Apple Watch Research Articles

Performance of apple watch for detecting atrial fibrillation among a known atrial fibrillation population

Abstract Background Photoplethysmography (PPG) based consumer accessible devices with algorithms for irregular rhythm detection are gaining popularity as an opportunistic screening tool for atrial arrhythmias, and are FDA cleared for use in patients without a history of atrial fibrillation (AF). However, these devices are increasingly used for the management of AF, a purpose for which some algorithms are not intended. Purpose To compare the performance of the Apple Watch Irregular Rhythm Notification (IRN) with an implantable cardiac monitor (ICM) for the detection of atrial fibrillation (AF) in patients with known AF from the DEFINE AFib study. Methods Patients with known AF, a LINQ or LINQ II ICM, and an Apple Watch with PPG-based IRN activated were included in the analysis. ICM detected AF episodes served as the standard to which Apple Watch IRNs were compared. All AF episodes detected by the ICM device were manually adjudicated by two trained, independent reviewers with a third independent reviewer in situations of disagreement. Only AF episodes ≥60 minutes during a 75-minute window in which the Apple Watch was monitoring were included in accordance with the known Apple Watch algorithm detection parameters. To eliminate oversampling of ICM detected episodes compared to Apple Watch IRNs, 1,000 simulations of one randomly selected episode per subject was done to avoid single-subject bias. Results In total, 97 subjects were analyzed of which 42 had an AF episode ≥60 minutes. Applying the 75-minute window of Apple Watch monitoring to episodes ≥60 minutes, 20 subjects had qualifying, adjudicated ICM detected AF. These 20 subjects accounted for 481 qualifying AF episodes. Of the ICM detected episodes, 191 (40%) occurred while an Apple Watch was not being worn. In total, 11/20 (55%) subjects had an IRN generated by the Apple Watch. Adjudication of the ICM episodes resulted in 290 true positive AF episodes ≥60 minutes during a 75-minute window while the watch was actively monitoring, with 74 (26%) of those episodes triggering an IRN resulting in a per episode sensitivity of 24 ± 7% and PPV of 91 ± 4% (Figure 1A). The efficacy of Apple Watch to detect AF episodes of differing lengths was assessed for episodes between 1-6 hours, 6-12 hours, 12-24 hours, and > 24 hours with sensitivities of 19 ± 19%, 38 ± 8%, 32 ± 10%, and 31 ± 8%, respectively (Figure 1B). The Apple Watch generated an annualized false positive rate of 2.25 IRNs for every patient year of monitoring. At the subject level, Apple Watch had a sensitivity of 48%, specificity of 100%, PPV of 100%, and NPV of 71% (Figure 1A). Conclusions In patients with a history of AF, the Apple Watch IRN was most sensitive at the individual subject level while watch performance was lower by episode. The current IRN algorithm favors longer episode durations. The limited sensitivity, dependence on user wear-time, and reliance on long-durations of AF limit the use of Apple Watch for AF management.

Accuracy of the Apple Watch in detecting atrial fibrillation among patients undergoing 24-hour Holter monitoring

Abstract Background As direct-to-consumer wearable devices like the Apple Watch become more common, an increasing number of patients arrive at clinics with reports of irregular rhythms captured on their devices. Purpose This study sought to assess the accuracy of a commonly used smart wearable device (Apple Watch) in detecting atrial fibrillation. Methods We enrolled 140 patients referred for 24-hour Holter monitoring in a Cardiovascular Center. Following thorough disclosure, participants were instructed to wear an Apple Watch provided by the researchers in addition to the Holter monitor to facilitate comparison. The Apple Watch was selected due to its widespread usage among patients. Participants were instructed to record an ECG using the Apple Watch hourly, or more frequently while awake. The objective of the study was to evaluate the sensitivity and specificity of the irregular rhythm notification and ECG-based detection features of the Apple Watch in comparison to the conventional 24-hour Holter monitoring for atrial fibrillation detection. Results The irregular rhythm notification feature of the Apple Watch exhibited a low sensitivity of 21.4% but achieved a high specificity of 100% in detecting atrial fibrillation. Meanwhile, the ECG-based detection feature of the Apple Watch analyzed from 1,295 ECGs captured by the participants when compared in real-time to their ECG rhythms recorded by the Holter monitor, revealed a high level of agreement with a sensitivity of 100% and a specificity of 99.1%. Conclusion The low sensitivity of the irregular notification feature of the Apple watch in detecting atrial fibrillation cautions against relying solely on it to rule out atrial fibrillation as the cause of a patient’s symptoms. Nevertheless, the high concordance of manually taken Apple watch ECGs positions the device as a reliable tool for detecting atrial fibrillation through its ECG-based detection feature.

