Wrist-worn Device Research Articles

Validity Study Comparing Polar Ignite’s® Estimated VO2max to Traditional Cardiopulmonary Exercise Testing in Normal Cardiac Anatomy & Fontan Pediatric Patients

Cardiorespiratory fitness as represented by maximum oxygen uptake, VO2max, is considered the best indicator for overall cardiovascular health. Fitness technology such as wrist worn devices, now allows for an easy method to monitor an individual’s VO2max. This study sought to compare the estimated VO2max measurements from the Polar Ignite (Polar Electro Oy, Kempele, Finland) fitness watch (Polar Ignite) against the gold standard, cardiopulmonary exercise testing (CPET) in normal cardiac anatomy and Fontan pediatric patients. This single-center, prospective study enrolled patients with normal cardiac anatomy and single ventricle patients with Fontan palliation (Fontan) between 13-21 years. The Polar Ignite was fastened on the patient’s right or left wrist while supine and estimated VO2max was generated based on a non-exercise test (Polar Fitness Test). A maximal voluntary CPET using the standard Bruce Treadmill Protocol was then completed. Measurements from the two tests were compared using the intraclass correlation coefficient (ICC), Pearson correlation coefficient and Bland-Altman method. Forty-seven patients were enrolled, including n=36 (77%) normal cardiac patients and n=11 (23%) Fontan patients. The majority of participants were white (n=41, 87%) and female (n=36, 66%). Patients were 15.3±1.9 years old, weighed 65.7±16.6 kg, and were 168.0±10.4 cm tall on average. The mean estimated Polar Ignite VO2max and peak CPET VO2 was 43.9±6.1 ml/kg/min vs 37.7±8.2 ml/kg/min, respectively in the normal cardiac group, and 42.6±3.9 ml/kg/min vs 22.6±5.6 ml/kg/min, respectively in the Fontan group. We found poor validity (ICC=0.21) and a weak correlation (r=0.31, p=0.07) between VO2max measured by the Polar Ignite and CPET in the normal cardiac group and poor validity (ICC=0.08) and strong correlation (r=0.84, p=0.001) in the Fontan group. In Bland-Altman analyses, the mean bias between VO2max as measured by the Polar Ignite and CPET was 6.85 ml/kg/min among the normal cardiac group and 20.05 ml/kg/min among the Fontan group. The Polar Ignite overestimated VO2max compared to CPET in both the normal cardiac and Fontan groups and cannot replace formal testing.

0278 Patient Satisfaction with a Novel Sleep Telehealth Platform in the US Military

Abstract Introduction Despite a high prevalence of sleep problems, there is a gross shortage of sleep clinicians within the military health system. To increase access to care, a novel telehealth platform was designed to empower patients and clinicians to make evidence-based sleep treatment decisions. Here, we present initial results from the implementation of this novel sleep telehealth platform in two military treatment facilities (MTFs). Methods Participants (N=305) with sleep complaints were recruited remotely and/or in-person from two MTFs. Participants were predominately men (53.4%), who self-identified as White (55.4%) or Black (24.1%) race and were retired or active-duty military servicemembers (69%). Average age was 45.9 (SD=13.3) years. Participants completed a baseline assessment including standardized research questionnaires via a mobile application (WellTap®). During a 10-day remote monitoring assessment, participants completed daily sleep diaries and twice-daily mood/daytime function surveys, while wearing a commercial-off-the-shelf device (Fitbit®). A personalized sleep report including evidence-based treatment recommendations was provided. Participants completed a satisfaction survey regarding the remote monitoring assessment. Results The most commonly reported sleep problems were difficulty staying asleep (65%), insufficient sleep (61%), and snoring (40%). Most participants described their sleep quality as “a little good” (51%) or “not at all good” (27%). Thirty-nine percent of participants reported moderate to severe insomnia complaints, 38% reported moderate to severe excessive daytime sleepiness, and 22% reported moderate to severe depressive symptoms. In terms of satisfaction, participants reported the sleep assessments were “easy” to complete (92%) and would consider sleep telehealth approaches in the future (95%). The vast majority of participants preferred to continue with sleep telehealth and preferred to combine a mobile app and remote monitoring with either virtual or face-to-face provider support. Conclusion Patients at two military treatment hospitals reported high levels of satisfaction with a novel 10-day intensive remote monitoring assessment including a mobile health assessment and wrist-wearable device. Future studies should evaluate the clinical and economic impact of sleep telehealth and remote monitoring in the U.S. military. Support (if any) Department of Defense (W81XWH1990006) via the Medical Technology Enterprise Consortium) to the University of Maryland, Baltimore (PI: EMW).

