Accuracy of the Apple Watch 4 to Measure Heart Rate in Patients With Atrial Fibrillation

Investigating Measurement Equivalence of Smartphone Sensor-Based Assessments: Remote, Digital, Bring-Your-Own-Device Study

BACKGROUND

Floodlight Open is a global, open-access, fully remote, digital-only study designed to understand the drivers and barriers in deployment and persistence of use of a smartphone app for measuring functional impairment in a naturalistic setting and broad study population.

OBJECTIVE

This study aims to assess measurement equivalence properties of the Floodlight Open app across operating system (OS) platforms, OS versions, and smartphone device models.

METHODS

Floodlight Open enrolled adult participants with and without self-declared multiple sclerosis (MS). The study used the Floodlight Open app, a "bring-your-own-device" (BYOD) solution that remotely measured MS-related functional ability via smartphone sensor-based active tests. Measurement equivalence was assessed in all evaluable participants by comparing the performance on the 6 active tests (ie, tests requiring active input from the user) included in the app across OS platforms (iOS vs Android), OS versions (iOS versions 11-15 and separately Android versions 8-10; comparing each OS version with the other OS versions pooled together), and device models (comparing each device model with all remaining device models pooled together). The tests in scope were Information Processing Speed, Information Processing Speed Digit-Digit (measuring reaction speed), Pinching Test (PT), Static Balance Test, U-Turn Test, and 2-Minute Walk Test. Group differences were assessed by permutation test for the mean difference after adjusting for age, sex, and self-declared MS disease status.

RESULTS

Overall, 1976 participants using 206 different device models were included in the analysis. Differences in test performance between subgroups were very small or small, with percent differences generally being ≤5% on the Information Processing Speed, Information Processing Speed Digit-Digit, U-Turn Test, and 2-Minute Walk Test; <20% on the PT; and <30% on the Static Balance Test. No statistically significant differences were observed between OS platforms other than on the PT (P<.001). Similarly, differences across iOS or Android versions were nonsignificant after correcting for multiple comparisons using false discovery rate correction (all adjusted P>.05). Comparing the different device models revealed a statistically significant difference only on the PT for 4 out of 17 models (adjusted P≤.001-.03).

CONCLUSIONS

Consistent with the hypothesis that smartphone sensor-based measurements obtained with different devices are equivalent, this study showed no evidence of a systematic lack of measurement equivalence across OS platforms, OS versions, and device models on 6 active tests included in the Floodlight Open app. These results are compatible with the use of smartphone-based tests in a bring-your-own-device setting, but more formal tests of equivalence would be needed.

The use of Apple smartwatches to obtain vital signs readings in surgical patients

Wearable devices, such as Apple smartwatches, have been widely used for medical and healthcare purposes. Although they provide a wide array of applications, such as heart rate and oxygen saturation determination, only a few studies have demonstrated the reliability and accuracy of these devices. Hence, this study aimed to assess the reliability of the Apple smartwatch series 8 (ASWs8) in determining heart rate and oxygen saturation against conventional monitoring devices (CMD). This cross-section study encompassed a cohort of 52 patients from King Abdulaziz University Dental Hospital in Jeddah, Saudi Arabia. We collected heart rate and oxygen saturation data via the ASWs8 and CMD. Subsequently, the mean measurement of each parameter was compared and evaluated for any significant differences. The ASWs8 yielded a mean heart rate measurement of 83.04 ± 13.4 BPM, while CMD produced 82.81 ± 13.5 BPM. The mean difference stood at - 0.23 ± 0.7 BPM, with limits of agreement ranging from - 25 to 25%. No significant difference emerged in the determination of heart rate (Cronbach α = 0.999, p = 0.311). Additionally, the ASWs8 recorded an average oxygen saturation measurement of 97.10 ± 1.6%, compared to CMD's 98.23 ± 1.0%. This resulted in a mean difference of 0.74% and limits of agreement spanning from - 3 to 1%. Once again, the analysis unveiled that no statistically significant difference existed in the obtained oxygen saturation levels (Cronbach α = 0.735, p = 0.094). In this study, we have uncovered compelling evidence that the ASWs8 stands as a reliable and accurate device. It exhibits strong concordance with the CMD, rendering it suitable for continuous vital signs, particularly for heart rate and oxygen saturation. Furthermore, this device can play a pivotal role in clinical management, facilitating the early detection of abnormal physiological parameters among pre-operative patients, thereby potentially reducing hospital admissions.