Smart watch for the detection of exercise-induced transient ST segment changes

Abstract Background Smartwatches are largely used and allowed to perform electrocardiogram one lead, usually a modified DI, used to diagnose atrial fibrillation. It is unknown whether or not smartwatches could be used to identify electrocardiographic transient ST changes during exercise-induced myocardial ischemia. The present study assessed the feasibility and reliability of using an Apple watch to record V5 in subjects scheduled for standard ECG stress tests. Methods One hundred eighty-nine subjects were included in this study. The mean age was 61+9 years, and 21.6 % were women (41 subjects). A single thoracic lead V5 was obtained with an Apple watch, positioned at the fifth intercostal space anterior axillary line, by creating a circuit between the back of the watch and the right index resting on the crown for 30 seconds. Four subjects were excluded because of the poor quality of the Apple watch signal. Forty-nine patients (25,92%) had a positive stress test with standard 12 lead ECG, 136 (71,96 %) negative stress test, and 4 (2,12%) the ECGs were non-diagnostic. The mean difference or bias between the Apple Watch and standard exercise-induced ST changes was quantified as 0.054, suggesting a generally minimal discrepancy in their readings. This consistency is critically affirmed by Cohen's Kappa (k) coefficient of 0.665. 9-1.0) Results A total of 189 subjects were included in the study; the mean age was 61 years (SD 9), and 21.6 % were women (41 subjects). Insightful outcomes were gleaned from the Bland-Altman analysis, which probed the concordance between the smartwatch ECG and the conventional standard ECG in measuring ST-segment deviations (Bias 0,054; SD 0.31, from -0.56 to 0.67, 95% limits of agreement), as vividly depicted in Figure. The dashed blue lines in Figure demarcate the 95% limits of agreement, which span from -0.56 to 0.67, offering a tangible visualization of the range within which most individual differences resided. A notable harmony was unveiled in the intricate exploration of the concordance between the two diagnostic methodologies, substantiated by an agreement of 87.6%. This demonstrates that, in most instances, the methodologies provided analogous results, exhibiting a commendable consistency between them in a substantial proportion of cases. Assuming the results of standard ECGs as the reference values, Apple Watch ST deviation showed a sensitivity of 69% and specificity of 94 %. The positive predictive value was 0.80952381, and the negative predictive value was 0,8951049. Conclusions In conclusion, this study demonstrated that the Apple Watch could detect transient ST changes with a sensitivity of 69% and a specificity of 94 %. Further prospective studies should be performed to assess the potential role of detecting transient myocardial ischemia with a smartwatch in a general population at risk.