0955 Evaluating state-of-the-art algorithms for sleep-wake classification using wrist-worn wearable devices

Abstract Introduction Over the last 40 years, a variety of algorithms have been proposed to classify sleep-wake from wrist acceleration data. Input features into these algorithms have been activity counts or raw acceleration. The algorithms proposed range from a single heuristic rule to logistic regression to machine learning and deep learning. The purpose of this work is to evaluate and compare the accuracy of these algorithms against polysomnography (PSG) annotations on a common dataset from a sleep laboratory. Methods The Newcastle PSG dataset was used to compare the various sleep-wake prediction algorithms to concurrent PSG annotations in thirty second epochs. This dataset contains 28 patients, 27 of which had wrist acceleration data for both the right and left wrist and one which had data for the left wrist only. Twenty of the participants had at least one sleep disorder. Sleep disorders included idiopathic hypersomnia, restless leg syndrome, sleep apnea, narcolepsy, sleep paralysis, nocturia, obstructive sleep apnea, RBD, parasomnia and insomnia. Results The domain adversarial convolutional neural network (DACNN) model showed the best overall results (sens=83.9, spec=57.6, f1=81.7, WASO-RMSE=80.9). The next best performing model according to WASO-RMSE was Cole-Kripke (sens=81.4, spec=50.3, f1=78.0, WASO-RMSE=90.4). This was followed by the van Hees heuristic model (sens=83.6, spec=47.5, f1=79.1, WASO-RMSE=91.1). The fourth best performing model was the Random Forest model (spec=77.5, sens=55.5, f1=76.4, WASO-RMSE=93.0). The fifth best performing model was Sadeh (sens=82.6, spec=49.7, f1=78.5, WASO-RMSE=93.6). The sixth best performing model was the LSTM model (sens=78.6, spec=58.9, f1=77.8, WASO-RMSE=93.9). The worst performing model was the CNN model (sens=79.8, spec=54.2, f1=77.8, WASO-RMSE=99.8). * sens=sensitivity, spec=specificity, WASO=wake after sleep onset, RMSE=root mean squared error Conclusion The DACNN algorithm outperformed all other algorithms on sleep-wake classification from wrist acceleration data. Despite being vastly different in input features’ types and algorithm complexity, all other algorithms performed similarly on the dataset, with f1 scores ranging from 76.4 to 79.1 and WASO-RMSE ranging from 90.4 to 99.8. Support (if any)

0158 Preliminary Study of the Links Between Adverse Childhood Experiences and Actigraphy-Measured Sleep Characteristics in Young Adults

Abstract Introduction Adverse Childhood Experiences (ACEs) are highly stressful events during the first 18 years of life that have been linked with adverse adult health outcomes. Sleep is an important behavioral risk factor for a myriad of health outcomes that have been linked to ACE exposure. A growing body of evidence indicates that ACEs are associated with low self-reported sleep durations, but these findings should be verified using objectively measured sleep characteristics. The purpose of this study was to examine associations of ACE exposure with actigraphy-measured sleep duration, timing, and variability. Methods Methods: In twenty-four young adults (20F/4M; mean±SD, age=24±5 y), we assessed ACE exposure using the 10-item ACE scale and measured sleep characteristics over a ≥6 day period using a wrist-worn actigraphy device. Participants with 0–1 ACEs were characterized as low-ACE (ACE-low, n=10) and participants with 2+ ACEs were characterized as moderate-high-ACE exposure (ACE-mh, n=14). Independent samples t-tests and Hedges’ g effect sizes were used to compare total sleep duration, bed-time, get-up time, time-in-bed, sleep onset latency and the standard deviation of bed time and total sleep duration between groups. Results Time-in-bed was greater in ACE-mh than in ACE-low (mean±SE difference=-60±25 min; p=0.027; g=-0.95), yet there was no difference in total sleep duration (-9±10 min; p=0.46; g=-0.37). This discrepancy was accompanied by a non-significant, but large-effect size difference between ACE-mh and ACE-low (-15±7 min; p=0.051; g=-0.83) in sleep onset latency. There were no differences between groups in either bed-time (p=0.34; g=0.39) or get-up time (p=0.35; g=-1.2), nor in either bed-time (p=0.84; g=-0.08) or total sleep duration (p=0.36; g=-0.37) variability. Conclusion In contrast to prior evidence based on self-report, our initial evidence indicates that ACE exposure is not linked with shorter sleep durations or higher sleep variability when measured using actigraphy. Instead, ACE exposure is linked to greater time in bed and greater sleep onset latencies, suggesting that individuals exposed to ACEs may have less efficient sleep. Support (if any) Support: Research reported in this publication was supported in part by the NCATS of the NIH (UL1TR002537) and by the Injury Prevention Research Center through the CDC (R49 CE003095).

