Wrist-worn Device Research Articles

Expert Demand for Consumer Sleep Technology Features and Wearable Devices: A Case Study

Global demand for sleep-tracking wearables, or consumer sleep technologies (CSTs), is steadily increasing. CST marketing campaigns often advertise the scientific merit of devices, but these claims may not align with consensus opinion from sleep research experts. Consensus opinion about CST features has not previously been established in a cohort of sleep researchers. This case study reports the results of the first survey of experts in real-world sleep research and a hypothetical purchase task (HPT) to establish economic valuation for devices with different features by price. Forty-six (N = 46) respondents with an average of 10 ± 6 years’ experience conducting research in real-world settings completed the online survey. Total sleep time was ranked as the most important measure of sleep, followed by objective sleep quality, while sleep architecture/depth and diagnostic information were ranked as least important. A total of 52% of experts preferred wrist-worn devices that could reliably determine sleep episodes as short as 20 min. The economic value was greater for hypothetical devices with a longer battery life. These data set a precedent for determining how scientific merit impacts the potential market value of a CST. This is the first known attempt to establish a consensus opinion or an economic valuation for scientifically desirable CST features and metrics using expert elicitation.

Examining Individual Factors Influencing Adherence and Response to Wholegrains and Nuts in a Series of Interventional N-of-1 Studies

ObjectivesInter-individual variations in response to dietary interventions are common, which can be affected by physiological and behavioural factors. N-of-1 studies, where a series of measurements are collected on an individual level over time, can provide insights into the factors which affect response for a single volunteer. The MI-DIET study is a series of N-of-1 studies to investigate what factors, at an individual level, influence a) adherence to a wholegrain and nuts intervention and b) blood pressure response.Methods14 volunteers with mildly elevated blood pressure (120/80–140/90mmHg), and who were low consumers of wholegrain foods, were recruited. Each volunteer carried out a 24-week N-of-1 study, composed of three 8-week periods (observation, intervention and follow-up). Throughout the study, volunteers responded to semi-personalised morning and evening questionnaires using a wrist-worn device (PRO-Diary, CamNtech Ltd.), which also monitored activity levels via actigraphy, and took their blood pressure daily using a wireless monitor (QardioArm, Qardio Inc.). During the intervention period, participants were provided with and asked to consume 3–4 portions of wholegrains and a handful of nuts each day, in line with the Dietary Approaches to Stop Hypertension (DASH) diet.ResultsTo date, 11 volunteers have completed the study, and 2 volunteers have withdrawn. Each volunteer's data is analyzed on an individual level using N-of-1 dynamic modelling analysis. Results to date include a significant association between menstrual cycle day and blood pressure in one volunteer (p < 0.01), while consumption of chocolate or cakes was associated with fewer portions of wholegrains being consumed by another volunteer (p < 0.05).ConclusionsCollecting repeated measurements on an individual level can identify relevant predictors of compliance and response to a dietary intervention for a given person. Subsequent aggregated analysis of variables measured across multiple volunteers will enable us to further understand factors which may underpin differential response to dietary interventions.Funding SourcesBiotechnology and Biological Sciences Research Council (BBSRC) UK and Unilever Foods Innovation Centre, Wageningen, The Netherlands: Collaborative Training Partnership (CTP) PhD.

Feasibility of a novel wrist-worn thermal device for management of vasomotor symptoms in patients with prostate cancer.

5067 Background: Vasomotor symptoms (VMS), or hot flashes, are a common side effect of androgen deprivation therapy (ADT) for prostate cancer (PCa). VMS negatively impact sleep, fatigue, and quality of life (QOL) in PCa survivors. There are few nonpharmacological treatments for VMS. The Embr Wave is a wearable thermal device that applies cooling to thermoreceptors on the inside of the wrist and has been associated with patient reported decreases in hot flash interference in women. We performed a single-arm feasibility study evaluating the Embr Wave for management of VMS in PCa survivors. Methods: 57 PCa survivors reporting bothersome hot flashes were enrolled and instructed to use the device as needed for VMS during the 4-week study. The primary outcome was device usage recorded by the device (minutes and sessions). Additional outcomes included the change in patient reported Hot Flash Related Daily Interference Scale (HFRDIS, range 0-10) and Patient Reported Outcomes Measurement Information System Sleep Disturbance 4a (PROMIS SD, range 0-100) and Sleep Related Impairment 8a (PROMIS SRI, range 0-100). Study procedures were conducted remotely using a virtual clinical trial management platform. Results: The study was conducted from May to Dec 2021 in the US. 44 men completed the study; 39 had retrievable usage data. Median age was 66 (range 57-78) years and median 3 (1-23) years since PCa diagnosis. The most common hormonal treatments were leuprolide (n=22), abiraterone (n=13), and enzalutamide (n=6). Baseline scores indicated moderate hot flash interference and mild sleep disturbance (Table). Mean±SD (median) usage of the Embr device was 3.2±2.5 (2.3) hours and 7.6±3.6 (7.5) sessions per day. 26 (67%) participants reported using the device 7 days/nights each week. Improvements were observed in HFRDIS, PROMIS SD, and PROMIS SRI scores (Table). There were also improvements in subjective ratings of hot flash frequency, duration, interference with daily life/sleep, bothersomeness and control (all p&lt;0.01). The majority (69%) of participants reported that the device was effective at helping them manage hot flashes. No adverse events were reported. Conclusions: Results of this study support the feasibility of use of the thermal device for management of bothersome hot flashes in PCa survivors. Future randomized controlled studies are warranted to evaluate patient reported outcomes related to frequency and severity of VMS, sleep quality, fatigue, and overall QOL, in addition to defining the potential utility of the Embr thermal device in PCa survivors experiencing VMS. Clinical trial information: NCT04892914. [Table: see text]