Validation of diagnostic wearable ECG data from high-risk patients and integration into the atrial fibrillation catheter ablation pathway

Abstract Background Wearables like the Apple Watch (AW) offer non-invasive, long-term monitoring and patient-led, single-lead ECG recordings. Although validated for Atrial Fibrillation (AF) detection in the general population, the accuracy and clinical utility of the single-lead ECG recording feature for the diagnosis of AF has not been evaluated in patients at high risk of atrial arrhythmias.(1) An evaluation of the accuracy of the Wearable-derived labels is important to determine the feasibility of integrating AW-based remote monitoring into clinical pathways. Objective To evaluate the accuracy and data burden from AW-based, patient-led ECG monitoring in patients undergoing AF catheter ablation. Methods Patients referred for first-time AF catheter ablation to the Study Institution between January 2022 and March 2023 were invited to enrol in the AF Follow-up with Apple Watch (AFFU-AW) Trial. Patients randomised to the active arm of the study received 12 months of post-ablation follow-up using a loaned Apple Watch. Participants underwent an education session and were asked to perform daily ECGs; sending through any labelled as AF, inconclusive, or in the context of symptoms. The accuracy of the automated ECG labelling was determined against blinded, independent labelling by two experienced Electrophysiologists. Results 1093 remote transmissions were emailed in for evaluation from 52 patients to the Study team. This was a median of 6 [2, 17] per patient and a receiver burden of 2 [1, 4] ECGs per day. 475 were categorised by the AW as AF with a sensitivity of 0.71 and specificity of 0.96 compared to expert labelling. 342 (31%) were not categorised by the wearable, with an ‘inconclusive’ interpretation (161 [47%]) or ‘high heart rate’ (108 [32%]) as the most cited reasons. 178 (55%) of the uncategorised ECGs were subsequently overread as AF. Accompanying symptoms were reported with 324 (30%) ECGs, most commonly palpitations. Conclusion This is the first reported evaluation of the AF diagnostic feature based on AW ECG recordings as part of a remote patient rhythm surveillance pathway after catheter ablation. The high positive predictive value of ‘Sinus Rhythm’ and ‘AF’ labels and the high specificity of the ‘AF’ label are comparable to diagnostic algorithms in implantable loop recorders.(2) The source code to automate data curation and structured databasing of wearable ECGs has been made available for care providers to integrate the service into device monitoring workflows.(3)

Listen to your heart: The effect of music on heart rate variability

Abstract Background Heart rate variability (HRV) is indicative of autonomic balance. High HRV during sinus rhythm is associated with reduced morbidity and mortality across a wide range of health disorders. Listening to music has been shown to induce psychological and physiological responses that increase HRV, a mechanism currently employed in music therapy to reduce anxiety and post-operative pain. However, it remains unclear which music characteristics have the most positive effect on HRV. Purpose This study aimed to assess the impact of different music genres, white noise, tempo, and personal music preferences on HRV. Methods Adults without atrial fibrillation or pacemaker dependency were included during the Netherlands largest three-day music festival. Participants were subjected to three-minute fragments of white noise, and subsequently a randomized order of classical, rock, electronic and the subject’s most-listened personal ("Spotify Wrapped 2022") music; all having an either fast (130-140 BPM) or slow tempo (70-80 BPM), which was randomized as well. Afterwards, participants ranked all music fragments in order of their preference. A tachogram was collected using the PPG sensor of an Apple Watch Series 8. Data was pre-processed to exclude non-sinus rhythm beats and measurement artefacts. HRV time-domain (SDNN, RMSSD, NN50, pNN50, triangular index) and frequency-domain (LF, LFn, HF, HFn, LF/HF) metrics were calculated from the tachograms using Python-software. HRV outcomes were corrected for age, sex, heart rate and music tempo group and analyzed using general linear mixed models in SPSS. Results In total 242 participants with a median age of 28 years (IQR 24-32) were included, of which 136 (56%) female. Classical music showed a significantly higher HRV response (RMSSD) compared to electronic (p=0.025) and the subject’s personal music (p=0.030, see figure). No other significant differences in HRV were found between the different genres and personal music. White noise showed a significantly higher HRV for all time domains, as well as LF and HF, compared to the different genres and personal music. A total of 181 (74.8%) participants ranked their personal music fragment as the most preferred. There was no difference between the fast and slow tempo group when performing an interaction analysis for tempo and the different music genres except for classical and personal music (RMSSD, p = 0.047). Conclusions This large study highlights that classical music enhances HRV more than other music, suggesting its therapeutic potential. Remarkably, white noise showed the greatest increase in HRV. Although these were bitter pills to swallow; white noise or classical music once a day might keep the doctor away.

