Measurement of the QT interval using the Apple Watch

Are Wearable ECG Devices Ready for Hospital at Home Application?

The increasing focus on improving care for high-cost patients has highlighted the potential of Hospital at Home (HaH) and remote patient monitoring (RPM) programs to optimize patient outcomes while reducing healthcare costs. This paper examines the role of wearable devices with electrocardiogram (ECG) capabilities for continuous cardiac monitoring, a crucial aspect for the timely detection and management of various cardiac conditions. The functionality of current wearable technology is scrutinized to determine its effectiveness in meeting clinical needs, employing a proposed ABCD guide (accuracy, benefit, compatibility, and data governance) for evaluation. While smartwatches show promise in detecting arrhythmias like atrial fibrillation, their broader diagnostic capabilities, including the potential for monitoring corrected QT (QTc) intervals during pharmacological interventions and approximating multi-lead ECG information for improved myocardial infarction detection, are also explored. Recent advancements in machine learning and deep learning for cardiac health monitoring are highlighted, alongside persistent challenges, particularly concerning signal quality and the need for further validation for widespread adoption in older adults and Hospital at Home settings. Ongoing improvements are necessary to overcome current limitations and fully realize the potential of wearable ECG technology in providing optimal care for high-risk patients.

Rhythm-Ready: Harnessing Smart Devices to Detect and Manage Arrhythmias

PURPOSE OF REVIEW

To survey recent progress in the application of implantable and wearable sensors to detection and management of cardiac arrhythmias.

RECENT FINDINGS

Sensor-enabled strategies are critical for the detection, prediction and management of arrhythmias. In the last several years, great innovation has occurred in the types of devices (implanted and wearable) that are available and the data they collect. The integration of artificial intelligence solutions into sensor-enabled strategies has set the stage for a new generation of smart devices that augment the human clinician. Smart devices enhanced by new sensor technologies and Artificial Intelligence (AI) algorithms promise to reshape the care of cardiac arrhythmias.

Relative utility of portable ECG devices in capturing arrhythmias in athletes

INTRODUCTION

Traditional monitoring of athletes with cardiac symptoms is limited due to sport-specific considerations and the intermittent nature of symptoms. Some portable electrocardiogram (ECG) devices may have more diagnostic utility than traditional monitoring. Their accuracy, advantages, and limitations should be considered when a clinician is considering the most appropriate device for investigation of an athlete's symptoms.

AREAS COVERED

There are six main categories of portable ECG devices: smartwatches, handheld devices, mobile cardiac telemetry (MCT), patches, rings, and chest sensors. The aim of this review is to highlight to a clinician the potential benefits of some devices over others to assist the physician in identifying the most appropriate device. We present peer-reviewed literature on the accuracy of each type of device along with advantages and limitations.

EXPERT OPINION

For a user-initiated capture of an ECG, smartwatches and handheld devices are easy to use and supported by peer-reviewed literature. Rings can also provide a user-initiated ECG, though there is limited evidence to support their usage. For continuous monitoring, patches and MCT are both useful, though there is limited access to these devices. Chest sensors show some promise, although access is currently limited in some countries.

Wearable electrocardiogram devices in patients with congenital long QT syndrome: The SMART-QT study

BACKGROUND

In patients with congenital long QT syndrome (LQTS), the risk of ventricular arrhythmia is correlated with the duration of the corrected QT interval and the changes in the ST-T wave pattern on the 12-lead surface electrocardiogram (12L-ECG). Remote monitoring of these variables could be useful.

AIM

To evaluate the abilities of two wearable electrocardiogram devices (Apple Watch and KardiaMobile 6L) to provide reliable electrocardiograms in terms of corrected QT interval and ST-T wave patterns in patients with LQTS.

METHODS

In a prospective multicentre study (ClinicalTrials.gov identifier: NCT04728100), a 12L-ECG, a 6-lead KardiaMobile 6L electrocardiogram and two single-lead Apple Watch electrocardiograms were recorded in patients with LQTS. The corrected QT interval and ST-T wave patterns were evaluated manually.

