Previously submitted to: JMIR mHealth and uHealth (no longer under consideration since Dec 30, 2021)
Date Submitted: Nov 24, 2020
Open Peer Review Period: Nov 23, 2020 - Jan 18, 2021
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Smartphone applications for the detection of atrial fibrillation in primary care: a review
ABSTRACT
Background:
Smartphone technology continues to advance at a fervid pace. In the field of cardiology, traditional physical tests recommended to detect cardiac arrhythmias may soon be superseded by low-cost, convenient and reliable smartphone apps. We aimed to determine the screening test accuracy of smartphone apps in detecting atrial fibrillation (AF) in patients within primary care.
Objective:
The aim of this review was to determine the screening test accuracy of smartphone apps in detecting atrial fibrillation (AF) in patients within primary care.
Methods:
Systematically searched MEDLINE, PUBMED, Web of Science, CINAHL and Cochrane Library until 01 February 2019. Articles were screened, and evaluated before relevant data extracted and study quality appraised using the QUADAS-2 tool. The raw test accuracy data was constructed into a 2x2 contingency table and the test accuracy statistics were calculated, and organised in descriptive plots.
Results:
Seven cross-sectional studies tested one, or two smartphone apps including the AliveCor 1-lead 30 sec ECG (seven studies; n=16,359), and the Cardiio Rhythm PPG (one study; n=1,012). The prevalence of AF ranged from 1.17%-12.29%, with a mean of 2.65%. The AliveCor 1-lead device reported a sensitivity ranging from 0.92 to 0.99, and specificity from 0.99 to 1.00. Sensitivity and PPV showed the greatest heterogeneity, with results ranging from suboptimal to excellent. The Cardiio Rhythm PPG app recorded a sensitivity of 0.71 (95% CI 0.51-0.87) and specificity of 0.99 (95% CI 0.98-1.00).
Conclusions:
Community screening for AF using smartphone electrocardiography (ECG) or photoplethysmography (PPG) is feasible. Smartphone apps that screen for AF in primary care demonstrate excellent specificity, but suboptimal sensitivity. Further optimisation of detection algorithms, to accommodate the spectrum of disease seen within the community, should be considered before such devices are used as a tool for systematic auto-screening.
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