Clinical Research

AliveCor Kardia and short continuous Holter monitoring were similarly effective at detecting AF recurrences in 126 post-ablation patients followed for 4 weeks: 95.3% sensitivity and 97.5% specificity for AF detection with the Kardia system. Patients were more satisfied with Kardia than with Holter monitoring and found it to be more convenient in daily usage than Holter (p<0.001).

The authors describe how describe the adoption of KardiaMobile and the Kardia Pro platform for uploading and assessing ECGs can be used for long-term AF detection after a successful AF ablation procedure. The single-centre, unblinded, randomised pilot study compared the AliveCor system with routine follow-up based on symptoms and intermittent cardiac monitoring as needed. In the 100 patients followed for 6 months there were no statistically significant differences the numbers of timing in AF detection, healthcare utilisation or anxiety. More patients required additional ECGs or cardiac monitors in the control group (27%) than in the KardiaMobile group (6%; p=0.004).

Currently, most of the commercially available portable or wearable ECG devices engage both hands to detect the ECG signal, which may be an issue when the user is driving or running or otherwise impaired. This study showed that Indeed, using a left in-ear region to the left hand lead, an ECG could be obtained with the same good amplitude as standard chest ECG: P wave and QRS wave morphology were clearly identifiable using the standard KardiaMobile factory setting without the need to amplify the ECG signal. The authors suggest that future wearable devices using this in-ear ECG technology could be dedicated earphones connected to a smartphone. These new devices could be more user friendly, sharable with other users and offer long-term monitoring.

This study presents the feasibility of a remote patient monitoring program in the Netherlands for managing arrhythmia, heart failure (weight) and blood pressure in symptomatic adults with congenital heart disease (CHD); the program used KardiaPro for the receipt and transfer of KardiaMobile ECG data. ECGs were assessed daily by trained nurses, under supervision of a cardiologist. Patients (median age 45; 35% male) were contacted by the treating cardiologist to adjust therapy, for surveillance or in order to provide reassurance. From June 2017 to March 2018, 55 symptomatic adult CHD patients participated; mean follow-up was 3 months and adherence was 97%. There were qualitatively fewer emergency room visits and hospitalizations (3) versus historical record (19). Serial patient-reported outcome measure (PROM) questionnaires were available for 12 patients at baseline and six patients after 6 months and showed a nonsignificant change in quality of life during telemonitoring. Nearly 75% of the 176 KardiaMobile ECGs were sinus rhythm; two patients were diagnosed with a new arrhythmia. In summary, a remote patient monitoring program featuring KardiaMobile is feasible with high adherence.

This case report describes a woman with congenital pulmonary valve stenosis treated with balloon valvulotomy, with several years of palpitations and unrevealing Holter monitor evaluations. She was enrolled in a remote patient monitoring program in the Netherlands using KardiaPro. The woman used KardiaMobile to successfully record atrial fibrillation with intermittent bundle branch block during an episode of palpitations.

Hartwacht Arrhythmia (HA) is a cardiac arrhythmia remote monitoring program in the Netherlands, initiated by Cardiologie Centra Nederland. This is the first evaluation of KardiaMobile in a real-world cohort of ambulatory patients for symptom-driven monitoring in that country. Between January 2017 and March 2018, 5,982 KardiaMobile ECGs from 233 participants were received, with a median of 28 ECGs per patient per year (mean age 58 years, 52% male); patients were instructed to record an ECG when they experienced palpitations or related complaints. KardiaMobile algorithms classified 59% as Normal, 22% as Possible AF, 17% as Unclassified, and 2% as Unreadable. According to the HA team, 8% of all ECGs were uninterpretable. The AF algorithm had a sensitivity of 92% and specificity of 95%; the negative predictive value (NPV) for detection of AF was high, at 98%, while the positive predictive value (PPV) was 80%, with 12% of AF ECGs being interpreted by the cardiologist as sinus rhythm. Conversely, the Normal algorithm had a high PPV, and a specificity of 91% and a sensitivity of 80%; 96% of all KardiaMobile Normal ECGs were interpreted by the cardiologist as being sinus rhythm. The authors call for a refinement in the detection of normal sinus rhythm with and without ectopy to reduce the need for manual assessment of this category of ECGs.

This systematic review of 14 studies from community and hospital settings reported the diagnostic accuracy of handheld ECG devices in detecting AF in adults, compared with a gold standard 12-lead ECG or Holter monitor. Six studies recruited from community (n=6064 ECGs) and eight studies from hospital (n=2116 ECGs) settings. The pooled sensitivity was 89% (95% CI 81% to 94%) in the community and 92% (95% CI 83% to 97%) in the hospital. The pooled specificity was 99% (95% CI 98% to 99%) in the community and 95% (95% CI 90% to 98%) in the hospital. Accuracy of ECG devices varied: sensitivity ranged from 54.5% to 100% and specificity ranged from 61.9% to 100%. Meta-regression showed that setting (p=0.032) and ECG device type (p=0.022) significantly contributed to variations in sensitivity and specificity. The pooled sensitivity and specificity of single-lead handheld ECG devices were high. Setting and handheld ECG device type were significant factors of variation in sensitivity and specificity.

The study compared AliveCor KardiaMobile (ACK) lead I recordings with the 12-lead ECG and introduce a novel parasternal lead (NPL). Consecutive cardiac inpatients were recruited. In all patients a 12-lead ECG, ACK lead I and NPL were obtained. Two experienced electrophysiologists were blinded and separately evaluated all ECG. Sensitivity, specificity, and predictive values of the ACK ECG compared to the 12-lead ECG were calculated. 296 ECGs from 99 patients (38 female, age 64±15 years, BMI 27.8±5.1 kg/m2) were analyzed. The electrophysiologists’ interpretation of the ACK recordings yielded a sensitivity of 100% and specificity of 94% for atrial fibrillation or flutter in lead I (κ=0.90) and a sensitivity of 96% and specificity of 97% in the NPL (κ = 0.92). The ACK diagnostic algorithm displayed a significantly lower sensitivity (55–70%), specificity (60–69%), and accuracy (κ = 0.4–0.53) but a high negative predictive value (100%). Patients with atrial flutter (n=5) and with ventricular stimulation (n=12) had a high likelihood of being misclassified by the algorithm.

This study evaluated smartphone-based electrocardiogram (ECG) recordings aimed at AF screening at Polish pharmacies. Patients aged 65 years and over were screened for AF at 10 pharmacies using KardiaMobile with a dedicated application (Kardia app). Prior AF was a study exclusion criterion. CHA2DS2-VASc score (congestive heart failure, hypertension, age, diabetes mellitus, previous stroke/transient ischemic attack, female sex, and vascular disease) was collected from every patient. Kardia app detection was evaluated by the cardiologist. A total of 525 ECGs were performed. Kardia app detection was provided in 490 cases. In 437 (89.18%) cases, it was “normal” rhythm, in 17 (3.47%) recordings “possible AF,” in 23 (4.69%) ECGs “unreadable,” and in 13 (2.65%) “unclassified”. After the cardiologist reevaluation, the new AF was identified in 7 (1.33%) patients. Sensitivity and specificity of Kardia app in detecting AF was 100% (95% confidence interval [CI]: 71.5%-100%) and 98.7% (95% CI: 97.3%-99.5%), respectively. The positive predictive value was 64.7% (95% CI: 38.3%-85.7%) and the negative predictive value was 100% (95% CI: 99.2%-100%). CHA2DS2-VASc score was 2.14+0.69 for those with new AF and 3.33+1.26 in the non-AF group. The results obtained in patients with low CHA2DS2-VASc score and “silent” AF confirm the importance of routine AF screening, and suggests that screening at pharmacies is a feasible option.

This was a case-control study to compare the performance of Kardia and WatchBP with that of two electrode-based consumer devices (PolarH7 and Firstbeat Bodyguard2 for AF detection in general-practice use. In 418 subjects the overall accuracy of all four devices was >94% when measurements were compared with 12-lead ECG at a study single visit. User-rated comfort and overall impression were highest with the Kardia and Bodyguard2 devices. There was no long-term use of the devices. The authors noted that the performance of all the devices would likely decline in unsupervised use.

A paucity of data exists on the accuracy of primary care physicians’ (PCP) interpretation of KardiaMobile ECGs compared with the device's automated diagnosis. Using 408 ECGs in 51 patients, before and after elective cardioversion, this study demonstrated variable accuracy in clinician interpretation, with a mean accuracy of 91% for the review by cardiologists, and 85% accuracy for the review by PCPs. With exclusion of Unclassified ECGs, the algorithm accuracy had a sensitivity and specificity of 100% and 95%, respectively. Accurate diagnosis of a KardiaMobile Unclassified ECG was established in 10/12 when assessed by a cardiologist, and 9/12 on review by a primary care physician. Combining the automated algorithm with cardiologist interpretation of only Unclassified traces yielded excellent results and provides an efficient, cost-effective workflow for the utilization of a smartphone-based ECG in clinical practice.

The accuracy of the KardiaMobile AF algorithm was evaluated in 52 patients admitted for antiarrhythmic drug initiation for AF. Patients performed KardiaMobile recordings immediately following twice daily 12-lead ECGs. There were 225 paired KardiaMobile and 12-lead ECG recordings. The algorithm interpretation was missing or labeled as non-interpretable in 62 (27.5%) of recordings for multiple reasons (truncated recording, noise, slow heart rate, other). When the algorithm did not provide a diagnosis, blinded electrophysiologists were able to provide interpretation in 92% of these recordings. After exclusion of non-interpretable recordings, the KardiaMobile AF algorithm had very good accuracy, with a sensitivity of 96.6% and specificity of 94% for the detection of AF when compared to physician interpreted ECGs, and a κ coefficient of 0.89. The majority of patients (93.6%) found KardiaMobile easy to use, and 59.6% noted that use lessened AF-diagnosis related anxiety. 63.8% of survey respondents preferred continued use of KardiaMobile for AF detection.

In this prospective study of 672 patients with AF or sinus rhythm at two university hospitals in Switzerland and Germany, physician review of KardiaMobile was used as the reference for evaluation of the accuracy of a photoplethysmography (PPG) heart rhythm analysis from a smartphone camera. Less than 3% of patients had KardiaMobile recordings with poor signal quality. Additionally, the study tested the accuracy of the KardiaMobile algorithms. 18.8% of KardiaMobile recordings were labeled “unclassified,” but cardiologists were able to identify the cardiac rhythm in all of these cases. The KardiaMobile AF algorithm had a sensitivity of 99.6% (95% CI 97.9-100%) and a specificity of 97.8% (95.3-99.2%).

