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FUNDAMENTAL PRINCIPLES

Objective: The aim of this paper was to investigate and describe the necessary elements in validating and comparing HR apps versus standard technology.
Methods: The FibriCheck (Qompium) app was used in two separate prospective nonrandomized studies. In the first study, the HR of the FibriCheck app was consecutively compared with 2 different Food and Drug Administration (FDA)-cleared HR devices: the Nonin oximeter and the AliveCor Mobile ECG. In the second study, a next step in validation was performed by comparing the beat-to-beat intervals of the FibriCheck app to a synchronized ECG recording.
Results: In the first study, the HR (BPM, beats per minute) of 88 random subjects consecutively measured with the 3 devices showed a correlation coefficient of .834 between FibriCheck and Nonin, .88 between FibriCheck and AliveCor, and .897 between Nonin and AliveCor. A single way analysis of variance (ANOVA; P=.61 was executed to test the hypothesis that there were no significant differences between the HRs as measured by the 3 devices. In the second study, 20,298 (ms) R-R intervals (RRI)–peak-to-peak intervals (PPI) from 229 subjects were analyzed. This resulted in a positive correlation (rs=.993, root mean square deviation [RMSE]=23.04 ms, and normalized root mean square error [NRMSE]=0.012) between the PPI from FibriCheck and the RRI from the wearable ECG. There was no significant difference (P=.92) between these intervals.
Conclusion: Our findings suggest that the most suitable method for the validation of an HR app is a simultaneous measurement of the HR by the smartphone app and an ECG system, compared on the basis of beat-to-beat analysis. This approach could lead to more correct assessments of the accuracy of HR apps.
Vandenberk T et al. Clinical Validation of Heart Rate Apps: Mixed-Methods Evaluation Study. JMIR 2018.

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Objective: To develop and validate a smartphone based acquisition and processing algorithm based on photopletyhsmographic (PPG) data collected in a controlled hospital environment.
Methods: A smartphone camera application was developed to record PPG data in synchronization with a reference electrocardiogram. Subjects were recorded while undergoing an electrophysiological examination. The PPG data acquisition was validated on 28 volunteers with sinus rhythm. After signal analysis an algorithm was developed for detection of ectopic beats. To characterize arrhythmias, supraventricular extrasystoles were induced every 10, 5 or 3 beats after 500 ms by applying a pacing train to the right atrium. The coupling interval was also examined by altering the intermediary time by 400, 500 or 600 ms.
Results: After signal conditioning, an accurate ectopic beat detection was obtained from the PPG signal. Premature atrial ectopic beats could be differentiated based on the interpeak distance at different coupling intervals.
Conclusion: By acquiring a PPG signal with the camera, the smartphone is not only capable of determining a regular sinus rhythm, but it also has the power to identify ectopic beats.
Drijkoningen L et al. Validation of a smartphone-based photoplethysmographic beat detection algorithm for normal and ectopic complexes. Computing in Cardiology, 2014.

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VALIDATION STUDIES

Methods: Patients were recruited from the cardiology ward. After obtaining written informed consent, demographic and medical information were collected. Patients were instructed to perform one measurement using a pulse-deriving smartphone app and one via a single-lead ECG handheld device. A 12-lead electrocardiogram (ECG) was collected and interpreted by a cardiologist as gold standard. Patients with atrial flutter were excluded, with additional exclusions for insufficient quality measurements and unsuccessful measurements resulting due to technical errors. Unclassified single-lead ECG measurements were handled as test-negative. Sensitivity, specificity and accuracy were calculated with respect to the reference diagnosis. McNemar’s analysis was performed to compare the sensitivity and specificity of the proprietary PPG and single-lead ECG AF detection algorithms.
Results: The median age in the study population (n = 300) was 70 years (interquartile range: 51-78), 56.3% were men, and the median CHA2DS2-VASc was 3 (interquartile range: 1-4) with an AF-prevalence of 32.3%. PPG signal and single‑lead ECG quality was sufficient in 272/300 (91.0%) and 278/298 (93.3%) participants, respectively. After excluding atrial flutter patients (n = 25) and insufficient quality measurements, the sensitivity and specificity were 97.6% (95% CI 93.8 to 99.3) and 94.1% (95% CI 86.8 to 98.1) for the PPG signal versus 95.7% (95% CI 91.4 to 98.3) and 91.1% (95% CI 83.2 to 96.1) for the single‑lead ECG signal, respectively. Results demonstrated a 96.4% (95% CI 93.2 to 98.3) accuracy for PPG and 94.1% (95% CI 90.4 to 96.6) for single-lead ECG. No significant differences in sensitivity (P = 0.453) or specificity (P = 0.219) between the proprietary PPG and single-lead ECG algorithms were found.
Conclusion: This study demonstrated equivalent diagnostic performance of PPG and single-lead ECG proprietary AF detection algorithms in smartphone apps.
Gruwez H et al. Head-to-head comparison of proprietary PPG and single-lead ECG algorithms for atrial fibrillation detection. EP Europace, 2021.

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Methods: Patients from the cardiology department were consecutively enrolled. Patients were handed 7 iOS models and 1 Android model and were asked to consecutively perform one PPG measurement per device. A 12-lead electrocardiogram (ECG) was collected during the same consultation and interpreted by a cardiologist as reference diagnosis. To allow an objective comparison across the devices, patients who failed to perform one successful measurement on each device were excluded. Additional exclusions were atrial flutter rhythms and insufficient quality results. Sensitivity, specificity and accuracy were calculated with respect to the reference diagnosis. McNemar’s analysis was used for the head-to-head comparison of the sensitivity and specificity of the proprietary algorithm on the different smartphone devices.
Results: A total of 150 patients participated in the study with a median CHA2DS2-VASc score of 3 (interquartile range: 1-5). The median age of the study population was 70 (interquartile range: 56-79) years. In total, 54.7% of the population was male and the AF-prevalence was 35.3%. After the exclusion of patients with atrial flutter (n = 14) and patients who did not successfully perform a PPG measurement on each device (n = 5), diagnostic-grade results of 131 patients were used to calculate the performance of the proprietary algorithm. The sensitivity and specificity of the AF detection algorithm ranged from 90.9% (95% CI 75.7-98.1) to 100.0% (95% CI 91.0-100) and 94.5% (95% CI 86.6-98.5) to 100.0% (95% CI 94.6-100), respectively. The overall accuracy across the devices ranged from 94.4% (95% CI 88.3-97.9) to 99.0% (95% CI 94.6-100). Head-to-head comparisons of the results did not reveal significant differences in sensitivity (P = 0.125-1.000) or specificity (P = 0.375-1.000) of the proprietary AF detection algorithm among the different devices.
Conclusion: This study demonstrated the device-independent nature of the PPG-deriving smartphone application with respect to 12-lead ECG diagnosis.
Gruwez H et al. Evaluation of the device independent nature of a photoplethysmography-deriving smartphone app. EP Europace, 2021.

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Methods: A phase II diagnostic accuracy study in a convenience sample of 242 subjects recruited in primary care. The majority of the participants were patients with a known history of AF (n = 160). A PPG measurement was obtained while patients held their index finger on the smartphone camera during one minute. A synchronized single-lead ECG was taken on the chest. Both traces were interpreted by the FibriCheck AF algorithm. First, the results of the FibriCheck algorithm were compared with 12-lead electrocardiographic recordings. Secondly, beat-to- beat comparison was done between the PPG and the single-lead ECG measurements.
Results: The signal quality filter of the application defined 29 PPG’s and 10 single-lead traces as poor and unreliable signal quality. For the PPG measurement and interpretation by the FibriCheck app, a sensitivity of 98% (95% CI 92 – 100), a specificity of 88% (95% CI 80 – 94) and an accuracy of 93% (95% CI 89 – 96) were obtained. False positive results were caused by atrial (n = 7) or ventricular (n = 1) extrasystoles and by failure of the quality filter of the application in recognizing a poor and unreliable signal (n = 4). For the single-lead ECG interpretation by the FibriCheck app, a sensitivity of 98% (95% CI 93 – 100), a specificity of 90% (95% CI 83 – 95) and an accuracy of 94% (95% CI 91 – 97) was found. The 11 false positive results were due to atrial (n = 10) and ventricular (n = 1) extrasystoles. Beat-to-beat analysis of the synchronized PPG and singlelead ECG traces showed a small difference in performance (99% uniform diagnoses), due to the different measurement method.
Conclusion: The FibriCheck app is an accessible standalone smartphone application that showed promising results for AF detection in a primary care convenience sample. The app scored a high accuracy and sensitivity and a moderate to high specificity. The PPG measurement method nearly matched single-lead ECG performance. These findings make the app a possible candidate to implement in future screening or case-finding programs for AF.
Mortelmans C et al. Validation of a new smartphone application (“FibriCheck”) for the diagnosis of atrial fibrillation in primary case. EHRA conference, Vienna, 2017.