Model-based estimation of heart movements using microwave Doppler radar sensor

BACKGROUND

Heart rate is one of the most crucial vital signs and can be measured remotely using microwave Doppler radar. As the distance between the body and the Doppler radar sensor increases, the output signal weakens, making it difficult to extract heartbeat waveforms. In this study, we propose a new template-matching method that addresses this issue by simulating Doppler radar signals. This method extracts the heartbeat waveform with higher accuracy while the participant is naturally sitting in a chair.

METHODS

An extended triangular wave model was created as a mathematical representation of cardiac physiology, taking into account heart movements. The Doppler radar output signal was then simulated based on this model to automatically obtain a template for one cycle. The validity of the proposed method was confirmed by calculating the PPIs using the template and comparing their accuracy to the R-R intervals (RRIs) of the electrocardiogram for five participants and by analyzing the signals of eight participants in their natural state using the mathematical model of heart movements. All measurements were conducted from a distance of 500 mm.

RESULTS

The correlation coefficients between the RRIs of the electrocardiogram and the PPIs using the proposed method were examined for five participants. The correlation coefficients were 0.93 without breathing and 0.70 with breathing. This demonstrates a higher correlation considering the long distance of 500 mm, and the fact that body movements were not specifically restricted, suggesting that the proposed method can successfully estimate RRI. The average correlation coefficients, calculated between the Doppler output signals and the templates for each of the eight participants, exceeded 0.95. Overall, the proposed method showed higher correlation coefficients than those reported in previous studies, indicating that our method performed well in extracting heartbeat waveforms.

CONCLUSIONS

Our results indicate that the proposed method of remote heart monitoring using microwave Doppler radar demonstrates higher accuracy in estimating the RRI of the electrocardiogram while at rest sitting in a chair, and the ability to extract the heartbeat waveforms from the measured Doppler output signal, eliminating the need to create templates in advance as required by conventional template matching methods. This approach offers more flexibility in the measurement environment than conventional methods.

Accuracy and role of consumer facing wearable technology for continuous monitoring during endoscopic procedures

Background

Consumer facing wearable devices capture significant amounts of biometric data. The primary aim of this study is to determine the accuracy of consumer-facing wearable technology for continuous monitoring compared to standard anesthesia monitoring during endoscopic procedures. Secondary aims were to assess patient and provider perceptions of these devices in clinical settings.

Methods

Patients undergoing endoscopy with anesthesia support from June 2021 to June 2022 were provided a smartwatch (Apple Watch Series 7, Apple Inc., Cupertino, CA) and accessories including continuous ECG monitor and pulse oximeter (Qardio Inc., San Francisco, CA) for the duration of their procedure. Vital sign data from the wearable devices was compared to in-room anesthesia monitors. Concordance with anesthesia monitoring was assessed with interclass correlation coefficients (ICC). Surveys were then distributed to patients and clinicians to assess patient and provider preferences regarding the use of the wearable devices during procedures.

Results

292 unique procedures were enrolled with a median anesthesia duration of 34 min (IQR 25-47). High fidelity readings were successfully recorded with wearable devices for heart rate in 279 (95.5%) cases, oxygen in 203 (69.5%), and respiratory rate in 154 (52.7%). ICCs for watch and accessories were 0.54 (95% CI 0.46-0.62) for tachycardia, 0.03 (95% CI 0-0.14) for bradycardia, and 0.33 (0.22-0.43) for oxygen desaturation. Patients generally felt the devices were more accurate (56.3% vs. 20.0% agree, p < 0.001) and more permissible (53.9% vs. 33.3% agree, p < 0.001) to wear during a procedure than providers.

Conclusion

Smartwatches perform poorly for continuous data collection compared to gold standard anesthesia monitoring. Refinement in software development is required if these devices are to be used for continuous, intensive vital sign monitoring.