This study compared the clinical equivalency of a 6-lead smartphone-based ECG device (AliveCor KardiaMobile 6L) with a 12-lead ECG. The KardiaMobile 6L has three conducting surfaces, touching the patient’s hands and left knee. Nineteen healthy volunteers and 25 patients seen at the cardiology clinic underwent a simultaneous recording of a regular 12-lead ECG and a 6-lead ECG using the KardiaMobile 6L. Specifically, a few seconds after a 30-second recording with the KardiaMobile 6L was initiated, a 10-second recording was obtained with 12-lead ECG. The median beats of all six limb leads, including derived leads (leads III, aVR, aVL, aVF) were calculated from the two devices. The QRS amplitude and morphology of the median beats in each lead was compared between the two devices and a correlation coefficient was calculated. Results: The KardiaMobile 6L and 12-lead ECG median beats were very similar. The Pearson correlation coefficient for all leads across all patients was 0.991. The six individual lead-specific correlation coefficients ranged from 0.993 for lead II to 0.980 for lead aVR (p < 0.0001 for each lead).

Heart rate (HR) detection from a smartphone-based photoplethysmography (PPG) app (FibriCheck) was compared with the KardiaMobile ECG and the Nonin pulse oximeter. The HR (BPM, beats per minute) of 88 random subjects consecutively measured for 10 seconds with the 3 devices showed a moderate-to-strong correlation coefficient of 0.834 between FibriCheck and Nonin, 0.88 between FibriCheck and AliveCor, and 0.897 between Nonin and AliveCor. The mean HR for FibriCheck was 71 BPM, for Nonin 69 BPM, and for AliveCor 69 BPM. A single way analysis of variance showed no significant differences between the HRs as measured by the 3 devices (p=0.61). This study reports the potential utility and limitations in use of the smartphone-based PPG signal for HR detection.

KardiaMobile was used to identify asymptomatic AF at the time of influenza vaccination in 5 practices in Sydney, Australia. Nurses used the automated algorithm to screen 973 patients aged ≥ 65 years between April-June 2015. Screening took on average 5 minutes (range 1.5 -10 minutes); abnormal recordings required additional time. Newly identified AF was found in 0.8% (8) of patients, and the overall prevalence of AF was 3.8% (37). The sensitivity and specificity of the automated algorithm for detecting AF was 95% and 99%, respectively. Screening by practice nurses was well accepted by practice staff. Key enablers were the confidence and competence of nurses and a ‘designated champion’ to lead screening at the practice. Barriers were practice specific, and mainly related to staff time and funding.

One thousand pharmacy customers (mean age 76 ± 7 years, 44% male) were screened with KardiaMobile. Newly identified AF was found in 1.5% (95% CI, 0.8-2.5%), and AF prevalence was 6.7%. The automated algorithm showed 98.5% sensitivity and 91.4% specificity for detecting AF. Using cost and outcome data from a United Kingdom study for AF screening, the incremental cost-effectiveness ratio of extending screening into the community with KardiaMobile, based on 55% warfarin prescription adherence, would be $USD4,066 per quality-adjusted life-year gained, and $USD20,695 for preventing one stroke. In summary, screening for AF with KardiaMobile is feasible and cost-effective.

KardiaMobile was used in a community screening of 109 patients (70 in sinus rhythm and 39 in AF) soon after a 12-lead ECG had been performed. The ECGs were interpreted by two cardiologists blinded to the rhythm diagnosis, and were processed to provide an automated diagnosis of sinus rhythm or AF. Results were compared with the 12-lead ECG diagnosis by a third cardiologist. An optimized algorithm performed extremely well in the validation set with high sensitivity, specificity, overall accuracy and Kappa (95% CI) of 98% (89%–100%), 97% (93%–99%), 97% (94%–99%) and 0.92 (0.86– 0.98) respectively. This study concluded that KardiaMobile can be used to simply and rapidly record a high quality single-lead ECG to accurately detect AF, making it an ideal technology for community screening programs to detect silent AF.

This pilot study reports that wearable PPG devices may have similar sensitivity and specificity for AF detection to one-lead-ECG wristbands. Subjects recruited from a community senior care organisation wore a PPG wristband on one arm and Kardia Band in combination with an Apple Watch on the other. Three consecutive measurements were performed with both devices simultaneously. 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 Kardia Band.

This paper describes the development of fully connected artificial neural network (RSL_ANN), receiving 19 ECG features (11 morphological, 4 on F waves and 4 on heart-rate variability). The network was created and tested on 8028 annotated ECGs acquired with the Kardia device. Less than 3% of the ECGs included in the database could not be used in this study due to high levels of noise. Performance of RSL_ANN was very good and very similar in all datasets, with AUC over 90%. The work shows the value of Kardia for providing high volumes of quality data to aid the development of advanced diagnostic algorithms for AF.

The KardiaBand, paired with a smartwatch, generated an automated detection of atrial fibrillation (AF) or sinus rhythm (SR). This was compared with a 12-Lead ECG performed immediately after iECG tracing. Cardiologist interpretation of unclassified tracings improved accuracy.

This study evaluated the accuracy of KardiaBand ECG and the automated AF algorithm. 100 patients (mean age 68 ± 11 yrs) with AF presenting for cardioversion (CV) were enrolled and received simultaneous 12-lead ECG and KardiaBand ECG before the procedure; if the CV was performed a post CV 12-lead ECG was then obtained along with another KardiaBand ECG. CV was canceled in 8 patients due to presentation in sinus rhythm. There were 169 simultaneous 12-lead ECG and KardiaBand ECGs. Compared to 12-lead ECG, the automated algorithm detected AF with 93% sensitivity, 84% specificity and K coefficient 0.77. Physician-interpretation of KardiaBand ECGs demonstrated 99% sensitivity, 83% specificity and K coefficient 0.83. The automated AF algorithm on KardiaBand, when supported by physician review, can accurately differentiate AF from sinus rhythm. This technology can help screen patients prior to elective CV and avoid unnecessary procedures.

Diagnosing inappropriate sinus tachycardia (IST) is important for treatment decisions as they are usually not suitable for ablation. But supraventricular tachycardias (SVT) are often short-lasting which makes them difficult to analyse. AliveCor Kardia sECG was able to differentiate between SVT and IST with a sensitivity of 89% and a specificity of 91% for the detection of SVT. In high-quality sECG recordings sensitivity rose to 95% with a specificity of 92%. With 12-lead ECG sensitivity was 100% and specificity of 98%. The authors commented that cardiologists should be encouraged to integrate wearables into clinical practice, possibly reducing unnecessary electrophysiology procedures.

This review describes the basic engineering principles of common wearable sensors and the role of these devices in the remote screening and diagnosis of common cardiovascular diseases. The authors develop a simple ABCD guide to potentially accelerate wearable integration into cardiovascular practice: Clinicians should Assess the device’s performance, determine whether the device Benefits their patients and clinical practice, integrate devices into Clinical workflow and finally, institute Data rights. The review highlighted the IPED study with KardiaMobile which, although not strictly a wearable device, indicated that wearables with single- lead ECG can be used to diagnose arrhythmias in patients with palpitations or presyncope.

The IPED study concluded that the use of AliveCor/Kardia is safe, non-invasive, and easy to use and should be considered for all patients presenting acutely to the emergency department with unexplained palpitations or pre-syncope. This would bring back patients to an ambulatory care setting and allow assessment to take place in a less chaotic environment than in the ED. The paper describes the establishment of a smartphone ambulatory ECG palpitation and pre-syncope ambulatory care clinic (SPACC). The service allows patients who present with pre-syncope and palpitations to be referred to a next-day assessment clinic for consideration of AliveCor/Kardia device fitting for long-term monitoring. Preliminary three-month data show an 8.8% detection rate of symptomatic cardiac dysrhythmia. This is comparable to the 8.9% of patients who had a symptomatic cardiac dysrhythmia detected in the IPED study and shows the successful implementation of the smartphone service in a pragmatic clinical setting.

This review summarised 43 studies on the performance of mobile health devices in diagnosing and screening for AF. Sensitivity, specificity and of devices, including AliveCor monitors, are all >90%. The authors conclude that while mobile health has the potential to change the paradigm of health care, the reliability and safety of different devices must be carefully assessed. National screening programmes for AF are likely to represent a cost-effective use of resources and mobile health devices offer a feasible option for mass screening in diverse settings as they are user-friendly and leadless. However, ECG confirmation is mandated by guidelines for the diagnosis of AF. The clinical benefit and cost-effectiveness of mobile health devices need to be evaluated in further studies.

This study examined whether KardiaBand could accurately and reliably differentiate normal rhythm from common, classic abnormalities, when compared with physician-interpreted 12-lead electrocardiograms in 1322 patients hospitalised in cardiology departments or in cardiac ICUs. Sensitivity and specificity were 82% and 92% for AF diagnosed by physicians, 95% and 86% for AF diagnosed by the automated algorithm, 70% and 100% for high-degree AV block, 88% and 82% for ST segment depression, 56% and 95% for T-wave inversion, 60% and 100% for wide-QRS tachycardia, 26% and 98% for other supraventricular tachycardias, 50% and 100% for sinoatrial block, and 33% and 97% for ST-T changes. The authors conclude that beyond AF detection, multi-electrode recordings or alternative positioning of the device electrodes may be necessary to improve diagnostic performance.

Dutch general practitioners (GPs) could safely rule out cardiac arrhythmias using KardiaMobile ECGs. Each GP was asked to interpret four single-lead ECGs, randomly drawn from a pool of 80 case-vignettes using AliveCor KardiaMobile. In total, 457 GPs interpreted a total of 1613 1L-ECGs. Sensitivity and specificity for atrial fibrillation/flutter were 92.5% and 89.8% respectively. Positive predictive value was 45.7% and negative predictive value 98.8%. Specificity was lower, 68.8%, when an ECG anomaly was present, and the positive predictive value was 43.9%. The authors conclude that confirmation by an expert-reader is warranted whenever an abnormality is suspected.

This cross-sectional study was conducted during the Arrhythmia Alliance World Heart Rhythm Week, and suggested pharmacists can contribute to greater outreach of awareness campaigns. The Atrial Fibrillation Association formed a partnership with the International Pharmacists for Anticoagulation Care Taskforce, and goals to test a model for raising awareness of AF involving pharmacists globally; and to identify barriers and enablers to its implementation. Pharmacists from 10 countries invited individuals (≥ 40 years; without anticoagulation therapy of AF) to participate in the awareness campaign. Participants agreeing were engaged in the early detection of AF (EDAF) using pulse palpation. Individuals with rhythm discrepancies were referred and prospectively assessed to have information on the proportion of confirmed diagnosis, leading to estimate the detection rate. Interviews with country coordinators explored barriers and enablers to implementation. The study involved 4,193 participants in the awareness campaign and 2,762 in the EDAF event (mean age 65.3 ± 13.0), of whom 46.2% individuals were asymptomatic, recruited across 120 sites. Most common CHA2DS2- VASc risk factor was hypertension. Among 161 patients referred to physician, feedback was obtained for 32 cases, of whom 12 new arrhythmia diagnoses were confirmed (5 for AF, 2 for atrial flutter), all among elders (≥ 65 years). Qualitative evaluation suggested a local champion to enable pharmacists’ success; technology enhanced engagement amongst patients and increased pharmacists’ confidence in referring to physicians; interprofessional relationship was crucial in success.