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Background: Mobile phone apps using photoplethysmography (PPG) technology through their built-in camera are becoming an attractive alternative for atrial fibrillation (AF) screening because of their low cost, convenience, and broad accessibility. However, some important questions concerning their diagnostic accuracy remain to be answered.
Objective: This study tested the diagnostic accuracy of the FibriCheck AF algorithm for the detection of AF on the basis of mobile phone PPG and single-lead electrocardiography (ECG) signals.
Methods: A convenience sample of patients aged 65 years and above, with or without a known history of AF, was recruited from 17 primary care facilities. Patients with an active pacemaker rhythm were excluded. A PPG signal was obtained with the rear camera of an iPhone 5S. Simultaneously, a single‑lead ECG was registered using a dermal patch with a wireless connection to the same mobile phone. PPG and single-lead ECG signals were analyzed using the FibriCheck AF algorithm. At the same time, a 12‑lead ECG was obtained and interpreted offline by independent cardiologists to determine the presence of AF.
Results: A total of 45.7% (102/223) subjects were having AF. PPG signal quality was sufficient for analysis in 93% and single‑lead ECG quality was sufficient in 94% of the participants. After removing insufficient quality measurements, the sensitivity and specificity were 96% (95% CI 89%-99%) and 97% (95% CI 91%-99%) for the PPG signal versus 95% (95% CI 88%-98%) and 97% (95% CI 91%-99%) for the single‑lead ECG, respectively. False-positive results were mainly because of premature ectopic beats. PPG and single‑lead ECG techniques yielded adequate signal quality in 196 subjects and a similar diagnosis in 98.0% (192/196) subjects.
Conclusions: The FibriCheck AF algorithm can accurately detect AF on the basis of mobile phone PPG and single-lead ECG signals in a primary care convenience sample.
Proesmans T et al. Mobile Phone–Based Use of the Photoplethysmography Technique to Detect Atrial Fibrillation in Primary Care: Diagnostic Accuracy Study of the FibriCheck App. JMIR, 2019.

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This study evaluated the diagnostic accuracy of a PPG-based pulse-deriving smartphone application (FibriCheck) with respect to handheld single-lead ECG and the gold standard, 12-lead ECG. In addition, the device dependent nature of the performance of the application was assessed.
Patients who were scheduled for a regular consultation or procedure (i.e. ablation or cardioversion) were recruited from the cardiology ward. Additionally, patients hospitalized for continuous cardiac monitoring were recruited to enrich the database with atrial fibrillation (AF) measurements. After obtaining written informed consent, patients filled in a questionnaire collecting demographic and medical information. Patients were handed 6 Android and 2 iOS devices and were asked to perform one PPG-measurement per device. They also performed a single-lead ECG measurement with a handheld device (Kardia Mobile). Subsequently, a 12-lead ECG was taken to obtain a reference diagnosis.
A total of 150 patients participated in the study. The mean age of the study population was 64 (±19) years, 58% was male. The AF-prevalence was 37%. On average, patients in AF had a higher CHA2DS2-VASc score; 3.93 (±1.80) compared to 2.02 (±1.63) for non-AF patients.
The amount of insufficient quality measurements recorded with the pulse-deriving smartphone application ranged from 4% (iOS) to 13% (Android). Averaged for all the smartphone devices, the pulse-deriving application scored 87.7% (±3%) sensitivity, 96.6% (±3%) specificity, 77.2% (±5%) NPV, 98.3% (±1%) PPV, and 90.3% (±2%) accuracy. The handheld singlelead ECG device had 87.6% sensitivity, 91.5% specificity, 78.2% NPV, 95.5% PPV, and 88.9% accuracy.
The same calculations were preformed after excluding regular atrial flutter measurements. On average, the pulse-deriving application scored 94.8% (±1%) sensitivity, 96.1% (±3%) specificity, 88.1% (±3%) NPV, 98.3% (±1%) PPV, and 95.2% (±1%) accuracy. The handheld single-lead ECG device had 95.5% sensitivity, 90.2% specificity, 90.2% NPV, 95.5% PPV, and 93.9% accuracy.
The diagnostic accuracy of the pulse-deriving smartphone application and the handheld single-lead ECG device was strongly influenced by the presence of regular atrial flutters, stressing the importance of further thorough validation. For the pulsederiving smartphone application, there was no significant influence from device type in terms of diagnostic accuracy.
Proesmans T et al. The diagnostic accuracy of a pulse-deriving smartphone application is device independent. European Heart Rhythm Meeting, 2019.

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INTERPRETATION OF PPG

By applying photoplethysmography (PPG), the camera of the mobile phone can be used to remotely assess heart rate and rhythm, which was widely used in conjunction with teleconsultations within the TeleCheck-AF project during the coronavirus disease 2019 (COVID-19) pandemic. Herein, we provide an educational, structured, stepwise practical guide on how to interpret PPG signals. A better understanding of PPG recordings is critical for the implementation of this widely available technology into clinical practice.

Betz K. et al. Interpretation of photoplethysmography: a step-by-step guide. Herzschr Elektrophys, 2021, doi: https://doi.org/10.1007/s00399-021-00795-y

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Methods: A double-blinded, randomised, prospective study was performed. Visualized traces (PDF-files) of simultaneously recorded PPG and single-lead ECG measurements were randomly mixed and divided into two bundles for review by two independent cardiologists. These files included AF rhythms, sinus rhythms, and inferior quality measurements.
Results: 322 paired PPG and single-lead ECG traces were reviewed by two independent cardiologists unfamiliar with PPG. 176 single-lead ECG traces (55%) were identified as AF, and 146 as sinus rhythm (45%). Averaged results from the two cardiologists for the PPG signal compared to single-lead ECG resulted in 82% sensitivity and 93% specificity, associated with a Positive Predictive Value (PPV) of 92% and a Negative Predictive Value (NPV) of 84%. McNemar’s chi-squared test showed a significant difference (P=0.001) between the interpretations based on PPG versus ECG. The inter-observer agreement was 0.83 and Cramer’s V test for association resulted in 0.75. After eliminating 47 inferior quality measurements (15%), further data-analysis was done. Sensitivity and specificity rates increased to 96% and 100% respectively, associated with a PPV of 100% and a NPV of 96%. No significant differences (P=0.13) were found between PPG and ECG interpretation. The inter-observer agreement was 0.95 and Cramer’s V test increased to 0.95.
Conclusion: The use of a PPG-based smartphone application for AF diagnosis resulted in a relatively high accuracy compared to single-lead ECG, although a significant difference was observed. After data-analysis of solely high-quality measurements, the sensitivity and specificity rates increased, and the results between PPG and single-lead ECG became non-significant. This stresses the importance to focus on algorithms that improve the quality or detect impaired quality issues related to the signal.
Vandenberk T et al. Atrial Fibrillation Diagnosis Based on a Smartphone Derived Ppg Waveform: A Diagnostic Accuracy Study versus Single-Lead. AHA Journals, 2018.

