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PURPOSE: A major barrier to the incorporation of biometric data into clinical practice is the lack of device integration with electronic medical records (EMRs). We developed infrastructure to transmit biometric data from an Apple Watch into the EMR for physician review. The study objective was to test feasibility of using this infrastructure for patients undergoing radiotherapy. METHODS: The study included patients with breast or prostate cancer receiving ≥3 weeks of radiotherapy who reported owning an Apple Watch. Daily resting heart rate (HR), HR variability, step count, and exercise minutes were automatically transferred to our EMR using a custom app installed on each patient's iPhone. Biometric data were presented to the treating radiation oncologist for review on a weekly basis during creation of the on-treatment note. Feasibility was defined a priori as physician review of biometric data for at least 90% of patients. Time trends in biometric data were tested using the Jonckheere-Terpstra test. Patient satisfaction was assessed using the System Usability Scale (SUS), with scores above 80 considered above-average user experience. RESULTS: Of the 20 patients enrolled, biometric data were successfully transmitted to the EMR and reviewed by the radiation oncologist for 95% (n = 19) of patients, thus meeting the a priori feasibility threshold. For patients with radiation courses ≥4 weeks, exercise minutes decreased over time (P =.01) and daily mean HR variability increased over time (P =.02). The median SUS was 82.5 (IQR, 70-87.5). CONCLUSION: Our study demonstrates the feasibility of real-time integration of biometric data collected from an Apple Watch into the EMR with subsequent physician review. The high rates of physician review and patient satisfaction provide support for further development of large-scale collection of wearable device data. [ABSTRACT FROM AUTHOR]

Wearable devices have become commonplace tools for tracking behavioral and physiological parameters in real-world settings. Nonetheless, the practical utility of these data for clinical and research applications, such as sleep analysis, is hindered by their noisy, large-scale, and multidimensional characteristics. Here, we develop a neural network algorithm that predicts sleep stages by tracking topological features (TFs) of wearable data and model-driven clock proxies (CPs) reflecting the circadian propensity for sleep. To evaluate its accuracy, we apply it to motion and heart rate data from the Apple Watch worn by young subjects undergoing polysomnography (PSG) and compare the predicted sleep stages with the corresponding ground truth PSG records. The neural network that includes TFs and CPs along with raw wearable data as inputs shows improved performance in classifying Wake/REM/NREM sleep. For example, it shows significant improvements in identifying REM and NREM sleep (AUROC/AUPRC improvements >13% and REM/NREM accuracy improvement of 12%) compared with the neural network using only raw data inputs. We find that this improvement is mainly attributed to the heart rate TFs. To further validate our algorithm on a different population, we test it on elderly subjects from the Multi-ethnic Study of Atherosclerosis cohort. This confirms that TFs and CPs contribute to the improvements in Wake/REM/NREM classification. We next compare the performance of our algorithm with previous state-of-the-art wearable-based sleep scoring algorithms and find that our algorithm outperforms them within and across different populations. This study demonstrates the benefits of combining topological data analysis and mathematical modeling to extract hidden inputs of neural networks from puzzling wearable data. [ABSTRACT FROM AUTHOR]

Objectives: Atrial fibrillation (AF) is a common early arrhythmia after heart valve surgery that limits physical activity. We aimed to evaluate the criterion validity of the Apple Watch Series 5 single-lead electrocardiogram (ECG) for detecting AF in patients after heart valve surgery. Design: We enrolled 105 patients from the University Hospital of North Norway, of whom 93 completed the study. All patients underwent single-lead ECG using the smartwatch three times or more daily on the second to third or third to fourth postoperative day. These results were compared with continuous 2–4 days ECG telemetry monitoring and a 12-lead ECG on the third postoperative day. Results: On comparing the Apple Watch ECGs with the ECG monitoring, the sensitivity and specificity to detect AF were 91% (75, 100) and 96% (91, 99), respectively. The accuracy was 95% (91, 99). On comparing Apple Watch ECG with a 12-lead ECG, the sensitivity was 71% (62, 100) and the specificity was 92% (92, 100). Conclusion: The Apple smartwatch single-lead ECG has high sensitivity and specificity, and might be a useful tool for detecting AF in patients after heart valve surgery. [ABSTRACT FROM AUTHOR]