Comprehensive cardiac interval analysis in the WEAR-TECH study cohort by comparing the Apple Watch Series 6 against a simultaneous 12-lead ECG

Abstract Background Wearable devices capable of single-lead electrocardiograms (ECGs) are becoming increasingly prevalent. Exploring their potential for screening conditions beyond atrial fibrillation presents a compelling research opportunity. Objective This study evaluates the agreement between the Apple Watch Series 6 (APW) and simultaneous 12-lead ECG (12L) recordings in measuring PR, QRS, RR, QT, and QTc intervals in a cohort of National Health Service (NHS) cardiology patients. Methods We analysed data from the WEAR-TECH study, which included 400 simultaneous recordings from the APW and 12L ECGs. We included patients with ECGs in sinus rhythm and excluded patients with poor-quality ECGs. No exclusions were made based on underlying cardiac conditions. Measurements of the best three consecutive waveforms in leads I, II, and V5 were performed using EP Calipers (EP Studio) by a single observer. Leads I and II were compared for PR, QRS, and RR intervals, while leads I, II, and V5 were compared for QT and QTc intervals. QTc was calculated using Bazett’s formula. Differences between APW and 12L ECGs were assessed using linear mixed effects regression models with random intercepts and type II Wald chi-square tests. Clinical agreement was measured by averaging the waveforms from 12L leads and the APW and calculating the difference between the means. Results Out of 324 paired ECGs from 162 patients, 8 were excluded due to missing or uninterpretable data. The median age was 64 (IQR: 54 - 74) years, with 117 (75%) being male. PR, QRS, and RR intervals from the APW agreed best with 12L lead II, and QT and QTc intervals from APW agreed best with 12L lead V5, with no significant difference found in any of these comparisons. PR and RR intervals significantly differed between APW and 12L lead I, with the APW tending to overestimate relative to the 12L. Additionally, significant differences were found in 12L leads I and II for QT and QTc, with APW also tending to overestimate these intervals (see Table 1). Clinical agreement between the APW and all 12L leads was a minimum of 21% for a <10ms difference, 45% for a <20ms difference, and 71% for a <40ms difference (see Figure 1). Conclusions The Apple Watch Series 6 demonstrates promising potential for initial outpatient monitoring of PR, QRS, RR, QT, and QTc intervals with reasonable agreement to 12L ECG. This is especially true in lead II for PR, QRS, and RR intervals and lead V5 for QT and QTc intervals. Further reductions in variance between APW and 12L ECG measurements could enhance clinical decision-making accuracy.Table 1:The p-values of APW vs 12L leadFigure 1:Agreement of APW to 12L leads

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

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

Automated, Real-Time Integration of Biometric Data From Wearable Devices With Electronic Medical Records: A Feasibility Study.

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. 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. 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). 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.

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

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

The provenance of Apple Health data: A timeline of update history

Fitness tracking smart watches are becoming more prevalent in investigations and the need to understand and document their forensic potential and limitations is important for practitioners and researchers. Such fitness devices have undergone several hardware and software upgrades, changing the way they operate and evolving as more sophisticated pieces of technology. One example is the Apple Watch, working in conjunction with the Apple iPhone, to measure and record a vast amount of health information in the Apple Health database, healthdb_secure.sqlite. Over time, an end user will update their devices, but their health data, uniquely, carries over from one device to the next. In this paper, we investigate and analyse the hardware and software provenance of a real 5+ year Apple Health dataset to determine changes, patterns and anomalies over time. This provenance investigation provides insights in the form of (1) a timeline, representing the dataset's history of device and firmware updates that can be used in the context of investigation validation, (2) anomaly detection and, (3) insights into cyber hygiene. Analysis of the non-health data recorded in the health database arguably provides just as much insightful information as the health data itself.

