Smart watch applications in atrial fibrillation detection: Current state and future directions.

INTRODUCTION: Atrial fibrillation (Afib) is a prevalent chronic arrhythmia associated with severe complications, including stroke, heart failure, and increased mortality. This review explores the use of smartwatches for Afib detection, addressing the limitations of current monitoring methods and emphasizing the potential of wearable technology in revolutionizing healthcare. RESULTS/OBSERVATION: Current Afib detection methods, such as electrocardiography, have limitations in sensitivity and specificity. Smartwatches with advanced sensors offer continuous monitoring, improving the chances of detecting asymptomatic and paroxysmal Afib. The review meticulously examines major clinical trials studying Afib detection using smartwatches, including the landmark Apple Heart Study and ongoing trials such as the Heart Watch, Heartline, and Fitbit Heart Study. Detailed summaries of participant numbers, smartwatch devices used, and key findings are presented. It also comments on the cost-effectiveness and scalability of smartwatch-based screening, highlighting the potential to reduce healthcare costs and improve patient outcomes. CONCLUSION/RELEVANCE: The integration of wearable technology into healthcare can lead to earlier diagnosis, improved patient engagement, and enhanced cardiac health monitoring. Despite ethical considerations and disparities, the potential benefits outweigh the challenges. This review calls for increased awareness, collaboration with insurance companies, and ongoing research efforts to optimize smartwatch accuracy and encourage widespread adoption of Afib detection. With insights from major trials, this review serves as a comprehensive reference for healthcare professionals and policymakers, guiding future strategies in the early diagnosis and management of atrial fibrillation.

Assessing the current utilization status of wearable devices in clinical research.

BACKGROUND/AIMS: Information regarding the use of wearable devices in clinical research, including disease areas, intervention techniques, trends in device types, and sample size targets, remains elusive. Therefore, we conducted a comprehensive review of clinical research trends related to wristband wearable devices in research planning and examined their applications in clinical investigations. METHODS: As this study identified trends in the adoption of wearable devices during the planning phase of clinical research, including specific disease areas and targeted number of intervention cases, we searched ClinicalTrials.gov-a prominent platform for registering and disseminating clinical research. Since wrist-worn devices represent a large share of the market, we focused on wrist-worn devices and selected the most representative models among them. The main analysis focused on major wearable devices to facilitate data analysis and interpretation, but other wearables were also surveyed for reference. We searched ClinicalTrials.gov with the keywords "ActiGraph,""Apple Watch,""Empatica,""Fitbit,""Garmin," and "wearable devices" to obtain studies published up to 21 August 2022. This initial search yielded 3214 studies. After excluding duplicate National Clinical Trial studies (the overlap was permissible among different device types except for wearable devices), our analysis focused on 2930 studies, including simple, time-series, and type-specific assessments of various variables. RESULTS: Overall, an increasing number of clinical studies have incorporated wearable devices since 2012. While ActiGraph and Fitbit initially dominated this landscape, the use of other devices has steadily increased, constituting approximately 10% of the total after 2015. Observational studies outnumbered intervention studies, with behavioral and device-based interventions being particularly prevalent. Regarding disease types, cancer and cardiovascular diseases accounted for approximately 20% of the total. Notably, 114 studies adopted multiple devices simultaneously within the context of their clinical investigations. CONCLUSIONS: Our findings revealed that the utilization of wearable devices for data collection and behavioral interventions in various disease areas has been increasing over time since 2012. The increase in the number of studies over the past 3 years has been particularly significant, suggesting that this trend will continue to accelerate in the future. Devices and their evaluation methods that have undergone thorough validation, confirmed their accuracy, and adhered to established legal regulations will likely assume a pivotal role in evaluations, allowing for remote clinical trials. Moreover, behavioral intervention therapy utilizing apps is becoming more extensive, and we expect to see more examples that will lead to their approval as programmed medical devices in the future.

Tracking Well-Being: A Comprehensive Analysis of Physical Activity and Mental Health in College Students Across COVID-19 Phases Using Ecological Momentary Assessment.