Non-Contact Measurement of Cardiopulmonary Activity Using Software Defined Radios

Vital signs are important indicators to evaluate the health status of patients. Channel state information (CSI) can sense the displacement of the chest wall caused by cardiorespiratory activity in a non-contact manner. Due to the influence of clutter, DC components, and respiratory harmonics, it is difficult to detect reliable heartbeat signals. To address this problem, this paper proposes a robust and novel method for simultaneously extracting breath and heartbeat signals using software defined radios (SDR). Specifically, we model and analyze the signal and propose singular value decomposition (SVD)-based clutter suppression method to enhance the vital sign signals. The DC is estimated and compensated by the circle fitting method. Then, the heartbeat signal and respiratory signal are obtained by the modified variational modal decomposition (VMD). The experimental results demonstrate that the proposed method can accurately separate the respiratory signal and the heartbeat signal from the filtered signal. The Bland-Altman analysis shows that the proposed system is in good agreement with the medical sensors. In addition, the proposed system can accurately measure the heart rate variability (HRV) within 0.5m. In summary, our system can be used as a preferred contactless alternative to traditional contact medical sensors, which can provide advanced patient-centered healthcare solutions.

Detection of Arrhythmias Using Smartwatches-A Systematic Literature Review

Smartwatches represent one of the most widely adopted technological innovations among wearable devices. Their evolution has equipped them with an increasing array of features, including the capability to record an electrocardiogram. This functionality allows users to detect potential arrhythmias, enabling prompt intervention or monitoring of existing arrhythmias, such as atrial fibrillation. In our research, we aimed to compile case reports, case series, and cohort studies from the Web of Science, PubMed, Scopus, and Embase databases published until 1 August 2023. The search employed keywords such as "Smart Watch", "Apple Watch", "Samsung Gear", "Samsung Galaxy Watch", "Google Pixel Watch", "Fitbit", "Huawei Watch", "Withings", "Garmin", "Atrial Fibrillation", "Supraventricular Tachycardia", "Cardiac Arrhythmia", "Ventricular Tachycardia", "Atrioventricular Nodal Reentrant Tachycardia", "Atrioventricular Reentrant Tachycardia", "Heart Block", "Atrial Flutter", "Ectopic Atrial Tachycardia", and "Bradyarrhythmia." We obtained a total of 758 results, from which we selected 57 articles, including 33 case reports and case series, as well as 24 cohort studies. Most of the scientific works focused on atrial fibrillation, which is often detected using Apple Watches. Nevertheless, we also included articles investigating arrhythmias with the potential for circulatory collapse without immediate intervention. This systematic literature review provides a comprehensive overview of the current state of research on arrhythmia detection using smartwatches. Through further research, it may be possible to develop a care protocol that integrates arrhythmias recorded by smartwatches, allowing for timely access to appropriate medical care for patients. Additionally, continuous monitoring of existing arrhythmias using smartwatches could facilitate the assessment of the effectiveness of prescribed therapies.

Advances and Opportunities of Mobile Health in the Postpandemic Era: Smartphonization of Wearable Devices and Wearable Deviceization of Smartphones

Mobile health (mHealth) with continuous real-time monitoring is leading the era of digital medical convergence. Wearable devices and smartphones optimized as personalized health management platforms enable disease prediction, prevention, diagnosis, and even treatment. Ubiquitous and accessible medical services offered through mHealth strengthen universal health coverage to facilitate service use without discrimination. This viewpoint investigates the latest trends in mHealth technology, which are comprehensive in terms of form factors and detection targets according to body attachment location and type. Insights and breakthroughs from the perspective of mHealth sensing through a new form factor and sensor-integrated display overcome the problems of existing mHealth by proposing a solution of smartphonization of wearable devices and the wearable deviceization of smartphones. This approach maximizes the infinite potential of stagnant mHealth technology and will present a new milestone leading to the popularization of mHealth. In the postpandemic era, innovative mHealth solutions through the smartphonization of wearable devices and the wearable deviceization of smartphones could become the standard for a new paradigm in the field of digital medicine.