0262 Comparing Wrist Actigraphy to a Novel Wearable (Actigpatch): Nonparametric Activity Estimation

Abstract Introduction Wrist actigraphy is a standard for monitoring sleep in the field; however, data quality is reduced if participants remove the device. The Actigpatch is a novel, adhesive water-resistant wearable that we have previously demonstrated as comparable to wrist actigraphy for traditional sleep-wake estimation. Here we compare assessment of nonparametric activity indexes. Methods While following fixed schedules, 39 participants simultaneously wore the Micro Motionlogger actigraph (Ambulatory Monitoring Inc., Ardley, NY) on their non-dominant wrist and the Actigpatch (Circadian Positioning Systems, Newport, RI) over the triceps of their dominant arm. Our analyses included 35 participants (21F; 32.9±13.2yrs) who contributed ≥four nights of data (range: 4-14 [mean: 10] nights). After matching devices’ tri-axial actimetry in one-minute epochs, we derived key non-parametric parameters of diurnal activity and calculated intraclass correlations to measure agreement. The non-parametric parameters include interdaily stability (IS), intradaily variability (IV), timing of the five hours of lowest activity (L5onset) and ten hours of highest activity (M10onset), and overall relative amplitude (RA). Results We observed agreement ranging from good for IS (ICC=0.77 [95%CI=0.59;0.88]; [all mean differences are patch-watch] mean difference=-0.21) to poor for IV (ICC=0.43 [0.12;0.67]; mean difference=0.29). ICC showed good agreement for M10onset (ICC=0.82 [0.67; 0.90]; mean difference=-37min) and excellent agreement for L5onset (ICC=0.91 [0.82;0.95]; mean difference=-2min). Finally, we identified good agreement when estimating the activity relative amplitude (RA ICC=0.86 [0.75; 0.93]; mean difference=0.03). An example of excellent agreement was manifested in the close estimation of L5onset (patch mean=1:04am, SD=70min; watch mean=1:06am, SD=68min). Conclusion Adding to our prior evidence that these two devices offer similar sleep-wake estimation using traditional algorithms, the present data indicate the Actigpatch offers good agreement to the Motionlogger for nonparametric analyses of IS, activity timing, and RA. Agreement was not as good for IV, with the Actigpatch showing more intradaily variability than the watch, perhaps due to the triceps placement. Because the Actigpatch is unobtrusive, water-resistant, and can be worn continuously for three weeks, it has potential benefits for studies of individuals who struggle with adherence to wearing wrist worn devices. Support (if any) R01AA025593, P20GM139743. Actigpatches provided by Circadian Positioning Systems.

Wrist-worn arrhythmia monitoring devices are well accepted for long-term rhythm assessment by the elderly

Abstract Funding Acknowledgements Type of funding sources: Private company. Main funding source(s): This study was funded by PulseOn Oy. Background Wearable devices such as patch ECG monitors and wrist-worn intermittent ECG devices have recently emerged to fill the gap between Holter monitors and implantable loop recorders in diagnosing paroxysmal cardiac arrhythmias. Unlike patch ECG, wrist-worn devices require active participation of the patient, for example, for taking the ECG recordings. The incidence of cardiac arrhythmias increases with age, so it is important that the devices are easy enough to use also for older people. Purpose We evaluated the usability of a wrist-worn arrhythmia monitoring device among elderly. The device is exclusively targeted for health care providers to be handed out to patients for planned monitoring period instead of being targeted directly to consumers. Another purpose was to obtain feedback on the comfortability of wearing the device and possible skin irritation caused by continuous wearing. Methods We recruited 200 subjects of at least 65 years of age (average 71, range 65-92) with no previous atrial fibrillation diagnosis through a newspaper advertisement. The subjects came for a visit in a local cardiac clinic where they received a short introduction for using the wrist device. The monitoring period was defined as two weeks. The participants were instructed to take four scheduled ECG measurements daily and every time they had arrhythmia symptoms or if the continuous photoplethysmography-based rhythm monitoring of the device detected irregular rhythm and notified the user. The subjects had to recharge the device once during the monitoring period using the accompanied charging dock. The subjects were instructed to keep a diary on the device usage. The subjects also filled a feedback questionnaire regarding device usability at the end of the monitoring period. Results On a scale of 1 to 5, the overall easiness of using the device obtained an average score of 4.4 (standard deviation 0.7), the easiness of taking the ECG recordings 4.7 (std 0.6), and the easiness of device charging 4.8 (std 0.6). All except one study subject were capable of using the device in the correct way for the whole monitoring period. Average score for the device causing skin irritation was 1.6 (std 1.0) when score 1 was considered ‘no irritation’. However, one subject had to stop using the device prematurely due to skin irritation. Three of the previously undiagnosed subjects had paroxysmal AF episode during the monitoring period and another eleven subjects had other atrial tachycardia events. Conclusion Based on the obtained feedback, the studied wrist-worn arrhythmia monitor was found easy to use and was well received by the study participants. The studied wrist-worn monitoring device seems thus suitable for arrhythmia diagnosis in elderly patients. A wearable device, being immediately available at the wrist for taking ECG measurements, additionally provides the benefit of being able to record also short-lasting atrial tachycardia episodes.

