All publications examining any type of AF were evaluated (paroxysmal, persistent or permanent as defined by the study), including original research and conference abstracts in participants aged 18 years and above.

All results for sensitivity, specificity, PPV, NPV and diagnostic accuracy for smartphone AF detection were visualised using forest plots. When not directly reported by study authors, we derived diagnostic values using 2×2 contingency tables from reported sensitivity, specificity and corresponding confidence intervals. Due to uncertainty in the number of patients with AF, we were unable to produce a contingency table for one study.9 Publication bias was assessed using a funnel plot of linear regression of the log ORs on the inverse root of the sample size, with asymmetry and/or a non-zero slope coefficient with p<0.1 indicative of small study bias. Bivariate mixed-effects regression modelling was used to meta-analyse study comparisons with confidence intervals for sensitivity and specificity. A summary receiver operating characteristic plot was constructed to provide information on the overall diagnostic accuracy of smartphones for AF detection with 95% prediction regions. Heterogeneity was assessed using the I2 statistic, and visually using the bivariate box plot approach. Statistical analysis was performed using Stata V.14.2 (StataCorp LP, Texas, USA) and the MIDAS program.

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