Bionanotechnology and bioMEMS (BNM): state-of-the-art applications, opportunities, and challenges

The development of micro- and nanotechnology for biomedical applications has defined the cutting edge of medical technology for over three decades, as advancements in fabrication technology developed originally in the semiconductor industry have been applied to solving ever-more complex problems in medicine and biology. These technologies are ideally suited to interfacing with life sciences, since they are on the scale lengths as cells (microns) and biomacromolecules (nanometers). In this paper, we review the state of the art in bionanotechnology and bioMEMS (collectively BNM), including developments and challenges in the areas of BNM, such as microfluidic organ-on-chip devices, oral drug delivery, emerging technologies for managing infectious diseases, 3D printed microfluidic devices, AC electrokinetics, flexible MEMS devices, implantable microdevices, paper-based microfluidic platforms for cellular analysis, and wearable sensors for point-of-care testing.

Technology-assisted behavioral intervention to encourage prehabilitation in frail older adults undergoing surgery: Development and design of the BeFitMe™ Apple Watch app

Objective

Increasing the physical activity of frail, older patients before surgery through prehabilitation (prehab) can hasten return to autonomy and reduce complications postoperatively. However, prehab participation is low in the clinical setting. In this study, we re-design an existing prehab smartphone application (BeFitMe™) using a novel standalone Apple Watch platform to increase accessibility and usability for vulnerable patients.

Methods

Design Science Research Methodology was used to (1) develop an approach to clinical research using standalone Apple Watches, (2) re-design BeFitMe™ for the Apple Watch platform, and (3) incorporate user feedback into app design. In phase 3, beta and user testers gave feedback via a follow-up phone call. Exercise data was extracted from the watch after testing. Descriptive statistics were used to summarize accessibility and usability.

Results

BeFitMe™ was redesigned for the Apple Watch with full functionality without requiring patients to have an iPhone or internet connectivity and the ability to passively collect exercise data without patient interaction. Three study staff participated in beta testing over 3 weeks. Six randomly chosen thoracic surgery patients participated in user testing over 12 weeks. Feedback from beta and user testers was addressed with updated software (versions 1.0-1.10), improved interface and notification schemes, and the development of educational materials used during enrollment. The majority of users (5/6, 83%) participated by responding to at least one notification and data was able to be collected for 54/82 (68%) of the days users had the watches. The amount of data collected in BeFitMe™ Watch app increased from 2/11 (16%) days with the first patient tester to 13/13 (100%) days with the final patient tester.

Conclusions

The BeFitMe™ Watch app is accessible and usable. The BeFitMe™ Watch app may help older patients, particularly those from vulnerable backgrounds with fewer resources, participate in prehab prior to surgery.

The Apple Watch for Monitoring Mental Health-Related Physiological Symptoms: Literature Review

BACKGROUND

An anticipated surge in mental health service demand related to COVID-19 has motivated the use of novel methods of care to meet demand, given workforce limitations. Digital health technologies in the form of self-tracking technology have been identified as a potential avenue, provided sufficient evidence exists to support their effectiveness in mental health contexts.

OBJECTIVE

This literature review aims to identify current and potential physiological or physiologically related monitoring capabilities of the Apple Watch relevant to mental health monitoring and examine the accuracy and validation status of these measures and their implications for mental health treatment.

METHODS

A literature review was conducted from June 2021 to July 2021 of both published and gray literature pertaining to the Apple Watch, mental health, and physiology. The literature review identified studies validating the sensor capabilities of the Apple Watch.

RESULTS

A total of 5583 paper titles were identified, with 115 (2.06%) reviewed in full. Of these 115 papers, 19 (16.5%) were related to Apple Watch validation or comparison studies. Most studies showed that the Apple Watch could measure heart rate acceptably with increased errors in case of movement. Accurate energy expenditure measurements are difficult for most wearables, with the Apple Watch generally providing the best results compared with peers, despite overestimation. Heart rate variability measurements were found to have gaps in data but were able to detect mild mental stress. Activity monitoring with step counting showed good agreement, although wheelchair use was found to be prone to overestimation and poor performance on overground tasks. Atrial fibrillation detection showed mixed results, in part because of a high inconclusive result rate, but may be useful for ongoing monitoring. No studies recorded validation of the Sleep app feature; however, accelerometer-based sleep monitoring showed high accuracy and sensitivity in detecting sleep.