Diagnostic accuracy of smart gadgets/wearable devices in detecting atrial fibrillation in primary prevention: a meta-analysis

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Atrial fibrillation (AF) is the most common sustained arrhythmia and an important risk factor for stroke and heart. Recent technology advances have allowed for heart rhythm monitoring using smart gadgets/wearable devices which can be used for early AF diagnosis. Purpose We performed a systemic review and meta-analysis to assess the accuracy of AF diagnosis by smart gadgets/wearable devices in primary prevention. Methods We systematically searched Medline, Embase and Cochrane electronic databases up to September 22th, 2022 for observational studies of the diagnostic accuracy of smartphone application, wrist-worn wearables and external devices in detecting AF in primary prevention. Studies with 12-lead ECG or single-lead ECG interpreted by a cardiologist as gold standard were included. We calculated the area under the curve (AUC) of the summary receiver operating characteristic curves (SROC) and pooled sensitivities and specificities. Results A total of 28 studies were included enrolling 13463 patients, 57%(95% CI: 55-59%) male with average age of 65.3years (95% CI: 60.1 – 70.4). The pooled prevalence of AF was found to be 13% (95% CI 10 – 15%). In the overall analysis of all devices, the AUC was 0.99 (95% CI: 0.97-0.99)(Figure 1), the sensitivity 93%(95% CI: 89 – 95%) and the specificity 97%(95% CI: 95 – 98%) (Figure 2). Wrist-worn wearables had AUC of 0.97 (95% CI: 0.94-0.99), the sensitivity 83%(95% CI: 52 – 96%) and the specificity 98%(95% CI: 87 – 100%). Smartphone applications had AUC of 0.97 (95% CI: 0.96-0.98), the sensitivity 92%(95% CI: 77 – 98%) and the specificity 95%(95% CI: 91 – 97%). External devices had AUC of 0.99 (95% CI: 0.97-0.99), the sensitivity 94%(95% CI: 91 – 96%) and the specificity 97%(95% CI: 95 – 98%). Single-lead ECG had AUC of 0.99 (95% CI: 0.97- 0.99), the sensitivity 93%(95% CI: 88 – 96%) and the specificity 91%(95% CI: 78 – 97%). photoplethysmography pulse waveform technology (PPG) had AUC of 0.97 (95% CI: 0.96-0.98), the sensitivity 91%(95% CI: 78 – 97%) and the specificity 95%(95% CI: 91 – 97%). Pulse beat interval technology had AUC of 0.99 (95% CI: 0.98-0.99), the sensitivity 95%(95% CI: 90 – 98%) and the specificity 96%(95% CI: 93 – 98%) Conclusions Smartphone application, wrist-worn devices and external devices have excellent diagnostic accuracy in atrial fibrillation diagnosis in primary prevention.

A lifestyle monitoring system for cardiovascular care: protocol of a prospective observational trial

Abstract Funding Acknowledgements Type of funding sources: Foundation. Main funding source(s): Dutch Heart Foundation. Background In cardiac rehabilitation, technological innovations can improve patient-relevant outcomes and are associated with reductions in health care costs by enabling personalized care. Lifestyle factors such as physical fitness, dietary habits, mental stress, and sleep quality, are strong predictors of the occurrence, clinical course, and overall treatment outcomes of common cardiovascular diseases such as coronary artery disease and atrial fibrillation. However, these lifestyle factors are rarely monitored, nor used in daily clinical practice and personalized cardiac care. Moreover, non-adherence to long-term self-reporting is common. Purpose The main purpose of this study is to evaluate adherence to a continuous unobtrusive and patient-friendly lifestyle monitoring system. Methods In a prospective observational trial we investigate usability and adherence of a monitoring system for lifestyle factors, i.e., daily physical activity levels, dietary habits, mental stress, smoking habit, and sleep quality. Patients (n = 100) scheduled for or who recently underwent coronary artery bypass surgery, an electrophysiology study and/or radiofrequency catheter ablation, a transcatheter aortic valve implantation, a fractional flow reserve test and/or a percutaneous coronary intervention, and/or valve surgery are asked to participate. The monitoring system consists of a care platform with a custom-built conversational interface—a chatbot—and a wrist-worn wearable medical device. The wrist-worn device collects continuous objective data on physical activity and the chatbot collects subjective data (e.g., sleep quality, nutrition intake and mental stress levels), both for 7 separate days per month repeatedly during the study period. Data collection starts one week before treatment and continues until 1-year after discharge. Via a dashboard, patients can observe their lifestyle measures and adherence to self-reporting, track personal goals, and share their lifestyle data with practitioners and relatives. The primary outcome of the trail is adherence to using the integrated platform for self-tracking data. The secondary outcomes include system usability, determinants of adherence and the relation between baseline lifestyle behaviour and long-term patient-relevant outcomes. Discussion Systematic monitoring during daily life is key to gain insights into patients’ lifestyle behaviours. In this context, adherence to monitoring systems is critical for cardiologists and other care providers to obtain crucial information to personalize care. In this project, we present insights into the evaluation of adherence to monitoring technology including a conversational agent and continuous monitoring, focusing on multiple lifestyle domains for one year. This work contributes to our understanding of adherence, patient-centred data collection and interpretation, to enable personalized cardiac interventions, improve patient-relevant outcomes, and reduce health care costs.

AI-Driven sleep staging from actigraphy and heart rate.

