Search

The purpose of this study was to evaluate the reliability and validity of the raw accelerometry output from research-grade and consumer wearable devices compared to accelerations produced by a mechanical shaker table. Raw accelerometry data from a total of 40 devices (i.e., n = 10 ActiGraph wGT3X-BT, n = 10 Apple Watch Series 7, n = 10 Garmin Vivoactive 4S, and n = 10 Fitbit Sense) were compared to reference accelerations produced by an orbital shaker table at speeds ranging from 0.6 Hz (4.4 milligravity-mg) to 3.2 Hz (124.7mg). Two-way random effects absolute intraclass correlation coefficients (ICC) tested inter-device reliability. Pearson product moment, Lin's concordance correlation coefficient (CCC), absolute error, mean bias, and equivalence testing were calculated to assess the validity between the raw estimates from the devices and the reference metric. Estimates from Apple, ActiGraph, Garmin, and Fitbit were reliable, with ICCs = 0.99, 0.97, 0.88, and 0.88, respectively. Estimates from ActiGraph, Apple, and Fitbit devices exhibited excellent concordance with the reference CCCs = 0.88, 0.83, and 0.85, respectively, while estimates from Garmin exhibited moderate concordance CCC = 0.59 based on the mean aggregation method. ActiGraph, Apple, and Fitbit produced similar absolute errors = 16.9mg, 21.6mg, and 22.0mg, respectively, while Garmin produced higher absolute error = 32.5mg compared to the reference. ActiGraph produced the lowest mean bias 0.0mg (95%CI = -40.0, 41.0). Equivalence testing revealed raw accelerometry data from all devices were not statistically significantly within the equivalence bounds of the shaker speed. Findings from this study provide evidence that raw accelerometry data from Apple, Garmin, and Fitbit devices can be used to reliably estimate movement; however, no estimates were statistically significantly equivalent to the reference. Future studies could explore device-agnostic and harmonization methods for estimating physical activity using the raw accelerometry signals from the consumer wearables studied herein. [ABSTRACT FROM AUTHOR]

The Apple Watch™ (AW) offers heart rate (HR) tracking by photoplethysmography (PPG) and single-lead electrocardiographic (ECG) recordings. The accuracy of AW-HR and diagnostic performance of AW-ECGs among children during both sinus rhythm and arrhythmias have not been explored. The purposes of this study were to assess the accuracy of AW-HR measurements compared to gold standard modalities in children during sinus rhythm and arrhythmias and to identify non–sinus rhythms using AW-ECGs. Subjects ≤18 years wore an AW during (1) telemetry admission, (2) electrophysiological study (EPS), or (3) exercise stress test (EST). AW-HRs were compared to gold standard modality values. Recorded AW-ECGs were reviewed by 3 blinded pediatric electrophysiologists. Eighty subjects (median age 13 years; interquartile range 1.0–16.0 years; 50% female) wore AW (telemetry 41% [n = 33]; EPS 34% [n = 27]; EST 25% [n = 20]). A total of 1090 AW-HR measurements were compared to time-synchronized gold standard modality HR values. Intraclass correlation coefficient (ICC) was high 0.99 (0.98–0.99) for AW-HR during sinus rhythm compared to gold standard modalities. ICC was poor comparing AW-HR to gold standard modality HR in tachyarrhythmias (ICC 0.24–0.27) due to systematic undercounting of AW-HR values. A total of 126 AW-ECGs were reviewed. Identification of non–sinus rhythm by AW-ECG showed sensitivity of 89%–96% and specificity of 78%–87%. We found high levels of agreement for AW-HR values with gold standard modalities during sinus rhythm and poor agreement during tachyarrhythmias, likely due to hemodynamic effects of tachyarrhythmias on PPG-based measurements. AW-ECGs had good sensitivity and moderate specificity in identification of non–sinus rhythm in children. [ABSTRACT FROM AUTHOR]

Wearable devices, particularly smartwatches like the Apple Watch (AW), can record important cardiac information, such as single‑lead 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. 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 12‑lead 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 12‑lead 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. 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. 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. [ABSTRACT FROM AUTHOR]