CONCLUSIONS

The results are encouraging regarding the application of the Apple Watch in mental health, particularly as heart rate variability is a key indicator of changes in both physical and emotional states. Particular benefits may be derived through avoidance of recall bias and collection of supporting ecological context data. However, a lack of methodologically robust and replicated evidence of user benefit, a supportive health economic analysis, and concerns about personal health information remain key factors that must be addressed to enable broader uptake.

Assessing the Clinical Utility of a Wearable Device for Physiological Monitoring of Heart Rate Variability in Military Service Members with Traumatic Brain Injury

Introduction: Autonomic dysfunction has been implicated as a consequence of traumatic brain injury (TBI). Heart rate variability (HRV) may be a viable measure of autonomic dysfunction that could enhance rehabilitative interventions for individuals with TBI. This pilot study sought to assess the feasibility and validity of using the Zeriscope™ platform system in a real-world clinical setting to measure HRV in active-duty service members with TBI who were participating in an intensive outpatient program. Methods: Twenty-five service members with a history of mild, moderate, or severe TBI were recruited from a military treatment facility. A baseline assessment was conducted in the cardiology clinic where point validity data were obtained by comparing a 5-min recording of a standard 12-lead electrocardiogram (ECG) output against the Zeriscope platform data. Results: Compared with the ECG device, the Zeriscope device had a concordance coefficient (rc) of 0.16, falling below the standard deemed to represent acceptable accuracy in HR measurement (i.e., 0.80). Follow-up analyses excluding outliers did not significantly improve the concordance coefficient to an acceptable standard for the total participant sample. System Usability Survey responses showed that participants rated the Zeriscope system as easy to use and something that most people would learn to use quickly. Conclusions: This study demonstrated promise in ambulatory HRV measurement in a representative military TBI sample. Future research should include further refinement of such ambulatory devices to meet the specifications required for use in a military active-duty TBI population.

Heart Watch Study: protocol for a pragmatic randomised controlled trial

INTRODUCTION

Personal digital devices that provide health information, such as the Apple Watch, have developed an increasing array of cardiopulmonary tracking features which have received regulatory clearance and are directly marketed to consumers. Despite their widespread and increasing use, data about the impact of personal digital device use on patient-reported outcomes and healthcare utilisation are sparse. Among a population of patients with atrial fibrillation and/or atrial flutter undergoing cardioversion, our primary aim is to determine the impact of the heart rate measurement, irregular rhythm notification, and ECG features of the Apple Watch on quality of life and healthcare utilisation.

METHODS AND ANALYSIS

We are conducting a prospective, open-label multicentre pragmatic randomised clinical trial, leveraging a unique patient-centred health data sharing platform for enrolment and follow-up. A total of 150 patients undergoing cardioversion for atrial fibrillation or atrial flutter will be randomised 1:1 to receive the Apple Watch Series 6 or Withings Move at the time of cardioversion. The primary outcome is the difference in the Atrial Fibrillation Effect on QualiTy-of-life global score at 6 months postcardioversion. Secondary outcomes include inpatient and outpatient healthcare utilisation. Additional secondary outcomes include a comparison of the Apple Watch ECG and pulse oximeter features with gold-standard data obtained in routine clinical care settings.

ETHICS AND DISSEMINATION

The Institutional Review Boards at Yale University, Mayo Clinic, and Duke University Health System have approved the trial protocol. This trial will provide important data to policymakers, clinicians and patients about the impact of the heart rate, irregular rhythm notification, and ECG features of widely used personal digital devices on patient quality of life and healthcare utilisation. Findings will be disseminated to study participants, at professional society meetings and in peer-reviewed journals.

TRIAL REGISTRATION NUMBER

NCT04468321.