Sleep is an important indicator of a person's health, and its accurate and cost-effective quantification is of great value in healthcare. The gold standard for sleep assessment and the clinical diagnosis of sleep disorders is polysomnography (PSG). However, PSG requires an overnight clinic visit and trained technicians to score the obtained multimodality data. Wrist-worn consumer devices, such as smartwatches, are a promising alternative to PSG because of their small form factor, continuous monitoring capability, and popularity. Unlike PSG, however, wearables-derived data are noisier and far less information-rich because of the fewer number of modalities and less accurate measurements due to their small form factor. Given these challenges, most consumer devices perform two-stage (i.e., sleep-wake) classification, which is inadequate for deep insights into a person's sleep health. The challenging multi-class (three, four, or five-class) staging of sleep using data from wrist-worn wearables remains unresolved. The difference in the data quality between consumer-grade wearables and lab-grade clinical equipment is the motivation behind this study. In this paper, we present an artificial intelligence (AI) technique termed sequence-to-sequence LSTM for automated mobile sleep staging (SLAMSS), which can perform three-class (wake, NREM, REM) and four-class (wake, light, deep, REM) sleep classification from activity (i.e., wrist-accelerometry-derived locomotion) and two coarse heart rate measures-both of which can be reliably obtained from a consumer-grade wrist-wearable device. Our method relies on raw time-series datasets and obviates the need for manual feature selection. We validated our model using actigraphy and coarse heart rate data from two independent study populations: the Multi-Ethnic Study of Atherosclerosis (MESA; N = 808) cohort and the Osteoporotic Fractures in Men (MrOS; N = 817) cohort. SLAMSS achieves an overall accuracy of 79%, weighted F1 score of 0.80, 77% sensitivity, and 89% specificity for three-class sleep staging and an overall accuracy of 70-72%, weighted F1 score of 0.72-0.73, 64-66% sensitivity, and 89-90% specificity for four-class sleep staging in the MESA cohort. It yielded an overall accuracy of 77%, weighted F1 score of 0.77, 74% sensitivity, and 88% specificity for three-class sleep staging and an overall accuracy of 68-69%, weighted F1 score of 0.68-0.69, 60-63% sensitivity, and 88-89% specificity for four-class sleep staging in the MrOS cohort. These results were achieved with feature-poor inputs with a low temporal resolution. In addition, we extended our three-class staging model to an unrelated Apple Watch dataset. Importantly, SLAMSS predicts the duration of each sleep stage with high accuracy. This is especially significant for four-class sleep staging, where deep sleep is severely underrepresented. We show that, by appropriately choosing the loss function to address the inherent class imbalance, our method can accurately estimate deep sleep time (SLAMSS/MESA: 0.61±0.69 hours, PSG/MESA ground truth: 0.60±0.60 hours; SLAMSS/MrOS: 0.53±0.66 hours, PSG/MrOS ground truth: 0.55±0.57 hours;). Deep sleep quality and quantity are vital metrics and early indicators for a number of diseases. Our method, which enables accurate deep sleep estimation from wearables-derived data, is therefore promising for a variety of clinical applications requiring long-term deep sleep monitoring.

The usability of daytime and night-time heart rate dynamics as digital biomarkers of depression severity.

Alterations in heart rate (HR) may provide new information about physiological signatures of depression severity. This 2-year study in individuals with a history of recurrent major depressive disorder (MDD) explored the intra-individual variations in HR parameters and their relationship with depression severity. Data from 510 participants (Number of observations of the HR parameters = 6666) were collected from three centres in the Netherlands, Spain, and the UK, as a part of the remote assessment of disease and relapse-MDD study. We analysed the relationship between depression severity, assessed every 2 weeks with the Patient Health Questionnaire-8, with HR parameters in the week before the assessment, such as HR features during all day, resting periods during the day and at night, and activity periods during the day evaluated with a wrist-worn Fitbit device. Linear mixed models were used with random intercepts for participants and countries. Covariates included in the models were age, sex, BMI, smoking and alcohol consumption, antidepressant use and co-morbidities with other medical health conditions. Decreases in HR variation during resting periods during the day were related with an increased severity of depression both in univariate and multivariate analyses. Mean HR during resting at night was higher in participants with more severe depressive symptoms. Our findings demonstrate that alterations in resting HR during all day and night are associated with depression severity. These findings may provide an early warning of worsening depression symptoms which could allow clinicians to take responsive treatment measures promptly.

Photoplethysmogram Biometric Authentication Using a 1D Siamese Network.

In the head-mounted display environment for experiencing metaverse or virtual reality, conventional input devices cannot be used, so a new type of nonintrusive and continuous biometric authentication technology is required. Since the wrist wearable device is equipped with a photoplethysmogram sensor, it is very suitable for use for nonintrusive and continuous biometric authentication purposes. In this study, we propose a one-dimensional Siamese network biometric identification model using a photoplethysmogram. To maintain the unique characteristics of each person and reduce noise in preprocessing, we adopted a multicycle averaging method without using a bandpass or low-pass filter. In addition, to verify the effectiveness of the multicycle averaging method, the number of cycles was changed and the results were compared. Genuine and impostor data were used to verify the biometric identification. We used the one-dimensional Siamese network to verify the similarity between the classes and found that the method with five overlapping cycles was the most effective. Tests were conducted on the overlapping data of five single-cycle signals and excellent identification results were observed, with an AUC score of 0.988 and an accuracy of 0.9723. Thus, the proposed biometric identification model is time-efficient and shows excellent security performance, even in devices with limited computational capabilities, such as wearable devices. Consequently, our proposed method has the following advantages compared with previous works. First, the effect of noise reduction and information preservation through multicycle averaging was experimentally verified by varying the number of photoplethysmogram cycles. Second, by analyzing authentication performance through genuine and impostor matching analysis based on a one-dimensional Siamese network, the accuracy that is not affected by the number of enrolled subjects was derived.