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Methods: PPG, single-lead ECG and 12-lead ECG data were simultaneously recorded in 30 patients. Diagnostic reference was the 12-lead ECG, read by two cardiologists. Cardiologists, electrophysiologists and cardiology fellows were invited to analyse the data of 30 patients (10 in SR, 10 in SR with extrasystoles and 10 in AF) through online surveys and classify the readings as ‘SR’, ‘ectopic/missed beats’, ‘AF’, ‘flutter’ or ‘unreadable’. For dichotomous analysis, ‘unreadable’ was reclassified as incorrect, the other options were reclassified as AF ‘present’ or ‘absent’. In the first survey, PPG data were presented subsequently as a waveform, stepwise adding the tachogram and Poincaré plot, and algorithm information. In the next two surveys, the single-lead and 12-lead ECG traces were presented. Sensitivity and specificity for all presentations were calculated with respect to the reference diagnosis. Diagnostic performances were compared with the Obuchowski-Rockette’s ANOVA approach with Jackknife covariance estimation and Benjamini-Hochberg correction.
Results: Sixty-five physicians completed the PPG survey and analysed the PPG waveforms with 88.8% sensitivity and 86.3% specificity for AF. The diagnostic metrics significantly increased to 95.5% sensitivity (P < 0.001) and 92.5% specificity (P < 0.001) after providing the tachogram and Poincaré plot. Fifty-seven physicians completed both ECG surveys and analysed the single-lead ECG outputs with 91.2% sensitivity and 93.9% specificity, while 12-lead ECG outputs were analysed with 93.9% sensitivity and 98.6% specificity. Hence, qualitative analysis of a PPG waveform with tachogram and Poincaré plot had a similar diagnostic performance to detect AF compared to single-lead ECG analysis and a similar sensitivity (P = 0.792) but lower specificity (P = 0.035) compared to 12-lead ECG.
Conclusion: PPG rhythm recordings, analysed by physicians as a waveform in combination with the corresponding tachogram and Poincaré plot, achieve similar diagnostic accuracy as single-lead ECG to detect AF.
Gruwez H et al. The accuracy of physician interpretation of PPG vs single-lead ECG vs 12-lead ECG for the detection of atrial fibrillation. EP Europace, 2021.

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Objective: Diagnosis of Atrial Fibrillation based on the visual interpretation of a PPG signal results in a high clinical accuracy compared to single lead ECG and the current gold 12-lead ECG-standard.
Methods: A double-blind, randomized, prospective study was performed. The visual signal of simultaneous measured PPG and one-lead ECG were selected for diagnosis by a cardiologist. These files included AF, sinus rhythms, bad signal (bad quality of PPG or/and ECG signal) and other arrhythmia measurements. The PDF-files were randomly mixed and divided over two bundles. Four doctors (one cardiologist, one electrophysiologist and two assistant cardiologists) were asked to review one of the bundles. The diagnosis of the PPG and one-lead ECG signals were compared to the diagnosis of the 12-lead ECG.
Results: 344 pairs of PPG, one-lead ECG and 12-lead ECG signals were reviewed by cardiologists. Out of the 12-lead ECG files, 173 were diagnosed as AF. Averaged results showed a PPG sensitivity rate of 83.52% and a specificity rate of 92.39% compared to a sensitivity rate of 93.09% and a specificity rate of 95.89% for the one-lead ECG. After eliminating other arrhythmias and bad signals, further data-analysis was done. Sensitivity and specificity rates increased to 96.28% and 99.08% for PPG compared to 95.64% and 98.42% for one-lead ECG. Considerable differences between reviewers were found for sensitivity rates.
Conclusion: The use of a smartphone application for AF patients results in a good accuracy for the diagnose of this heart rhythm disorder. Although possible problems could arise round education and training for cardiologist. After enabling dataanalysis, sensitivity and specificity rates increase to very high accuracy corresponding 12-lead ECG. Algorithms could be important to process PPG measurements to adjust the quality of the data. This study concludes the potential to detect and
diagnose AF in patients by the use of a smartphone with FibriCheck.
Vandenberk T et al. PPG versus single lead ECG for the diagnose of Atrial Fibrillation. e-Cardiology, Berlin 2017.

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Methods: During an online conference, the structured stepwise practical guide on how to interpret PPG signals was discussed and further refined during an internal review process. We provide the number of respective PPG recordings (FibriCheck®) and number of patients managed within a clinical scenario during the TeleCheck-AF project.
Results: To interpret PPG recordings, we introduce a structured stepwise practical guide and provide representative PPG recordings. In the TeleCheck-AF project, 2522 subjects collected 90616 recordings in total. The majority of these recordings was classified by the PPG algorithm as sinus rhythm (57.6%), followed by AF (23.6%). In 9.7% of recordings the quality was too low to interpret. The most frequently clinical scenarios where PPG technology was used in the TeleCheck-AF project was follow-up after AF ablation (1110 patients) followed by heart rate and rhythm assessment around (tele)consultation (966 patients).
Conclusion: We introduce a newly developed structured stepwise practical guide on PPG signal interpretation developed based on presented experiences from TeleCheck-AF. The present clinical scenarios for the use of on-demand PPG technology derived from the TeleCheck-AF project will help to implement PPG technology in the management of AF patients.

van der Velden R. et al. The photoplethysmography dictionary: Practical guidance on signal interpretation and clinical scenarios from TeleCheck-AF. European Heart Journal – Digital Health, 2021, https://doi.org/10.1093/ehjdh/ztab050

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MANAGING PATIENTS WITH AF

Objective: Despite improvements of outcome of ablation for AF, early arrhythmia recurrence is not uncommon up to 3 months post-ablation. Although these arrhythmias are transient and do not represent treatment failure, it is widely recognized as a risk factor for long-term recurrence. To date, a better understanding in the correlation between early and long-term recurrence is hindered by an inability to continuously monitor these patients. We hypothesize that the implementation of a pulse-deriving smartphone application in this population offers the potential to detect early as well as late recurrence in order to initiate proper treatment in a timely manner.
Methods: Four clinical centers included a total of 80 participants who underwent successful AF treatment using ablation therapy. All participants were instructed to measure twice daily with a pulse-deriving smartphone application (FibriCheck) and additionally when experiencing symptoms, for a monitoring period of 4 months post-ablation. The planned usual-care pathway was registered at study inclusion. All measurements were revised algorithmically and confirmed by the treating physicians and healthcare professionals from the FibriCheck monitoring center. At time of inclusion and study end a 12-lead ECG was performed.
Results: The mean age of the study population was 66 (±13) years from which 25 % was female. Using the CHA2DS2-VASc score, 61% of the participants had an increased stroke risk (i.e. a score of 2 or more). Overall compliance to conduct measurements was recorded at 91% with 2 measurements per day. The smartphone app was able to identify 29 AF-cases (36%) of which 27 paroxysmal and 2 persistent. Only 37% of the AF cases were symptomatic. In the usual care path only 3/29 (10%) cases were identified with 12-lead ECG at the next scheduled consult and 9 (31%) patients identified with AF would been monitored by Holter.
Conclusion: Pulse-deriving smartphone applications implemented in combination with a structured care path proved to be a promising methodology for short- and long-term outcome monitoring of post-ablation patients and are capable in the detection of silent intermittent atrial fibrillation episodes.
Proesmans T et al. Post-ablation outcome monitoring using a pulse-deriving smartphone application. European Society of Cardiology conference, Munich 2018.