Background: The validity of the Apple Watch to measure the heart rate (HR) and oxygen saturation (Spo2) for patients with chronic diseases such as diabetes mellitus (DM), dyslipidemia, and hypertension is still unclear. Therefore, this study aims to investigate the accuracy of the Apple Watch in measuring the Spo2 and HR in patients with chronic diseases. Methods: Forty-one patients with chronic diseases, including 20 with hypertension, 10 with diabetes, and 11 with dyslipidemia, completed a cross-sectional study. All participants used the Apple Watch against the Polar chest strap and the pulse oximeter at rest and during moderate intensity exercise sessions to measure HR and the SpO2 at rest for 5 minutes, during exercise for 16 minutes, and followed by 3 minutes of rest. The HR was measured during all previous periods, but evaluation of the Spo2 included 5 measures, done only before and after exercise, with a minute interval between each measure. Results: Overall, a strong correlation exists between measuring the SpO2 using the Apple Watch against the pulse oximeter (Contec) at rest (r = 0.92, p < 0.001) and after exercise (r = 0.86, p < 0.001) in all patients. The HR had a very strong correlation between the Apple Watch and the Polar chest strap (r = 0.99, p < 0.001) in all patients. There was no significant difference (p = 0.76) between the twenty-seven white and fourteen brown-skinned patients. Conclusion: The Apple Watch is valid to measure the HR and SpO2 in patients with chronic diseases. Clinical Trial Registration No: NCT05271864. [ABSTRACT FROM AUTHOR]

Objective: Panic attacks are an impairing mental health problem that affects 11% of adults every year. Current criteria describe them as occurring without warning, despite evidence suggesting individuals can often identify attack triggers. We aimed to prospectively explore qualitative and quantitative factors associated with the onset of panic attacks. Results: Of 87 participants, 95% retrospectively identified a trigger for their panic attacks. Worse individually reported mood and state-level mood, as indicated by Twitter ratings, were related to greater likelihood of next-day panic attack. In a subsample of participants who uploaded their wearable sensor data (n = 32), louder ambient noise and higher resting heart rate were related to greater likelihood of next-day panic attack. Conclusions: These promising results suggest that individuals who experience panic attacks may be able to anticipate their next attack which could be used to inform future prevention and intervention efforts.

OBJECTIVES In the modern era, there is heightened interest in understanding energy expenditure during exercise. Consequently, wearable devices such as the Galaxy Watch and Apple Watch have emerged as pivotal tools for daily health monitoring, given their convenience and increasing popularity. This study aimed to compare the calculated energy expenditure derived from the graded exercise test with readings from Galaxy and Apple Watches during a 30-min exercise session among Korean university students. Through this, we anticipate offering both motivation and clear insights into energy expenditure, thereby potentially aiding in weight management strategies for contemporary individuals. METHODS This study involved 27 college students from Korea National University of Transportation in Chungcheongbuk-do, Korea. We utilized COSMED's exercise load respiratory gas analysis system (Quark-CPET, COSMED, Rome, Italy), along with the Galaxy Watch (Galaxy Watch 5, Samsung, Seoul, Korea) and the Apple Watch (Apple watch series 5, Apple, Cupertino, USA) for measurements. Energy expenditure was monitored in real-time every 5 min throughout the 30-min exercise session. For statistical evaluations, we employed a one-way analysis of variance. Subsequent post-tests utilized the Tukey post-hoc test and Pearson correlation, with a significance level set at p0.05). Conversely, a notable difference was observed when comparing energy expenditure data from the graded exercise test to that of the Apple Watch for time intervals of 10, 15, 20, 25, and 30 min (p>0.05), although the 5-min interval did not exhibit a significant difference (p>0.05). Furthermore, a robust positive correlation was evident between the energy expenditure values derived from the graded exercise test and those from both the Galaxy Watch (r=0.952, p

In this paper, I use auto‐netnography data to explore my experiences of self‐tracking with my Apple watch to uncover some of the ways in which the materiality of self‐tracking led me to experience an intensified form of surveillance around my body. The paper contributes to literature within digital geographies which considers the blurring of online and offline boundaries. I consider this in relation to auto‐netnography and auto‐ethnography to question the distinction between the two. I contribute to debates in fat studies around the blurring of the personal and researcher identity when supporting the Health at Every Size Approach, furthering these debates by exemplifying how the materiality of self‐tracking can intensify feelings of guilt and shame when researching the body. The paper concludes with some ethical recommendations for self‐care in the research process, arguing that future research should consider how the researcher should hold space to deal with the unintended emotional consequences that may come from research. [ABSTRACT FROM AUTHOR]