Health Provider's Feedback on Physical Activity Surveillance Using Wearable Device-Smartphone Application for Adults with Metabolic Syndrome; a 12-Week Randomized Control Study.

Research on whether wearable device interventions can effectively prevent metabolic syndrome remains insufficient. This study aimed to evaluate the effect of feedback on clinical indicators in patients with metabolic syndrome on activities measured using wearable devices, such as smartphone apps. Patients with metabolic syndrome were recruited and prescribed to live for 12 weeks using a wrist-wearable device (B.BAND, B Life Inc., Korea). A block randomization method was used to distribute participants between the intervention (n=35) and control groups (n=32). In the intervention group, an experienced study coordinator provided feedback on physical activity to individuals through telephonic counseling every other week. The mean number of steps in the control group was 8892.86 (4473.53), and those in the intervention group was 10,129.31 (4224.11). After 12 weeks, metabolic syndrome was resolved. Notably, there were statistically significant differences in the metabolic composition among the participants who completed the intervention. The mean number of metabolic disorder components per person remained at 3 in the control group, and decreased from 4 to 3 in the intervention group. Additionally, waist circumference, systolic and diastolic blood pressure, and triglyceride levels were significantly reduced, while HDL-cholesterol levels were significantly increased in the intervention group. Overall, 12 weeks of telephonic counseling intervention using wearable device-based physical activity confirmation improved the damaged metabolic components of patients with metabolic syndrome. Telephonic intervention can help increase physical activity and reduce waist circumference, which is a typical clinical indicator of metabolic syndrome.

Stress Detection Through Wrist-Based Electrodermal Activity Monitoring and Machine Learning.

Stress is an inevitable part of modern life. While stress can negatively impact a person's life and health, positive and under-controlled stress can also enable people to generate creative solutions to problems encountered in their daily lives. Although it is hard to eliminate stress, we can learn to monitor and control its physical and psychological effects. It is essential to provide feasible and immediate solutions for more mental health counselling and support programs to help people relieve stress and improve their mental health. Popular wearable devices, such as smartwatches with several sensing capabilities, including physiological signal monitoring, can alleviate the problem. This work investigates the feasibility of using wrist-based electrodermal activity (EDA) signals collected from wearable devices to predict people's stress status and identify possible factors impacting stress classification accuracy. We use data collected from wrist-worn devices to examine the binary classification discriminating stress from non-stress. For efficient classification, five machine learning-based classifiers were examined. We explore the classification performance on four available EDA databases under different feature selections. According to the results, Support Vector Machine (SVM) outperforms the other machine learning approaches with an accuracy of 92.9 for stress prediction. Additionally, when the subject classification included gender information, the performance analysis showed significant differences between males and females. We further examine a multimodal approach for stress classifications. The results indicate that wearable devices with EDA sensors have a great potential to provide helpful insight for improved mental health monitoring.

Measure by measure: Resting heart rate across the 24-hour cycle.

Photoplethysmography (PPG) sensors, typically found in wrist-worn devices, can continuously monitor heart rate (HR) in large populations in real-world settings. Resting heart rate (RHR) is an important biomarker of morbidities and mortality, but no universally accepted definition nor measurement criteria exist. In this study, we provide a working definition of RHR and describe a method for accurate measurement of this biomarker, recorded using PPG derived from wristband measurement across the 24-hour cycle. 433 healthy subjects wore a wrist device that measured activity and HR for up to 3 months. HR during inactivity was recorded and the duration of inactivity needed for HR to stabilise was ascertained. We identified the lowest HR during each 24-hour cycle (true RHR) and examined the time of day or night this occurred. The variation of HR during inactivity through the 24-hour cycle was also assessed. The sample was also subdivided according to daily activity levels for subset analysis. Adequate data was obtained for 19,242 days and 18,520 nights. HR stabilised in most subjects after 4 minutes of inactivity. Mean (SD) RHR for the sample was 54.5 (8.0) bpm (day) and 50.5 (7.6) bpm (night). RHR values were highest in the least active group (lowest MET quartile). A circadian variation of HR during inactivity was confirmed, with the lowest values being between 0300 and 0700 hours for most subjects. RHR measured using a PPG-based wrist-worn device is significantly lower at night than in the day, and a circadian rhythm of HR during inactivity was confirmed. Since RHR is such an important health metric, clarity on the definition and measurement methodology used is important. For most subjects, a minimum rest time of 4 minutes provides a reliable measurement of HR during inactivity and true RHR in a 24-hour cycle is best measured between 0300 and 0700 hours. Funding: This study was funded by Google.