In 10 Dutch general practices, KardiaMobile ECG and AF algorithm were compared with simultaneous 12-lead ECG. Three cardiologists reviewed ECG data from 214 patients (mean age 64.1 y, 54% male). The 12-lead ECG diagnosed AF/AFL, any rhythm abnormality, and any conduction abnormality (AV block, BBB, LAD, LAFB) in 23, 44, and 28 patients, respectively. KardiaMobile ECG as assessed by the cardiologists had a sensitivity and specificity for AF/AFL of 100% (95% CI, 85.2%-100%) and 100% (95% CI, 98.1%-100%). The AF Instant Analysis algorithm identified 20 or 23 AF cases and incorrectly classified 4 cases of sinus rhythm as possible AF (sensitivity and specificity of 87.0% (95% CI, 66.4%-97.2%) and 97.9% (95% CI, 94.7%-99.4%)). KardiaMobile recordings as assessed by cardiologists had a sensitivity and specificity for any rhythm abnormality of 90.9% (95% CI, 78.3%-97.5%) and 93.5% (95% CI, 88.7%-96.7%) and for any conduction abnormality of 46.4% (95% CI, 27.5%-66.1%) and 100% (95% CI, 98.0%-100%). For conduction abnormality, the 15 false negatives were comprised of first-degree AVB (n=6), LAFB (n=8), and RBBB (n=1); on the other hand, cardiologists were able to accurately identify BBB in 13 patients’ KardiaMobile ECGs. The authors concluded that in a primary care population, the KardiaMobile ECG recording showed excellent diagnostic accuracy for AF/AFL and good diagnostic accuracy for other rhythm abnormalities. The 1L-ECG device was less sensitive for left anterior fascicular block and first-degree AV block.

Diagnostic pathways for identification of clinically significant paroxysmal arrhythmia have historically relied on ambulatory ECG monitoring. While useful as a risk stratification tool in certain patient groups, in general, it has a limited yield for infrequent arrhythmia. This is inherently cost-inefficient, and time to diagnosis can be delayed. Smartphone-based ECG devices are now well established in the public market. However, their adoption into standard investigatory pathways is not yet widespread. Recently, the National Institute for Health and Care Excellence (NICE) published a diagnostics guidance document, which reviewed the smartphone-based devices, KardiaMobile and imPulse, with respect to atrial fibrillation (AF) detection. NICE concluded there was insufficient evidence to recommend routine adoption in primary care and recommended further research. Regardless, given the evidence that is available and their uptake by the general public, cardiologists are increasingly likely to encounter them. This article focuses on KardiaMobile as the device is currently easily available to the general public. The authors conclude there is growing evidence to support its use as a screening tool for the detection of subclinical AF in high-risk populations, but further studies are required in order to equate this benefit to stroke and mortality reduction.

This guidance document for the United Kingdom evaluated use of lead-I ECG devices for single time point testing of people in primary care with symptoms of atrial fibrillation and an irregular pulse. The authors concluded there is not enough evidence to recommend routine adoption of lead-I ECG devices for this use case. They recommended further research to show how using lead-I ECG affects the number of people with atrial fibrillation detected, as well the staff time needed to interpret the ECG tracings. Of note, a de novo economic model was designed to evaluate cost effectiveness, and KardiaMobile dominated all other lead-I ECG devices, costing less and producing more quality-adjusted life years [QALYs].

Palpitations and pre-syncope are together responsible for 300,000 annual Emergency Department (ED) attendances in the United Kingdom (UK) alone. This multicenter randomized controlled trial compared the symptomatic rhythm detection rate of KardiaMobile versus standard care alone (no planned ambulatory ECG monitoring), for 243 participants presenting to 10 emergency departments in the UK with palpitations and pre-syncope with no obvious cause evident at initial consultation. A symptomatic rhythm was detected at 90 days in 69 (n=124; 55.6%; 95% CI 46.9–64.4%) participants in the intervention group versus 11 (n=116; 9.5%; 95% CI 4.2–14.8) in the control group (RR 5.9, 95% CI 3.3–10.5; p<0.0001). Mean time to symptomatic rhythm detection in the intervention group was 9.5 days (SD 16.1, range 0–83) versus 42.9 days (SD 16.0, range 12–66; p<0.0001) in the control group. Use of KardiaMobile increased the number of patients with symptomatic rhythm detection over five-fold, to more than 55%, at 90 days. The authors recommend that KardiaMobile be considered part of on-going care to all patients presenting acutely with unexplained palpitations or pre-syncope.

This case report presents a patient enrolled in a precision medicine study in which remote patient monitoring helped detect the presence of atrial fibrillation-atrial flutter(AFib-Flutter). A 64-year-old male with a history of ischemic heart disease used KardiaMobile after noting chest pain, and received an instant analysis of Atrial Fibrillation with a heart rate of 139 bpm. He called the paramedics and was taken by ambulance to the emergency department, where AFib-Flutter with rapid ventricular response was confirmed by 12-lead ECG, and successfully treated.

In this Viewpoint, the author describes the potential utility of photoplethymographic (PPG) sensors in consumer-grade wearable devices, such as Fitbit or Apple Watch, to aid in detection of arrhythmias. He also opines that it is essential to validate abnormal PPG findings with direct electrocardiographic recordings, such as from KardiaMobile or Apple Watch series 4. The opinion piece includes a figure showing the use of KardiaMobile ECG in the lead I and lead II orientations during normal sinus rhythm and during supraventricular tachycardia (long RP tachycardia).

This is the first study to evaluate the use of KardiaMobile in the urgent care setting. Those seeking care at urgent care centers constitute the fastest growing segment of patients in the U.S., and appropriately triaging those with palpitations will positively affect the entire American healthcare system. The purpose of this study is to compare KardiaMobile for 30 days with 24-hour Holter monitoring for the detection of symptomatic arrhythmias in an urgent care population. All Holter reports and KardiaMobile ECGs were reviewed by a general practitioner and a cardiologist. Data from the first 50 of 100 patients from 6 urgent care centers across Southern Arizona was performed. KardiaMobile was diagnostically superior to (10%) or concordant with (72%) Holter monitoring in 82% of patients. Holter monitoring was superior in 16% of patients. Arrhythmias detected included atrial and ventricular ectopy, supraventricular tachycardia, atrial fibrillation and inappropriate sinus tachycardia. This ongoing study will ultimately analyze noninferiority for diagnosis and management, perform a cost-comparison, and suggest the most effective clinical uses for KardiaMobile in an urgent care setting.

This study evaluated the use KardiaMobile in the lead II position (right hand to left leg) to improve visualization of flutter waves and clinician diagnosis of atrial flutter (AFL), compared to traditional lead-I tracings. Fifty patients were recruited (25 in sinus rhythm, 14 in AF, 11 in AFL). Lead-I AFL sensitivity was 27% for both electrophysiologists (EP), which individually improved to 73% and 55% in lead-II. KardiaMobile appropriately diagnosed lead-I AFL as unclassified in 18% of cases, compared to 55% in lead-II. Overall clinician agreement (AF, sinus rhythm and AFL) was modest utilizing lead-I position (EP1: κ=0.71, EP2: κ=0.73, p<0.001), which improved with lead-II tracings (EP1: κ=0.87, EP2: κ=0.83, both p<0.001). In summary, lead II position of KardiaMobile improves clinician diagnosis of atrial flutter.

This study evaluated the between and within rater validity and reliability of KardiaMobile in recording ECG rate, rhythm, and intervals in healthy college athletes in a pre-participation screening program. First, 10 athletes’ KardiaMobile ECG were reviewed by three physicians. Second, the physicians compared a 30-second simultaneous KardiaMobile and lead I from a 12-lead ECG, from 12 athletes. The between rater and between device reliability for the rate, QT interval and QRS duration parameters ranged from good to very good (intraclass correlation coefficients [ICC] = 0.667 – 0.981). The current investigation showed that the reliability of the ECG parameters measured using the smartphone technology ranged from good to very good. This paper serves as support for a technological advancement that will help advance the debate on the utility of ECG testing as part of the athletic pre-participation physical.

148 patients (mean age 41 years) with intermittent palpitations were asked to use KardiaMobile and record an ECG when symptomatic. Over a median period of use of 244 days, 113 patients (76.4%) made 516 symptomatic recordings. A symptom-rhythm correlation was possible for all patients who submitted recordings. Diagnoses were: sinus rhythm n=47 (41.6%), sinus tachycardia n=21 (18.6%), supraventricular/ventricular ectopics n=31 (27.4%), atrial fibrillation n=8 (7.1%), and supraventricular tachycardia n=6 (5.3%). Median time to diagnosis was nine days (range 1–287 days). In conclusion, KardiaMobile diagnosed the cause of intermittent palpitations in the majority of patients referred for evaluation.

This trial evaluated the diagnostic yield of KardiaMobile versus a 14-30 day external loop recorder (ELR). 38 patients were instructed to transmit ECGs via KardiaMobile and activate the ELR whenever they had symptoms. More patients had a potential diagnosis for their symptoms (i.e., at least one symptomatic recording during the entire monitoring period) with KardiaMobile than with the ELR (KardiaMobile= 34 (89.5%) vs ELR = 26 (68.4%); χ² = 5.1, p = 0.024). In the per-protocol analysis, all 33 patients (100%) had a potential diagnosis using the KardiaMobile device, which was significantly higher compared to 24 patients (72.2%) using the ELR (χ² = 10.4, p = 0.001). In summary, KardiaMobile is non-inferior to an ELR for detecting arrhythmias in the outpatient setting. The ease of use and portability of this device make it an attractive option for the detection of symptomatic arrhythmias.

Three patients with implantable cardioverter defibrillators (ICD) used KardiaMobile; device usage and satisfaction, patient engagement, quality of life (QoL), and cardiac anxiety were assessed. Each patient used the technology approximately daily or every other day as prescribed. At the 30-day follow-up, participants recorded an average of 32 ECGs per month. At 90-day follow-up, participants recorded an average of 34 ECGs per month. Two of the three participants self-reported a significant improvement in their physical QoL from baseline to completion, while simultaneously self-reporting a significant decrease in their mental QoL. All three participants reported high levels of device acceptance and technology satisfaction.