The association between salivary IL-6 and poor sleep quality assessed using Apple watches in stressed workers in Japan.

Insomnia is a major concern among stressed workers worldwide. Although stress generally has a detrimental effect on sleep quality, the impact of biological stress, especially the immunological stress response, on sleep quality is not yet fully understood. Stressed workers were recruited through a screening process using a Brief Job Stress Questionnaire. The participants were asked to complete various occupation-related questionnaires. Additionally, saliva samples were collected to assess interleukin-6 (IL-6) levels as an immunological stress response. Subsequently, they were asked to wear an Apple Watch to record their sleep pattern for one week. Their sleep architecture was estimated using a previously published and validated method. Finally, data from 73 participants were analyzed. Our multivariable analysis revealed that shorter durations of slow-wave sleep (non-rapid eye movement sleep stage 3) were significantly associated with elevated IL-6 levels (p = 0.005) and greater daytime sleepiness (p = 0.002) after controlling for total sleep time. Our finding that a higher immunological stress response is associated with poor sleep quality contributes to a better understanding of insomnia in stressed workers and emphasizes the importance of stress management in this population.

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

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

Impacts of Campus Green Spaces Exposure on Psychological Stress of College Students Based on Apple

Current research has revealed significant benefits of green space exposure (GSE) on psychological stress. However, few studies have explained the cumulative effects of GSE and how these effects vary across different demographic groups, such as gender and education level. This research aims to evaluate the long-term effects of GSE on psychological stress and heart rate variability (HRV) while examining how these effects vary across different demographic groups, such as gender and education level, utilizing Apple Watch data to provide a more objective measure of these effects. In a 6-week experiment, 43 college students were required to engage in GSE at least three times a week, each lasting no less than 10 minutes, based on their schedules. During the experiment, participants wore the Apple Watch continuously to collect physiological data automatically. This study demonstrates a significant reduction in emotional disturbance and notable changes in HRV amplitude following GSE, with variations observed based on gender and education levels. These findings underscore the substantial psychological health benefits of GSE.

A Remote Patient Monitoring System with Feedback Mechanisms using a Smartwatch: Concept, Implementation and Evaluation based on the activeDCM Randomized Controlled Trial.

Technological advances allow recording and sharing of health-related data in a patient-centric way using smartphones and wearables. Secure sharing of such patient-generated data with physicians would enable a dense management of individual health trajectories, monitoring of risk factors and asynchronous feedback. However, most Remote Patient Monitoring (RPM) systems currently available are not fully integrated into hospital IT systems or lack the patient-centric design. The objective was to conceptualize and implement a user-friendly, reusable, interoperable and secure RPM system incorporating asynchronous feedback mechanisms, using a broadly available consumer wearable (Apple Watch). Additionally, the study sought to evaluate factors influencing patient acceptance of such systems. The RPM system requirements were established through focus group sessions. Subsequently, a system concept was designed and implemented using an iterative approach, ensuring technical feasibility from the beginning. To assess clinical feasibility, the system was employed as part of the activeDCM prospective, randomized, interventional study focusing on Dilated Cardiomyopathy (DCM). Each patient used the system for at least 12 months. The System Usability Scale (SUS) was employed to measure usability from a subjective patient perspective. Additionally, an evaluation was conducted on the objective wearable interaction frequency as well as the completeness of transmitted data, classified into Sensor-based Health Data (SHD) and Patient Reported Outcome Measures (PROM). Descriptive statistics using boxplots, along bootstrapped multiple linear regression with a 95% confidence interval (CI) were utilized for evaluation, analyzing the influence of age, sex, device experience and intervention group membership. The RPM system consists of four interoperable components: patient-devices, data-server, data-viewer and notification-service. The evaluation of the system was conducted with 95 consecutive DCM patients (female: 28 of 95 (29%), age: 50±12 years) completing the activeDCM study protocol. The wearable/ smartphone application of the system achieved a mean SUS score of 78±17, which was most influenced by device experience. 83 of 95 patients (87%) could integrate the wearable application (very) well into their daily routine and 67 of 95 (70%) saw a benefit of the RPM system for management of their health condition. Patients interacted on average with the wearable on 61%±26% of days enrolled in the study, corresponding to 239±99 of 396±39 days. SHD was available on average for 78%±23% of days and PROM data 64%±27% of weeks enrolled in the study, corresponding to 307±87 of 396±39 days and 35±15 of 56±5 weeks, respectively. Wearable interaction frequency, SHD and PROM completeness were most influenced by intervention group membership. Our results mark a first step towards integrating RPM systems, based on a consumer wearable device for primary patient input, into standardized clinical workflows. They can serve as a blueprint for creating a user-friendly, reusable, interoperable and secure RPM system, that can be integrated into patients' daily routines. ClinicalTrials.gov-Identifier: NCT04359238.