Monitoring Faciobrachial Dystonic Seizures with Wearable Devices (P5-5.015)

<h3>Objective:</h3> This study sought to assess whether faciobrachial dystonic seizures (FBDS) can be reliably identified and differentiated from normal nocturnal arousals using a wrist-worn device. <h3>Background:</h3> Detection of FBDS in patients with Leucine-rich glioma inactivated-1 (LGI1-IgG) associated autoimmune encephalitis is challenging due to high event frequency and lack of a robust EEG correlate. Wearable devices may provide a convenient, noninvasive means to measure these events and provide an objective seizure diary. <h3>Design/Methods:</h3> Two LGI1-IgG seropositive patients (P1 and P2) and four control subjects, wearing Empatica E4 devices, were recruited for this study. Two eight-hour sleep periods of each patient: pre- and post-treatment, were analyzed. An algorithm was developed based on accelerometry (ACC) signals to detect events of interest. After event identification, we calculated the ACC magnitude, duration, and electrodermal activity (EDA) characteristics of detected events. Presently, we are actively expanding the patient cohort and are investigating machine learning methods to improve the accuracy of event detection. <h3>Results:</h3> Statistically significant differences were found in device signals pre- and post-treatment, and between FBDS events in LGI1-IgG and nocturnal arousals in control subjects. The frequency of actigraphy events was higher in both patients (P1: pre-treatment 13.93±3.82 ev/h, post-treatment 9.66±3.37 ev/h; P2: pre-treatment 26.21±13.16 ev/h, post-treatment 29.06±9.79 ev/h), than controls (3.47±2.77 ev/h). For ACC magnitude and event duration, the medians were lower post-treatment and significantly lower for ACC magnitude in P2. Post-treatment, significant differences were only observed between controls and P1. For EDA activity, tonic and phasic components for controls were significantly lower than for patients pre-treatment. Additionally, means and medians decreased post-treatment in both patients. <h3>Conclusions:</h3> Robust characteristics were identified in actigraphy and EDA signals from a wrist-worn device during FBDS events. This evidence suggests an accurate monitor can be developed to automatically detect FBDS, providing objective measurements for treatment guidance and clinical decisions. <b>Disclosure:</b> Dr. Cui has nothing to disclose. Dr. Faust has nothing to disclose. Boney Joseph has nothing to disclose. The institution of Dr. Dubey has received personal compensation in the range of $500-$4,999 for serving as a Consultant for UCB. The institution of Dr. Dubey has received personal compensation in the range of $5,000-$9,999 for serving as a Consultant for Astellas. The institution of Dr. Dubey has received personal compensation in the range of $500-$4,999 for serving as a Consultant for Argenx. Dr. Dubey has received personal compensation in the range of $0-$499 for serving on a Speakers Bureau for AGRIMS. Dr. Dubey has received personal compensation in the range of $0-$499 for serving on a Speakers Bureau for Advances in Neurology . Dr. Dubey has received personal compensation in the range of $0-$499 for serving on a Speakers Bureau for Moffit Cancer Center . Dr. Dubey has received research support from Department of Defense . Dr. Dubey has received intellectual property interests from a discovery or technology relating to health care. Dr. Dubey has received intellectual property interests from a discovery or technology relating to health care. Dr. Dubey has received intellectual property interests from a discovery or technology relating to health care. Dr. Brinkmann has received personal compensation in the range of $0-$499 for serving as a Consultant for Otsuka Pharmaceuticals. Dr. Brinkmann has stock in Cadence Neuroscience. The institution of Dr. Brinkmann has received research support from Epilepsy Foundation of America. The institution of Dr. Brinkmann has received research support from National Institutes of Health. The institution of Dr. Brinkmann has received research support from National Institutes of Health. The institution of Dr. Brinkmann has received research support from UNEEG A/S. The institution of Dr. Brinkmann has received research support from Seer Medical Pty. Dr. Brinkmann has received intellectual property interests from a discovery or technology relating to health care. Dr. Brinkmann has a non-compensated relationship as a Co-Investigator with Medtronic Inc. that is relevant to AAN interests or activities.

Cala Trio wrist-worn medical device for tremor improvement in patient with Spinocerebellar Ataxia-12 (P8-11.014)

Cala Trio wrist-worn medical device for tremor improvement in patient with Spinocerebellar Ataxia-12 (P8-11.014)

Measuring Energy Expenditure and Heart Rate during Maximum Aerobic Testing with the Apple Watch Series 7

Introduction: Wrist-worn devices such as the Apple Watch have emerged as technology for tracking physical activity. The aim of this research study is to analyze the Apple Watch Series 7 (AW7) with measurements of the maximum heart rate (MHR) and maximum energy expenditure (MEE) during a maximal aerobic capacity test on the treadmill. AW7 measurements will be compared to the Polar Heart Rate Monitor (Polar) and the PARVO Metabolic Cart (PARVO). Methods: 22 healthy and active subjects (mean ± SD: age 23.8 ± 4.0 years; BMI 23.0 ± 5.9 kg/m2) volunteered for the study. The subjects confirmed their activity, health status, and were measured for body composition and aerobic capacity. Results: No significant difference was found in MEE between PARVO (109.6 ± 41.7 kcal) and AW7 (98.7 ± 24.3 kcal) conditions; t(21)=1.5, p = 0.153. In addition, there was no significant difference in MHR between PARVO (186.2 ± 16.2 BPM) and AW7 (189.3 ± 8.5 BPM) conditions; t(21)=-0.9, p = 0.379. Conclusions: The main findings of this study show that the MEE as well as the MHR between the AW7 compared to the PARVO are not different.