Background: Smartwatches have gained tremendous attention in recent years and have become widely accepted by patients, despite not being intended for medical diagnosis. Objective: This study aimed to determine the accuracy of Apple Watch oxygen saturation measurement in patients with acute exacerbation of COPD by comparing it with medical-grade pulse oximetry and ABG. Method: This single-center, prospective, cross-sectional study involved 167 patients. Patients presenting with cardiac arrest, life-threatening symptoms, severe hypoxia, or obvious jaundice were excluded. Additionally, patients whose SpO2 measurements with the Apple Watch took more than 2 min or required eight attempts were also excluded. Vital signs were measured simultaneously using the IntelliVue MX500 monitor with the Masimo Rainbow Set pulse oximeter and the Apple Watch. Concurrently, arterial blood gas (ABG) samples were drawn. Results: A strong correlation between the Apple Watch 6 and medical-grade pulse oximetry (r = 0.89, ICC = 0.940) was noted. The Bland-Altman analysis revealed a mean error of 0.458% between the Apple Watch 6 and ABG (SD: 2.78, level of agreement: − 5.912 to 4.996). The mean error between pulse oximetry and ABG (SD: 5.086, level of agreement; − 10.983 to 8.953) was 1.015%. There was a correlation between respiratory rate and the number of attempts to measure SpO2 with the Apple Watch 6 (r = 0.75, p < 0.05). Conclusion: Apple Watch 6 is an accurate and reliable method for measuring SpO2 levels in emergency patients who presented with acute exacerbation of COPD. However, tachypneic patients may encounter challenges due to the potential need for multiple attempts to measure their oxygen saturation. [ABSTRACT FROM AUTHOR]

Background The Apple Watch has the capability to record a lead 1 electrocardiogram (ECG) and can identify and report atrial fibrillation. The use for detecting myocardial ischaemia is not endorsed by Apple but is documented in this case. Case summary A 76-year-old man made a lead 1 ECG with his Apple Watch immediately after exercising on a cross trainer. He was fully asymptomatic. The ECG showed an unusual negative T-wave in this lead 1 that deepened in a few minutes and returned to normal after 22 min. He consulted a cardiologist and a standard exercise ECG confirmed the negative T-wave in lead 1 after maximal exercise and in addition showed widespread ST-depression indicating myocardial ischaemia, again without any clinical symptoms. Further studies revealed severe obstructive three-vessel coronary artery disease that was considered not suitable for percutaneous intervention. A coronary artery bypass operation on all involved vessels was performed successfully. Recovery was uneventful and an exercise ECG repeated 11 weeks later was normal. Discussion We demonstrated that the lead 1 ECG made with the Apple Watch can reliably record T-wave changes indicating myocardial ischaemia. The use of the Apple Watch to document ischaemic changes should be studied systematically for its potential to identify myocardial ischaemia, mainly triggered by symptoms but maybe for asymptomatic persons as well. [ABSTRACT FROM AUTHOR]

Background: For older, frail adults, exercise before surgery through prehabilitation (prehab) may hasten return recovery and reduce postoperative complications. We developed a smartwatch-based prehab program (BeFitMe) for older adults that encourages and tracks at-home exercise. The objective of this study was to assess patient perceptions about facilitators and barriers to prehab generally and to using a smartwatch prehab program among older adult thoracic surgery patients to optimize future program implementation. Methods: We recruited patients, aged ≥50 years who had or were having surgery and were screened for frailty (Fried's Frailty Phenotype) at a thoracic surgery clinic at a single academic institution. Semi-structured interviews were conducted by telephone after obtaining informed consent. Participants were given a description of the BeFitMe program. The interview questions were informed by The Five "Rights" of Clinical Decision-Making framework (Information, Person, Time, Channel, and Format) and sought to identify the factors perceived to influence smartwatch prehab program participation. Interview transcripts were transcribed and independently coded to identify themes in for each of the Five "Rights" domains. Results: A total of 29 interviews were conducted. Participants were 52% men (n = 15), 48% Black (n = 14), and 59% pre-frail (n = 11) or frail (n = 6) with a mean age of 68 ± 9 years. Eleven total themes emerged. Facilitator themes included the importance of providers (right person) clearly explaining the significance of prehab (right information) during the preoperative visit (right time); providing written instructions and exercise prescriptions; and providing a preprogrammed and set-up (right format) Apple Watch (right channel). Barrier themes included pre-existing conditions and disinterest in exercise and/or technology. Participants provided suggestions to overcome the technology barrier, which included individualized training and support on usage and responsibilities. Conclusions: This study reports the perceived facilitators and barriers to a smartwatch-based prehab program for pre-frail and frail thoracic surgery patients. The future BeFitMe implementation protocol must ensure surgical providers emphasize the beneficial impact of participating in prehab before surgery and provide a written prehab prescription; must include a thorough guide on smartwatch use along with the preprogrammed device to be successful. The findings are relevant to other smartwatch-based interventions for older adults. [ABSTRACT FROM AUTHOR]