Harnessing the Power of Smart and Connected Health to Tackle COVID-19: IoT, AI, Robotics, and Blockchain for a Better World

As COVID-19 hounds the world, the common cause of finding a swift solution to manage the pandemic has brought together researchers, institutions, governments, and society at large. The Internet of Things (IoT), artificial intelligence (AI)-including machine learning (ML) and Big Data analytics-as well as Robotics and Blockchain, are the four decisive areas of technological innovation that have been ingenuity harnessed to fight this pandemic and future ones. While these highly interrelated smart and connected health technologies cannot resolve the pandemic overnight and may not be the only answer to the crisis, they can provide greater insight into the disease and support frontline efforts to prevent and control the pandemic. This article provides a blend of discussions on the contribution of these digital technologies, propose several complementary and multidisciplinary techniques to combat COVID-19, offer opportunities for more holistic studies, and accelerate knowledge acquisition and scientific discoveries in pandemic research. First, four areas, where IoT can contribute are discussed, namely: 1) tracking and tracing; 2) remote patient monitoring (RPM) by wearable IoT (WIoT); 3) personal digital twins (PDTs); and 4) real-life use case: ICT/IoT solution in South Korea. Second, the role and novel applications of AI are explained, namely: 1) diagnosis and prognosis; 2) risk prediction; 3) vaccine and drug development; 4) research data set; 5) early warnings and alerts; 6) social control and fake news detection; and 7) communication and chatbot. Third, the main uses of robotics and drone technology are analyzed, including: 1) crowd surveillance; 2) public announcements; 3) screening and diagnosis; and 4) essential supply delivery. Finally, we discuss how distributed ledger technologies (DLTs), of which blockchain is a common example, can be combined with other technologies for tackling COVID-19.

Portable Device Improves the Detection of Atrial Fibrillation After Ablation

Asymptomatic recurrences of atrial fibrillation (AF) have been found to be common after ablation.A randomized controlled trial of AF screening using a handheld single-lead ECG monitor (BigThumb^®) or a traditional follow-up strategy was conducted in patients with non-valvular AF after catheter ablation. Consecutive patients were randomized to either BigThumb Group (BT Group) or Traditional Follow-up Group (TF Group). The ECGs collected via BigThumb were compared using the automated AF detection algorithm, artificial intelligence (AI) algorithm, and cardiologists' manual review. Subsequent changes in adherence to oral anticoagulation of patients were also recorded. In this study, we examined 218 patients (109 in each group). After a follow-up of 345.4 ± 60.2 days, AF-free survival rate was 64.2% in BT Group and 78.9% in TF Group (P = 0.0163), with more adherence to oral anticoagulation in BT Group (P = 0.0052). The participants in the BT Group recorded 26133 ECGs, among which 3299 (12.6%) were diagnosed as AF by cardiologists' manual review. The sensitivity and specificity of the AI algorithm were 94.4% and 98.5% respectively, which are significantly higher than the automated AF detection algorithm (90.7% and 96.2%).As per our findings, it was determined that follow-up after AF ablation using BigThumb leads to a more frequent detection of AF recurrence and more adherence to oral anticoagulation. AI algorithm improves the accuracy of ECG diagnosis and has the potential to reduce the manual review.

Association of Wearable Device Use With Pulse Rate and Health Care Use in Adults With Atrial Fibrillation

Importance

Increasingly, individuals with atrial fibrillation (AF) use wearable devices (hereafter wearables) that measure pulse rate and detect arrhythmia. The associations of wearables with health outcomes and health care use are unknown.

Objective

To characterize patients with AF who use wearables and compare pulse rate and health care use between individuals who use wearables and those who do not.

Design, Setting, and Participants

This retrospective, propensity-matched cohort study included 90 days of follow-up of patients in a tertiary care, academic health system. Included patients were adults with at least 1 AF-specific International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10) code from 2017 through 2019. Electronic medical records were reviewed to identify 125 individuals who used wearables and had adequate pulse-rate follow-up who were then matched using propensity scores 4 to 1 with 500 individuals who did not use wearables. Data were analyzed from June 2020 through February 2021.

Exposure

Using commercially available wearables with pulse rate or rhythm evaluation capabilities.

Main Outcomes and Measures

Mean pulse rates from measures taken in the clinic or hospital and a composite health care use score were recorded. The composite outcome included evaluation and management, ablation, cardioversion, telephone encounters, and number of rate or rhythm control medication orders.