The aim of this randomized controlled trial is to investigate the clinical effectiveness and patient satisfaction of a smart technology intervention in patients admitted with ST elevation myocardial infarction (STEMI) or non-ST acute coronary syndrome (NST-ACS). Patients will be followed up to one year after the index event. The intervention group will take daily measurements with KardiaMobile, as well as a blood pressure monitor, weight scale, and activity tracker. Furthermore, two of four outpatient clinic visits will be replaced by electronic visits (1 and 6 months after index event). The control group will receive regular care, consisting of four outpatient clinic visits (1, 3, 6, and 12 months after index event). All patients will be asked to fill in validated questionnaires about patient satisfaction, quality of life, propensity of medication adherence, and physical activity. The primary outcome of this trial will be percentage of patients with controlled BP. In summary, this trial will investigate whether usage of smart technology can improve clinical- and cost-effectiveness of care.

This study prospectively associated acute alcohol consumption with cardiac arrhythmias using KardiaMobile. At the 2015 Munich Octoberfest, 3028 volunteers received a KardiaMobile ECG and breath alcohol concentration (BAC) measurements. ECGs were analyzed for cardiac arrhythmias (sinus tachycardia, sinus arrhythmia, premature atrial/ventricular complexes, atrial fibrillation/flutter) and respiratory sinus arrhythmia. Mean BAC was 0.85 +/- 0.54 g/kg. Cardiac arrhythmias occurred in 30.5% (sinus tachycardia 25.9%; other arrhythmia subtypes 5.4%). Breath alcohol concentration was significantly associated with cardiac arrhythmias overall (odds ratio (OR) per 1-unit change 1.75, 95% confidence interval (CI) 1.50-2.05; P < 0.001) and sinus tachycardia in particular (OR 1.96, 95%CI 1.66-2.31; P < 0.001). Respiratory sinus arrhythmia measuring autonomic tone was significantly reduced under the influence of alcohol.

This is a case report of paroxysmal supraventricular tachycardia, unable to be diagnosed through typical evaluation with an event monitor despite several years of symptoms. Here, the patient diagnosed himself through purchase of KardiaMobile, capturing an atypical atrioventricular node re-entrant tachycardia (AVNRT). He emailed his cardiologist the tracing, which eventually led to an electrophysiology study and successful ablation procedure.

Mobile health (mHealth) devices were used as clinical decision support tools in resource-limited areas to investigate the impact on long-term outcomes among patients with rheumatic and structural heart diseases. Patients randomized to the mHealth clinics (n=139) received, among other mHealth devices, point-of-care ECG using KardiaMobile. An initial mHealth assessment was associated with a shorter time to referral for valvuloplasty and/or valve replacement and was associated with an increased probability for valvuloplasty/valve replacement compared to standard care. Patients randomized to mHealth were associated with a lower risk of a hospitalization and/or death on follow-up (15% vs. 28%, adjusted hazard ratio: 0.41; 95% CI: 0.21 to 0.83; p = 0.013).

This consensus document by the Heart Rhythm Society describes the evolution and advancement of ambulatory ECG technology and its impact on clinical decision-making and practice. It includes a section devoted to smartphone-based ECG recording systems, highlighting the vast literature on KardiaMobile. The document describes the benefits and challenges of KardiaMobile, blurring the traditional models of prescribed device and physician interpretation, and also definitions of patient versus consumer.

This case report demonstrates the use of KardiaMobile ECG to record atrial flutter waves by placing the electrodes in the right hand and on the left knee, similar to lead II of a traditional 12-lead ECG.

This case report documents a 76-year-old gentleman with witnessed syncope. When he regained consciousness, a bystander cardiologist used KardiaMobile on the patient, which showed sinus tachycardia with complete heart block and a narrow complex escape rhythm. The patient later underwent pacemaker implantation. Although KardiaMobile is patient triggered, it can be used in syncope if applied by bystanders. The authors conclude there is a potential for widespread use of KardiaMobile to usher in a new era of democratized, crowd-sourced, syncope diagnostic capability.

Six college athletes presented to their athletic trainers complaining of palpitations during exercise. A single lead ECG was performed using KardiaMobile and sent wirelessly to the team cardiologist who con rmed an absence of dangerous arrhythmia. KardiaMobile has the potential to enhance evaluation of symptomatic athletes by allowing trainers and team physicians to make diagnoses in real-time and facilitate faster return to play.

This is a case study of a 22-year old admitted to the hospital for an episode of tachycardia at a rate of 150 BPM. Upon discharge from the hospital, the patient had an exercise ECG test in attempt to provoke the arrhythmia, then wore a 24-hour ambulatory ECG, and finally was issued a patient-activated event recorder for two weeks. None of these methods captured abnormalities despite symptoms recurring approximately every three months. The patient then purchased KardiaMobile and used this device to record when symptomatic. A consultant cardiologist reviewed recordings to diagnose probable atrioventricular nodal re-entrant tachycardia. Treatment reviewed.

This review paper discusses the evolution of the ECG and highlights the rise of digital health devices and wearable technologies. It identifies KardiaMobile as a useful tool to reduce clinic visits and lower the cost of monitoring while increasing the speed and accuracy of diagnoses. Clinical usability of KardiaMobile is described for narrow complex tachycardia in a patient with palpitations, complete heart block in a patient with intermittent giddiness, arrhythmia in a patient following catheter ablation, and AF in an asymptomatic individual.

This case report discusses the use of KardiaMobile to diagnose RVOT ventricular tachycardia in diagnosis of a 62-year old man experiencing frequent, sudden episodes of exertional near-syncope and syncope with monomorphic RVOT VT. KardiaMobile may improve diagnostic yield in patients with symptoms of palpitations, light-headedness, or near-syncope. However, the lack of adhesive electrodes and variable contact between the patient and the device can lead to superimposed noise and artifact that may, in some cases, obscure the correct electrocardiographic diagnosis. Further, the device records cardiac rhythms only upon proper activation.

Fifty-three attendees at a computing conference (mean age 43 ±11 years, 77% male) transmitted KardiaMobile recordings weekly for eight weeks. Transmission interpretation was normal sinus rhythm (68%); sinus bradycardia or tachycardia (16%); extra atrial or ventricular systoles (2%); QRS delay (1%); and noise (13%). Symptomatic ventricular tachycardia and asymptomatic ST segment depression were detected in two participants, suggesting that early detection of abnormalities provides a window of diagnostic and therapeutic opportunity for intervention to prevent significant cardiac events. The majority of participants (82%) reported that the device was beneficial, 33% felt that they were more health conscious after participating in the study, and 88% thought that the device was transmitting accurate information. Use of the device caused 24% of subjects to reach out to see a physician for a consultation.

This single center, randomized controlled trial evaluated the use of daily KardiaMobile recording and receipt of motivational text messages 3 times per week (the iHeart Intervention), on time to recurrent AF/AFL and time to treatment of recurrent arrhythmias in patients undergoing catheter radiofrequency ablation (RFA) or direct current cardioversion (DCCV). The study also evaluated patterns of smartphone ECG use over a 6 month period. 238 were randomized to standard of care (n=123) or the iHeart intervention (n=115). Data were collected from the KardiaMobile ECG and from the electronic health records. The likelihood of recurrence detection was greater in the intervention group (hazard ratio 1.56, 95% CI 1.06-2.30, p=0.24), and did not differ significantly for RFA and DCCV procedures. Recurrence during the first month after ablation strongly predicted later recurrence (hazard ratio = 4.53, 95% CI: 2.05-10.00, p = .0006). Time from detection of recurrent arrhythmia to treatment was shorter for the control group (HR 0.33, 95% CI 0.57-2.92, p<0.0001). The authors hypothesize longer time from detection to treatment in the intervention arm due to physicians being less likely to proceed to treatment with short (asymptomatic) AF recurrences; meanwhile the first documentation of arrhythmia in the control arm was often when they came in for treatment. Of note, there was a trend towards lower healthcare utilization (hospitalizations, ER visits) in the intervention arm. Regarding Kardia usage, 36% recorded > 180 ECGs, 56% of patients recorded at least 90 ECGs, and 75% used the device in the last 3 months of the study. In summary, KardiaMobile with motivational text messages enabled earlier detection of recurrent arrhythmias, with a trend toward less treatment and healthcare utilization.

This document, written by an international task force of electrophysiologists, provides updated definitions, mechanisms, and rationale for AF ablation and consensus recommendations concerning indications, strategies, techniques, and endpoints, technology and tools, and follow-up considerations for AF ablation. Specifically, it references the iTransmit study featuring KardiaMobile as an example of the use of smartphone-based ECG monitors that can be helpful for long-term intermittent surveillance after AF ablation.

Fifty-five patients (mean age 60 ± 12 years) with AF undergoing ablation recorded their rhythm using KardiaMobile and a traditional transtelephonic monitor (TTM) whenever they had symptoms, or at least once a week, for 3-4 months following ablation. All were interpreted by electrophysiologists. There were 831 KardiaMobile recordings, and 7 were noninterpretable. Of the 389 simultaneous recordings with KardiaMobile and TTM, there was excellent agreement (K statistic 0.82). KardiaMobile detected sinus rhythm 97% of the time and correctly detected AF and atrial flutter 100% of the time, with 3% false-positive results. For manual review of KardiaMobile versus TTM for detection of AF, KardiaMobile had 97% specificity and 100% sensitivity. P waves could be difficult to discern, and occasionally this resulted in mislabeling sinus rhythm with atrial ectopy as AF. KardiaMobile is an alternative method for monitoring patients after AF ablation, with patients agreeing on ease of use.

This is a case study of a 58-year-old patient with AF with multiple cardiac risk factors who failed to remain in normal sinus rhythm after two ablations and one cardioversion. Following a second cardioversion, the patient was given KardiaMobile for mobile monitoring of any symptomatic events. Within days, the patient began feeling symptomatic again and used his device to transmit an ECG to his healthcare provider. The novel technology led to more timely detection of recurrent AF. Since approximately one-third of patients with AF are asymptomatic, a daily ECG transmission in those who have undergone a prior cardioversion or AF ablation may prove useful in detecting silent AF.

This analysis of the iHEART trial aimed to provide insights into the predictors of moderate and frequent use of the KardiaMobile digital health tool over 6 months. Participants (with documented AF and at least one AF-related risk factor) were randomised to usual care or to KardiaMobile plus behaviour-altering motivational text messages three times per week for 6 months. KardiaMobile use over 6 months was stratified into infrequent (≤5 times/week), moderate (5-and including 11 times/week) and frequent (≥11 times/week). Three predictors of greater use of KardiaMobile were identified: premature atrial contractions, lower symptom burden and less treatment concern. Neither age nor technology experience were associated with use over 6 months.

iHEART was a single-centre randomised controlled trial in 238 patients with AF in which half the population were randomised to receive Kardia mobile in addition to usual care. These participants also received regular text messages about AF management and about lifestyle factors. Over 6 months, Atrial Fibrillation Effect on Quality of Life improved significantly more in the Kardia mobile group (18.5 vs 11.2 points) than in the control group (p<0.05). The differences were driven by greater improvements in ‘treatment concern’ and ‘satisfaction with current treatment’ subscales in the Kardia mobile group. AF symptom severity was similar in both groups over the course of the study.