Physical activity (PA) is a critical factor in maintaining mental health, particularly among college students who are vulnerable to stress, anxiety, and mood disturbances. The COVID-19 pandemic introduced unprecedented disruptions to daily routines. The purpose of this cohort study was to examine the longitudinal PA behaviors before and during COVID-19 using device-based assessment in a sample of college students. The participants were a convenience sample of 1-year college students from the University of Vermont Wellness Environment study. A daily survey was distributed to the participants every night on a study app measured three mental health outcomes of mood, anxiety, and stress for 16 weeks. Participants wore Apple Watches to monitor PA. A total of 167 participants completed at least 50% of daily surveys and had 20 days of valid Apple Watch data, which resulted in 11 387 participant-days' of observations. Changes in average daily moderate-to-vigorous physical activity (MVPA) and step counts were examined week-over-week from an 8-week period before COVID to an 8-week period during COVID using cluster-robust piecewise regression (16-weeks total). Linear mixed models examined the association between PA and mental health outcomes, while also examining the moderating influence of COVID phase. Significantly lower MVPA was observed from the end of pre-COVID to start of COVID by -18.2 min/day (p < 0.001) and significantly fewer steps/day was observed from end of pre-COVID to start of COVID by -3277 steps/day (p < 0.001). An MVPA "catch-up" effect was observed as there were small but positive week-over-week improvements during COVID for MVPA (b = 1.32 min/day, p < 0.001). The influence of COVID-19 phases had a notable impact on the relationships between PA/exercise and mental health outcomes. A discernible trend emerged, indicating stronger connections during the COVID period for anxiety and stress compared to the pre-COVID era. Interestingly, the moderating effect of COVID was opposite for mood and exercise. The COVID-19 pandemic led to a dramatic decline in PA among college students, coinciding with a period of heightened stress and anxiety. Despite a slight recovery in PA levels during the pandemic, the strengthened association between exercise and anxiety/stress during this time underscores the vital role of PA in promoting mental health. These findings highlight the importance of implementing behavior change strategies to maintain and promote student wellbeing.

Diagnostic accuracy of apple watch ECG outputs in identifying dysrhythmias: A comparison with 12-Lead ECG in emergency department.

BACKGROUND: Wearable devices, particularly smartwatches like the Apple Watch (AW), can record important cardiac information, such as single­lead electrocardiograms (ECGs). Although they are increasingly used to detect conditions such as atrial fibrillation (AF), research on their effectiveness in detecting a wider range of dysrhythmias and abnormal ECG findings remains limited. The primary objective of this study is to evaluate the accuracy of the AW in detecting various cardiac rhythms by comparing it with standard ECG's lead-I. METHODS: This single-center prospective observational study was conducted in a tertiary care emergency department (ED) between 1.10.2023 and 31.10.2023. The study population consisted of all patients assessed in the critical care areas of the ED, all of whom underwent standard 12­lead ECGs for various clinical reasons. Participants in the study were included consecutively. An AW was attached to patients' wrists and an ECG lead-I printout was obtained. Heart rate, rhythm and abnormal findings were evaluated and compared with the lead-I of standard ECG. Two emergency medicine specialists performed the ECG evaluations. Rhythms were categorized as normal sinus rhythm and abnormal rhythms, while ECG findings were categorized as the presence or absence of abnormal findings. AW and 12­lead ECG outputs were compared using the McNemar test. Predictive performance analyses were also performed for subgroups. Bland-Altman analysis using absolute mean differences and concordance correlation coefficients was used to assess the level of heart rate agreement between devices. RESULTS: The study was carried out on 721 patients. When analyzing ECG rhythms and abnormal findings in lead-I, the effectiveness of AW in distinguishing between normal and abnormal rhythms was similar to standard ECGs (p = 0.52). However, there was a significant difference between AW and standard ECGs in identifying abnormal findings in lead-I (p < 0.05). Using Bland-Altman analysis for heart rate assessment, the absolute mean difference for heart rate was 0.81 ± 6.12 bpm (r = 0.94). There was strong agreement in 658 out of 700 (94%) heart rate measurements. CONCLUSION: Our study indicates that the AW has the potential to detect cardiac rhythms beyond AF. ECG tracings obtained from the AW may help evaluate cardiac rhythms prior to the patient's arrival in the ED. However, further research with a larger patient cohort is essential, especially for specific diagnoses.