RESULTS

Overall, 98 patients with LQTS were included; 12.2% were children and 92.8% had a pathogenic variant in an LQTS gene. The main genotypes were LQTS type 1 (40.8%), LQTS type 2 (36.7%) and LQTS type 3 (7.1%); rarer genotypes were also represented. When comparing the ST-T wave patterns obtained with the 12L-ECG, the level of agreement was moderate with the Apple Watch (k=0.593) and substantial with the KardiaMobile 6L (k=0.651). Regarding the corrected QT interval, the correlation with 12L-ECG was strong for the Apple Watch (r=0.703 in lead II) and moderate for the KardiaMobile 6L (r=0.593). There was a slight overestimation of corrected QT interval with the Apple Watch and a subtle underestimation with the KardiaMobile 6L.

CONCLUSIONS

In patients with LQTS, the corrected QT interval and ST-T wave patterns obtained with the Apple Watch and the KardiaMobile 6L correlated with the 12L-ECG. Although wearable electrocardiogram devices cannot replace the 12L-ECG for the follow-up of these patients, they could be interesting additional monitoring tools.

The use of smartwatch electrocardiogram beyond arrhythmia detection

The adoption of wearables in medicine has expanded worldwide with a rapidly growing number of consumers and new features capable of real-time monitoring of health parameters such as the ability to record and transmit a single-lead electrocardiogram (ECG). Smartwatch ECGs are increasingly used but current smartwatches only screen for atrial fibrillation (AF). Most of the literature has focused on analyzing the smartwatch ECG accuracy for the detection of AF or other tachycardias. As with the conventional ECG, this tool may be used for many more purposes than only detection of AF. The objectives of this review are to describe the published literature regarding the accuracy and clinical value of recording a smartwatch ECG in other situations than diagnosis of tachycardia and discuss possible techniques to optimize the diagnostic yield.

An Artificial Intelligence Analysis of Electrocardiograms for the Clinical Diagnosis of Cardiovascular Diseases: A Narrative Review

Artificial intelligence (AI) applied to cardiovascular disease (CVD) is enjoying great success in the field of scientific research. Electrocardiograms (ECGs) are the cornerstone form of examination in cardiology and are the most widely used diagnostic tool because they are widely available, inexpensive, and fast. Applications of AI to ECGs, especially deep learning (DL) methods using convolutional neural networks (CNNs), have been developed in many fields of cardiology in recent years. Deep learning methods provide valuable support for rapid ECG interpretation, demonstrating a diagnostic capability overlapping with specialists in the diagnosis of CVD by a classical analysis of macroscopic changes in the ECG trace. Through photoplethysmography, wearable devices can obtain single-derivative ECGs for the recognition of AI-diagnosed arrhythmias. In addition, CNNs have been developed that recognize no macroscopic electrocardiographic changes and can predict, from a 12-lead ECG, atrial fibrillation, even from sinus rhythm; left and right ventricular function; hypertrophic cardiomyopathy; acute coronary syndromes; or aortic stenosis. The fields of application are many, but numerous are the limitations, mainly associated with the reliability of the acquired data, an inability to verify black box processes, and medico-legal and ethical problems. The challenge of modern medicine is to recognize the limitations of AI and overcome them.

The Reliability of the Apple Watch's Electrocardiogram

Background An electrocardiogram (ECG) is a standard tool used to detect various cardiovascular abnormalities. Detection sensitivity for atrial fibrillation (AF) was recently shown to be greatly increased by using short, intermittent ECG recordings. Modern mobile ECG recording devices that can monitor patients' heart activities around the clock have made this a reality. The Apple Watch is one of these portable ECG devices that can detect heart rhythms and is approved by the American FDA for screening and detecting AF. Objectives We compared the results of the Apple Watch I lead ECG with conventional ECG results to assess the sensitivity and specificity of the Apple Watch I lead ECG. We then determined the abnormalities that can be detected by the Apple Watch I lead ECG. Methods This study was conducted on outpatient cardiac clinics at King Abdullah bin Abdulaziz University Hospital (KAAUH), and Prince Sultan Cardiac Center (PSCC), from May to October 2021. A standard 12-lead ECG was recorded and compared with the Apple Watch I lead ECG. A total of 469 ECG comparisons were included in this study and evaluated by two investigators. The data on patient demographics, medical and medication history, and ECG data were reviewed and analyzed using IBM SPSS Statistics for Windows, Version 23 (Released 2015; IBM Corp., Armonk, New York, United States). Results No significant differences were seen between the Apple Watch and the 12-lead ECG in terms of the studied ECG characteristics. A significant and strong positive correlation between the heart rate measurements in the 12-lead ECG and Apple Watch ECG was documented. The most commonly found abnormalities in the Apple Watch ECG were AF in 37 (7.9%), followed by first-degree atrioventricular (AV) block in 32 (6.8%). The sensitivity of Apple Watch's automated interpretation to detect an AF was 99.54%, while the manual interpretation yielded a sensitivity of 100%. Conclusion The results of this study demonstrated a robust relationship between the 12-lead ECG and Apple Watch ECG in the diagnosis of arrhythmias. Consequently, cardiac patients may consider the Apple Watch ECG a trustworthy remote monitoring technique.