Understanding activity and physiology at scale: The Apple Heart & Movement Study

Physical activity or structured exercise is beneficial in a wide range of circumstances. Nevertheless, individual-level data on differential responses to various types of activity are not yet sufficient in scale, duration or level of annotation to understand the mechanisms of discrete outcomes nor to support personalized recommendations. The Apple Heart & Movement Study was designed to passively collect the dense physiologic data accessible on Apple Watch and iPhone from a large real-world cohort distributed across the US in order to address these knowledge gaps.

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

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. 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. 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. 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.

Efficacy of wearable cardiac monitoring devices versus traditional methods in detecting atrial fibrillation: a systematic review and meta-analysis

Introduction: Atrial fibrillation (AF) is a prevalent arrhythmia with significant health and economic impacts. Traditional detection methods like 12-lead ECGs and Holter monitors are effective but limited by cost and patient compliance. Wearable devices, such as smartwatches and patches, offer a promising alternative for real-time AF detection.Objective: This systematic review aims to evaluate the efficacy of wearable cardiac monitoring devices compared to traditional methods in detecting AF.Methods: A comprehensive literature search identified studies comparing wearable devices to traditional methods for AF detection. Data on sensitivity, specificity, and usability were extracted and analyzed. A pooled analysis using both fixed-effect and random-effects models assessed overall sensitivity and specificity.Results: This systematic review analyzed data from 15 studies comparing wearable devices to traditional methods for AF detection. Wearable devices, including smartwatches, patch-type ECGs, and PPG-based technologies, showed high sensitivity and specificity, with the fixed-effect model estimating overall sensitivity at 91.59% and specificity at 92.13%. The random-effects model provided slightly higher sensitivity (94.03%) and specificity (95.96%). Smartwatches like the Apple Watch with KardiaBand demonstrated up to 97.5% sensitivity, comparable to insertable cardiac monitors (ICMs). Patch-type ECGs, such as MobiCARE-MC100 and Zio XT, matched Holter monitors in accuracy, with extended monitoring enhancing AF detection. PPG-based technologies, exemplified by the WATCH AF trial, showed 93.7% sensitivity and 98.2% specificity. Despite high accuracy, significant heterogeneity among studies highlighted the need for standardized protocols.Conclusion: Wearable devices show high sensitivity and specificity for AF detection, comparable to traditional methods. However, substantial heterogeneity indicates the need for standardized protocols and further research to optimize these technologies for clinical use.

A Crowdsourced AI Framework for Atrial Fibrillation Detection in Apple Watch and Kardia Mobile ECGs.

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.