Oxygen saturation measurement in cyanotic heart disease with the Apple watch.

BACKGROUND: Accurate measurement of transcutaneous oxygen saturation is important for the assessment of cyanosis in CHD. Aim of this study was the evaluation of a supplementary transcutaneous oxygen saturation measurement with an Apple watch® in children with cyanotic heart disease. MATERIAL AND METHODS: During a six-minute walk test, measurement of transcutaneous oxygen saturation was performed simultaneously with an Oximeter (Nellcor, Medtronic, USA) and an Apple watch® Series 7 (Apple inc, USA) in 36 children with cyanotic heart disease. RESULTS: Median age was 9.2 (IQR 5.7-13.8) years. Transcutaneous oxygen saturation measurement with the Apple watch® was possible in 35/36 and 34/36 subjects before and after six-minute walk test. Children, in whom Apple watch® measurement was not possible, had a transcutaneous oxygen saturation < 85% on oximeter. Before six-minute walk test, median transcutaneous oxygen saturation was 93 (IQR 91-97) % measured by oximeter and 95 (IQR 93-96) % by the Apple watch®. After a median walking distance of 437 (IQR 360-487) m, transcutaneous oxygen saturation dropped to 92 (IQR 88-95, p < 0.001) % by oximeter and to 94 (IQR 90-96, p = 0.013) % measured with the Apple watch®. CONCLUSION: In children with mild cyanosis measurement of transcutaneous oxygen saturation with an Apple watch® showed only valid results if transcutaneous oxygen saturation was > 85%, with higher values being measured with the smart watch. In children with moderate or severe cyanosis transcutaneous oxygen saturation, measurement with the Apple watch® was not reliable and cannot be recommended to monitor oxygen saturation at home.

The Validity of Apple Watch Series 9 and Ultra 2 for Serial Measurements of Heart Rate Variability and Resting Heart Rate.

The widespread use of wearable devices has enabled continuous monitoring of biometric data, including heart rate variability (HRV) and resting heart rate (RHR). However, the validity of these measurements, particularly from consumer devices like Apple Watch, remains underexplored. This study aimed to validate HRV measurements obtained from Apple Watch Series 9 and Ultra 2 against the Polar H10 chest strap paired with the Kubios HRV software, which together served as the reference standard. A prospective cohort of 39 healthy adults provided 316 HRV measurements over a 14-day period. Generalized Estimating Equations were used to assess the difference in HRV between devices, accounting for repeated measures. Apple Watch tended to underestimate HRV by an average of 8.31 ms compared to the Polar H10 (p = 0.025), with a mean absolute percentage error (MAPE) of 28.88% and a mean absolute error (MAE) of 20.46 ms. The study found no significant impact of RHR discrepancies on HRV differences (p = 0.156), with RHR showing a mean difference of -0.08 bpm, an MAPE of 5.91%, and an MAE of 3.73 bpm. Equivalence testing indicated that the HRV measurements from Apple Watch did not fall within the pre-specified equivalence margin of ±10 ms. Despite accurate RHR measurements, these findings underscore the need for improved HRV algorithms in consumer wearables and caution in interpreting HRV data for clinical or performance monitoring.

Accuracy of Three Commercial Wearable Devices for Sleep Tracking in Healthy Adults.