Feasibility and reliability of whintings scanwatch to record 4-lead Electrocardiogram: A comparative analysis with a standard ECG

The recent technological advances allowed us to produce some new wearable devices, such as smartphones and smartwatches (SW). These devices provide different services to their users through different software applications installed even in the SW or smartphones. Health monitoring functionalities, among these services, are nowadays the new technological fashion. In fact, the monitoring is ensured by the sensor incorporated in the SW. The SW allows the record of only one single lead Electrocardiogram (ECG), which is sufficient to screen or diagnosis of rhythm and conduction disorders, especially during the onset of cardiac symptoms, but insufficient for the detection of ischemic disease and cardiomyopathies. In this context, this paper aims to evaluate the feasibility, and reliability of a SW to obtain ECG recordings in comparison with a standard ECG. For that purpose, 140 patients were recruited for this analysis. At the first step, the 12 lead ECG followed with four lead SW-ECG; using the Withings Scanwatch device, were recorded in the same resting conditions. The four lead SW-ECG consists of Einthoven DI lead recorded with the SW, where the SW was on the left wrist and the right index finger on the crown, and three Wilson-type leads, in the which the V1 was recorded in the fourth right parasternal intercostal space, V3 was recorded in the fifth intercostal space on the midclavicular line, and V6 was recorded in the fifth intercostal space on the left midaxillary line with the right index finger placed on the crown and the left hand encompassing the right wrist. 700 ECGs recordings were collected and statistically analyzed in this study. In total, 97 % of the patients were able to obtain an ECG through the SW. A strong correlation was observed between the two recording methods concerning the duration of the studied parameters (r >90 %). The correlation coefficient showed that 33 out of 44 parameters have a strong correlation with the standard ECG. The similarity of the combined leads in the 4 established subgroups was significantly higher, meaning that increasing the number of leads would improve the detection of electrical anomalies. Our findings confirm the existing data on the high similarity between SW and standard 12-leads ECG. Despite SW not having the accuracy and utility of the standard ECG machine, they should be considered as an interesting screening tool for cardiac rhythm disorders, and a compelling solution to electrical documentation of general cardiac symptoms.

Contemporary wearable and handheld technology for the diagnosis of cardiac arrhythmias in Singapore

Twelve-lead electrocardiography (ECG) remains the gold standard for the diagnosis of cardiac arrhythmias. It provides a snapshot of the cardiac electrical activity while the leads are attached to the patient. As medical training is required to use the ECG machine, its use remains restricted to the clinic and hospital settings. These aspects limit the usefulness of 12-lead ECG in the diagnosis of cardiac arrhythmias, especially in individuals with short-lasting and infrequent paroxysmal symptoms. The introduction of ECG recording features in wearable and handheld smart devices has changed the paradigm of cardiac arrhythmia diagnosis, empowering patients to record their ECG as and when symptoms occur. This review describes contemporary ambulatory heart rhythm monitors commonly available in Singapore and their expanding role in the diagnosis of cardiac rhythm abnormalities.