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This multicenter national study addresses the implementation and evaluation of the FibriCheck application in high-risk patient populations as a solution for primary and secondary stroke prevention. This study was performed for the Cabinet of Minister of Health De Block and the National Institute for Health and Disability Insurance (RIZIV). The study was organized in 8 clinical centers in the Northern part of Belgium. Patients were included in the following groups: (1) patients with relevant comorbidities, (2) patients with structural heart disease, (3) patients with an increased CHARGE-AF score, (4) postcryptogenic stroke patients, (5) post-cardioversion, and (6) post-ablation patients. Patients with a pacemaker rhythm were excluded. To quantify the number of AF-patients identified with the application versus during usual care, the initially planned care-path of each patient was documented. Patients were requested to perform 2 measurements per day and log their symptoms with each recording. A 12-lead ECG was taken at inclusion and study end. In total, 460 patients were included, 168 were female (37%). The mean age was 66 ± 12 years. The mean CHA2DS2-VASc score was 2.2 ± 2.5, 97 patients (21%) were anticoagulated. On average, patients enrolled for 1.86 ± 1.1 months. 47,667 measurements were performed, 81% was normal and 2% indicative for AF. 34% of the AF-measurements was symptomatic. A 91% compliance in performing 2 measurements per day was observed. 61 AF-patients (13%) were identified by the algorithm and confirmed by physician interpretation, 25 were previously undiagnosed. The mean age of the AF-group was 66 ± 11 years. The mean CHA2DS2-VASc score was 2.4 ± 1.6. Following these findings, 49 therapeutic interventions were carried out. Of the 61 AF-patients, 11 (18%) were also identified on 12-lead ECG. When questioning the physicians concerning the planned usual care-path, it appeared that 37 AF-patients (61%) would have received 12-lead ECG checks during follow-up consultations. 6 AF-patients (10%) would have received 24-hour Holter monitoring. Patients with structural heart disease, post-cardioversion, and postablation patients had the greatest chance of being monitored during follow-up consultations.
This study presents the first results of prolonged PPG-monitoring in high-risk populations for AF detection. Considering the high number of asymptomatic registrations, repeated PPG spot-checks proved to be valuable for the detection of new or recurrent, (a)symptomatic, paroxysmal or persistent AF.
Proesmans T et al. Implementation of a pulse-deriving smartphone application in highrisk populations for primary and secondary prevention of stroke. Belgian Heart Rhythm Meeting, 2018.

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SCREENING FOR AF

Key points:

  • Undetected atrial fibrillation (AF) is common and can be detected by screening.
  • Clinical AF refers to symptomatic or asymptomatic AF documented by surface ECG, whereas subclinical AF (SCAF) refers to AF detected by screening or continuous monitoring in whom clinical AF is not present.
  • Evidence suggests that anticoagulation and rhythm-control therapy for screen-detected AF might lead to better clinical outcomes. The existing evidence is for clinical AF and randomized clinical trials for SCAF are needed.
  • The AF detection rate of screening is determined by the population, the tool, the frequency, and the duration of screening. In general, longer and more frequent screening in a population at higher risk for AF results in a higher detection rate.
  • Implantable cardiac rhythm devices have the highest AF detection rates. Single-lead ECG and PPG devices are potentially more cost-effective and are more convenient for population-wide screening.

Gruwez H. et al. Atrial Fibrillation Population Screening, Cardiac Electrophysiology Clinics, 2021.

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Methods: Four general practices across Flanders provided patient data for the study. Inclusion criteria for participants were aged 65 or older and a CHARGE-AF score of at least 10%. We excluded patients with known AF or a pacemaker. Participants were asked to measure at least twice a day with FibriCheck (for at least 14 days). They were provided the 36-Item Short Form Survey (SF-36) questionnaire both before and after the study, as well as different surveys concerning their user experience and general perception of technology.

Results: There were 92 participants (36 women and 56 men). The study population was relatively homogenous concerning risk factors and medication use at baseline. During the study period, 5/86 (6%) participants were found to have AF (6 dropouts). The average study period was 23 days and the average number of measurements per day was 2.1. Patient compliance was variable, but high. On the whole, there were no appreciable changes in quality of life. The overall user experience and satisfaction were very high.

Conclusions: FibriCheck is a relatively easy-to-use smartphone app to complement AF screening in primary care. Its implementation in this setting is certainly achievable, and one can expect high rates of patient compliance. Based on these results, a planned cluster randomized trial will be going ahead.

Beerten S et al. A Heart Rate Monitoring App (FibriCheck) for Atrial Fibrillation in General Practice: Pilot Usability Study, JMIR Form Res, 2021.

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Methods: In this prospective, single-center trial, patients recovering from cardiac surgery were asked to register their heart rhythm 3 times daily using a Food and Drug Administration-approved PPG-based app, for either 30 or 60 days after discharge home. Patients with permanent AF or a permanent pacemaker were excluded.

Results: We included 24 patients (mean age 60.2 years, SD 12 years; 15/23, 65% male) who underwent coronary artery bypass grafting and/or valve surgery. During hospitalization, 39% (9/23) experienced postoperative AF. After discharge, the PPG app reported AF or atrial flutter in 5 patients. While the app notified flutter in 1 patient, this was a false positive, as electrocardiogram revealed a 2nd-degree, 2:1 atrioventricular block necessitating a permanent pacemaker. AF was confirmed in 4 patients (4/23, 17%) and interestingly, was associated with an underlying postoperative complication in 2 participants (pneumonia n=1, pericardial tamponade n=1). A significant increase in the proportion of measurements indicating sinus rhythm was observed when comparing the first to the second month of follow-up (P<.001). In the second month of follow-up, compliance was significantly lower with 2.2 (SD 0.7) measurements per day versus 3.0 (SD 0.8) measurements per day in the first month (P=.002). The majority of participants (17/23, 74%), as well as the surveyed primary care physicians, experienced positive value by using the app as they felt more involved in the postoperative rehabilitation.

Conclusions: Implementation of smartphone-based PPG technology enables detection of AF and other rhythm-related complications after cardiac surgery. An association between AF detection and an underlying complication was found in 2 patients. Therefore, smartphone-based PPG technology may supplement rehabilitation after cardiac surgery by acting as a sentinel for underlying complications, rhythm-related or otherwise.

Lamberigts M et al. Remote Heart Rhythm Monitoring by Photoplethysmography-Based Smartphone Technology After Cardiac Surgery: Prospective Observational Study, MIR Mhealth Uhealth, 2021.

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Methods: Participants presented themselves voluntarily at two screening sites. Screening was done using sequential measurements of a single lead ECG device (AliveCor) and a software only smartphone application based on PPG (FibriCheck). AliveCor measurements were performed by placing both hands on two electrodes while FibriCheck requires to place the finger on the smartphone camera. If one of the applications indicated an irregular rhythm a 12-lead ECG was taken for verification.
Results: In total 1056 participants were screened, 41% was male. The mean age was 59±15 years with a mean BMI of 26±10.
In total 31% had no risk factors for AF, 34% had 1 risk factor, 19% had 2 risk factors and 16% had +2 risk factors. The screening resulted in the identification of 8 AF cases, 1026 regular sinus rhythms and 22 other irregular rhythms. The AF cases had a CHADS2-VASc score of 3±1.18. AliveCor had a sensitivity of 100% and specificity of 99.6% for the detection of AF, while FibriCheck had a sensitivity of 100% and a specificity of 97.2%. Overall quality of the FibriCheck and AliveCor measurements was automatically determined and was unreadable in 2.9% and 3.8% of the cases respectively. No correlation was found between the cases with bad quality for both measurement techniques or demographics.
Conclusion: The obtained results indicate that detection of pulse intervals based on PPG is a feasible, sensitive and accurate screening tool for the detection of AF with a high level of agreement when compared to the results obtained using the single lead ECG. The use of a smartphone-only application could unlock the potential of digital screening and support case finding in selected population at risk for AF.
Grieten L et al. Evaluating smartphone based photoplethysmography as a screening solution for atrial fibrillation: a digital tool to detect AF? JACC, 2017.

