Feasibility of Using Mobile ECG Recording Technology to Detect Atrial
                        Fibrillation in Low-Resource Settings

Evans, Grahame F.; Shirk, Arianna; Muturi, Peter; Soliman, Elsayed Z.

doi:10.1016/j.gheart.2016.12.003

Cited by 44 publications

(59 citation statements)

References 7 publications

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“…The KM device has also been studied as an AF screening instrument in asymptomatic patients aged 65 years or older [ 7 – 11 ], which resulted in 0.8–1.5% cases of newly diagnosed atrial fibrillation. The present study shows that when using the KM device for screening a general population for AF, two issues need to be considered.…”

Section: Discussionmentioning

confidence: 99%

A mobile one-lead ECG device incorporated in a symptom-driven remote arrhythmia monitoring program. The first 5,982 Hartwacht ECGs

Selder

Breukel

Blok³

et al. 2018

Neth Heart J

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BackgroundIn recent years many mobile devices able to record health-related data in ambulatory patients have emerged. However, well-organised programs to incorporate these devices are sparse. Hartwacht Arrhythmia (HA) is such a program, focusing on remote arrhythmia detection using the AliveCor Kardia Mobile (KM) and its algorithm.ObjectivesThe aim of this study was to assess the benefit of the KM device and its algorithm in detecting cardiac arrhythmias in a real-world cohort of ambulatory patients.MethodsAll KM ECGs recorded in the HA program between January 2017 and March 2018 were included. Classification by the KM algorithm was compared with that of the Hartwacht team led by a cardiologist. Statistical analyses were performed with respect to detection of sinus rhythm (SR), atrial fibrillation (AF) and other arrhythmias.Results5,982 KM ECGs were received from 233 patients (mean age 58 years, 52% male). The KM algorithm categorised 59% as SR, 22% as possible AF, 17% as unclassified and 2% as unreadable. According to the Hartwacht team, 498 (8%) ECGs were uninterpretable. Negative predictive value for detection of AF was 98%. However, positive predictive value as well as detection of other arrhythmias was poor. In 81% of the unclassified ECGs, the Hartwacht team was able to provide a diagnosis.ConclusionsThis study reports on the first symptom-driven remote arrhythmia monitoring program in the Netherlands. Less than 10% of the ECGs were uninterpretable. However, the current performance of the KM algorithm makes the device inadequate as a stand-alone application, supporting the need for manual ECG analysis in HA and similar programs.

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Section: Discussionmentioning

confidence: 99%

A mobile one-lead ECG device incorporated in a symptom-driven remote arrhythmia monitoring program. The first 5,982 Hartwacht ECGs

Selder

Breukel

Blok³

et al. 2018

Neth Heart J

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“…[ 22 ] Processes relating to recruitment and retention of participants were favorable in the eligible studies, with lower numbers of recruitment evident in some studies and this can be seen in Table 4 , which also highlights the ability to screen all participants involved. [ 14 , 16 , 17 , 20 ] Drop-out rates were low in all eligible studies, perhaps reflecting the study design, compliance and ease of use with the AliveCor device. The minimal number (n = 5/60) who did drop-out of the research studies were for reasons including moving away from the geographical study location (n = 1), purchasing a replacement mobile phone of an alternative brand (n = 1) and withdrawal of consent (n = 3).…”

Section: Resultsmentioning

confidence: 99%

Effectiveness of a single lead AliveCor electrocardiogram application for the screening of atrial fibrillation

Hall¹,

Mitchell²,

Wood³

et al. 2020

Medicine

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“…The incidence of newly diagnosed AF varied according to the characteristics of the screened population, ranging from 0.12%, in healthy community-dwelling citizens 55 to 8% in a Kenyan inpatient cohort with an increased risk of AF. 50 The study comparing the diagnostic performance of a smartphone PPG application and the AliveCor device showed higher sensitivity of PPG than AliveCor (92.9% vs. 71.4%) with comparable specificity (97.7% vs. 99.4%), lower PPV (53.1% vs. 76.9%) and similar NPV (99.8% vs. 99.2%) relative to AliveCor. 51 Interestingly; in the Huawei Heart Study (HHS) automatic periodic measurements were more likely to identify episodes of AF compared to patient triggered events (37.0% vs. 7.5%).…”

Section: Main Outcome Measuresmentioning

confidence: 87%

Mobile health applications for the detection of atrial fibrillation: a systematic review

et al. 2020

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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.

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Feasibility of Using Mobile ECG Recording Technology to Detect Atrial Fibrillation in Low-Resource Settings

Cited by 44 publications

References 7 publications

A mobile one-lead ECG device incorporated in a symptom-driven remote arrhythmia monitoring program. The first 5,982 Hartwacht ECGs

A mobile one-lead ECG device incorporated in a symptom-driven remote arrhythmia monitoring program. The first 5,982 Hartwacht ECGs

Effectiveness of a single lead AliveCor electrocardiogram application for the screening of atrial fibrillation

Mobile health applications for the detection of atrial fibrillation: a systematic review

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