Sleep tracking by consumers is becoming increasingly prevalent; yet, few studies have evaluated the accuracy of such devices. We sought to evaluate the accuracy of three devices (Oura Ring Gen3, Fitbit Sense 2, and Apple Watch Series 8) compared to the gold standard sleep assessment (polysomnography (PSG)). Thirty-five participants (aged 20-50 years) without a sleep disorder were enrolled in a single-night inpatient study, during which they wore the Oura Ring, Fitbit, and Apple Watch, and were monitored with PSG. For detecting sleep vs. wake, the sensitivity was ≥95% for all devices. For discriminating between sleep stages, the sensitivity ranged from 50 to 86%, as follows: Oura ring sensitivity 76.0-79.5% and precision 77.0-79.5%; Fitbit sensitivity 61.7-78.0% and precision 72.8-73.2%; and Apple sensitivity 50.5-86.1% and precision 72.7-87.8%. The Oura ring was not different from PSG in terms of wake, light sleep, deep sleep, or REM sleep estimation. The Fitbit overestimated light (18 min; p < 0.001) sleep and underestimated deep (15 min; p < 0.001) sleep. The Apple underestimated the duration of wake (7 min; p < 0.01) and deep (43 min; p < 0.001) sleep and overestimated light (45 min; p < 0.001) sleep. In adults with healthy sleep, all the devices were similar to PSG in the estimation of sleep duration, with the devices also showing moderate to substantial agreement with PSG-derived sleep stages.

Apple Watch 6 vs. Galaxy Watch 4: A Validity Study of Step-Count Estimation in Daily Activities.

The purpose of this study was to examine the validity of two wearable smartwatches (the Apple Watch 6 (AW) and the Galaxy Watch 4 (GW)) and smartphone applications (Apple Health for iPhone mobiles and Samsung Health for Android mobiles) for estimating step counts in daily life. A total of 104 healthy adults (36 AW, 25 GW, and 43 smartphone application users) were engaged in daily activities for 24 h while wearing an ActivPAL accelerometer on the thigh and a smartwatch on the wrist. The validities of the smartwatch and smartphone estimates of step counts were evaluated relative to criterion values obtained from an ActivPAL accelerometer. The strongest relationship between the ActivPAL accelerometer and the devices was found for the AW (r = 0.99, p < 0.001), followed by the GW (r = 0.82, p < 0.001), and the smartphone applications (r = 0.93, p < 0.001). For overall group comparisons, the MAPE (Mean Absolute Percentage Error) values (computed as the average absolute value of the group-level errors) were 6.4%, 10.5%, and 29.6% for the AW, GW, and smartphone applications, respectively. The results of the present study indicate that the AW and GW showed strong validity in measuring steps, while the smartphone applications did not provide reliable step counts in free-living conditions.

Smartwatch: Looking beyond what you see.

BACKGROUND: The clinical utility of the Apple Inc.® smartwatch in scenarios beyond detecting atrial fibrillation has been debated. Although the device has the capability to record electrocardiograms (ECG) and detect arrhythmias, voltage limitations hinder its accuracy in measuring real voltage when recording precordial leads. This limitation poses challenges for its clinical use in diagnosing ischemia and screening cardiomyopathies. This review aims to analyze the ECG recording capacity of the Apple Watch, investigate the reasons for voltage limitations, and explore alternative approaches for its use in these clinical scenarios. METHODS: A comprehensive literature review was conducted to examine the ECG recording capacity of the Apple Watch and the limitations encountered when recording precordial leads. Data in CSV format files were analyzed to gain insights into the underlying causes of voltage limitations. RESULTS: The Apple Watch demonstrates effectiveness in detecting cardiac arrhythmias such as atrial fibrillation using photoplethysmography and ECG recording. However, voltage limitations during precordial lead recordings impede accurate voltage measurement, thereby limiting its clinical utility. Analysis of the data stored in the CSV files revealed that these voltage limitations are primarily attributed to the presentation format. Exploring alternative approaches for data processing could potentially overcome this challenge. CONCLUSIONS: This review highlights the potential for addressing voltage limitations through alternative data processing approaches. Further research is necessary to identify suitable alternatives that enable the Apple Watch to be effectively utilized in these clinical scenarios.

Accuracy of the Apple Watch Oxygen Saturation Measurement in Adults and Children with Congenital Heart Disease.

