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Meta-Analysis
. 2021 Aug 27;23(8):e28974.
doi: 10.2196/28974.

Diagnostic Accuracy of Smartwatches for the Detection of Cardiac Arrhythmia: Systematic Review and Meta-analysis

Scarlet Nazarian #  1 , Kyle Lam #  1 , Ara Darzi  1 , Hutan Ashrafian  1
Affiliations
Meta-Analysis

Diagnostic Accuracy of Smartwatches for the Detection of Cardiac Arrhythmia: Systematic Review and Meta-analysis

Scarlet Nazarian et al. J Med Internet Res. .

Abstract

Background: Significant morbidity, mortality, and financial burden are associated with cardiac rhythm abnormalities. Conventional investigative tools are often unsuccessful in detecting cardiac arrhythmias because of their episodic nature. Smartwatches have gained popularity in recent years as a health tool for the detection of cardiac rhythms.

Objective: This study aims to systematically review and meta-analyze the diagnostic accuracy of smartwatches in the detection of cardiac arrhythmias.

Methods: A systematic literature search of the Embase, MEDLINE, and Cochrane Library databases was performed in accordance with the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines to identify studies reporting the use of a smartwatch for the detection of cardiac arrhythmia. Summary estimates of sensitivity, specificity, and area under the curve were attempted using a bivariate model for the diagnostic meta-analysis. Studies were examined for quality using the Quality Assessment of Diagnostic Accuracy Studies 2 tool.

Results: A total of 18 studies examining atrial fibrillation detection, bradyarrhythmias and tachyarrhythmias, and premature contractions were analyzed, measuring diagnostic accuracy in 424,371 subjects in total. The signals analyzed by smartwatches were based on photoplethysmography. The overall sensitivity, specificity, and accuracy of smartwatches for detecting cardiac arrhythmias were 100% (95% CI 0.99-1.00), 95% (95% CI 0.93-0.97), and 97% (95% CI 0.96-0.99), respectively. The pooled positive predictive value and negative predictive value for detecting cardiac arrhythmias were 85% (95% CI 0.79-0.90) and 100% (95% CI 1.0-1.0), respectively.

Conclusions: This review demonstrates the evolving field of digital disease detection. The current diagnostic accuracy of smartwatch technology for the detection of cardiac arrhythmias is high. Although the innovative drive of digital devices in health care will continue to gain momentum toward screening, the process of accurate evidence accrual and regulatory standards ready to accept their introduction is strongly needed.

Trial registration: PROSPERO International Prospective Register of Systematic Reviews CRD42020213237; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=213237.

Keywords: accuracy; atrial fibrillation; cardiac arrhythmia; cardiology; diagnostics; mHealth; screening; smartwatch; wearable devices; wearables.

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Conflict of interest statement

Conflicts of Interest: AD is Chair of the Health Security initiative at Flagship Pioneering UK Ltd. The remaining authors declare no conflicts of interest.

Figures

Figure 1
Figure 1
PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) flow diagram for study selection.
Figure 2
Figure 2
Pooled analysis for sensitivity of cardiac arrhythmia detection by smartwatches. Effect sizes are shown with 95% CIs. A random effects model was used. ES: effect sizes.
Figure 3
Figure 3
Pooled analysis for specificity of cardiac arrhythmia detection by smartwatches. Effect sizes are shown with 95% CIs. A random effects model was used. ES: effect sizes.
Figure 4
Figure 4
Pooled analysis for accuracy of cardiac arrhythmia detection by smartwatches. Effect sizes are shown with 95% CIs. A random effects model was used. ES: effect sizes.
Figure 5
Figure 5
Pooled analysis for PPV of cardiac arrhythmia detection by smartwatches. Effect sizes are shown with 95% CIs. A random effects model was used. ES: effect sizes; PPV: positive predictive value.
Figure 6
Figure 6
Pooled analysis for NPV of cardiac arrhythmia detection by smartwatches. Effect sizes are shown with 95% CIs. A random effects model was used. ES: effect sizes; NPV: negative predictive value.
Figure 7
Figure 7
Overview of photoplethysmography sensor detection of arrhythmia. PPG: photoplethysmography.
See this image and copyright information in PMC

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