Validity of Wearable Trackers for Measuring Steps, Energy Expenditure and Heart Rate in Korean Elderly

PURPOSE: This study aimed to assess the validity of a physical activity tracking device by comparing physical activity data from older adults, including the number of steps, energy expenditure, and heart rate (HR), with data obtained from research accelerometers, wearable watches, and mobile apps.METHODS: A total of 24 healthy older adults (aged 65-79 years) wore a wearable tracking device and used a respiratory gas analyzer while walking and running on a treadmill at speeds of 3.8 km/h, 4.8 km/h, 5.6 km/h, 6.4 km/h, and 8.0 km/h, in a randomized order. This study evaluated seven wearable devices: one research accelerometer (ActiGraph GT3X+), five smartwatches/fitness trackers (Samsung Galaxy Watch Active 2, Fitbit Versa 2, Xiaomi Mi Band 4, Garmin Forerunner 945, and Apple Watch Series 2), and one mobile application. Step counts were measured directly, HR was compared using a Polar H7 HR Sensor, and energy expenditure was assessed using a Quarkb2 respiratory gas analyzer.RESULTS: Commercial wearable watches and mobile applications were more accurate than research-grade accelerometers for monitoring steps during walking and running in older adults. HR measurements from wearable watches were more accurate than those from reference devices. Although energy expenditure measurements from mobile applications and wearable watches were less accurate than measurements of steps and HRs their accuracy was comparable to that of research-grade accelerometers.CONCLUSIONS: This study provides insights into the strengths of using wearable devices in healthcare and fitness fields, which seek to monitor and actively manage the physical activity of older adults using wearable trackers.

Real-World Accuracy of Wearable Activity Trackers for Detecting Medical Conditions: Systematic Review and Meta-Analysis.

Wearable activity trackers, including fitness bands and smartwatches, offer the potential for disease detection by monitoring physiological parameters. However, their accuracy as specific disease diagnostic tools remains uncertain. This systematic review and meta-analysis aims to evaluate whether wearable activity trackers can be used to detect disease and medical events. Ten electronic databases were searched for studies published from inception to April 1, 2023. Studies were eligible if they used a wearable activity tracker to diagnose or detect a medical condition or event (eg, falls) in free-living conditions in adults. Meta-analyses were performed to assess the overall area under the curve (%), accuracy (%), sensitivity (%), specificity (%), and positive predictive value (%). Subgroup analyses were performed to assess device type (Fitbit, Oura ring, and mixed). The risk of bias was assessed using the Joanna Briggs Institute Critical Appraisal Checklist for Diagnostic Test Accuracy Studies. A total of 28 studies were included, involving a total of 1,226,801 participants (age range 28.6-78.3). In total, 16 (57%) studies used wearables for diagnosis of COVID-19, 5 (18%) studies for atrial fibrillation, 3 (11%) studies for arrhythmia or abnormal pulse, 3 (11%) studies for falls, and 1 (4%) study for viral symptoms. The devices used were Fitbit (n=6), Apple watch (n=6), Oura ring (n=3), a combination of devices (n=7), Empatica E4 (n=1), Dynaport MoveMonitor (n=2), Samsung Galaxy Watch (n=1), and other or not specified (n=2). For COVID-19 detection, meta-analyses showed a pooled area under the curve of 80.2% (95% CI 71.0%-89.3%), an accuracy of 87.5% (95% CI 81.6%-93.5%), a sensitivity of 79.5% (95% CI 67.7%-91.3%), and specificity of 76.8% (95% CI 69.4%-84.1%). For atrial fibrillation detection, pooled positive predictive value was 87.4% (95% CI 75.7%-99.1%), sensitivity was 94.2% (95% CI 88.7%-99.7%), and specificity was 95.3% (95% CI 91.8%-98.8%). For fall detection, pooled sensitivity was 81.9% (95% CI 75.1%-88.1%) and specificity was 62.5% (95% CI 14.4%-100%). Wearable activity trackers show promise in disease detection, with notable accuracy in identifying atrial fibrillation and COVID-19. While these findings are encouraging, further research and improvements are required to enhance their diagnostic precision and applicability. Prospero CRD42023407867; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=407867.