As new customer health devices have been spread throughout the consumer market in recent years, it now needs to be evaluated if they also fulfill the requirements of clinical use. The Apple Watch Series 6 provides a new health feature with its oxygen saturation measurement. The aim of this prospective, investigator-initiated, single-arm study was to compare transcutaneous oxygen saturation measurements using the Apple Watch 6 with the conventional method of pulse oximetry in patients with congenital heart disease. Patients of any age presenting at the Leipzig Heart Center, Department for pediatric cardiology, were included. After obtaining informed consent, the routine oxygen saturation measurement with the pulse oximeter was taken and simultaneously three measurements with the Apple Watch. A total of 508 patients were enrolled. Comparing children and adults in terms of measurement success shows a statistically significant difference with a higher proportion of unsuccessful measurements in children, but no difference concerning correct versus incorrect Apple Watch measurements. Noticeable, strapping on the watch properly around the patient's wrists significantly improved the measurements compared to a watch only laid on. The study demonstrated that oxygen saturation measurement with the Apple Watch 6 is not yet up to the medical standard of pulse oximetry, too large a proportion of the measurements remain either unsuccessful or incorrect. While a high proportion of unsuccessful measurements in children can be attributed to movement, the cause in adults usually remains unclear. Further influencing factors on a correct, or successful measurement could not be found.

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.

Improving Automatic Smartwatch Electrocardiogram Diagnosis of Atrial Fibrillation by Identifying Regularity within Irregularity.

Smartwatches equipped with automatic atrial fibrillation (AF) detection through electrocardiogram (ECG) recording are increasingly prevalent. We have recently reported the limitations of the Apple Watch (AW) in correctly diagnosing AF. In this study, we aim to apply a data science approach to a large dataset of smartwatch ECGs in order to deliver an improved algorithm. We included 723 patients (579 patients for algorithm development and 144 patients for validation) who underwent ECG recording with an AW and a 12-lead ECG (21% had AF and 24% had no ECG abnormalities). Similar to the existing algorithm, we first screened for AF by detecting irregularities in ventricular intervals. However, as opposed to the existing algorithm, we included all ECGs (not applying quality or heart rate exclusion criteria) but we excluded ECGs in which we identified regular patterns within the irregular rhythms by screening for interval clusters. This "irregularly irregular" approach resulted in a significant improvement in accuracy compared to the existing AW algorithm (sensitivity of 90% versus 83%, specificity of 92% versus 79%, p < 0.01). Identifying regularity within irregular rhythms is an accurate yet inclusive method to detect AF using a smartwatch ECG.

A Clinician's Guide to Smartwatch "Interrogation".

PURPOSE OF REVIEW: Wearable technology is rapidly evolving and the data that it can provide regarding an individual's health is becoming increasingly important for clinicians to consider. The purpose of this review is to help inform health care providers of the benefits of smartwatch interrogation, with a focus on reviewing the various parameters and how to apply the data in a meaningful way. RECENT FINDINGS: This review details interpretation of various parameters found commonly in newer smartwatches such as heart rate, step count, ECG, heart rate recovery (HRR), and heart rate variability (HRV), while also discussing potential pitfalls that a clinician should be aware of. Smartwatch interrogation is becoming increasingly relevant as the continuous data it provides helps health care providers make more informed decisions regarding diagnosis and treatment. For this reason, we recommend health care providers familiarize themselves with the technology and integrate it into clinical practice.

Accuracy and Systematic Biases of Heart Rate Measurements by Consumer-Grade Fitness Trackers in Postoperative Patients: Prospective Clinical Trial.

BACKGROUND: Over the recent years, technological advances of wrist-worn fitness trackers heralded a new era in the continuous monitoring of vital signs. So far, these devices have primarily been used for sports. OBJECTIVE: However, for using these technologies in health care, further validations of the measurement accuracy in hospitalized patients are essential but lacking to date. METHODS: We conducted a prospective validation study with 201 patients after moderate to major surgery in a controlled setting to benchmark the accuracy of heart rate measurements in 4 consumer-grade fitness trackers (Apple Watch 7, Garmin Fenix 6 Pro, Withings ScanWatch, and Fitbit Sense) against the clinical gold standard (electrocardiography). RESULTS: All devices exhibited high correlation (r≥0.95; P<.001) and concordance (rc≥0.94) coefficients, with a relative error as low as mean absolute percentage error <5% based on 1630 valid measurements. We identified confounders significantly biasing the measurement accuracy, although not at clinically relevant levels (mean absolute error<5 beats per minute). CONCLUSIONS: Consumer-grade fitness trackers appear promising in hospitalized patients for monitoring heart rate. TRIAL REGISTRATION: ClinicalTrials.gov NCT05418881; https://www.clinicaltrials.gov/ct2/show/NCT05418881.