Digital health tools are emerging as a promising solution for optimizing Deep Brain Stimulation (DBS) therapy for neurodegenerative diseases such as Parkinson's Disease (PD), addressing challenges of therapy maintenance, care access, and discrete assessments. Wearable technology and mobile health platforms can offer remote monitoring capabilities, allowing for real-time virtual programming with optimization of patients' therapy, use of digital biomarkers that can help identify the onset, symptoms, medication-related fluctuations and side-effects, and response to DBS treatment. Through an investigational remote monitoring application (RM app) integrating patient-reported outcomes (PROs) and objective data using wearables, we aimed to develop an accessible, data-driven digital tool to monitor patient symptoms and deliver low-burden and easy-to-access DBS therapy. The platform is designed to be compatible with several consumer-grade wearables and ensures patient data privacy and security. We conducted a feasibility study to test the tool's effectiveness in a large-scale DBS study, where subjects received a home-monitoring kit consisting of an iPhone and Apple Watch, pre-configured with the RM app to enable remote data collection. Compliance in this paper is defined as adherence to the RM app and Apple watch usage. Analysis of 67 subjects from the study demonstrated an average compliance rate of 63.3% for daily check-ins, 85.5% for monthly surveys and 61.9% for passive data with an average of 55 days of post-implant data. The study highlights the feasibility of frequent monitoring in the early post-implant period that could lead to optimal outcomes for patients. The platform developed can further optimize study execution and be extended to other chronic conditions treated with neuromodulation implants such as depression and pain. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The study investigates the potential of anxiety clusters in predicting programming performance in two distinct coding environments. Participants comprised 83 second-year programming students who were randomly assigned to either a block-based or a text-based group. Anxiety-induced behaviors were assessed using physiological measures (Apple Watch and Electrocardiogram machine), behavioral observation, and self-report. Utilizing the Hidden Markov Model and Optimal Matching algorithm, we found three representative clusters in each group. In the block-based group, clusters were designated as follows: "stay calm" (students allocating more of their time to a calm state), "stay hesitant" (students allocating more of their time to a hesitant state), and "to-calm" (those allocating minimal time to a hesitant and anxious state but displaying a pronounced propensity to transition to a calm state). In contrast, clusters in the text-based group were labeled as: "to-hesitant" (exhibiting a higher propensity to transition to a hesitant state), "stay hesitant" (allocating significant time to a hesitant state), and "stay anxious" (remaining persistently anxious in a majority of the coding time). Additionally, our results indicate that novice programmers are more likely to experience anxiety during text-based coding. We discussed the findings and highlighted the policy implications of the study. [ABSTRACT FROM AUTHOR]

Background: Dilated cardiomyopathy (DCM) is a leading cause of heart failure, particularly in younger individuals. Low physical strength is a global risk factor for cardiovascular mortality, and physical activity and a healthy lifestyle have been shown to improve outcomes in patients with heart failure. However, inappropriate exercise may increase the risk of arrhythmias in certain individuals with DCM. The determinants for predicting individual risks in this setting are poorly understood, and clinicians are hesitant to recommend sports for cardiomyopathy patients. The activeDCM trial aims to assess the safety and efficacy of a personalized exercise and activity programme for individuals with DCM. Study Design: The activeDCM trial is a prospective, randomized, interventional trial with a 12 month follow‐up. Three hundred patients, aged 18–75 years with DCM, left ventricular ejection fraction (LVEF) ≤ 50% and New York Heart Association (NYHA) classes I–III, will be enrolled. The intervention includes a personalized exercise and activity programme. The primary outcome is the increase in peak oxygen uptake (VO2max, mL/kg/min) from baseline to 12 months. Secondary endpoints include adherence to personalized activity programmes, freedom from clinically relevant arrhythmia, unplanned hospitalization for heart failure and changes in NYHA class, quality of life scores, 6 min walk distance, muscular strength, N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP) and high‐sensitivity troponin T (hsTnT) levels and cardiac function. Advanced research questions include high‐density phenome and omics analysis combined with digital biomarkers derived from Apple Watch devices. Discussion: The activeDCM trial will provide valuable insights into the safety and efficacy of personalized exercise training in DCM patients, inform clinical practice and contribute to the development of heart failure management programmes. The study will generate data on the impact of exercise on various aspects of cardiovascular disease, including genetic, metabolic, phenotypic and longitudinal aspects, facilitating the development of future digital tools and strategies, including the incorporation of smart wearable devices. [ABSTRACT FROM AUTHOR]

