Table 1.
Summary of full-text studies
| Study | Study design and key enrolment criteria | Setting and sample size | Population characteristics | Technology for AF detection | Reference test |
| Brasier et al 201930 | Prospective, multicentre Age >18 years, capable of written consent Supported by industry |
Secondary care N=672 AF prevalence 42% |
Age 78 years (median); female 45%; hypertension 72%; diabetes 31%, heart failure 36%; stroke 16%, OAC 49% | iPhone 4S; Preventicus app; 300 s recording; data quality check performed prior to rhythm analysis that used beat-to-beat changes of pulse wave time intervals and morphology | Blinded interpretation of single-lead ECG by two cardiologists with group consensus; three study comparisons with PPG signal analysed at (1) 60 s; (2) 120 s; and (3) 300 s. |
| Chan et al 201611 | Prospective, single centre Age≥65, history of hypertension, diabetes Supported by industry |
Primary care, N=1013 AF prevalence 3% |
Age 68 years (mean); female 53%; hypertension 90%; diabetes 37%; heart failure 4%; stroke 11% | iPhone 4S; CRMA app; 3×17 s recordings, baseline wander and noise filtered. AF detection based on a lack of repeating patterns in the PPG waveform, using SVM. Labelled AF if 2 of 3 recordings irregular. | Blinded interpretation of single-lead ECG by two cardiologists with group consensus. |
| Fan et al 201912 | Prospective, single centre Age >18 years Excluded if unable to use smartphone or had memory impairment Supported by industry |
Secondary care n=108 AF prevalence 48% |
Female 42%; diabetes 30%, heart failure 13%; stroke 12%; OAC 46% | Huawei Mate 9, Huawei Honor 7X; Preventicus app; 180-second recording analysed | 12-lead ECG interpreted by two cardiologists with group consensus. |
| McManus et al 201313 | Prospective single centre AF for DCCV |
Secondary care N=76 AF prevalence 100% |
Age 65 years (mean); female 35%; hypertension 71%; diabetes 28%; heart failure 21%; stroke 12% | iPhone 4S; unknown app; 120 s recording, analysed using two statistical techniques (RMSSD and ShE) | 12-lead ECG interpreted by trained physicians with group consensus. |
| McManus et al 201614 | Prospective single centre AF for DCCV and premature beats |
Secondary care, N=121 AF prevalence 81% |
Age 66 years (mean); female 18% | iPhone 4S; PULSESMART app; 120 s recording analysed using three statistical techniques (RMSSD, ShE, Poincare plot) | 12 or 3-lead ECG, interpreted by trained physicians with group consensus. |
| Mutke et al 202031 | Prospective, multicentre; data from two trials (WATCH AF and DETECT PRO) Supported by industry |
Secondary care N=1330 AF prevalence 47% |
iPhone 4S; Preventicus app; 60 s recording analysed using beat-to-beat variations via a non-linear rhythm analysis, signal quality check not performed | Single-lead ECG. Interpretation by two cardiologists with group consensus. | |
| Poh et al 201836 | Retrospective analysis with DCNN for AF detection Supported by industry |
Validation data from primary care N=1013 AF prevalence 3% |
Age 68 years (mean); female 53%; hypertension 90%; diabetes 37%; stroke 11%; heart failure 4% | iPhone4S; unknown app; 3×17 s recordings analysed using six AF detection algorithms (CoV,5 CoSEn, nRMSSD +ShE, RMSSD +SD1/SD2, Poincaré plot and SVM) | Blinded interpretation of single-lead ECG by two cardiologists with group consensus. |
| Proesmans et al 201932 | Prospective multicentre Age≥65 years, paroxysmal or persistent AF Supported by industry |
Primary care N=223 AF prevalence 46% |
Age 77 years (mean); female 53%; diabetes 20%; heart failure 29%; stroke 22%; OAC 56% | iPhone 5S; Fibricheck app; 3×60 s recordings; signal quality evaluated using RR-interval variability analysis; AF detection based on recurrent neural network algorithm | Blinded 12-lead ECG interpretation by two cardiologists with group consensus. |
| Rozen et al 201815 | Prospective single centre Age >18 years, AF for DCCV Supported by industry |
Secondary care N=97 AF prevalence 90% |
Age 68 years (mean); female 25% | iPhone; CRMA app; 3×20 s recordings analysed using SVM to classify PPG waveforms; feature extraction used to determine self-similarity of waveform; labelled AF if at least 2 of the three recordings irregular | Blinded 12-lead ECG interpretation by two cardiologists with group consensus. |
| Yan et al 201816 | Prospective single centre Supported by industry |
Secondary care; N=233 AF prevalence 35% |
Age 70 years (mean); female 30%; hypertension 60%; diabetes 35%; heart failure 32%; stroke 19% | iPhone 6S; CRMA app; 3×20 s recordings, baseline wander and noise filtered; AF detection using SVM (based on lack of repeating patterns); AF if irregular in ≥1, or three consecutive uninterpretable measurements | Blinded interpretation by cardiologist of 12-lead ECG; two study comparisons of (1) facial PPG and (2) finger PPG. |
See online supplemental table S1 for summary of conference abstracts.
AF, atrial fibrillation; CoSEn, coefficient of sample entropy; CoV, coefficient of variation; CRMA, cardiio rhythm smartphone application; DCCV, direct current cardioversion; DCNN, deep convolutional neural network; ECG, electrocardiogram; OAC, oral anticoagulation; PPG, photoplethysmography; RMSSD, root mean square of successive RR differences; ShE, Shannon entropy; SVE, support vector machine.