Kardia mobile in combination with a specially developed relational agent improved quality of life, (p=0.03), daily activity score (p=0.009) and self-reported adherence to anticoagulant medication (p<0.001) over 30 days compared with usual care in this pilot trial of 120 individuals with chronic AF. The relational agent provided instructions on how to employ Kardia mobile, as well as health education, monitoring, and problem-solving for users. A larger trial is planned. The authors concluded that Kardia mobile combined with the relational agent may improve patient-centred care and provide a low-cost, effective means of reducing the social and medical morbidity associated with AF.

This randomized clinical trial will implement a novel, smartphone-based intervention to address the patient experience of AF. One hundred eighty patients with AF who are receiving anticoagulation for stroke prevention will be randomized to 30 days of an embodied conversational agent and KardiaMobile, or to usual care, which includes a symptom and adherence journal. The primary endpoints are improvement in health related quality of life, and self-reported adherence to anticoagulation.

A pilot cohort from within the larger ongoing NIH randomized trial, iPhone Helping Evaluate Atrial Fibrillation Rhythm through Technology (iHEART), was evaluated to determine differences in AF/AFL recurrence rates (after undergoing treatment to restore normal rhythm) and quality-of-life over a 6-month follow-up period among 23 patients utilizing KardiaMobile on a daily basis, and 23 control patients. In the KardiaMobile group, 61% had recurrent AF/AFL versus 30% of controls (hazard ratio 2.55, 95% CI 1.06-6.11, p=0.04). Among the 13 patients with baseline and 6 month QoL assessments, significant improvements were observed in the physical functioning (p = 0.009), role physical (p = 0.007), vitality (p = 0.03), and mental health domains (p = 0.02). In summary, self-monitoring of AF is feasible using KardiaMobile, and improves self-reported quality-of-life.

The iHEART study is a single center, prospective, randomized controlled trial. A total of 300 participants with a recent history of atrial fibrillation will be enrolled. Participants will be randomized 1:1 to receive the iHEART intervention, receiving an iPhone® equipped with a KardiaMobile and behavioral altering motivational text messages or usual cardiac care for 6 months. This will be the first study to investigate the utility of a mobile health intervention in a “real world” setting. This study will assess the impact of KardiaMobile on clinical outcomes, quality of life, quality-adjusted life-years and disease-specific knowledge.

This study, called The Box 2.0, will compare the detection rate of AF diagnosed with an mobile Health solution to the detection rate of AF diagnosed with standard care. Secondary objectives include detection of sternal wound infection and cardiac decompensation, as well as assessment of quality of life, patient satisfaction, and cost effectiveness. This study uses a prospective intervention group and a historical control group for comparison. Patients undergoing cardiac surgery at Leiden University Medical Center are eligible for enrollment. In this study, 365 historical patients will be used as controls and 365 other participants will be asked to receive either The Box 2.0 intervention consisting of seven home measurement devices along with a video consultation two weeks after discharge or standard cardiac care for three months. Patient information will be analyzed according to the intention-to-treat principle. The Box 2.0 devices include a blood pressure monitor, thermometer, weight scale, step count watch, single-lead electrocardiogram (ECG) device, 12-lead ECG device, and pulse oximeter. The primary outcome of this study is the detection rate of AF in both groups. Quality of life and cost-effectiveness are also studied. The first results are expected in September 2020.

This study aimed to determine the feasibility of patients self-monitoring with KardiaMobile to identify recurrence of post-operative AF (POAF) in the post-discharge period following cardiac surgery. Forty-two participants with no prior history of AF, and discharged home in stable sinus rhythm, used KardiaMobile 4 times per day for 4 weeks post-discharge. Self-monitoring for POAF recurrence using KardiaMobile was feasible and acceptable, and participants felt empowered. Self-monitoring identified 24% (95% CI 12–39%) with an AF recurrence within 17 days of hospital discharge. 80% of patients with recurrence were at high enough stroke risk to warrant consideration of anticoagulation. The study concluded that KardiaMobile is a non-invasive, inexpensive, convenient and feasible way to monitor for AF recurrence in post-cardiac surgery patients. It also provides a mechanism to provide knowledge about the condition and also potentially reduces anxiety.

KardiaMobile smartphone monitoring for 30 days significantly improved the detection of AF compared with standard repeat 24-h Holter monitoring in this multicentre study. Patients (105 KardiaMobile and 98 standard Holter) were without known AF, and had a history of ischaemic stroke or transient ischaemic attack within the preceding 12 months. AF lasting ≥30 s was detected 9.5% of KardiaMobile patients vs. 2.0% of Holter patients (p=0.024). The number needed to screen to detect one AF was 13. KardiaMobile also drove changes in clinical practice: 9.5% of patients had received oral anticoagulation therapy at 3 months, compared with 0% in the Holter group (0=0.002).

The minimum subclinical AF duration required on ECG monitoring poststroke/transient ischemic attack to recommend OAC therapy is debated. Monitoring duration, quality of analysis, AF episode definition, interval from stroke to monitoring commencement, and patient characteristics including old age, certain ECG alterations, and stroke type, determine AF detection rate. This white paper by experts from the AF-SCREEN International Collaboration summarizes existing evidence and knowledge gaps on searching for AF after a stroke by using ECG monitoring. New AF can be detected by routine plus intensive ECG monitoring in approximately one-quarter of patients with ischemic stroke. After acute ischemic stroke, patients should undergo 72 hours of electrocardiographic monitoring to detect AF.

The aim of this international multicenter study was to use KardiaMobile to identify AF in patients admitted to the hospital with stroke/transient ischemic attack, compared with 24-h Holter monitoring performed after discharge. 1056 patients had daily KardiaMobile ECG recordings while in the hospital. Patients also received standard cardiac investigations according to local institutional guidelines. Detection rates of AF was compared with Holter monitoring when available. 251 underwent Holter monitoring, generally over a 3 month period after discharge. Of the 251 patients, Holter detected AF in 7 (2.8%) and KardiaMobile detected AF in 28 (11.2%). 6 patients had AF detected on both Holter and KardiaMobile. The authors recommended that KardiaMobile could be instituted to complement local standard cardiac investigations especially when Holter monitoring was not readily available.

This multicenter randomized clinical trial will investigate the effectiveness of prolonged ECG monitoring with KardiaMobile for AF detection in patients with cryptogenic stroke or transient ischemic attack (TIA). One hundred patients in the intervention group will use KardiaMobile to record their ECG twice daily; 100 patients in the control group will complete a 7-day Holter monitor. The primary outcome of this study is the percentage of patients in which AF is detected in the first year after the index ischaemic stroke or TIA. Secondary outcomes include markers for AF prediction, orally administered anticoagulation therapy changes, as well as the incidence of recurrent stroke and major bleeds.

This review investigated the literature on feasibility, validity, and utility of the AliveCor device as a tool for atrial fibrillation detection in clinical practice and in wider research. Eleven studies were included for evaluation. The feasibility of implementation of the AliveCor device as a heart rhythm screening aid in AF screening studies was high. The sensitivity for AF detection varied across the included studies, ranging from 54.5% to 100%. The AliveCor device proved cost effective in the analysis in the review. Overall quality reporting was moderate and only limited ethical detail was provided throughout the studies. The authors concluded that the AliveCor device offers a mobile, validated and secure option for heart rhythm screening and is feasible for both patients and health professionals to use in hospital and the community.

Primary care practice is the ideal location for large-scale health screening. Five clinical pharmacists were trained to use Kardia Mobile Device to screen for AF among 604 people aged ≥65 years, attending influenza vaccination clinics at general practitioner practices in England during the vaccination season. Only 6 of 604 Kardia recordings were uninterpretable. Total prevalence of AF was 4.3%. All participants with AF qualified for anticoagulation. Feedback from participating subjects was generally positive. The AF screening strategy was found to be cost-effective in 71.8% of the estimates in a Markov simulation model. The authors conclude that the study highlights the need to move towards the adoption of specifically purposed modern technology as the first line of AF detection.

In this article the potential benefits of opportunistic AF screening and detection in a community setting using easy-to-use “self-service health check-up stations” installed in public spaces, such as supermarkets and pharmacies, with digital ECG capture.

The purpose of this study was to evaluate the accuracy and practicality of screening high risk asymptomatic nursing home residents having ≥2 risk factors for AF and no previous history of AF using KardiaMobile (AliveCor, Mountain View, CA). Study participants had ≥2 risk factors, consisting of age ≥75 years, female sex, obstructive sleep apnea, peripheral vascular disease, diabetes mellitus, obesity, hypertension, and congestive heart failure. Using KardiaMobile, 30-second heart rhythm recordings were obtained on four different occasions. All tracings were reviewed by a cardiologist and, if uncertain, by an electrophysiologist. The nursing facility was notified of any diagnosis of AF, prompting further evaluation by the primary physician. Of the 245 residents screened, 18 (7.4%) had a diagnostic tracing for AF, 15 (83.3%) of whom had AF on the initial screen. There were no significant differences in demographics or individual risk factors between residents with and those without AF.

This review provides an overview of the gaps in the current evidence and a summary of the arguments for and against screening. Large randomized controlled trials have commenced to determine the cost-effectiveness and clinical benefit of screening using a range of devices and across different populations. Screening has been suggested as one approach to increase AF detection rates and reduce the incidence of ischemic stroke by earlier initiation of anticoagulation therapy. However, international taskforces currently recommend against screening, citing the cost implications and uncertainty over the benefits of a systematic screening program compared to usual care. Health care professionals should be aware of the implications of these emerging data for diagnostic pathways and treatment.

KardiaMobile and WatchBP to opportunistically test groups at increased risk of AF is feasible across a range of different healthcare and non-healthcare settings, allowing more of the population to receive pulse rhythm checks to detect possible AF, with the greatest detection rates are to be found in testing groups of older people and those with existing CVD.

This community-based AF screening study found that AF is underdiagnosed and under-treated and suggested that the early detection of AF using mobile devices is needed in Korea. The study included two parts. The preliminary study examined 2,422 participants in a community dementia screening program who were aged 60 years or older in the preliminary study. The expanded study included 5,366 participants at nine Senior Welfare Centers aged 60 years or older. AF screening was conducted using an automated SL-ECG (KardiaMobile by AliveCor, Mountain View, CA, USA). AF was confirmed with a 12-lead electrocardiogram in subjects classified as having AF on the SL-ECG. In the preliminary study, of the 2,422 subjects, 124 had AF on the SL-ECG. The prevalence of AF was 3.0% (95% confidence interval [CI]: 2.4-3.8). The positive predictive value (PPV) of SL-ECG was 58.9% (95% CI: 50.1-67.1). Of the subjects diagnosed with AF, 65.8% (95% CI: 54.3-75.6) were newly diagnosed. In the expanded study, of the 5,366 subjects, 289 had AF on SL-ECG. The prevalence was 2.6% (95% CI: 2.2-3.1) and PPV of SL-ECG was 48.8% (95% CI: 43.1-54.5).

