF) detection to underserved populations, but they have not been rigorously evaluated.
objectiveThe purpose of this study was to systematically review and evaluate the quality, functionality, and adherence to self-management behaviors of existing mobile apps for AF.
methodsWe systematically searched 3 app stores for apps that were free, available in English, and intended for use by patients to detect and manage AF. A minimum of 2 reviewers evaluated (1) app quality, using the Mobile Application Rating Scale (MARS); (2) functionality using published criteria; and (3) features that support 4 self-management behaviors (including PPG waveform monitoring) identified using evidence-based guidelines. Interrater reliability between the reviewers was calculated.
resultsOf 12 included apps, 5 (42%) scored above average for quality (MARS score ≥3.0). App quality was highest for their ease of use, navigation, layout, and visual appeal (eg, functionality and aesthetics) and lowest for their behavioral change support and subjective impressions of quality. The most common app functionalities were capturing and graphically displaying user-entered data (n = 9 [75%]). Nearly all apps (n = 11 [92%]) supported PPG waveform monitoring, but only 2 (17%) supported all 4 self-management behaviors. Interrater reliability was high (0.75-0.83).
conclusionThe reviewed apps had wide variability in quality, functionality, and adherence to self-management behaviors. Given the accessibility of these apps to underserved populations and the tremendous potential they hold for improving AF detection and management, high priority should be given to improving app quality and functionality.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.