Non-Invasive Risk Assessment and Prediction of Mortality in Patients Undergoing Coronary Artery Bypass Graft Surgery

Objectives: Heart rate turbulence (HRT) and T-wave alternans (TWA), non-invasive markers of cardiac autonomic dysfunction, and ventricular repolarization abnormality, reportedly, predict the risk of cardiovascular death after myocardial infarction. We investigated whether pre-operative assessment of HRT and/or TWA could predict long-term mortality following coronary artery bypass graft (CABG) surgery. Methods: From May 2010 to December 2017, patients undergoing elective CABG and receiving 24 h ambulatory electrocardiogram monitoring 1 to 5 days prior to CABG surgery were prospectively enrolled. Pre-operative HRT and TWA were measured using a 24 h ambulatory electrocardiogram. The relative risk of cardiac or overall death was assessed according to abnormalities of HRT, TWA, or left ventricular ejection fraction (LV EF). Results: During the mean follow-up period of 4.6 ± 3.9 years, 40 adjudicated overall (5.9%/yr) and 5 cardiac deaths (0.9%/yr) occurred in 146 enrolled patients (64.9 ± 9.3 years; 108 males). Patients with abnormal HRT exhibited significantly higher relative risks of cardiac death (adjusted hazard ratio [HR] 24.9, 95% confidence interval [CI] 1.46-427) and all-cause death (adjusted HR 5.77, 95% CI 2.34-14.2) compared to those with normal HRT. Moreover, abnormal HRT plus abnormal TWA and LV EF < 50% was associated with a greater elevation in cardiac and overall mortality risk. The predictive role of abnormal HRT with/without abnormal TWA for all-cause death was likely more prominent in patients with mildly reduced (35 to 50%) or preserved (≥50%) LV EF. Abnormal HRT plus abnormal TWA and LV EF < 50% showed high negative predictive value in cardiac and overall mortality risk. Conclusions: Assessment of pre-operative HRT and/or TWA predicted mortality risk in patients undergoing elective CABG. Combined analysis of HRT, TWA, and LVEF enhanced the prognostic power. In particular, the predictive value of HRT was enhanced in patients with preserved or mid-range LV EF.

Detection of QT interval prolongation using Apple Watch electrocardiogram in children and adolescents with congenital long QT syndrome

Background

Apple watch-derived electrocardiogram (awECG) may help identify prolongation of corrected QT (QTc) interval. This study aimed to determine its usefulness for assessment of prolongation of QTc interval in children and adolescents with long QT syndrome (LQTS).

Methods

Children and adolescents with and without LQTS were recruited for measurement of QTc intervals based on standard 12-lead (sECG) and awECG lead I, II and V5 tracings. Bland-Altman analysis of reproducibility, concordance assessment of T wave morphologies, and receiver operating characteristic (ROC) analysis of sensitivity and specificity of awECG-derived QTc interval for detecting QTc prolongation were performed.

Results

Forty-nine patients, 19 with and 30 without LQTS, aged 3-22 years were studied. The intraclass correlation coefficient was 1.00 for both intra- and inter-observer variability in the measurement of QTc interval. The awECG- and sECG-derived QTc intervals correlated strongly in all three leads (r = 0.90-0.93, all p < 0.001). Concordance between awECG and sECG in assessing T wave morphologies was 84% (16/19). For detection of QTc prolongation, awECG lead V5 had the best specificity (94.4% and 87.5%, respectively) and positive predictive value (87.5% and 80.0%, respectively), and for identification of patients with LQTS, awECG leads II and V5 had the greatest specificity (92.3%-94.1%) and positive predictive value (85.7% to 91.7%) in both males and females.

Conclusions

Apple Watch leads II and V5 tracings can be used for reproducible and accurate measurement of QTc interval, ascertainment of abnormal T wave morphologies, and detection of prolonged QTc interval in children and adolescents with LQTS.

Electrocardiogram Devices for Home Use: Technological and Clinical Scoping Review

BACKGROUND

Electrocardiograms (ECGs) are used by physicians to record, monitor, and diagnose the electrical activity of the heart. Recent technological advances have allowed ECG devices to move out of the clinic and into the home environment. There is a great variety of mobile ECG devices with the capabilities to be used in home environments.

OBJECTIVE

This scoping review aimed to provide a comprehensive overview of the current landscape of mobile ECG devices, including the technology used, intended clinical use, and available clinical evidence.

METHODS

We conducted a scoping review to identify studies concerning mobile ECG devices in the electronic database PubMed. Secondarily, an internet search was performed to identify other ECG devices available in the market. We summarized the devices' technical information and usability characteristics based on manufacturer data such as datasheets and user manuals. For each device, we searched for clinical evidence on the capabilities to record heart disorders by performing individual searches in PubMed and ClinicalTrials.gov, as well as the Food and Drug Administration (FDA) 510(k) Premarket Notification and De Novo databases.