Gait variability (GV) is a crucial measure of inconsistency of muscular activities or body segmental movements during repeated tasks. Hence, GV might serve as a relevant and sensitive measure to quantify adjustments of walking control. However, it has not been clarified whether GV is associated with walking speed, a clarification needed to exploit effective better bilateral coordination level. For this aim, fourteen male students (age 22.4 ± 2.7 years, body mass 74.9 ± 6.8 kg, and body height 1.78 ± 0.05 m) took part in this study. After three days of walking 1 km each day at a self-selected speed (SS) on asphalt with an Apple Watch S. 7 (AppleTM, Cupertino, CA, USA), the participants were randomly evaluated on a treadmill at three different walking speed intensities for 10 min at each one, SS − 20%/SS + 20%/ SS, with 5 min of passive recovery in-between. Heart rate (HR) was monitored and normalized as %HRmax, while the rate of perceived exertion (RPE) (CR-10 scale) was asked after each trial. Kinematic analysis was performed, assessing the Contact Time (CT), Swing Time (ST), Stride Length (SL), Stride Cycle (SC), and Gait Variability as Phase Coordination Index (PCI). RPE and HR increased as the walking speed increased (p = 0.005 and p = 0.035, respectively). CT and SC decreased as the speed increased (p = 0.0001 and p = 0.013, respectively), while ST remained unchanged (p = 0.277). SL increased with higher walking speed (p = 0.0001). Conversely, PCI was 3.81 ± 0.88% (high variability) at 3.96 ± 0.47 km·h−1, 2.64 ± 0.75% (low variability) at SS (4.94 ± 0.58 km·h−1), and 3.36 ± 1.09% (high variability) at 5.94 ± 0.70 km·h−1 (p = 0.001). These results indicate that while the metabolic demand and kinematics variables change linearly with increasing speed, the most effective GV was observed at SS. Therefore, SS could be a new methodological approach to choose the individual walking speed, normalize the speed intensity, and avoid a gait pattern alteration. [ABSTRACT FROM AUTHOR]

Predicting cardiorespiratory fitness levels can be useful for measuring progress in an exercise program as well as for stratifying cardiovascular risk in asymptomatic adults. This study proposes a model to predict fitness level in terms of maximal oxygen uptake using anthropometric, heart rate, and step count data. The model was trained on a diverse cohort of 3115 healthy subjects (1035 women and 2080 men) aged 42 ± 10.6 years and tested on a cohort of 779 healthy subjects (260 women and 519 men) aged 42 ± 10.18 years. The developed model is capable of making accurate and reliable predictions with the average test set error of 3.946 ml/kg/min. The maximal oxygen uptake labels were obtained using wearable devices (Apple Watch and Garmin) during recorded workout sessions. Additionally, the model was validated on a sample of 10 subjects with maximal oxygen uptake determined directly using a treadmill protocol in a laboratory setting and showed an error of 4.982 ml/kg/min. Unlike most other models, which use accelerometer readings as additional input data, the proposed model relies solely on heart rate and step counts—data readily available on the majority of fitness trackers. The proposed model provides a point estimation and a probabilistic prediction of cardiorespiratory fitness level, thus it can estimate the prediction's uncertainty and construct confidence intervals. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Although smartwatches are not considered medical devices, experimental validation of their accuracy in detecting hypoxemia is necessary due to their potential use in monitoring conditions manifested by a prolonged decrease in peripheral blood oxygen saturation (SpO2), such as chronic obstructive pulmonary disease, sleep apnea syndrome, and COVID-19, or at high altitudes, e.g., during sport climbing, where the use of finger-sensor-based pulse oximeters may be limited. The aim of this study was to experimentally compare the accuracy of SpO2 measurement of popular smartwatches with a clinically used pulse oximeter according to the requirements of ISO 80601-2-61. Each of the 18 young and healthy participants underwent the experimental assessment three times in randomized order—wearing Apple Watch 8, Samsung Galaxy Watch 5, or Withings ScanWatch—resulting in 54 individual experimental assessments and complete datasets. The accuracy of the SpO2 measurements was compared to that of the Radical-7 (Masimo Corporation, Irvine, CA, USA) during short-term hypoxemia induced by consecutive inhalation of three prepared gas mixtures with reduced oxygen concentrations (14%, 12%, and 10%). All three smartwatch models met the maximum acceptable root-mean-square deviation (≤4%) from the reference measurement at both normal oxygen levels and induced desaturation with SpO2 less than 90%. Apple Watch 8 reached the highest reliability due to its lowest mean bias and root-mean-square deviation, highest Pearson correlation coefficient, and accuracy in detecting hypoxemia. Our findings support the use of smartwatches to reliably detect hypoxemia in situations where the use of standard finger pulse oximeters may be limited. [ABSTRACT FROM AUTHOR]