Automated seizure detection with noninvasive wearable devices: A systematic review and meta-analysis.

ObjectiveThis study was undertaken to review the reported performance of noninvasive wearable devices in detecting epileptic seizures and psychogenic nonepileptic seizures (PNES).MethodsWe conducted a systematic review and meta‐analysis of studies reported up to November 15, 2021. We included studies that used video‐electroencephalographic (EEG) monitoring as the gold standard to determine the sensitivity and false alarm rate (FAR) of noninvasive wearables for automated seizure detection.ResultsTwenty‐eight studies met the criteria for the systematic review, of which 23 were eligible for meta‐analysis. These studies (1269 patients in total, median recording time = 52.9 h per patient) investigated devices for tonic–clonic seizures using wrist‐worn and/or ankle‐worn devices to measure three‐dimensional accelerometry (15 studies), and/or wearable surface devices to measure electromyography (eight studies). The mean sensitivity for detecting tonic–clonic seizures was .91 (95% confidence interval [CI] = .85–.96, I 2 = 83.8%); sensitivity was similar between the wrist‐worn (.93) and surface devices (.90). The overall FAR was 2.1/24 h (95% CI = 1.7–2.6, I 2 = 99.7%); FAR was higher in wrist‐worn (2.5/24 h) than in wearable surface devices (.96/24 h). Three of the 23 studies also detected PNES; the mean sensitivity and FAR from these studies were 62.9% and .79/24 h, respectively. Four studies detected both focal and tonic–clonic seizures, and one study detected focal seizures only; the sensitivities ranged from 31.1% to 93.1% in these studies.SignificanceReported noninvasive wearable devices had high sensitivity but relatively high FARs in detecting tonic–clonic seizures during limited recording time in a video‐EEG setting. Future studies should focus on reducing FAR, detection of other seizure types and PNES, and longer recording in the community.

0159 Sleep-wake stability and variability in the middle-aged adult population: a UK Biobank study

Abstract Introduction Given the increasing use of consumer, wrist-worn devices with triaxial accelerometry (actigraphy), understanding whether 24-hr activity patterns are associated with specific mental and physical health deficits is of paramount importance. The UK Biobank, a community-based sample of adults in the United Kingdom, provides an opportunity to examine this question given its size (more than 100,000 actigraphy records) and scope (association health records). As a first step in understanding these relationships, understanding the impact of missing data, ways to interpolate missing data, and the general characteristics of 24-hr patterns is necessary. Methods A subset (n=70) of complete (i.e., no missing data) actigraphy records from individuals participating in the UK Biobank were used to examine the impact of missing data on intradaily variability (IV; rhythm fragmentation) and interdaily stability (IS; rhythm regularity). Data were intentionally removed and imputed and the impact of the manipulations was examined with Bland-Altman statistics. After determining the best imputation method and the limits of the method, it was applied to missing data in the full cohort, resulting in 82,840 actigraphy records. These were examined for IV and IS, as well as relative amplitude and the timing of L5 and M10. Data were categorized by age, gender, ethnicity, BMI and material deprivation. ANOVA with eta² effect sizes were used for comparisons. Data shown as median (interquartile range). Results Relative to IV and IS, median and mean imputation methods corrected for missing data of up to 24 hours. Linear imputation of long (&amp;gt;3 hours) missing data worsened IV and IS scores. Non-parametric patterns in the general population were within expected range, with IV=0.89(0.74-1.05), IS=0.55(0.46-0.63), RA=0.90(0.85-0.99), L5-timing=00:52 (00:07-01:37), and M10-timing=08:25(07:40-09:20). Stratifying by gender, age, ethnicity, BMI and material deprivation resulted in significant differences within all groups (p&amp;lt;0.0001), but small effect sizes (0.0019-0.00052). Conclusion Median imputation is useful for correcting for missing data (&amp;lt;24 hours) when examining IV and IS. Non-parametric analysis of the general population indicated values within the expected range. Even though there were significant differences in non-parametric outcomes between genders, age groups, ethnicities, body-mass index and material deprivation, none of these had significant effect sizes. Support (If Any) This research has been conducted using the UK Biobank Resource