Sleep medicine provider perceptions and attitudes regarding consumer sleep technology.

This study assessed perceptions and attitudes of sleep medicine providers regarding consumer sleep technology (CST). A convenience sample of n = 176 practicing sleep medicine and behavioral sleep medicine experts was obtained using social media and the American Academy of Sleep Medicine directory. Providers completed a questionnaire that assessed perceptions and attitudes about patient use of CST in the clinical setting. The sample included both adult and pediatric psychologists, physicians, and advanced practice providers from a variety of health settings. Providers reported 36% (3%-95%) of patients used CST, and the most common devices seen by providers were wrist-worn devices followed by smartphone apps. The most common perceived patient motivations for frequent use were to measure sleep and self-discovery. Across sleep disorders, clinicians did not endorse frequent CST use; the highest reported use was for assisting patients in the completion of sleep diaries. Overall devices were rated as somewhat accurate and neutral regarding helpfulness. In qualitative responses, providers associated CST use with increased patient engagement but increased orthosomnia and misperceptions about sleep. CST is frequently encountered in the sleep medicine clinic, and providers view CST as somewhat accurate but neither helpful nor unhelpful in clinical practice. Although providers viewed these devices as useful to drive patient engagement/awareness and track sleep patterns, providers also viewed them as a contributor to orthosomnia and misperceptions about sleep. Addison C, Grandner MA, Baron KG. Sleep medicine provider perceptions and attitudes regarding consumer sleep technology. J Clin Sleep Med. 2023;19(8):1457-1463.

Adherence to a lifestyle monitoring system in patients with heart disease: protocol for the care-on prospective observational trial

BackgroundLifestyle factors such as physical fitness, dietary habits, mental stress, and sleep quality, are strong predictors of the occurrence, clinical course, and overall treatment outcomes of common cardiovascular diseases. However, these lifestyle factors are rarely monitored, nor used in daily clinical practice and personalized cardiac care. Moreover, non-adherence to long-term self-reporting of these lifestyle factors is common. In the present study, we evaluate adherence to a continuous unobtrusive and patient-friendly lifestyle monitoring system using evidence-based assessment tools.MethodsIn a prospective observational trial (N = 100), the project investigates usability of and adherence to a monitoring system for multiple lifestyle factors relevant to cardiovascular disease, i.e., daily physical activity levels, dietary habits, mental stress, smoking, and sleep quality. Patients with coronary artery disease, valvular disease and arrhythmias undergoing an elective intervention are asked to participate. The monitoring system consists of a secured online platform with a custom-built conversational interface—a chatbot—and a wrist-worn wearable medical device. The wrist-worn device collects continuous objective data on physical activity and the chatbot is used to collect self-report data. Participants collect self-reported lifestyle data via the chatbot for a maximum of 4 days every other week; in the same week physiological data are collected for 7 days for 24 h. Data collection starts one week before the intervention and continues until 1-year after discharge. Via a dashboard, patients can observe their lifestyle measures and adherence to self-reporting, set and track personal goals, and share their lifestyle data with practitioners and relatives. The primary outcome of the trial is adherence to using the integrated platform for self-tracking data. The secondary outcomes include system usability, determinants of adherence and the relation between baseline lifestyle behaviour and long-term patient-relevant outcomes.DiscussionSystematic monitoring during daily life is essential to gain insights into patients’ lifestyle behaviour. In this context, adherence to monitoring systems is critical for cardiologists and other care providers to monitor recovery after a cardiac intervention and to detect clinical deterioration. With this project, we will evaluate patients’ adherence to lifestyle monitoring technology. This work contributes to the understanding of patient-centered data collection and interpretation, to enable personalized care after cardiac interventions in order to ultimately improve patient-relevant outcomes and reduce health care costs.Trial registrationNetherlands Trial Registry (NTR) NL9861. Registered 6th of November 2021.

The role of wrist-worn technology in the management of Parkinson's disease in daily life: A narrative review.

Parkinson's disease (PD) is a neurodegenerative disorder that affects millions of people worldwide. Its slow and heterogeneous progression over time makes timely diagnosis challenging. Wrist-worn digital devices, particularly smartwatches, are currently the most popular tools in the PD research field due to their convenience for long-term daily life monitoring. While wrist-worn sensing devices have garnered significant interest, their value for daily practice is still unclear. In this narrative review, we survey demographic, clinical and technological information from 39 articles across four public databases. Wrist-worn technology mainly monitors motor symptoms and sleep disorders of patients in daily life. We find that accelerometers are the most commonly used sensors to measure the movement of people living with PD. There are few studies on monitoring the disease progression compared to symptom classification. We conclude that wrist-worn sensing technology might be useful to assist in the management of PD through an automatic assessment based on patient-provided daily living information.