Accuracy of the Apple Watch iECG in Children With and Without Congenital Heart Disease.

The development of smart technologies paves the way for new diagnostic modalities. The Apple Watch provides an FDA approved iECG function for users from 22 years of age. Yet, there are currently no data on the accuracy of the Apple Watch iECG in children. While arrhythmias are a frequent phenomenon in children, especially those with congenital heart disease, the increasing spread of smart watches provides the possibility to use a smart watch as mobile event recorder in case of suspected arrhythmia. This may help to provide valuable information to the treating physician, without having the patient to come to the hospital. Necessary treatment adjustments might be provided without timely delay. The aim of this study was therefore to evaluate the agreement of measured values of rate, interval, and amplitude with those obtained by a diagnostic quality ECG recording to an Apple Watch iECG in children with and without congenital heart disease. In this prospective, single-arm study, consecutive patients aged 0-16 years presenting to the Heart Center Leipzig, Department for pediatric cardiology were included. After obtaining informed consent from participants' parents, a 12-lead ECG and an iECG using an Apple Watch were performed. Cardiac rhythm was classified, amplitudes and timing intervals were measured and analyzed in iECG and 12-lead ECG for comparability. These measurements were performed blinded to the patients' history by two experienced pediatric cardiologists. Patient demographic data, medical and cardiac history were assessed. 215 children between 0 and 16 years were enrolled. Comparison of amplitudes and timing intervals between ECG and iECG showed excellent correlation (K > 0.7, p < 0.01) in all parameters except for the p-waves. Automatic rhythm classification was inferior to manual interpretation of ECG / iECG, while iECG interpretation was reliable in 94.86% of cases. The study demonstrates equal quality of the Apple Watch derived iECG compared to a lead I in 12-lead ECG in children of all age groups and independent from cardiac anatomy.

Accuracy of the Apple Watch single-lead ECG recordings in pre-term neonates.

INTRODUCTION: Telemedicine gained an increasing use throughout the last years. Lifestyle tools like the Apple watch seem to have an increasing spread even in remote areas and underdeveloped regions. The increasing availability of these tools offers the chance to use the health care functions of these devices to improve provision of professional medical care. First data on the use of the Apple Watch as a remote monitoring device in children have been reported, showing good acceptability and usability of the Apple Watch for symptom monitoring in children. This study aimed to evaluate the accuracy of the Apple Watch iECG in comparison to a standard 12-lead ECG in pre-term babies. METHODS: In this prospective, single-arm study, consecutive preterm neonates hospitalised in Leipzig University Hospital neonatal ICU were eligible. A 12-lead ECG and an iECG using Apple Watch 4 were performed. iECG and 12-lead ECG measurements were performed by a paediatric cardiologist. Cardiac rhythm was classified and amplitudes and timing intervals were analysed for comparability. RESULTS: Fifty preterm neonates, gestational week (23-36 weeks), and body weight (0.65-3.09 kg) were enrolled. Overall good quality and excellent correlation of the Apple Watch generated iECG in comparison to the standard 12-lead ECG could be demonstrated (p < 0.001). When interpreted by a paediatric cardiologist, a correct rhythm classification could be done in 100% of cases. CONCLUSION: The Apple Watch iECG seems to be a valuable tool to record an ECG comparable to lead I of the standard 12-lead ECG even in pre-term neonates. With a widespread availability and excellent connectivity, the Apple Watch iECG function may provide practitioners with a tool to send an iECG for interpretation to a paediatric cardiac specialist.

Effects of Missing Data on Heart Rate Variability Metrics.