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Objective: Opportunistic screening for Atrial Fibrillation (AF) is proven to be important and effective in identifying cases of untreated, frequently asymptomatic AF. This work focuses on the performance evaluation of using a smartphone application FibriCheck as a screening tool during the week of the heart rhythm (WHR).
Methods: Participants presented themselves voluntarily at the screening sites (AZ Delta, Roeselare or Ziekenhuis Oost-Limburg, Genk) during the WHR. Screening was done using sequential measurements a single lead ECG device (AliveCor, 30 seconds) and a software only smartphone application based on photoplethysmography (PPG) (FibriCheck, 60 seconds). AliveCor measurements were performed by placing both hands on two electrodes while the FibriCheck requires to place the finger on the smartphone camera. Additionally, demographic and background questionnaires were obtained. If one of the screening technologies indicated an irregular rhythm a 12-lead ECG was taken for verification by the cardiologist on site.
Results: In total 1056 participants were screened, 41% was male. The overall mean age was 59±15 years with a mean BMI of 26±10. In total 31% had no risk factors for AF, 34% had 1 risk factor, 19% had 2 risk factors and 16% had two or more risk factors. The screening resulted in the identification of 8 AF cases, 1026 regular sinus rhythms and 22 irregular rhythms (bigeminy, trigeminy, supraventricular arrhythmia). The AF cases had a CHADS2-VASc score of 3±1.18. The AliveCor application had a sensitivity of 100% and specificity of 99.6% for the detection of atrial fibrillation, while the FibriCheck application had a sensitivity of 100% and a specificity of 95.8% for the detection of atrial fibrillation. Overall quality of the FibriCheck and AliveCor measurements was automatically determined and was unreadable/unusable in 2.9% and 3.8% of the cases respectively. These cases required an additional measurement to obtain a diagnosis. No correlation was found between the cases with bad quality measurements for both measurement techniques.
Conclusion: The obtained results indicate that detection of pulse intervals based on PPG is a sensitive and accurate screening tool for the detection of atrial fibrillation and has a high level of agreement with the results obtained using the single lead ECG. The use of a smartphone-only application could unlock the potential of digital screening and support case finding of atrial fibrillation in selected population at risk for atrial fibrillation.
Grieten G et al. Screening for atrial fibrillation using only a smartphone application: a new tool to unlock digital screening? Belgian Heart Rhythm Meeting, 2016.

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Objective: This study was organized to assess the efficacy and feasibility of a nationwide voluntary screening program in the Belgian general population over 40 years of age.
Methods: Participants were ad-hoc invited for a complementary screening in 2 clinical centers in Belgium over a period of 5 consecutive days during the heart rhythm week. Participants filled in a questionnaire assessing eligibility and CHA2DS2-VASc parameters. A total of 1359 participants were screened, 1179 of whom were older than 40 years. From this general screening population, high quality measurements of 1095 participants captured by a PPG smartphone application (FibriCheck) and a single-lead ECG tool (KardiaMobile), evaluated by both an automatic algorithm and visual interpretation, were included for data analysis. Additionally, the accuracy of the algorithm-based tools with respect to manual rhythminterpretation was assessed.
Results: This study reports a prevalence of AF in the general screening population of 0.5% with a higher prevalence in men (0.9%) compared to in women (0.2%). The average age of the AF-group was 76 (±3) years. Using the CHA2DS2-VASc score, all participants with a positive AF-screening had a high risk score for stroke (i.e. a score of 2 or more). Although the intent of screening for AF is straightforward (either a positive or negative result), both screening tools have a subdivision to categorize irregular heart rhythms in AF, sinus rhythm or other arrhythmias. This extra category provides a clinical challenge on how to interpret screening results. Therefore, a dual assessment was made in this study: aiming for maximum sensitivity by combining the AF and the other arrhythmia categories versus maximum specificity by combining the normal and the other arrhythmia categories. When striving for maximal sensitivity, the algorithm-based interpretation of the PPG trace scored a sensitivity/specificity/accuracy (SSA) of 100%/97%/97%. Algorithm based interpretation of the single-lead ECG trace had an SSA of 100%/95%/95%. When striving for maximal specificity, the algorithm-based interpretation of the PPG trace scored SSA of 100%/98%/98%. Algorithm based interpretation of the single-lead ECG trace had a SSA of 100%/99%/99%.
Conclusion: AF was present in 0.5% of the participants. All participants with a positive AF-screening had an increased risk for thromboembolism. The present study shows that a voluntary screening program using high accuracy PPG-based and single-lead ECG tools was able to detect an important number of patients with previously undetected AF.
Grieten L et al. Evaluation of screening technologies and assessments in a voluntary screening program in the general Belgian population. Heart Rhythm Society, Boston 2018.

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Objective: This cross-sectional study was set up to assess the feasibility of mass screening for atrial fibrillation (AF) with only the use of a smartphone.
Methods & results: A local newspaper published an article, allowing to subscribe for a 7-day screening period to detect AF. Screening was performed through an application that uses photo-plethysmography (PPG) technology by exploiting a smartphone camera. Participants received instructions on how to perform correct measurements twice daily, with notifications pushed through the application’s software. In case of heart rhythm irregularities, raw PPG signals underwent secondary offline analysis to confirm a final diagnosis. From 12 328 readers who voluntarily signed up for screening (49 ± 14 years; 58% men), 120 446 unique PPG traces were obtained. Photo-plethysmography signal quality was adequate for analysis in 92% of cases. Possible AF was detected in 136 individuals (1.1%). They were older (P < 0.001), more frequently men (P < 0.001), and had higher body mass index (P = 0.004). In addition, participants who strictly adhered to the recommended screening frequency (i.e. twice daily) were more often diagnosed with possible AF (1.9% vs. 1.0% in individuals who did not adhere; P = 0.008). Symptoms of palpitations, confusion, and shortness of breath were more frequent in case of AF (P < 0.001). The cumulative diagnostic yield for possible AF increased from 0.4% with a single heart rhythm assessment to 1.4% with screening during the entire 7-day screening period.
Conclusion: Mass screening for AF using only a smartphone with dedicated application based on PPG technology is feasible and attractive because of its low cost and logistic requirements.
Verbrugge F. et al. Atrial fibrillation screening with photo-plethysmography through a smartphone camera. Europace, 2019.

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Aims: Atrial fibrillation (AF) is the most common sustained arrhythmia and an important risk factor for stroke and heart failure. We aimed to conduct a systematic review of the literature and summarize the performance of mobile health (mHealth) devices in diagnosing and screening for AF.
Methods and results: We conducted a systematic search of MEDLINE, Embase, and the Cochrane Central Register of Controlled Trials. Forty-three studies met the inclusion criteria and were divided into two groups: 28 studies aimed at validating smart devices for AF diagnosis, and 15 studies used smart devices to screen for AF. Evaluated technologies included smartphones, with photoplethysmographic (PPG) pulse waveform measurement or accelerometer sensors, smartbands, external electrodes that can provide a smartphone single-lead electrocardiogram (iECG), such as AliveCor, Zenicor and MyDiagnostick, and earlobe monitor. The accuracy of these devices depended on the technology and the population, AliveCor and smartphone PPG sensors being the most frequent systems analysed. The iECG provided by AliveCor demonstrated a sensitivity and specificity between 66.7% and 98.5% and 99.4% and 99.0%, respectively. The PPG sensors detected AF with a sensitivity of 85.0–100% and a specificity of 93.5–99.0%. The incidence of newly diagnosed arrhythmia ranged from 0.12% in a healthy population to 8% among hospitalized patients.
Conclusion: Although the evidence for clinical effectiveness is limited, these devices may be useful in detecting AF. While mHealth is growing in popularity, its clinical, economic, and policy implications merit further investigation. More head-to-head comparisons between mHealth and medical devices are needed to establish their comparative effectiveness.
Lopez Perales CR, et al. Mobile health applications for the detection of atrial fibrillation: a systematic review. Europace. 2020 Oct 12:euaa139.