Background: The Apple Watch provides promising health data that could aid in increasing exercise adherence; regular exercise can help individuals manage and prevent diseases such as obesity and cardiovascular disease. However, the impact of skin pigmentation on the accuracy of the Apple Watch Series 9 for measures of energy expenditure (EE) and heart rate (HR) is unknown. Purpose: The purpose of this study was to determine the accuracy of the Apple Watch Series 9 on various skin pigmentations for measures of EE and HR. Methods: Thirty young, healthy individuals were assigned to one of three groups based on their scores on the Fitzpatrick skin survey. Participants completed a 10 min treadmill protocol with varying speeds and inclines while wearing an Apple Watch Series 9, a two-way non-rebreathing mouthpiece connected to a Parvo Medics metabolic cart, and a Polar H7 chest strap to measure EE and HR. Results: Overall, EE was found to be inconsistent for all skin pigmentation groups. However, for HR, the Apple Watch Series 9 was more variable (i.e., less accurate) for darker skin pigmentations compared to lighter skin pigmentations. Conclusions: The Apple Watch Series 9 was found to vary in both EE and HR measures from criterion across intensity and skin pigmentation, with greater discrepancies for individuals in Group 3 for measures of HR. Further investigation might aim to study the impact of skin pigmentations and wrist subcutaneous fat on the accuracy of the latest Apple Watch Series 9 for measures of EE and HR. [ABSTRACT FROM AUTHOR]

Abstract Background Dilated cardiomyopathy (DCM) is a leading cause of heart failure, particularly in younger individuals. Low physical strength is a global risk factor for cardiovascular mortality, and physical activity and a healthy lifestyle have been shown to improve outcomes in patients with heart failure. However, inappropriate exercise may increase the risk of arrhythmias in certain individuals with DCM. The determinants for predicting individual risks in this setting are poorly understood, and clinicians are hesitant to recommend sports for cardiomyopathy patients. The activeDCM trial aims to assess the safety and efficacy of a personalized exercise and activity programme for individuals with DCM. Study Design The activeDCM trial is a prospective, randomized, interventional trial with a 12 month follow‐up. Three hundred patients, aged 18–75 years with DCM, left ventricular ejection fraction (LVEF) ≤ 50% and New York Heart Association (NYHA) classes I–III, will be enrolled. The intervention includes a personalized exercise and activity programme. The primary outcome is the increase in peak oxygen uptake (VO2max, mL/kg/min) from baseline to 12 months. Secondary endpoints include adherence to personalized activity programmes, freedom from clinically relevant arrhythmia, unplanned hospitalization for heart failure and changes in NYHA class, quality of life scores, 6 min walk distance, muscular strength, N‐terminal pro‐B‐type natriuretic peptide (NT‐proBNP) and high‐sensitivity troponin T (hsTnT) levels and cardiac function. Advanced research questions include high‐density phenome and omics analysis combined with digital biomarkers derived from Apple Watch devices. Discussion The activeDCM trial will provide valuable insights into the safety and efficacy of personalized exercise training in DCM patients, inform clinical practice and contribute to the development of heart failure management programmes. The study will generate data on the impact of exercise on various aspects of cardiovascular disease, including genetic, metabolic, phenotypic and longitudinal aspects, facilitating the development of future digital tools and strategies, including the incorporation of smart wearable devices.