Results

Among 16 320 patients with AF included in the analysis, 348 patients used wearables and 15 972 individuals did not use wearables. Prior to matching, patients using wearables were younger (mean [SD] age, 64.0 [13.0] years vs 70.0 [13.8] years; P < .001) and healthier (mean [SD] CHA2DS2-VASc [congestive heart failure, hypertension, age ≥ 65 years or 65-74 years, diabetes, prior stroke/transient ischemic attack, vascular disease, sex] score, 3.6 [2.0] vs 4.4 [2.0]; P < .001) compared with individuals not using wearables, with similar gender distribution (148 [42.5%] women vs 6722 women [42.1%]; P = .91). After matching, mean pulse rate was similar between 125 patients using wearables and 500 patients not using wearables (75.01 [95% CI, 72.74-77.27] vs 75.79 [95% CI, 74.68-76.90] beats per minute [bpm]; P = .54), whereas mean composite use score was higher among individuals using wearables (3.55 [95% CI, 3.31-3.80] vs 3.27 [95% CI, 3.14-3.40]; P = .04). Among measures in the composite outcome, there was a significant difference in use of ablation, occurring in 22 individuals who used wearables (17.6%) vs 37 individuals who did not use wearables (7.4%) (P = .001). In the regression analyses, mean composite use score was 0.28 points (95% CI, 0.01 to 0.56 points) higher among individuals using wearables compared with those not using wearables and mean pulse was similar, with a -0.79 bpm (95% CI -3.28 to 1.71 bpm) difference between the groups.

Conclusions and Relevance

This study found that follow-up health care use among individuals with AF was increased among those who used wearables compared with those with similar pulse rates who did not use wearables. Given the increasing use of wearables by patients with AF, prospective, randomized, long-term evaluation of the associations of wearable technology with health outcomes and health care use is needed.

Artificial Intelligence and Early Detection of Pancreatic Cancer: 2020 Summative Review

Despite considerable research efforts, pancreatic cancer is associated with a dire prognosis and a 5-year survival rate of only 10%. Early symptoms of the disease are mostly nonspecific. The premise of improved survival through early detection is that more individuals will benefit from potentially curative treatment. Artificial intelligence (AI) methodology has emerged as a successful tool for risk stratification and identification in general health care. In response to the maturity of AI, Kenner Family Research Fund conducted the 2020 AI and Early Detection of Pancreatic Cancer Virtual Summit (www.pdac-virtualsummit.org) in conjunction with the American Pancreatic Association, with a focus on the potential of AI to advance early detection efforts in this disease. This comprehensive presummit article was prepared based on information provided by each of the interdisciplinary participants on one of the 5 following topics: Progress, Problems, and Prospects for Early Detection; AI and Machine Learning; AI and Pancreatic Cancer-Current Efforts; Collaborative Opportunities; and Moving Forward-Reflections from Government, Industry, and Advocacy. The outcome from the robust Summit conversations, to be presented in a future white paper, indicate that significant progress must be the result of strategic collaboration among investigators and institutions from multidisciplinary backgrounds, supported by committed funders.

Comparison of Continuous ECG Monitoring by Wearable Patch Device and Conventional Telemonitoring Device

BACKGROUND

Detection of arrhythmias is crucial for the treatment of cardiovascular diseases. However, conventional devices do not provide sufficient diagnostic accuracy while patients should suffer from bothersome diagnostic process. We sought to evaluate diagnostic capability and safety of the new adhesive electrocardiogram (ECG) monitoring device in patients who need ECG monitoring during admission.

METHODS

We enrolled 10 patients who admitted to Seoul National University Bundang Hospital and required continuous ECG monitoring between October 31, 2019 and December 18, 2019. New adhesive ECG monitoring device and conventional ECG monitoring device were simultaneously applied to the patients and maintained for 48 hours. From each patient, 48 pairs of ECG signal were collected and analyzed by two cardiologists independently. Discrepancy of diagnosis and frequency of noise or signal loss were compared between the two devices.

RESULTS

From analyzable ECG data, discrepancy of arrhythmia diagnosis was not observed between the two devices. Noise rate was higher in conventional ECG monitoring device (2.5% vs. 17.3%, P < 0.001) and signal loss was not observed in new adhesive device while there was 9.4% of signal losses in conventional Holter recorder group. The new device was well-tolerated among 48 hours of monitoring period and no adverse event was observed.

CONCLUSION

A newer adhesive ECG monitoring device demonstrated similar diagnostic accuracy compared to conventional ECG monitoring device.