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QUALITY RELATED STUDIES

Objective: This work focuses on comparing the performance between photoplethysmography (PPG) and single lead ECG based smartphone applications during a national incentivized screening initiative and evaluate the quality related issues between these technologies.
Methods: Participants presented themselves voluntarily during the screening initiative. Screening was done using sequential measurements. First the circumference of the index finger and temperature of the finger was recorded. Next a single lead ECG device (AliveCor, 30 seconds) and a software only smartphone application based on photoplethysmography (FibriCheck, 60 seconds). AliveCor measurements were performed by placing both hands on two electrodes while the FibriCheck requires to place the finger on the smartphone camera. Additionally, demographic and background questionnaires were obtained. If one of the screening technologies indicated an irregular rhythm a 12-lead ECG was taken for verification by the cardiologist on site.
Results: In total 1056 participants were screened, 41% was male. The overall mean age was 59±15 years with a mean BMI of 26±10. In total 31% had no risk factors for AF, 34% had 1 risk factor, 19% had 2 risk factors and 16% had two or more risk factors. The screening resulted in the identification of 8 AF cases, 1026 regular sinus rhythms and 22 irregular rhythms (bigeminy, trigeminy, supraventricular arrhythmia). The AF cases had a CHADS2-VASc score of 3±1.18. The AliveCor had a sensitivity of 100% and specificity of 99.6% for the detection of AF, while the FibriCheck application had a sensitivity of 100% and a specificity of 97.2. The proprietary quality algorithms of AliveCor and FibriCheck indicated if the quality of the signal was insufficient for analysis. The quality was unreadable in 2.9% and 3.8% of the cases respectively. The main indicator
were cold hands, tremor or callus formation at the hands of the users. Interestingly, no correlation was observed between both technologies. Only in 10% of the bad quality signals there was a correlation between both technologies. The other cases the majority of the findings favored normal recordings.
Conclusion: The obtained results indicate that detection of pulse intervals based on PPG is a sensitive and accurate screening tool for the detection of atrial fibrillation and has a high level of agreement with the results obtained using the single lead ECG. Despite the quality challenges of PPG signals, there is no correlation found in the cause nor the agreement between both technologies indicating that for the general population the quality parameters are properly tuned to prevent misdiagnosis as much as possible. These quality parameters will be a fundamental requirement further leverage PPG signals as a suitable signal for heart rhythm analysis.
Grieten L et al. Using smartphone enabled technologies for detection atrial fibrillation: is there a difference in signal quality between ECG and PPG? Heart Rhythm Society, Boston 2018.

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This observational study evaluated the applicability and robustness of the FibriCheck smartphone application implemented in a broad population in a free-living setting.
A local newspaper published a free 7-day access code for a pulse-deriving smartphone application. Participants to this screening program received instructions on how to perform high quality measurements twice daily. To obtain a high quality signal, participants were instructed to adopt a sitting position with both arms resting on a firm surface, holding the smartphone in a vertical position with their dominant hand. Subsequently, the index finger of their non dominant hand should cover the flashlight and backside camera horizontally, without putting firm pressure. The FibriCheck application firstly checks acquired PPG signals for their quality. Compromised signals are not used for analysis to avoid inaccurate diagnostic results. Study participants with frequent poor quality PPG measurements received notifications through the application, guiding them on how to perform better measurements.
From 12,328 readers who voluntarily signed up for screening (49±14 years; 58% men), 120,446 unique PPG traces were obtained. AF was detected in 136 individuals (1.1%).
PPG signal quality was sufficient for analysis in 110,713 measurements (92%). The frequency of measurements with insufficient quality for analysis decreased significantly during the screening period, from 17% on day 1 to 2% on day 7 (Pvalue<0.001), indicating a steep learning curve. 8,683 participants only performed high quality PPG measurements. They were significantly younger compared to participants with at least one insufficient quality measurement (49 versus 51 years old, P-value<0.001).
This study demonstrates the applicability and robustness of a pulse-deriving smartphone application in a broad population in an unsupervised setting, provided that efforts are focused on training and education. Awaiting further validation studies, these results indicate the potential of a pulse-deriving smartphone application to detect atrial fibrillation.
Proesmans T et al. The quality of smartphone based heart rhythm monitoring using PPG technology in a large-scale free-living setting. European Heart Rhythm Meeting, 2019.

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This work investigates the effect of age on the usability of the FibriCheck smartphone application. A usability test was performed on 95 participants divided in three age categories; young adults (18-29), adults (30-65) and pensioners (65+).
Participants were instructed to use the application and perform critical tasks with minimum supervision or assistance. The tasks included multiple functionalities of the app (e.g. downloading, create an account, perform a measurement, add context, log-in, etc.). The time to perform each task and amount of insufficient quality measurements were documented.
Subjective usability estimation was inquired via a survey.
Although significant differences were observed between the time required to perform each task, all the tasks were completed successfully. In general, the elderly required more time to perform a task and reported higher difficulty levels in the subjective usability estimation. No significant difference was found between the groups regarding the number of insufficient quality measurements.
Although there is a difference in time to perform tasks and in the difficulty experienced between age categories, it does not affect the usability of the smartphone application. These results demonstrate that the FibriCheck application can be implemented in the relevant target groups.
Elaâchiri A et al. Effect of age on the usability of a photoplethysmography based smartphone application. European Heart Rhythm Meeting, 2019.

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HEALTH ECONOMICS

Objective: This study will address this by implementing a smartphone application for AF monitoring in post-cryptogenic stroke patients and assessing its cost-effectiveness.
Methods: In a multi-center prospective trial, 63 patients experiencing a cryptogenic stroke in the past year since the start of the study were enrolled. Patients were instructed to measure the heart rhythm twice daily with a pulse-deriving smartphone application (FibriCheck) and additionally when experiencing symptoms over a period of 3 months. At time of inclusion and study end, a 12-lead ECG was performed.
In addition, the cost-effectiveness of monitoring in patients after a recent cryptogenic stroke was assessed using a Markov model. The model simulated the health status of 1000 patients over a period of 35 years. Rates of AF detection and anticoagulation therapy from this study and published literature, together with epidemiological data from Belgium, were used to predict lifetime costs and effectiveness. The alternatives being investigated were opportunistic screening, usual care and screening with FibriCheck.
Results: This study reports 3 (5.2%) newly diagnosed AF cases and 1 (1.7%) recurrent AF case. None of these cases were identified with 12-lead ECG, neither at inclusion nor at the end of the study. Only 1 patient detected by FibriCheck during the monitoring period would been monitored by Holter as part of the usual care strategy.
The Markov model indicated that both opportunistic screening and usual care were inferior to FibriCheck in terms of costeffectiveness. Comparing FibriCheck as screening tool with usual care for patients post-cryptogenic stroke, the implementation of FibriCheck in a population of 1000 patients resulted in 26 quality adjusted life years (QALY) and substantial cost savings of -1.189 €/QALY.
Conclusion: After a cryptogenic stroke, 3-month FibriCheck monitoring proved to be cost-effective for preventing recurrent strokes. These results strengthen the evidence base for prolonged monitoring in secondary stroke prevention.
Proesmans T et al. Health economic assessment of smartphone implementation for atrial fibrillation monitoring in cryptogenic stroke patients. European Society of Cardiology Conference, Munich 2018.

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WEARABLE SENSORS

Based on the review of Carpenter and Frontera, we present a case of a 66-year old female patient with a history of unexplained syncope, and symptoms of palpitations which was implanted with an Implantable Loop Recorder (ILR) (LinQ, Medtronic). After the procedure the patient was provided with a smartwatch device (E4, Empatica) able to measure the photoplethysmography (PPG) signal at the wrist and an iPhone 5S smartphone with a custom-made application (FibriCheck®), able to measure the PPG signal in the tip of the finger using the smartphone camera.
She was instructed to wear the smartwatch during the nights to perform at least 7 hours of continuous measurements. After waking and removing the watch for charging, she was instructed to perform spot-check measurements (60 seconds) using a smartphone application (FibriCheck®). In total 2 recordings standard recordings were obtained per day, and additional measurements upon presentation of symptoms.
All of these measurements were performed over a period of 4 months. After every recording the data was automatically sent to a secure database (in the cloud). All data are available and presented on a dashboard for over-reading by a clinical technician or cardiologist. Overall compliance rates for the smartwatch measurements were 98% and for the smartphone measurements 107%, indicating a good adherence for long term monitoring application. In total, the ILR detected 9 episodes of paroxysmal AF with episodes ranging between 40 minutes to 3 hours.
After synchronizing the data streams between the ILR, smartwatch and smartphone, all AF events that occurred while wearing or using one of the smart devices were picked-up and identified as AF. Our in-house developed algorithms identified episodes of AF based on RR variability, which is commonly accepted in literature.
To conclude, persuasive technologies such as smartphones and smartwatches can provide a new potential in the detection and management of patients with AF. Opening a unique way of long-term and cost-effective case-finding initiatives.
Grieten L et al. “Smart” solutions for paroxysmal atrial fibrillation? Europace, 2017.