RESULTS

From the PubMed database and internet search, we identified 58 ECG devices with available manufacturer information. Technical characteristics such as shape, number of electrodes, and signal processing influence the capabilities of the devices to record cardiac disorders. Of the 58 devices, only 26 (45%) had clinical evidence available regarding their ability to detect heart disorders such as rhythm disorders, more specifically atrial fibrillation.

CONCLUSIONS

ECG devices available in the market are mainly intended to be used for the detection of arrhythmias. No devices are intended to be used for the detection of other cardiac disorders. Technical and design characteristics influence the intended use of the devices and use environments. For mobile ECG devices to be intended to detect other cardiac disorders, challenges regarding signal processing and sensor characteristics should be solved to increase their detection capabilities. Devices recently released include the use of other sensors on ECG devices to increase their detection capabilities.

Using a Smartwatch to Record Precordial Electrocardiograms: A Validation Study

Smartwatches that support the recording of a single-lead electrocardiogram (ECG) are increasingly being used beyond the wrist, by placement on the ankle and on the chest. However, the reliability of frontal and precordial ECGs other than lead I is unknown. This clinical validation study assessed the reliability of an Apple Watch (AW) to obtain conventional frontal and precordial leads as compared to standard 12-lead ECGs in both subjects without known cardiac anomalies and patients with underlying heart disease. In 200 subjects (67% with ECG anomalies), a standard 12-lead ECG was performed, followed by AW recordings of the standard Einthoven leads (leads I, II, and III) and precordial leads V1, V3, and V6. Seven parameters (P, QRS, ST, and T-wave amplitudes, PR, QRS, and QT intervals) were compared through a Bland-Altman analysis, including the bias, absolute offset, and 95% limits of agreement. AW-ECGs recorded on the wrist but also beyond the wrist had similar durations and amplitudes compared to standard 12-lead ECGs. Significantly greater amplitudes were measured by the AW for R-waves in precordial leads V1, V3, and V6 (+0.094 mV, +0.149 mV, +0.129 mV, respectively, all p < 0.001), indicating a positive bias for the AW. AW can be used to record frontal, and precordial ECG leads, paving the way for broader clinical applications.

Accurate QT correction method from transfer entropy

Background

The QT interval in the electrocardiogram (ECG) is a fundamental risk measure for arrhythmic adverse cardiac events. However, the QT interval depends on the heart rate and must be corrected accordingly. The present QT correction (QTc) methods are either simple models leading to under- or overcorrection, or impractical in requiring long-term empirical data. In general, there is no consensus on the best QTc method.

Objective

We introduce a model-free QTc method-AccuQT-that computes QTc by minimizing the information transfer from R-R to QT intervals. The objective is to establish and validate a QTc method that provides superior stability and reliability without models or empirical data.

Methods

We tested AccuQT against the most commonly used QT correction methods by using long-term ECG recordings of more than 200 healthy subjects from PhysioNet and THEW databases.

Results

AccuQT overperforms the previously reported correction methods: the proportion of false-positives is reduced from 16% (Bazett) to 3% (AccuQT) for the PhysioNet data. In particular, the QTc variance is significantly reduced and thus the RR-QT stability is increased.

Conclusion

AccuQT has significant potential to become the QTc method of choice in clinical studies and drug development. The method can be implemented in any device recording R-R and QT intervals.

Comparison Between QT and Corrected QT Interval Assessment by an Apple Watch With the AccurBeat Platform and by a 12‑Lead Electrocardiogram With Manual Annotation: Prospective Observational Study

Background

Abnormal prolongation or shortening of the QT interval is associated with increased risk for ventricular arrhythmias and sudden cardiac death. For continuous monitoring, widespread use, and prevention of cardiac events, advanced wearable technologies are emerging as promising surrogates for conventional 12‑lead electrocardiogram (ECG) QT interval assessment. Previous studies have shown a good agreement between QT and corrected QT (QTc) intervals measured on a smartwatch ECG and a 12-lead ECG, but the clinical accuracy of computerized algorithms for QT and QTc interval measurement from smartwatch ECGs is unclear.