This study suggested that student pharmacist driven health fairs are a feasible model to screen for AF and are effective in providing AF education to the public. The study evaluated AF screening and education at student pharmacist driven health fairs. Screening for AF was performed by student members of the American Pharmacist Association Academy of Student Pharmacists with preceptor oversight. Participants were screened using the KardiaMobile device (AliveCor, Mountain View, CA). Participant education was provided using an American Heart Association AF patient information sheet. Learning assessment was evaluated with three multiple choice questions. Results: Students screened a total of 697 participants over a six-month period at 13 health fairs. Overall, 71% of the participants were women aged 56 ± 15 years (mean ± SD). Sixteen of the participants (2.3%) who were screened received results indicating possible AF. None of the participants with a possible positive finding had symptoms suggestive of AF. Of these 16 participants, 11 (69%) had a CHA₂DS₂-VASc score greater than or equal to 2 (2.7 ± 0.7). Most participants answered each learning assessment question correctly. More than 95% of participants believed that screening for AF at health fairs was important or very important.

AliveCor monitoring in conjunction with eHealth tools improves clinical management decisions to adhere to guidelines. The Atrial Fibrillation Screen, Management And guideline-Recommended Therapy (AF-SMART) studies of opportunistic AF screening in 16 metropolitan and rural general practices were conducted from November 2016–June 2019. These studies investigated custom-designed eHealth tools to support all stages of AF screening in general practice. GPs/nurses liked the eHealth tools, although technical problems sometimes disrupted screening. Time was the main barrier to screening for GPs/nurses, so systems need to be very efficient. Practices with leadership from a senior GP ‘screening champion’ had broader uptake, especially from the nursing team. Providing regular feedback on screening data was beneficial for quality improvement and motivation. Clear protocols for follow-up of abnormal results were required for successful nurse-led screening in a hierarchical system. Participation in the program had broader benefits of improving AF knowledge and raising the profile of cardiovascular health in the practice. Screening for a shorter, more intense period (e.g., during influenza vaccination) worked well for practices where sufficient staff time was allocated.

Prospective AF screening among patients aged 65 years of age and older was conducted at 10 pharmacies in Poland using KardiaMobile, between December 2017 and November 2018. A total of 525 ECGs were performed; participants had a mean age of 73.7 years; 68% were female. A total of 24 ECGs were deemed by the cardiologists as noninterpretable (4.9%). Kardia Instant Analysis was provided in 490 cases. In 17 (3.5%) recordings were “possible AF,” in 23 (4.7%) ECGs “unreadable,” and in 13 (2.7%) “unclassified”. After the cardiologist reevaluation, new AF was identified in 7 (1.33%) patients, and a previous diagnosis in 4 patients. Sensitivity and specificity of Kardia app in detecting AF was 100% (95% confidence interval [CI]: 71.5%-100%) and 98.7% (95% CI: 97.3%-99.5%), respectively. The positive predictive value was 64.7% (95% CI: 38.3%-85.7%) and the negative predictive value was 100% (95% CI: 99.2%-100%). CHA2DS2-VASc score was 2.14+0.69 for those with new AF and 3.33+1.26 in the non-AF group. The authors concluded that the Kardia app is capable of fast screening and detecting AF with high sensitivity and specificity. The possible diagnosis of AF deserves additional cardiological evaluation. The results obtained in patients with low CHA2DS2-VASc score and “silent” AF confirm the importance of routine AF screening. Cardiovascular screening with the use of mobile health technology is feasible at pharmacies.

One-third of the 19 studies of this meta analysis used AliveCor devices. The availability of large numbers of screened patients allowed the researchers to quantify the yield and stroke risk for AF in 5-year age brackets. The AF detection rate was 1.44% for screening people ≥65 years. The number needed to screen (NNS) to detect one treatable AF was 83 for people ≥65 years. In the bracket aged 60-64 years NNS for treatable AF was 926, compared with 53 in the bracket aged 80-84 years. The detection rate was not influenced by the screening method, recruitment setting, country, or year screened. The authors conclude that screening for AF in a general population is likely to be cost-effective if screening is commenced at age 65, in line with current international guidelines.

This study aimed to test the feasibility of an awareness event including opportunistic screening for AF and to test the reliability of KardiaMobile. During two weeks, at a community pharmacy, a nursing home, and an outpatient cardiology clinic in Portugal, individuals aged 40 years and older, without a history of atrial fibrillation, participated in a pharmacist-led detection event. Participants received a manual pulse check, provided a clinical history, and received a KardiaMobile ECG recording. ECGs highlighted as possible AF were confirmed by the cardiologist and if AF was diagnosed, they were referred to their physician. The awareness event involved 223 individuals, among which 205 were screened. Mean age was 66 years (SD=15) and hypertension was the most frequently reported (n=107; 52.2%). Mean CHAD2DS2- VASc score was 3 (SD=1.8). Cardiac irregularities were identified in 45 individuals, 14 confirmed to be new cases of AF (6.8%) by the cardiologist; detection rate varied between 1% to 13%, depending on the setting. There was one unreadable trace (0.5%). The sensitivity and specificity of the AF algorithm were 90.9% and 97.4%. The authors conclude KardiaMobile to be potentially useful for opportunistic early detection of AF, provided interprofessional collaboration is guaranteed so that suspect cases are adequately managed and in a timely way.

The aim of this project was to describe the feasibility of KardiaMobile for AF screening in a large-scale, undifferentiated population. 184 Canadian primary care physicians were provided with a KardiaMobile ECG and asked to obtain a single 30-second ECG recording in all patients seen in their daily practice ≥ 65 years and not previously diagnosed with AF. Physician evaluation of KardiaMobile was measured using a Likert-scale based questionnaire. 133 physicians (72%) reported their findings and completed the survey. Over 3 months, 7585 patients were screened (42% of eligible patients). AF was detected in 471 patients (6.2%). Anticoagulation therapy was initiated in 270 patients (57%). Physicians generally reported a high perceived clinical value (94%) and ease of integration (89%) of the device. In conclusion, previously undiagnosed AF is common in older individuals attending primary care clinics. KardiaMobile appears to be an effective screening tool for AF with high physician acceptability. More research on the feasibility of such novel technologies is warranted for future consideration of integration in population-based screening programs.

With support from the Heart Rhythm Society (HRS) and the American College of Physicians (ACP), this initiative demonstrated the feasibility and yield, both in identifying previously undiagnosed AF and educating patients and caregivers about AF, of systematic screening events in Internal Medicine practices using a KardiaMobile ECG. Five Internal Medicine practices performed systematic screening and education of patients at higher risk for AF using KardiaMobile and a variety of educational materials. Participants were required to have at least one of the following AF risk factors: ischemic heart disease, diabetes, hypertension, congestive heart failure, chronic obstructive pulmonary disease, obesity, obstructive sleep apnea, age > 65 years old, a history of smoking, thyroid disease or female gender. Patients screened as “Unclassified” or “Possible AF” were referred for further evaluation. A total of 772 patients were screened. The average age was 65.2 + 15.4 years, and 28.2% were 75 years old or older. The majority, 521 (67.5%), were female, and 75.7% had a CHA2DS2-VASc Score > 2. Six hundred seventy (86.8%), screened as “Normal,” 85 (11.0%) as “Unclassified” and 17 (2.2%) as “Possible AF.” Participants demonstrated a significant knowledge deficit about stroke and AF prior to the screening events, and the majority felt that their awareness of these issues increased significantly as a result of their participation. The authors conclude that systematic screening using KardiaMobile was feasible, although with relatively modest yield of non-Normal algorithmic findings.

In this focused update of the AHA/ACC/HRS guidelines, in 7.12 Device Detection of AF and Atrial Flutter, the authors cite AliveCor research to support the following statement: “A role in screening for silent AF may also exist for remote electrocardiographic acquisition and transmission with a ‘smart’ worn or handheld WiFi-enabled device with remote interpretation.”

This eHealth implementation study aimed to evaluate strategies to promote opportunistic AF screening using electronic screening prompts and improve treatment using electronic decision support (EDS) software. An electronic screening prompt appeared whenever an eligible patient's (aged ≥65 years, no AF diagnosis) medical record was opened in participating general practices. General practitioners and practice nurses offered screening using KardiaMobile ECG. Guideline-based EDS was provided to assist treatment decisions. Deidentified data were collected from practices using a data extraction tool. General practices (n=8) across Sydney, Australia, screened for a median of 6 months. A total of 1805 of 11,476 (16%) eligible patients who attended were screened (44% men, mean age 75.7 years). Screening identified 19 (1.1%) new cases of AF (mean age, 79 years; mean CHA2DS2-VASc, 3.7; 53% men). General practitioners (n=30) performed 70% of all screenings (range 1-448 patients per general practitioner). The proportion of patients with AF prescribed oral anticoagulants was higher for those diagnosed during the study: 15 of 18 (83%) for screen-detected and 39 of 46 (85%) for clinically detected, compared with 933 of 1306 (71%) patients diagnosed before the study ( P<0.001). The EDS was accessed 111 times for patients with AF and for 4 of 19 screen-detected patients.

This population-based study in India used KardiaMobile to derive age and sex-stratified AF prevalence by screening 7 participants in each of six age and sex strata (age 40-55, 56-65, 65+, and male and female) from 50 villages (2100 participants). A health worker from each village used a KardiaMobile to screen for AF on 3 separate days, and administered a questionnaire. All abnormal (AF or unclassified) ECGs were reviewed by the Indian cardiologist and AF determination confirmed by a US-based cardiac electrophysiologist. Among 2074 participants, AF was identified in 33 participants (1.6%), two-thirds on the first ECG. AF prevalence was higher among males (2.3% vs 1.0%, p = 0.03) and in older people (0.6%, 0.9%, 2.1%, 5.6%; p < 0.01). The authors conclude that the prevalence of AF observed is comparable to rates found in studies from North America and Western Europe and increases similarly with age. AF screening with KardiaMobile using village health workers in rural India is feasible and presents an opportunity for a strategy to address the stroke epidemic in India through primary prevention.