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Introduction: Atrial fibrillation (AF) is the most common cardiac arrhythmia in the developed world. Using photoplethysmography (PPG) and software algorithms, AF can be detected with high accuracy using smartphone camera-derived data. However, reports of diagnostic accuracy of standalone algorithms using wristband-derived PPG data are sparse, while this provides a means to perform long-term AF screening and monitoring. This study evaluated the diagnostic accuracy of a well-known standalone algorithm using wristband-derived PPG data.
Materials and methods: Subjects recruited from a community senior care organization were instructed to wear the Wavelet PPG wristband on one arm and the Alivecor KardiaBand one-lead-ECG wristband on the other. Three consecutive measurements (duration per measurement: 60 s for PPG and 30 s for one-lead ECG) were performed with both devices, simultaneously. The PPG data were analyzed by the Fibricheck standalone algorithm and the ECG data by the Kardia algorithm. The results were compared to a reference standard (interpretation of the one-lead ECG by two independent cardiologists).
Results: A total of 180 PPGs and one-lead ECGs were recorded in 60 subjects, with a mean age of 70±17. AF was identified in 6 (10%) of the users, two users (3%) were not classifiable by the PPG algorithm and 1 user (2%) was not classifiable by the one-lead ECG algorithm. The diagnostic performance (sensitivity/specificity/positive predictive value/negative predictive value/accuracy) on user level was 100/96/75/100/97% for the PPG wristband and 100/98/86/100/98% for the one-lead ECG wristband.
Conclusions: In a small real-world cohort of elderly people, the standalone Fibricheck AF algorithm can accurately detect AF using Wavelet wristband-derived PPG data. Results are comparable to the Alivecor Kardia one-lead ECG device, with an acceptable unclassifiable/bad quality rate. This opens the door for long-term AF screening and monitoring.
Selder JL et al. Assessment of a standalone photoplethysmography (PPG) algorithm for detection of atrial fibrillation on wristband-derived data. ScienceDirect, 2020, doi: 10.1016/j.cmpb.2020.105753.

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REMOTE PATIENT MANAGEMENT

TeleCheck-AF is a multicentre international project initiated to maintain care delivery for patients with atrial fibrillation (AF) during COVID-19 through teleconsultations supported by an on-demand photoplethysmography-based heart rate and rhythm monitoring app (FibriCheck). We describe the characteristics, inclusion rates, and experiences from participating centres according the TeleCheck-AF infrastructure as well as characteristics and experiences from recruited patients.

Gawalko M. et al. The European TeleCheck-AF project on remote app-based management of atrial fibrillation during the COVID-19 pandemic: centre and patient experiences. EP Europace, 2021, doi: https://doi.org/10.1093/europace/euab050

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Recently, we introduced the TeleCheck-AF approach, an on-demand mobile health (mHealth) infrastructure using app-based heart rate and rhythm monitoring for 7 days, to support long-term atrial fibrillation (AF) management through teleconsultation. Herein, we extend the mHealth approach to patients with recent-onset AF at the emergency department (ED).

In the proposed TeleWAS-AF approach, on-demand heart rate and rhythm monitoring are used to support a wait-and-see strategy at the ED. All stable patients who present to the ED with recent-onset symptomatic AF and who are able to use mHealth solutions for heart rate and rhythm monitoring are eligible for this approach. Patients will receive both education on AF and instructions on the use of the mHealth technology before discharge from the ED. A case coordinator will subsequently check whether patients are able to activate the mHealth solution and to perform heart rate and rhythm measurements.

Forty hours after AF onset, the first assessment teleconsultation with the physician will take place, determining the need for delayed cardioversion. After maximal 7 days of remote monitoring, a second assessment teleconsultation may occur, in which the rhythm can be reassessed and further treatment strategy can be discussed with the patients. This on-demand mHealth prescription increases patient involvement in the care process and treatment decision-making by encouraging self-management, while avoiding excess data-load requiring work-intensive and expensive data management. Implementation of the TeleWAS-AF approach may facilitate the management of AF in the ED and reduce the burden on the ED system, which enhances the capacity for health care utilization.

Pluymaekers N. et al. On-Demand Mobile Health Infrastructure for Remote Rhythm Monitoring within a Wait-and-See Strategy for Recent-Onset Atrial Fibrillation: TeleWAS-AF. Cardiology 2021, doi: 10.1159/000514156

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During the coronavirus 2019 (COVID-19) pandemic, traditional face-to-face outpatient consultations in atrial fibrillation (AF) clinics were transformed into teleconsultations. Herein, we describe how we implemented a remote on-demand mobile health (mHealth) infrastructure, which was based on a mobile phone app using photoplethysmography (PPG) technology allowing rate and rhythm monitoring through teleconsultations.
Pluymaekers N et al. On-demand app-based rate and rhythm monitoring to manage atrial fibrillation through teleconsultations during COVID-19. IJC Heart & Vasculature, 2020, doi: 10.1016/j.ijcha.2020.100533.

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The COVID-19 pandemic has accelerated how healthcare providers are working to deliver healthcare at distance. Many cardiac patients are now relying on phone and videoconference to receive medical care from home. The situation is pushing healthcare towards the future, leading to a leap forward for cardiac telemedicine.
Klompstra L et al. Delivering healthcare at distance to cardiac patients during the COVID-19 pandemic: Experiences from clinical practice. European Journal of Cardiovascular Nursing, 2020, doi: 10.1177/1474515120930558.

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During the coronavirus 2019 (COVID-19) pandemic, outpatient visits for patients with atrial fibrillation (AF), were converted into teleconsultations. As a response to this, a novel mobile health (mHealth) intervention was developed to support these teleconsultations with AF patients: TeleCheck-AF. This approach incorporates three fundamental components: 1) “Tele”: A structured teleconsultation. 2) “Check”: An app-based on-demand heart rate and rhythm monitoring infrastructure. 3) “AF”: comprehensive AF management.

This report highlights the significant importance of coordination of the TeleCheck-AF approach at multiple levels and underlines the importance of streamlining care processes provided by a multidisciplinary team, using an mHealth intervention, during the COVID-19 pandemic. Moreover, this report reflects on how the TeleCheck-AF approach has contributed to strengthening the health system in maintaining management of this prevalent sustained cardiac arrhythmia, whilst keeping patients out of hospital, during the pandemic and beyond.
MJ Van De Velden R et al. Coordination of a remote mHealth infrastructure for atrial fibrillation management during COVID-19 and beyond: TeleCheck-AF. SAGE Journals, 2020, doi: 10.1177/2053434520954619.