Objective

The prospective observational study compared the smartwatch-recorded QT and QTc assessed using AccurKardia’s AccurBeat platform with the conventional 12‑lead ECG annotated manually by a cardiologist.

Methods

ECGs were collected from healthy participants (without any known cardiovascular disease) aged >22 years. Two consecutive 30-second ECG readings followed by (within 15 minutes) a 10-second standard 12-lead ECG were recorded for each participant. Characteristics of the participants were compared by sex using a 2-sample t test and Wilcoxon rank sum test. Statistical comparisons of heart rate (HR), QT interval, and QTc interval between the platform and the 12-lead ECG, ECG lead I, and ECG lead II were done using the Wilcoxon sign rank test. Linear regression was used to predict QTc and QT intervals from the ECG based on the platform’s QTc/QT intervals with adjustment for age, sex, and difference in HR measurement. The Bland-Altman method was used to check agreement between various QT and QTc interval measurements.

Results

A total of 50 participants (32 female, mean age 46 years, SD 1 year) were included in the study. The result of the regression model using the platform measurements to predict the 12-lead ECG measurements indicated that, in univariate analysis, QT/QTc intervals from the platform significantly predicted QT/QTc intervals from the 12-lead ECG, ECG lead I, and ECG lead II, and this remained significant after adjustment for sex, age, and change in HR. The Bland-Altman plot results found that 96% of the average QTc interval measurements between the platform and QTc intervals from the 12-lead ECG were within the 95% confidence limit of the average difference between the two measurements, with a mean difference of –10.5 (95% limits of agreement –71.43, 50.43). A total of 94% of the average QT interval measurements between the platform and the 12-lead ECG were within the 95% CI of the average difference between the two measurements, with a mean difference of –6.3 (95% limits of agreement –54.54, 41.94).

Conclusions

QT and QTc intervals obtained by a smartwatch coupled with the platform’s assessment were comparable to those from a 12-lead ECG. Accordingly, with further refinements, remote monitoring using this technology holds promise for the identification of QT interval prolongation.

The Role of Contemporary Wearable and Handheld Devices in the Diagnosis and Management of Cardiac Arrhythmias

Cardiac arrhythmias are associated with significant morbidity, mortality and economic burden on the health care system. Detection and surveillance of cardiac arrhythmias using medical grade non-invasive methods (electrocardiogram, Holter monitoring) is the accepted standard of care. Whilst their accuracy is excellent, significant limitations remain in terms of accessibility, ease of use, cost, and a suboptimal diagnostic yield (up to ∼50%) which is critically dependent on the duration of monitoring. Contemporary wearable and handheld devices that utilise photoplethysmography and the electrocardiogram present a novel opportunity for remote screening and diagnosis of arrhythmias. They have significant advantages in terms of accessibility and availability with the potential of enhancing the diagnostic yield of episodic arrhythmias. However, there is limited data on the accuracy and diagnostic utility of these devices and their role in therapeutic decision making in clinical practice remains unclear. Evidence is mounting that they may be useful in screening for atrial fibrillation, and anecdotally, for the diagnosis of other brady and tachyarrhythmias. Recently, there has been an explosion of patient uptake of such devices for self-monitoring of arrhythmias. Frequently, the clinician is presented such information for review and comment, which may influence clinical decisions about treatment. Further studies are needed before incorporation of such technologies in routine clinical practice, given the lack of systematic data on their accuracy and utility. Moreover, challenges with regulation of quality standards and privacy remain. This state-of-the-art review summarises the role of novel ambulatory, commercially available, heart rhythm monitors in the diagnosis and management of cardiac arrhythmias and their expanding role in the diagnostic and therapeutic paradigm in cardiology.

Smart Consumer Wearables as Digital Diagnostic Tools: A Review

The increasing usage of smart wearable devices has made an impact not only on the lifestyle of the users, but also on biological research and personalized healthcare services. These devices, which carry different types of sensors, have emerged as personalized digital diagnostic tools. Data from such devices have enabled the prediction and detection of various physiological as well as psychological conditions and diseases. In this review, we have focused on the diagnostic applications of wrist-worn wearables to detect multiple diseases such as cardiovascular diseases, neurological disorders, fatty liver diseases, and metabolic disorders, including diabetes, sleep quality, and psychological illnesses. The fruitful usage of wearables requires fast and insightful data analysis, which is feasible through machine learning. In this review, we have also discussed various machine-learning applications and outcomes for wearable data analyses. Finally, we have discussed the current challenges with wearable usage and data, and the future perspectives of wearable devices as diagnostic tools for research and personalized healthcare domains.