Wearable Sensors for Monitoring and Preventing Noncommunicable Diseases: A Systematic Review

Ensuring healthy lives and promoting a healthy well-being for all at all ages are listed as some of the goals in Agenda 2030 for Sustainable Development. Considering that noncommunicable diseases (NCDs) are the leading cause of death worldwide, reducing the mortality of NCDs is an important target. To reach this goal, means for detecting and reacting to warning signals are necessary. Here, remote health monitoring in real time has great potential. This article provides a systematic review of the use of wearable sensors for the monitoring and prevention of NCDs. In addition, this article not only provides in-depth information about the retrieved articles, but also discusses examples of studies assessing warning signals that may result in serious health conditions, such as stroke and cardiac arrest, if left untreated. One finding is that even though many good examples of wearable sensor systems for monitoring and controlling NCDs are presented, many issues also remain to be solved. One major issue is the lack of testing on representative people from a sociodemographic perspective. Even though substantial work remains, the use of wearable sensor systems has a great potential to be used in the battle against NCDs by providing the means to diagnose, monitor and prevent NCDs.

A comparison of manual electrocardiographic interval and waveform analysis in lead 1 of 12-lead ECG and Apple Watch ECG: A validation study

Background

The Apple Watch Series 4 (AW) can detect atrial fibrillation and perform a single-lead electrocardiogram (ECG), but the clinical accuracy of AW ECG waveforms compared to lead 1 of a 12-lead ECG is unclear.

Objective

The purpose of this study was to assess the accuracy of interval measurements on AW ECG tracings in comparison to lead 1 on a 12-lead ECG.

Methods

We obtained ECGs at a university hospital of healthy volunteers age >18 years. ECG waveforms were measured with calipers to the nearest 0.25 mm. When possible, 3 consecutive waveforms in lead 1 were measured. Waveform properties, including intervals, were recorded. Concordance correlation coefficients and Bland-Altman plots were used to assess level of agreement between devices.

Results

Twelve-lead (n = 113) and AW (n = 129) ECG waveforms from 43 volunteers (mean age 31 years; 65% female) were analyzed. Sinus rhythm interpretation between devices was 100% concordant. No arrhythmias were recorded. Mean difference (d) for heart rate was 1.16 ± 4.33 bpm (r = 0.94); 3.83 ± 113.54 ms for RR interval (r = 0.79); 5.43 ± 17 ms for PR interval (r = 0.83); –6.89 ± 14.81 ms for QRS interval (r = 0.65); –11.27 ± 22.9 ms for QT interval (r = 0.79); and –11.67 ± 27 ms for QTc interval (r = 0.57). There was moderate (d <40 ms) to strong (d <20 ms or < 5 bpm) agreement between devices represented by Bland-Altman plots.

Conclusion

The AW produces accurate ECGs in healthy adults with moderate to strong agreement of basic ECG intervals.

Wearable Sensors for COVID-19: A Call to Action to Harness Our Digital Infrastructure for Remote Patient Monitoring and Virtual Assessments

The COVID-19 pandemic has brought into sharp focus the need to harness and leverage our digital infrastructure for remote patient monitoring. As current viral tests and vaccines are slow to emerge, we see a need for more robust disease detection and monitoring of individual and population health, which could be aided by wearable sensors. While the utility of this technology has been used to correlate physiological metrics to daily living and human performance, the translation of such technology toward predicting the incidence of COVID-19 remains a necessity. When used in conjunction with predictive platforms, users of wearable devices could be alerted when changes in their metrics match those associated with COVID-19. Anonymous data localized to regions such as neighborhoods or zip codes could provide public health officials and researchers a valuable tool to track and mitigate the spread of the virus, particularly during a second wave. Identifiable data, for example remote monitoring of cohorts (family, businesses, and facilities) associated with individuals diagnosed with COVID-19, can provide valuable data such as acceleration of transmission and symptom onset. This manuscript describes clinically relevant physiological metrics which can be measured from commercial devices today and highlights their role in tracking the health, stability, and recovery of COVID-19+ individuals and front-line workers. Our goal disseminating from this paper is to initiate a call to action among front-line workers and engineers toward developing digital health platforms for monitoring and managing this pandemic.