Between November 2015 to September 2016, 11,574 Hong Kong citizens voluntarily participated in the AFinder Program, a nongovernmental organization (NGO)–led community- based AF screening program using KardiaMobile. A total of 118 screening sessions in 108 community centers was carried out by 84 trained layperson volunteers older than 50 years. Citizens with AF were contacted by telephone for completing the baseline and 9-month follow-up questionnaires. The ECG reports were mailed to those participants with AF, and they were advised to seek medical attention. Participants who had uninterpretable ECGs were advised to seek medical attention and undergo conventional ECG tests. Among all participants (9236 female citizens [79.8%]; mean age 78.6 years), KardiaMobile ECGs were interpretable in 10,735 citizens (92.8%). 244 (2.3%) had AF; a new diagnosis of AF was found in 74 participants (0.69%), with a mean CHA2DS2-VASc score of 3.9 +/- 1.5. 36 of the 74 were asymptomatic. Of 72 participants with newly diagnosed AF and indicated for oral anticoagulation, 47 sought medical attention and 17 (23.6%; 95% CI 13.8-33.4%) received oral anticoagulants. This NGO-led community-based AF screening program was effective in identifying citizens with newly diagnosed AF. However, the effectiveness of the program in subsequently leading them to receive appropriate oral anticoagulation therapy was weakened by the lack of a more structured downstream management pathway.

The purpose of this study was to evaluate the utility of screening for AF in patients presenting to Kaiser Permanente ambulatory clinics for routine care using KardiaMobile during intake. A total of 2286 patients 65 years and older were screened; mean age of the patients was 80 ± 11 years (range 65-96), 60% were males and 40% were females. AF was detected in 117 (5.1%) patients, 81 of whom had a history of AF (3.5% of the total screened). There were 36 (1.6%) patients who had undiagnosed AF, and only 2/36 (6%) were on anticoagulant therapy. In summary, up to 1.6% of patients 65 years and older presenting to an ambulatory clinic may have undiagnosed AF, most of whom are at significant risk of stroke (CHADsVASc score of ≥2), and would benefit from screening and treatment for AF to prevent stroke.

1041 patients age 65 years of age or greater were screened in 9 primary care clinics in Hong Kong using KardiaMobile. All ECGs were over-read by a cardiologist. Overall AF prevalence was 2.6%, and newly identified AF was 1.5%. Mean age of newly diagnosed AF patients was 77 years, with a mean CHA2DS2-VASc score of 3.9. Patient awareness of AF was low with 36.4% unfamiliar with AF and 63.6% unaware of the risk of AF related stroke. All patients agreed that KardiaMobile was easy to operate and willing to undergo repeated screening in future primary care visits. 86% of primary care physicians considered KardiaMobile useful for AF screening and would use it in their daily practice. At baseline, 47% of primary care physicians used CHA2D2-VASc score to assess AF related stroke risk, which increased to 71% at the end of the study.

This is the first prospective randomized trial of AF screening using a remote, handheld ECG device over an extended period of time (1 year). 1001 adults ≥ 65 years of age with a CHADS-VASc score ≥2 (mean score 3.0) were randomized to AF screening using KardiaMobile or usual care. Patients randomized to KardiaMobile acquired ECGs twice weekly over 12 months (plus additional ECGs if symptomatic). 19/500 (3.8%) patients in the KardiaMobile group were diagnosed with AF, versus 5/501 (1.0%) in the usual care group (hazard ratio, 3.9; 95% confidence interval=1.4-10.4; P=0.007) at a cost per AF diagnosis of $10,780 (£8255). There was a statistically similar number of stroke/transient ischemic attack/systemic embolic events. The majority of KardiaMobile patients were satisfied with the device, finding it easy to use without restricting activities or causing anxiety. This trial found that extended AF screening with KardiaMobile is significantly more likely to identify incident AF than usual care.

This study used KardiaMobile to screen 50 adults in Kenya (mean age 54 years, 66% women) attending Kijabe Hospital outpatient internal medicine or diabetes clinics; 44% had hypertension, 32% had diabetes, and 4% had stroke. ECG tracings in 4 of the 50 patients (8%) showed AF, and none had been previously diagnosed with AF. The authors concluded that KardiaMobile can be used to screen for AF in low-resource settings.

This protocol is for a mixed methods study that will recruit and train Aboriginal health workers to use KardiaMobile to consecutively screen 1500 Aboriginal people aged 45 years and older. The study will quantify the proportion of people who presented for follow-up assessment and/or treatment following a non-normal screening and then estimate the prevalence and age distribution of AF of the Australian Aboriginal population.

KardiaMobile was used to identify asymptomatic AF at the time of influenza vaccination in 5 practices in Sydney, Australia. Nurses used the automated algorithm to screen 973 patients aged ≥ 65 years between April-June 2015. Screening took on average 5 minutes (range 1.5 -10 minutes); abnormal recordings required additional time. Newly identified AF was found in 0.8% (8) of patients, and the overall prevalence of AF was 3.8% (37). The sensitivity and specificity of the automated algorithm for detecting AF was 95% and 99%, respectively. Screening by practice nurses was well accepted by practice staff. Key enablers were the confidence and competence of nurses and a ‘designated champion’ to lead screening at the practice. Barriers were practice specific, and mainly related to staff time and funding.

Residents from 6 villages in Gujarat, India, were screened for AF using KardiaMobile. A total of 235 participants aged 50 years and older (half female) used KardiaMobile for 2 minutes on 5 consecutive days. Community health workers helped to screen participants. The prevalence of AF increased by the number of screenings, from 3.0% with 1 screening to 5.1% with 5 screenings.

From May 1, 2014, to April 30, 2015, adults aged 18 and above were informed by media promotion for a community-wide AF screening program in Hong Kong. A group of non-medical volunteers used KardiaMobile to screen 13,122 Hong Kong citizens (mean age 65.5 ± 13.3 years). All recordings were overread by a cardiologist within 1 month of the recording, and all participants with AF detected were referred for medical consultation. Fifty-six (0.4%) out of 13,122 KardiaMobile recordings were uninterpretable. Newly diagnosed AF was discovered in 101 (0.8%) participants. The overall prevalence for AF was 1.8% (239/13,122, 95% CI 1.6-2%). Systematic population-based ECG screening for AF with KardiaMobile was feasible and identified a proportion of Hong Kong citizens with AF that was comparable with that of contemporary US and European populations.

This study evaluated the use of AliveCor and MyDiagnostick handheld ECG monitors (in 2015) in 445 hospitalised patients at risk for atrial fibrillation (AF). In this setting, both devices were wound to be useful for a screening strategy that was reasonable from a cost-effectiveness perspective. However, the authors welcomed future improvements to the sensitivity and specificity of both devices. Manual review of readings improved the performance of both systems; notably with the AliveCor device there was no need for additional standard ECGs to rule out false positives with manual review.

Ninety-five patients, 29 with AF and 66 in sinus rhythm, were assessed with KardiaMobile and a standard 12-lead EKG by two physicians in clinic. For one practitioner’s review, the sensitivity of KardiaMobile was 90% and the specificity was 86%; for the other practitioner, the sensitivity was 93% and the specificity was 76%. The high sensitivity of KardiaMobile suggests this test is a good ‘rule-out’ for AF. A positive test should be combined with a 12-lead EKG to confirm the diagnosis of AF.

KardiaMobile was used to screen 954 participants aged 12-99. There were 54 (5.6%) people noted to have a potential abnormality (conduction defect, increased voltage, rhythm abnormality); of these 23 (43%) were abnormal with two confirming AF and 2 showing atrial utter. Other abnormalities detected included atrial and ventricular ectopy, bundle branch block, and left ventricular hypertrophy. One patient with increased voltages was later diagnosed with hypertrophic cardiomyopathy. In conclusion, KardiaMobile was quick and easy to use and led to new diagnoses of arrhythmia, bundle branch block, left ventricular hypertrophy and cardiomyopathy.

One hundred twenty-one Maori and Pacific people age ≥ 55 years were screened for AF with KardiaMobile in New Zealand community pharmacies; if the automatic algorithm was positive, participants were referred to primary care for confirmatory 12-lead ECG. Two of the 121 participants screened had a new diagnosis of AF (1.7%), and two known AF cases appeared to not be receiving warfarin, giving a total of four people (3%) that could benefit from initiation of anticoagulation. There were 2 false positives, which were thought to occur due to incorrect handling of the device, which was corrected through further training of the pharmacists. The study determined that KardiaMobile is highly acceptable to patient populations as well as health professionals in this environment.

Receptionists and practice nurses screened patients aged ≥65 years using KardiaMobile. General practitioner (GP) review was then provided during the patient’s consultation. Eighty-eight patients (51% male; mean age 74.8 ± 8.8 years) were screened: 17 patients (19%) were in AF (all previously diagnosed). KardiaMobile was well accepted by GPs, nurses and patients. Receptionists were reluctant, whereas nurses were confident in using the device to explain and provide screening.

One thousand pharmacy customers (mean age 76 ± 7 years, 44% male) were screened with KardiaMobile. Newly identified AF was found in 1.5% (95% CI, 0.8-2.5%), and AF prevalence was 6.7%. The automated algorithm showed 98.5% sensitivity and 91.4% specificity for detecting AF. Using cost and outcome data from a United Kingdom study for AF screening, the incremental cost-effectiveness ratio of extending screening into the community with KardiaMobile, based on 55% warfarin prescription adherence, would be $USD4,066 per quality-adjusted life-year gained, and $USD20,695 for preventing one stroke. In summary, screening for AF with KardiaMobile is feasible and cost-effective.

KardiaMobile was used in a community screening of 109 patients (70 in sinus rhythm and 39 in AF) soon after a 12-lead ECG had been performed. The ECGs were interpreted by two cardiologists blinded to the rhythm diagnosis, and were processed to provide an automated diagnosis of sinus rhythm or AF. Results were compared with the 12-lead ECG diagnosis by a third cardiologist. An optimized algorithm performed extremely well in the validation set with high sensitivity, specificity, overall accuracy and Kappa (95% CI) of 98% (89%–100%), 97% (93%–99%), 97% (94%–99%) and 0.92 (0.86– 0.98) respectively. This study concluded that KardiaMobile can be used to simply and rapidly record a high quality single-lead ECG to accurately detect AF, making it an ideal technology for community screening programs to detect silent AF.

Use of KardiaMobile was associated with a decrease in healthcare resource use. Per patient and year, KardiaMobile users had 1.14 fewer office visits, 0.17 fewer cardiac-specific ED and/or urgent care visits, 0.20 fewer arrhythmia-related ED and/or urgent care visits, 0.18 fewer unplanned arrhythmia-related hospital admissions, and 0.19 fewer cardiac monitor uses.

The investigators conducted a retrospective review of their institutional electronic health records and KardiaPro database to identify 128 KardiaMobile patients with information on healthcare resource utilisation in the year before they started to use the mobile ECG device. Patients were then followed for one year and resource utilisation captured. Most patients used KardiaMobile for paroxysmal (60%) or persistent (16%) AF.

This pilot initiative aimed to test the feasibility of integrating the AliveCor Kardia Mobile system into community monitoring of treatment in 74 patients with recently diagnosed fast AF and opportunistic community diagnosis of AF. Subjects had known fast AF requiring monitoring and management, and suspected AF due to an abnormal pulse on manual pulse check. During the 6-month monitoring period, the AliveCor device was found to be easy to use, more time-effective and cost-effective (saving up to £134.49 per patient), and successfully prevented the need for serial 12-lead ECGs in the community. Of 37 patients requiring ECG monitoring, 113 iECGs were needed and of the 53 patients with an ‘abnormal’ pulse, 15% were found to be in new-onset AF and were appropriately anticoagulated.