Remote and wearable ECG devices with diagnostic abilities in adults: A state-of-the-science scoping review

The electrocardiogram (ECG) records the electrical activity in the heart in real time, providing an important opportunity to detecting various cardiac pathologies. The 12-lead ECG currently serves as the "standard" ECG acquisition technique for diagnostic purposes for many cardiac pathologies other than arrhythmias. However, the technical aspects of acquiring a 12-lead ECG are not easy. and its usage currently is restricted to trained medical personnel, which limits the scope of its usefulness. Remote and wearable ECG devices have attempted to bridge this gap by enabling patients to take their own ECG using a simplified method at the expense of a reduced number of leads, usually a single-lead ECG. In this review, we summarize the studies that investigated the use of remote ECG devices and their clinical utility in diagnosing cardiac pathologies. Eligible studies discussed Food and Drug Administration-cleared, commercially available devices that were validated in an adult population. We summarize technical logistics of signal quality and device reliability, dimensional and functional features, and diagnostic value. Our synthesis shows that reduced-set ECG wearables have huge potential for long-term monitoring, particularly if paired with real-time notification techniques. Such capabilities make them primarily useful for abnormal rhythm detection, and there is sufficient evidence that a remote ECG device can be superior to the traditional 12-lead ECG in diagnosing specific arrhythmias such as atrial fibrillation. However, this review identifies important challenges faced by this technology and highlights the limited availability of clinical research examining their usefulness.

Clinical Validation of Automated Corrected QT-Interval Measurements From a Single Lead Electrocardiogram Using a Novel Smartwatch

Introduction

The Withings Scanwatch (Withings SA, Issy les Moulineaux, France) offers automated analysis of the QTc. We aimed to compare automated QTc-measurements using a single lead ECG of a novel smartwatch (Withings Scanwatch, SW-ECG) with manual-measured QTc from a nearly simultaneously recorded 12-lead ECG.

Methods

We enrolled consecutive patients referred to a tertiary hospital for cardiac workup in a prospective, observational study. The QT-interval of the 12-lead ECG was manually interpreted by two blinded, independent cardiologists through the tangent-method. Bazett's formula was used to calculate QTc. Results were compared using the Bland-Altman method.

Results

A total of 317 patients (48% female, mean age 63 ± 17 years) were enrolled. HR-, QRS-, and QT-intervals were automatically calculated by the SW in 295 (93%), 249 (79%), and 177 patients (56%), respectively. Diagnostic accuracy of SW-ECG for detection of QTc-intervals ≥ 460 ms (women) and ≥ 440 ms (men) as quantified by the area under the curve was 0.91 and 0.89. The Bland-Altman analysis resulted in a bias of 6.6 ms [95% limit of agreement (LoA) -59 to 72 ms] comparing automated QTc-measurements (SW-ECG) with manual QTc-measurement (12-lead ECG). In 12 patients (6.9%) the difference between the two measurements was greater than the LoA.

Conclusion

In this clinical validation of a direct-to-consumer smartwatch we found fair to good agreement between automated-SW-ECG QTc-measurements and manual 12-lead-QTc measurements. The SW-ECG was able to automatically calculate QTc-intervals in one half of all assessed patients. Our work shows, that the automated algorithm of the SW-ECG needs improvement to be useful in a clinical setting.

Applications and Current Medico-Legal Challenges of Telemedicine in Ophthalmology

BACKGROUND

The digital revolution is redesigning the healthcare model, and telemedicine offers a good example of the best cost-effectiveness ratio. The COVID-19 pandemic has catalysed the use of the telemedicine. The aim of this review is to describe and discuss the role and the main applications of telemedicine in the ophthalmic clinical practice as well as the related medico-legal aspects.

METHODS

45 original articles and 5 reviews focused on this topic and published in English language from 1997 and 2021 were searched on the online databases of Pubmed, Scopus, Web of Sciences and Embase, by using the following key words: "telemedicine", "privacy", "ophthalmology", "COVID-19" and "informed consent".