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

A study was conducted to test a selection of commercially available wearable devices to determine their feasibility for triggering incident detection during a variety of simulated agricultural incidents with high risk of causing injury. The goal was to ultimately increase survivability outcomes for victims by enhancing notification and reducing response time from emergency services. A 50th percentile adult male anthropomorphic test device (ATD). was fitted with a convenient selection of commercially available wearable smart technologies to measure the responsiveness of the technology's incident detection software. Devices used for this testing were: (1) Garmin Vivoactive 4 smartwatch; (2) Apple Watch Series 7 (Bluetooth only and cellular models); and (3) Movesense Active tracking device. A Samsung Galaxy S22 smartphone and an Apple iPhone 12 smartphone were used to connect the wearable devices and measured impact through their internal inertial measurement unit (IMU) sensors. Simulated ejections from equipment, vertical falls, and vehicle overturns were performed with the ATD. Side upsets were simulated with the ATD positioned in the operator station of a 52-drawbar horsepower (dbp), two-wheel drive, standard front axle, diesel tractor, weighing 6500 pounds. The tractor was equipped with an approved ROPS. Side upsets were also simulated using a 22-horsepower zero-turn mower, with the ATD positioned in the operator seat. Falls were simulated from heights of up to 4.57 meters. After each simulated incident, devices were examined to determine whether or not incident detection was successfully triggered. Data was then collected from an internal sensor logging application installed on the selected devices. It was found that the incident detection feature on the identified wearable devices only triggered in specific scenarios. Only 2 of the 27 simulated incidents successfully triggered incident detection on one device. Only the Garmin Vivoactive 4 smartwatch triggered incident detection. No device was triggered during the ATD impact in simulated tractor upset testing or in simulated zero-turn mower upset testing. It was concluded that these devices, in their current form, are not reliable for use in detecting serious agricultural-related injuries, especially considering the lack of adequate cell phone coverage in the areas in which these incidents are most likely to occur. [ABSTRACT FROM AUTHOR]

Detection of gait cycle events is a crucial step toward an effective evaluation and rehabilitation of pathologies or injuries in human locomotion. Recently, methods based on the Discrete Wavelet Transform (DWT) have been useful for this detection due to their robustness and the wide variety of options for analyzing and decomposing signals in time/frequency domains, as well as their ability to extract relevant features embedded in the signals. In this study, a detection method of main gait cycle events, using the Wavelet Symlets and Daubechies families, was developed. These events are the heel-strike (HS) and the toe-off (TO). Inertial signals were acquired by three different devices: a G-WALK (reference equipment), an Apple Watch (AW), and a noncommercial device based on Inertial Measurement Units (IMUs). The dataset was obtained from six-minute walking tests performed by 22 healthy subjects. First, the dataset was processed, and then the signals were synchronized regarding the reference system. Subsequently, the signals were decomposed into 6 levels using sym4 and db5 Wavelets to obtain multiple perspectives of the signals. Then, using automatic threshold techniques and symmetric windows, it was possible to detect HS and TO events. Finally, the IMUs-based system obtained a 94.398 % of recall, 100 % of precision, and 97.117 % of F1- score, with absolute values delays in the detection between 10-20 ms. In contrast, the AW system performance was 90.168 %, 100 %, and 94.828 % for recall, precision, and F1- score, respectively, with absolute values delays in the detection of 10-28 ms. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study was to understand how objective measures of daily activity change following unicompartmental knee arthroplasty (UKA). Objective data on post‐operative changes in daily physical activity following UKA are limited, highlighting the need for studies using wearable technologies to provide real‐time assessments of recovery. Methods: This pilot study included a secondary analysis of a prospective study of 33 consecutive UKA patients, with data collected using an Apple Watch and a digital care management application. Objective metrics, including step count, steadiness, standing duration and performance on the six‐minute walk test, were analyzed at different post‐operative time points. Descriptive statistics and the Wilcoxon signed‐rank test were used for analysis. Results: Post‐operatively, there was a significant increase in daily step count at 6 weeks (p = 0.017), 6 months (p < 0.001) and 12 months (p = 0.0018). Steadiness improved significantly at 6 months (p = 0.049) and 12 months (p = 0.039) post‐operatively. Standing duration increased significantly at all the post‐operative time points (p < 0.001). Gait speed did not show significant changes post‐operatively. The estimated six‐minute walk test distance improved significantly at 6 months (p = 0.027) and 12 months (p = 0.031) post‐operatively. Conclusion: The study findings suggest that UKA improves daily physical activity levels, reflected by enhanced mobility and function. While gait speed did not significantly change, improvements in step count, steadiness, standing duration and the six‐minute walk test distance indicate enhanced functional capacity and endurance post‐operatively. The study highlights the benefits of UKA in improving functional outcomes in patients with knee osteoarthritis. Further research with larger sample sizes and longer follow‐ups is warranted to confirm these findings. Levels of Evidence: II. [ABSTRACT FROM AUTHOR]