This is a systematic review reporting the estimates of diagnostic accuracy, and cost-effectiveness of lead-I ECG devices. The diagnostic accuracy and clinical impact results presented are derived from an asymptomatic population (used as a proxy for people with signs or symptoms of AF). The summary sensitivity of lead-I ECG devices was 93.9% [95% confidence interval (CI) 86.2% to 97.4%] and summary specificity was 96.5% (95% CI 90.4% to 98.8%). One study reported limited clinical outcome data. Acceptability of lead-I ECG devices was reported in four studies, with generally positive views. The de novo economic model yielded incremental cost-effectiveness ratios (ICERs) per quality-adjusted life-year (QALY) gained. The results of the pairwise analysis show that all lead-I ECG devices generated ICERs per QALY gained below the £20,000–30,000 threshold. KardiaMobile (AliveCor, Mountain View, CA, USA) is the most cost-effective option in a full incremental analysis.

This guidance document for the United Kingdom evaluated use of lead-I ECG devices for single time point testing of people in primary care with symptoms of atrial fibrillation and an irregular pulse. The authors concluded there is not enough evidence to recommend routine adoption of lead-I ECG devices for this use case. They recommended further research to show how using lead-I ECG affects the number of people with atrial fibrillation detected, as well the staff time needed to interpret the ECG tracings. Of note, a de novo economic model was designed to evaluate cost effectiveness, and KardiaMobile dominated all other lead-I ECG devices, costing less and producing more quality-adjusted life years [QALYs].

Palpitations and pre-syncope are together responsible for 300,000 annual Emergency Department (ED) attendances in the United Kingdom (UK) alone. This multicenter randomized controlled trial compared the symptomatic rhythm detection rate of KardiaMobile versus standard care alone (no planned ambulatory ECG monitoring), for 243 participants presenting to 10 emergency departments in the UK with palpitations and pre-syncope with no obvious cause evident at initial consultation. A symptomatic rhythm was detected at 90 days in 69 (n=124; 55.6%; 95% CI 46.9–64.4%) participants in the intervention group versus 11 (n=116; 9.5%; 95% CI 4.2–14.8) in the control group (RR 5.9, 95% CI 3.3–10.5; p<0.0001). Mean time to symptomatic rhythm detection in the intervention group was 9.5 days (SD 16.1, range 0–83) versus 42.9 days (SD 16.0, range 12–66; p<0.0001) in the control group. Use of KardiaMobile increased the number of patients with symptomatic rhythm detection over five-fold, to more than 55%, at 90 days. The authors recommend that KardiaMobile be considered part of on-going care to all patients presenting acutely with unexplained palpitations or pre-syncope.

This case study from the York Health Economics Consortium focuses on the potential return on investment of replacing a typical AF diagnostic pathway with a KardiaMobile pathway, for the purposes of diagnosing AF. The analysis was developed in spring 2017 and was based on the information and evidence specific for UK general practitioner care available at the time. The authors ascertained a cost savings of £968 per patient per year from a National Health System perspective.

This is the first prospective randomized trial of AF screening using a remote, handheld ECG device over an extended period of time (1 year). 1001 adults ≥ 65 years of age with a CHADS-VASc score ≥2 (mean score 3.0) were randomized to AF screening using KardiaMobile or usual care. Patients randomized to KardiaMobile acquired ECGs twice weekly over 12 months (plus additional ECGs if symptomatic). 19/500 (3.8%) patients in the KardiaMobile group were diagnosed with AF, versus 5/501 (1.0%) in the usual care group (hazard ratio, 3.9; 95% confidence interval=1.4-10.4; P=0.007) at a cost per AF diagnosis of $10,780 (£8255). There was a statistically similar number of stroke/transient ischemic attack/systemic embolic events. The majority of KardiaMobile patients were satisfied with the device, finding it easy to use without restricting activities or causing anxiety. This trial found that extended AF screening with KardiaMobile is significantly more likely to identify incident AF than usual care.

One thousand pharmacy customers (mean age 76 ± 7 years, 44% male) were screened with KardiaMobile. Newly identified AF was found in 1.5% (95% CI, 0.8-2.5%), and AF prevalence was 6.7%. The automated algorithm showed 98.5% sensitivity and 91.4% specificity for detecting AF. Using cost and outcome data from a United Kingdom study for AF screening, the incremental cost-effectiveness ratio of extending screening into the community with KardiaMobile, based on 55% warfarin prescription adherence, would be $USD4,066 per quality-adjusted life-year gained, and $USD20,695 for preventing one stroke. In summary, screening for AF with KardiaMobile is feasible and cost-effective.

This study reported on a method to generate multi‑lead ECGs using an existing single‑lead KardiaMobile mECG device by using alligator clips connected to an insulated copper wire to attach the device to adhesive electrodes on the patient's limbs and torso according to standard lead configurations. Readings were obtained of 6 electrocardiographic diagnoses (sinus rhythm, typical atrial flutter, atrial fibrillation, sinus with right bundle branch block, left anterior fascicular block, sinus with left bundle branch block, and bi-ventricular pacing. Recordings were compared with patients’ own baseline 12‑lead ECG obtained on the same day, as well as with single-lead ECGs. Both diagnostic accuracy and confidence were significantly greater with the modified KardiaMobile than with single-lead ECG: agreement with the correct diagnosis was 81.6%, vs. 48.2% with single-lead and 88.6 with 12‑lead ECG (p<0.01) and fellows’ confidence score for the diagnosis was 4.35 out of 5, vs 3.34 with single-lead and 4.53 with 12‑lead ECG (p=0.09). The authors conclude that is would be possible to enhance existing single‑lead KardiaMobile devices with minimal added cost.

This review focuses on the real-world use and evolution of wearable cardiac monitoring devices for arrhythmias, cardiovascular diseases and some of their risk factors beyond atrial fibrillation. The AliveCor smartphone-based 12-lead equivalent ECG system was discussed as a demonstration that it is feasible to use a wearable two-electrode system to replace a standard 12-lead ECG. The authors note that in order to provide a benefit in different clinical scenarios or patient cohorts, appropriate clinical pathways need to be developed and evaluated using wearables. This also includes appropriate training of users, patients and prescribing physicians.

The use of the KardiaMobile iOS- and Android enabled ECG modality to improve the hands-on learning experience and interpretation of cardiovascular physiology was evaluated in an online survey toward the completion of medical students’ MS1 academic year. KardiaMobile ECG device was easily integrated into the preclerkship curriculum and readily deployed in the lecture hall, improving the clinical relevance of cardiovascular physiology instruction. Sixty-seven percent of students agreed or strongly agreed that the AliveCor KardiaMobile device was a valuable addition to ECG instruction. Ninety-two percent of students surveyed agreed or strongly agreed that using mobile medical devices will help further their medical education and that knowing about mobile medical devices will be important in their future practice.

This review surveys the ambulatory monitoring landscape and the development of analytical methods such as machine learning to increase the accuracy and improve the actionability of device-based diagnoses. The authors highlight the increased signal fidelity in the KardiaMobile six-lead device. Novel biosignal definition and biosensor acquisition, automated diagnosis and expert-level triage, secure data transmission and patient-centric disease management has the potential to drive profound change in cardiovascular monitoring. Coupled with interoperability of data to widen access to all stakeholders, seamless connectivity (an internet of things) and maintenance of anonymity, this approach could ultimately facilitate near-real-time diagnosis and therapy.

The authors of this community health centre study used KardiaMobile devices to perform single–time point screening for AF in 4531 residents aged 65 years or older recruited from 5 Chinese community health centres. Overall AF prevalence was 4.0%. Although 85% of those patients were recommended for oral anticoagulants, the prescription rate for known AF was only 20%. The study illustrates the usefulness of handheld ECG devices in rapid screening for AF to identify potential treatment gaps in community settings.

Use of combined remote monitoring systems with AliveCor after acute MI could replace up to two physical outpatient clinic visits with two digital outpatient clinic visits, patients were able to accurately measure and transfer BP, a single-lead ECG, and weight. Furthermore, patients can more easily send in clinically relevant measures (ECG and BP) to the hospital if indicated (e.g., in case of palpitations). Patients indicated that they appreciated extra control from the hospital, as well as the possibility to view their own health data.

The study was performed to assess the risk that electromagnetic interference (EMI) introduced by smart devices may affect cardiac implantable electronic devices (CIEDs). This risk is relevant as more than one million CIEDs are implanted each year globally. The investigators analysed data from 251 patients with an CIED (59% with a pacemaker and 41% with an implantable defibrillator). All subjects used the Kardia device to record a single channel ECG for 30 seconds. The results confirmed that that the Kardia device has an excellent safety profile in patients with CIEDs: no EMI was observed on the EGMs nor were there any clinical events at the time of the Kardia recordings. Recordings were correctly interpreted in 90% of paced recordings and 94.7% of nonpaced recordings.

Patients with human immunodeficiency virus (HIV) are at higher risk for cardiac arrhythmias, which can be recorded by Kardia. However, quality of KardiaMobile ECG depends on the skin condition; drying of the skin is observed in HIV patients with lower CD4 count and as a side effect of applied pharmacotherapy. This study examined the quality of the KardiaMobile ECG signal in 263 Kenyan adults with different clinical stages of human immunodeficiency virus (HIV) infection. The recordings were made during routine check-ups at the outpatient clinics. The ECG was readable in 201 patients (76.4%) and unreadable in 62 (23.6%). The World Health Organization AIDS Clinical Staging (WACS) score > 1 was associated with OR 3.95 (95% CI 2.14-7.29, p < 0.0001) for acquiring an unreadable ECG. The authors conclude that KardiaMobile ECG accuracy is limited in HIV patients, but could be improved with moisturizing the skin before recording.

The AliveCor portable ECG monitor and the Santamedical SM-110 pulse oximeter were the only two health-monitoring devices which met the clinically acceptable criteria for each biosignal they measured in laboratory testing, using widely accepted clinical and industry criteria. Only four tested devices meet target requirements for accuracy. Test participants were young and healthy: 38 men and women aged on average 23 years and with a mean BMI of 24.6. Devices were tested at rest and immediately following exercise and were assessed according to their respective sensing modalities: activity trackers, thermometers, blood pressure monitors, electrocardiographs, pulse oximeters and respiratory rate monitors. The authors expressed concern that most of the devices are inaccurate during nonrest use conditions and that they may not include safeguards, such as error checking and user warnings. In the absence of FDA or comparable clearance, the use of consumer-marketed health-monitoring devices for clinical or medical purposes should be undertaken with caution.