Heart rate variability (HRV) has been studied for decades in clinical environments. Currently, the exponential growth of wearable devices in health monitoring is leading to new challenges that need to be solved. These devices have relatively poor signal quality and are affected by numerous motion artifacts, with data loss being the main stumbling block for their use in HRV analysis. In the present paper, it is shown how data loss affects HRV metrics in the time domain and frequency domain and Poincaré plots. A gap-filling method is proposed and compared to other existing approaches to alleviate these effects, both with simulated (16 subjects) and real (20 subjects) missing data. Two different data loss scenarios have been simulated: (i) scattered missing beats, related to a low signal to noise ratio; and (ii) bursts of missing beats, with the most common due to motion artifacts. In addition, a real database of photoplethysmography-derived pulse detection series provided by Apple Watch during a protocol including relax and stress stages is analyzed. The best correction method and maximum acceptable missing beats are given. Results suggest that correction without gap filling is the best option for the standard deviation of the normal-to-normal intervals (SDNN), root mean square of successive differences (RMSSD) and Poincaré plot metrics in datasets with bursts of missing beats predominance (p<0.05), whereas they benefit from gap-filling approaches in the case of scattered missing beats (p<0.05). Gap-filling approaches are also the best for frequency-domain metrics (p<0.05). The findings of this work are useful for the design of robust HRV applications depending on missing data tolerance and the desired HRV metrics.

Feasibility and Reliability of Smartwatch to Obtain Precordial Lead Electrocardiogram Recordings.

The Apple Watch is capable of recording single-lead electrocardiograms (ECGs). To incorporate such devices in routine medical care, the reliability of such devices to obtain precordial leads needs to be validated. The purpose of this study was to assess the feasibility and reliability of a smartwatch (SW) to obtain precordial leads compared to standard ECGs. We included 100 participants (62 male, aged 62.8 ± 13.1 years) with sinus rhythm and recorded a standard 12-lead ECG and the precordial leads with the Apple Watch. The ECGs were quantitively compared. A total of 98 patients were able to record precordial leads without assistance. A strong correlation was observed between the amplitude of the standard and SW-ECGs' waves, in terms of P waves, QRS-complexes, and T waves (all p-values < 0.01). A significant correlation was observed between the two methods regarding the duration of the ECG waves (all p-values < 0.01). Assessment of polarity showed a significant and a strong concordance between the ECGs' waves in all six leads (91-100%, all p-values < 0.001). In conclusion, 98% of patients were able to record precordial leads using a SW without assistance. The SW is feasible and reliable for obtaining valid precordial-lead ECG recordings as a validated alternative to a standard ECG.

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.

Reliability and agreement between two wearable inertial sensor devices for measurement of arm activity during walking and running gait.

STUDY DESIGN: This is a validation study. BACKGROUND: Tracking limb movement with body worn sensors allows clinicians to measure limb dynamics to guide treatment for patients with movement disorders. The current gold standard, 3-dimensional optical motion capture, is costly, time-consuming, requires specific training, and is conducted in specialized laboratories. PURPOSE: The purpose of our study was to a compare consumer-grade inertial sensor to a laboratory-grade sensor to provide additional methods for capturing limb dynamics. METHODS: The participants wore an Apple Watch and a laboratory-grade Xsens sensor on each wrist during 3 conditions: walk, fast-walk, and run. Acceleration data were collected simultaneously on each device per wrist for all conditions. Intraclass correlation coefficients and Bland-Altman plots were calculated to measure intra-/interdevice reliability, evaluate bias, and limits of agreement. RESULTS: Intradevice ICCs showed good reliability during walk and fast-walk (0.79-0.87) and excellent reliability during run (0.94-0.97) conditions. Inter-device ICCs yielded moderate reliability during walk (0.52 ± 0.22) and excellent reliability in fast-walk and run (0.93 ± 0.02, 1.00 ± 0.01) conditions. Bland-Altman plots showed small biases with 90% or more of the data contained within the limits of agreement. DISCUSSION: Our study demonstrates reliability and agreement between the two devices, suggesting that both can reliably capture upper extremity motion data during gait trials. CONCLUSION: Our findings support further study of consumer-grade motion trackers to measure arm activity for clinical use. These devices are inexpensive, user-friendly, and allow for data collection outside of the laboratory.