Rheumatoid arthritis (RA) is a fluctuating progressive disease requiring frequent symptom assessment for appropriate management. Continuous tracking using digital technologies may provide greater insights of a patient's experience. This prospective study assessed the feasibility, reliability, and clinical utility of using novel digital technologies to remotely monitor participants with RA. Participants with moderate to severe RA and non-RA controls were monitored continuously for 14 days using an iPhone with an integrated bespoke application and an Apple Watch. Participants completed patient-reported outcome measures and objective guided tests designed to assess disease-related impact on physical function. The study was completed by 28 participants with RA, 28 matched controls, and 2 unmatched controls. Completion rates for all assessments were > 97% and were reproducible over time. Several guided tests distinguished between RA and control cohorts (e.g., mean lie-to-stand time [seconds]: RA: 4.77, control: 3.25; P < 0.001). Participants with RA reporting greater stiffness, pain, and fatigue had worse guided test performances (e.g., wrist movement [P < 0.001] and sit-to-stand transition time [P = 0.009]) compared with those reporting lower stiffness, pain, and fatigue. This study demonstrates that digital technologies can be used in a well-controlled, remote clinical setting to assess the daily impact of RA. [ABSTRACT FROM AUTHOR]

In the modern world, stress has become a pervasive concern that affects individuals' physical and mental well-being. To address this issue, many wearable devices have emerged as potential tools for stress detection and management by measuring heart rate, heart rate variability (HRV), and various metrics related to it. This literature review aims to provide a comprehensive analysis of existing research on HRV tracking and biofeedback using smartwatches pairing with reliable 3rd party mobile apps like Elite HRV, Welltory, and HRV4Training specifically designed for stress detection and management. We apply various algorithms and methodologies employed for HRV analysis and stress detection including time-domain, frequency-domain, and non-linear analysis techniques. Prominent smartwatches, such as Apple Watch, Garmin, Fitbit, Polar, and Samsung Galaxy Watch, are evaluated based on their HRV measurement accuracy, data quality, sensor technology, and integration with stress management features. We describe the efficacy of smartwatches in providing real-time stress feedback, personalized stress management interventions, and promoting overall well-being. To assist researchers, doctors, and developers with using smartwatch technology to address stress and promote holistic well-being, we discuss the data's advantages and limitations, future developments, and the significance of user-centered design and personalized interventions. [ABSTRACT FROM AUTHOR]

The purpose of this study was to explore the concurrent validity of 2 commercial smartwatches (Apple Watch Series 6 and 7) against a clinical criterion device (12-lead electrocardiogram [ECG]) and a field criterion device (Polar H-10) during exercise. Twenty-four male collegiate football players and 20 recreationally active young adults (10 men and 10 women) were recruited and participated in a treadmill- based exercise session. The testing protocol included 3 minutes of standing still (resting), then walking at low intensity, jogging at a moderate intensity, running at a high intensity, and postexercise recovery. The intraclass correlation (ICC2,k), and Bland-Altman plot analyses showed a good validity of the Apple Watch Series 6 and Series 7 with increased error (bias) as jogging and running speed increased in the football and recreational athletes. The Apple Watch Series 6 and 7 are highly valid smartwatches at rest and different exercise intensities, with validity decreasing with increased running speed. Strength and conditioning professionals and athletes can confidently use the Apple Watch Series 6 and 7 when tracking heart rate; however, caution must be taken when running at moderate or higher speeds. The Polar H-10 can surrogate a clinical ECG for practical applications. [ABSTRACT FROM AUTHOR]

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. 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. 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. 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. • The Apple Watch has demonstrated the capability to record ECGs from a single lead and detect atrial fibrillation using photoplethysmography. • Strategically placing the smartwatch on body parts allows recording up to 9 leads, mimicking a standard ECG's morphology. • The device's format limits voltage in precordial leads, leading to inaccuracies and reducing its clinical utility. • This review analyzes Apple Watch's ECG recording capacity and CSV file utilization to explore and mitigate this limitations. [ABSTRACT FROM AUTHOR]

Copyright of Angle Health Law Review is the property of Angle Publishing Co., Ltd. and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)