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During the coronavirus 2019 (COVID-19) pandemic, outpatient visits in the atrial fibrillation (AF) clinic of the Maastricht University Medical Centre (MUMC+) were transferred into teleconsultations. The aim was to develop anon-demand app-based heart rate and rhythm monitoring infrastructure to allow appropriatmanagement of AF through teleconsultation. In line with the fundamental aspects of integrated care, including actively involving patients in the care process and providing comprehensive care by a multidisciplinary team, we implemented a mobile health (mHealth) intervention to support teleconsultations with AF patients: TeleCheck-AF. The TeleCheck-AF approach guarantees the continuity of comprehensive AF management and supports integrated care through teleconsultation during COVID-19. It incorporates three important components: (i) a structured teleconsultation (‘Tele’), (ii) a CE-marked app-based on-demand heart rate and rhythm monitoring infrastructure (‘Check’), and (iii) comprehensive AF management (‘AF’). In this article, we describe the components and implementation of the TeleCheck-AF approach in an integrated and specialized AF-clinic through teleconsultation. The TeleCheck-AF approach is currently implemented in numerous European centres during COVID-19.
A H A Pluymaekers N et al. Implementation of an on-demand app-based heart rate and rhythm monitoring infrastructure for the management of atrial fibrillation through teleconsultation: TeleCheck-AF. EP Europace, 2020, doi: 10.1093/europace/euaa201.

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During the coronavirus 2019 (COVID-19) pandemic, traditional face-to-face consultations in atrial fibrillation (AF) outpatient clinics were rapidly transferred into teleconsultations, which were initially conducted without any information on heart rhythm or heart rate of the patients. To guarantee the continuity of comprehensive AF management through teleconsultation during COVID-19, we developed a mobile health (mHealth) intervention at the Maastricht Medical University Centre to support AF teleconsultations: TeleCheck-AF.
Linz D et al. TeleCheck-AF for COVID-19: A European mHealth project to facilitate atrial fibrillation management through teleconsultation during COVID19. European Heart Journal, 2020, doi: 10.1093/eurheartj/ehaa404.

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Background: Although novel teleconsultation solutions can deliver remote situations that are relatively similar to face‐to‐face interaction, remote assessment of heart rate and rhythm as well as risk factors remains challenging in patients with atrial fibrillation (AF).
Hypothesis.
Mobile health (mHealth) solutions can support remote AF management.
Methods: Herein, we discuss available mHealth tools and strategies on how to incorporate the remote assessment of heart rate, rhythm and risk factors to allow comprehensive AF management through teleconsultation.
Results: Particularly, in the light of the coronavirus disease 2019 (COVID‐19) pandemic, there is decreased capacity to see patients in the outpatient clinic and mHealth has become an important component of many AF outpatient clinics. Several validated mHealth solutions are available for remote heart rate and rhythm monitoring as well as for risk factor assessment. mHealth technologies can be used for (semi‐)continuous longitudinal monitoring or for short‐term on‐demand monitoring, dependent on the respective requirements and clinical scenarios. As a possible solution to improve remote AF care through teleconsultation, we introduce the on‐demand TeleCheck‐AF mHealth approach that allows remote app‐based assessment of heart rate and rhythm around teleconsultations, which has been developed and implemented during the COVID‐19 pandemic in Europe.
Conclusion: Large scale international mHealth projects, such as TeleCheck‐AF, will provide insight into the additional value and potential limitations of mHealth strategies to remotely manage AF patients. Such mHealth infrastructures may be well suited within an integrated AF‐clinic, which may require redesign of practice and reform of health care systems.
Hermans N.L. A et al. On‐demand mobile health infrastructures to allow comprehensive remote atrial fibrillation and risk factor management through teleconsultation. Clinical Cardiology, 2020, doi: 0.1002/clc.23469.

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VARIOUS PUBLICATIONS

Current atrial fibrillation (AF) guidelines recommend screening for AF in individuals above 65 years or with other characteristics suggestive of increased stroke risk. Several mobile health (mHealth) approaches are available to identify AF. Although most wearables or ECG machines include algorithms to detect AF, an ECG confirmation of AF is necessary to establish a suspected diagnosis of AF. Early detection of AF is important to allow early initiation of AF management, and early rhythm control therapy lowered risk of adverse cardiovascular outcomes among patients with early AF aged >75 or with a CHA2DS2-VASc score ≥2 and cardiovascular conditions in the EAST-AFNET 4 study. Strategies for early AF detection should be always linked to a comprehensive work-up infrastructure organized within an integrated care pathway to allow early initiation and guidance of AF treatment in newly detected AF patients. In this review article, we summarize strategies and mHealth approaches for early AF detection and the transition to early AF management including AF symptoms evaluation and assessment of AF progression as well as AF risk factors.

Linz D. et al. Early atrial fibrillation detection and the transition to comprehensive management. EP Europace, Volume 23, Issue Supplement_2, 2021, doi: https://doi.org/10.1093/europace/euaa424

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Novel wearable devices for heart rhythm analysis using either photoplethysmography (PPG) or electrocardiogram (ECG) are in daily clinical practice. This survey aimed to assess impact of these technologies on physicians’ clinical decision-making and to define, how data from these devices should be presented and integrated into clinical practice.

Manningner M. et al. Current perspectives on wearable rhythm recordings for clinical decision-making: the wEHRAbles 2 survey. EP Europace, 2021, doi: https://doi.org/10.1093/europace/euab064

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Many digital health technologies capable of atrial fibrillation (AF) detection are directly available to patients. However, adaptation into clinical practice by heart rhythm healthcare practitioners (HCPs) is unclear.

Ding E. et al. Survey of current perspectives on consumer-available digital health devices for detecting atrial fibrillation. Cardiovascular Digital Health Journal,
2020, doi: https://doi.org/10.1016/j.cvdhj.2020.06.002

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Atrial fibrillation (AF) affects more than 6 million people in the United States; however, much AF remains undiagnosed. Given that more than 265 million people in the United States own smartphones (>80% of the population), smartphone applications have been proposed for detecting AF, but the accuracy of these applications remains unclear.

O’Sullivan J. et al. Accuracy of Smartphone Camera Applications for Detecting Atrial Fibrillation
A Systematic Review and Meta-analysis. JAMA Netw Open, 2020, doi:10.1001/jamanetworkopen.2020.2064.

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This collaborative statement from the International Society for Holter and Noninvasive Electrocardiology/ Heart Rhythm Society/ European Heart Rhythm Association/ Asia Pacific Heart Rhythm Society describes the current status of mobile health (“mHealth”) technologies in arrhythmia management. The range of digital medical tools and heart rhythm disorders that they may be applied to and clinical decisions that may be enabled are discussed. The facilitation of comorbidity and lifestyle management (increasingly recognized to play a role in heart rhythm disorders) and patient self‐management are novel aspects of mHealth. The promises of predictive analytics but also operational challenges in embedding mHealth into routine clinical care are explored.
Varma N. et al. 2021 ISHNE/ HRS/ EHRA/ APHRS collaborative statement on mHealth in Arrhythmia Management: Digital Medical Tools for Heart Rhythm Professionals. Ann Noninvasive Electrocardiol. 2021. https://doi.org/10.1111/anec.12795

During the coronavirus 2019 (COVID-19) pandemic, outpatient visits in the atrial fibrillation (AF) clinic of the Maastricht University Medical Centre (MUMC+) were transferred into teleconsultations. The aim was to develop anon-demand app-based heart rate and rhythm monitoring infrastructure to allow appropriatmanagement of AF through teleconsultation. In line with the fundamental aspects of integrated care, including actively involving patients in the care process and providing comprehensive care by a multidisciplinary team, we implemented a mobile health (mHealth) intervention to support teleconsultations with AF patients: TeleCheck-AF. The TeleCheck-AF approach guarantees the continuity of comprehensive AF management and supports integrated care through teleconsultation during COVID-19. It incorporates three important components: (i) a structured teleconsultation (‘Tele’), (ii) a CE-marked app-based on-demand heart rate and rhythm monitoring infrastructure (‘Check’), and (iii) comprehensive AF management (‘AF’). In this article, we describe the components and implementation of the TeleCheck-AF approach in an integrated and specialized AF-clinic through teleconsultation. The TeleCheck-AF approach is currently implemented in numerous European centres during COVID-19.
A H A Pluymaekers N et al. Implementation of an on-demand app-based heart rate and rhythm monitoring infrastructure for the management of atrial fibrillation through teleconsultation: TeleCheck-AF. EP Europace, 2020, doi: 10.1093/europace/euaa201.

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