Background: Atrial fibrillation (AFib) detection via mobile ECG devices is promising, but algorithms often struggle to generalize across diverse datasets and platforms, limiting their real-world applicability. Objective: This study aims to develop a robust, generalizable AFib detection approach for mobile ECG devices using crowdsourced algorithms. Methods: We developed a voting algorithm using random forest, integrating six open-source AFib detection algorithms from the PhysioNet Challenge. The algorithm was trained on an AliveCor dataset and tested on two disjoint AliveCor datasets and one Apple Watch dataset. Results: The voting algorithm outperformed the base algorithms across all metrics: the average of sensitivity (0.884), specificity (0.988), PPV (0.917), NPV (0.985), and F1-score (0.943) on all datasets. It also demonstrated the least variability among datasets, signifying its highest robustness and effectiveness in diverse data environments. Moreover, it surpassed Apple's algorithm on all metrics and showed higher specificity but lower sensitivity than AliveCor's Kardia algorithm. Conclusions: This study demonstrates the potential of crowdsourced, multi-algorithmic strategies in enhancing AFib detection. Our approach shows robust cross-platform performance, addressing key generalization challenges in AI-enabled cardiac monitoring and underlining the potential for collaborative algorithms in wearable monitoring devices. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Background: The advancements in wearable technology have made the detection of arrhythmias more accessible. While smartwatches are commonly used to detect patients with atrial fibrillation, their effectiveness in the differential diagnosis of supraventricular tachycardias (SVT) lacks consensus. Methods: A study was conducted on 47 patients with documented SVTs on a 12-lead ECG. All patients in the cohort underwent electrophysiology study with induction of SVT. A 6th generation Apple Watch was used to record ECG tracings during baseline sinus rhythm and during induced SVT. Cardiology residents and attending cardiologists evaluated these recordings to diagnose the differential diagnosis of SVT. Results: The evaluation revealed 27 cases of typical atrioventricular nodal reentrant tachycardia (AVNRT), 11 cases of atrioventricular reentrant tachycardia (AVRT), and 9 cases of atrial tachycardia/atrial flutter (AT/AFL) among the induced tachycardias. Attending physicians achieved an accuracy of 66.0 to 76.6%, and residents demonstrated accuracy rates between 68.1 and 74.5%. Interrater reliability was assessed using Fleiss's Kappa method, resulting in a moderate level of agreement between residents (Kappa = 0.465, p < 0.001, 95% CI 0.30–0.63) and attendings (Kappa = 0.519, p < 0.001, 95% CI 0.35–0.68). The overall Kappa value was 0.417 (p < 0.001, 95% CI 0.34–0.49). Conclusions: Smartwatch recordings demonstrate moderate feasibility in diagnosing SVT when following a pre-specified algorithm. However, this diagnostic performance was lower than the accuracy obtained from 12-lead ECG tracings when blinded to procedure outcomes. [ABSTRACT FROM AUTHOR]

2. Abstract: Background: Traditional screening methods, such as 12-lead electrocardiograms (ECGs) and Holter monitors, often fall short in detecting transient arrhythmias. However, advancements in wearable technology, like the Apple Watch®, enable real-time rhythm monitoring and specific arrhythmia detection through dedicated algorithms. Case description: A 60-year-old man with a history of arrhythmogenic cardiomyopathy and an implanted cardioverter-defibrillator (ICD), during a walk, experienced palpitations and dizziness; the Apple Watch® alerted him of an elevated heart rate. He recorded a single-lead ECG with the watch, recognizing an abnormal electrocardiogram, then he sought for immediate medical assistance. After few minutes ICD delivered a shock. The patient underwent further evaluation and radiofrequency ablation to successfully treat the ventricular tachycardia. Discussion: Wearable technologies, like the Apple Watch®, hold a promise in detecting arrhythmias, as demonstrated by the Apple Heart Study's high sensitivity and specificity for atrial fibrillation detection. This case emphasizes the importance of correlating symptoms with smartwatch-recorded ECGs, especially when receiving a elevated heart rate alert. Limitations include reliance on user activation and potential challenges in recognizing artifacts in single-lead ECGs. However, in high-risk patients, smartwatches may complement existing devices, potentially reducing mortality by aiding in early intervention and optimizing ICD programming. Conclusion: The presented case emphasizes the potential of smartwatches in identifying ventricular arrhythmias, especially in high-risk individuals. Despite the existence of many challenges, the integration of wearable technologies into clinical care could enhance arrhythmia detection, reduce unnecessary shocks, and therefore improve patient outcomes. Future research could explore the role of smartwatches as a non-invasive alternative to implantable rhythm monitoring devices.