RESULTS

Telemedicine is able to guarantee patient care using information and communication technologies. Technology creates an opportunity to link doctors with the aim of assessing clinical cases and maintaining high standards of care while performing and saving time as well. Ophthalmology is one of the fields in which telemedicine is most commonly used for patient management.

CONCLUSIONS

Telemedicine offers benefits to patients in terms of saving time and costs and avoiding physical contact; however, it is necessary to point out significant limitations such as the absence of physical examinations, the possibility of transmission failure and potential violations of privacy and confidentiality.

Remote Cardiac Rhythm Monitoring in the Era of Smart Wearables: Present Assets and Future Perspectives

Remote monitoring and control of heart function are of primary importance for patient evaluation and management, especially in the modern era of precision medicine and personalized approach. Breaking technological developments have brought to the frontline a variety of smart wearable devices, such as smartwatches, chest patches/straps, or sensors integrated into clothing and footwear, which allow continuous and real-time recording of heart rate, facilitating the detection of cardiac arrhythmias. However, there is great diversity and significant differences in the type and quality of the information they provide, thus impairing their integration into daily clinical practice and the relevant familiarization of practicing physicians. This review will summarize the different types and dominant functions of cardiac smart wearables available in the market. Furthermore, we report the devices certified by official American and/or European authorities and the respective sources of evidence. Finally, we comment pertinent limitations and caveats as well as the potential answers that flow from the latest technological achievements and future perspectives.

Assessment of Non-Invasive Measurements of Oxygen Saturation and Heart Rate with an Apple Smartwatch: Comparison with a Standard Pulse Oximeter

The most commonly used method to assess peripheral oxygen saturation (SpO2) in clinical practice is pulse oximetry. The smartwatch Apple Watch 6 was developed with a new sensor and an app that allows taking on-demand readings of blood oxygen and background readings, day and night. The present study aimed to assess the feasibility and agreement of the Apple Watch 6 compared with a standard SpO2 monitoring system to assess normal and pathological oxygen saturation. We recruited study participants with lung disease or cardiovascular disease and healthy subjects. A total of 265 subjects were screened for enrolment in this study. We observed a strong positive correlation between the smartwatch and the standard commercial device in the evaluation of SpO2 measurements (r = 0.89, p < 0.0001) and HR measurements (r = 0.98, p < 0.0001). A very good concordance was found between SpO2 (bias, −0.2289; SD, 1.66; lower limit, −3.49; and upper limit, 3.04) and HR (bias, −0.1052; SD, 2.93; lower limit, −5.84; and upper limit, 5.63) measured by the smartwatch in comparison with the standard commercial device using Bland−Altman analysis. We observed similar agreements and concordance even in the different subgroups. In conclusion, our study demonstrates that the wearable device used in the present study could be used to assess SpO2 in patients with cardiovascular or lung diseases and in healthy subjects.

Parents' perspectives on a smartwatch intervention for children with ADHD: Rapid deployment and feasibility evaluation of a pilot intervention to support distance learning during COVID-19

Distance learning in response to the COVID-19 pandemic presented tremendous challenges for many families. Parents were expected to support children's learning, often while also working from home. Students with Attention Deficit Hyperactivity Disorder (ADHD) are at particularly high risk for setbacks due to difficulties with organization and increased risk of not participating in scheduled online learning. This paper explores how smartwatch technology, including timing notifications, can support children with ADHD during distance learning due to COVID-19. We implemented a 6-week pilot study of a Digital Health Intervention (DHI) with ten families. The DHI included a smartwatch and a smartphone. Google calendars were synchronized across devices to guide children through daily schedules. After the sixth week, we conducted parent interviews to understand the use of smartwatches and the impact on children's functioning, and we collected physiological data directly from the smartwatch. Our results demonstrated that children successfully adopted the use of the smartwatch, and parents believed the intervention was helpful, especially in supporting the development of organizational skills in their children. Overall, we illustrate how even simple DHIs, such as using smartwatches to promote daily organization and task completion, have the potential to support children and families, particularly during periods of distance learning. We include practical suggestions to help professionals teach children with ADHD to use smartwatches to improve organization and task completion, especially as it applies to supporting remote instruction.