0084 Consumer Sleep Technologies (CSTs) for Use in Real-World Sleep Research Environments: A Survey of Experts

Abstract Introduction Consumer sleep technologies (CSTs) have been designed for the everyday consumer rather than as a reliable scientific tool, but are becoming sufficiently accurate for use in the research landscape. Despite the growing conversation about the viability of CSTs for research, manufacturers may not be interested in increasing scientific accuracy in their devices unless doing so is expected to result in greater consumer sales. To establish concensus opinion about important device features and economic demand for CSTs for sleep research, professional opinions from sleep medicine experts were elicited to identify what metrics and device features for measuring sleep outside the laboratory are most desirable to the scientific community. This is the first known attempt to establish consensus opinion or economic valuation for scientifically-desirable CST device features and metrics using expert elicitation within the sleep science community. Methods Sleep and/or circadian researchers with experience collecting data in real-world environments were recruited from international academic, government, clinical, and industry research backgrounds using social media and nonprobability sampling techniques. The anonymous survey was hosted through the online tool Qualtrics between April to July 2021. Exposure to survey recruitment techniques were tracked through Twitter Analytics and Qualtrics. Respondents were asked to rank sleep metrics and consumer sleep technology features to estimate their average level of importance (low, medium, or high). A hypothetical purchase task estimated economic valuation for devices with different features by price. Results Survey respondents were 46 real-world sleep research experts with, on average, 11 (range: 1-27; mode: 10) years of experience conducting human research related to sleep in real-world environments. Sixty-six percent (66%) preferred wrist-worn devices and 52% indicated that sleep onset/offset could be determined from a period of inactivity ≤20 minutes. Eighty-three percent (83%) collected data related to napping. Forty-four percent (44%) preferred a continuous observation window between 4-14 days long. Respondents ranked battery life as the most important factor limiting the observation window. Total sleep time was ranked as the most important measure of sleep followed by objective sleep quality while sleep architecture/depth and diagnostic information were ranked as least important. Economic value was greater for hypothetical devices with longer battery life. Conclusion Real-world sleep experts prefer wrist-worn devices that can reliably estimate sleep and short naps. Battery life is important. Based on responses to both rank order questions and the hypothetical purchase task, estimating sleep depth is less important to researchers than measures of sleep duration and quality. These data set a precedent for future studies to determine the scientific relevance of metrics or how scientific endorsement of a product impacts the potential market value of a CST device. Support (If Any) NA

0278 Isolated REM sleep behavior disorder is associated with 24-hour rhythm disruption

Abstract Introduction Isolated REM sleep behavior disorder (iRBD), the loss of motor inhibition during REM sleep, is a symptom of prodromal Lewy body disease, with over 80% of iRBD patients eventually phenoconverting to Parkinson’s disease or Dementia with Lewy bodies. Rest-activity rhythm disruption, also an established predictor of Parkinson’s disease, has not been well characterized in patients with iRBD. Here, we tested the hypothesis that accelerometer-based measures of 24-hour rhythms would indicate greater fragmentation and variability in patients with iRBD relative to matched healthy controls. Methods N=36 patients with iRBD recruited from the Stanford Sleep Clinic had 24-hour activity continuously monitored for (mean ± SD) 25.3 ± 8.4 days using an Axivity wristworn device. A control dataset of age, sex, and body mass index matched healthy older adults (N=126) was selected from the UK Biobank accelerometer dataset. Raw accelerometer data were processed using the GGIR, nparACT, and ActCR software packages in R, with a focus on nonparametric and 5-parameter cosinor measures of 24-hour rhythms. Functional PCA analyses (fPCA) were applied to detect overall differences in 24-hour rhythms and during sleep. Results Patients with iRBD had lower interdaily stability and higher intradaily variability than controls (IS, Cohen’s d=-1.15, Mann-Whitney test p&amp;lt;0.001; IV, d=0.46, p=0.04). Cosine amplitude was lower in iRBD patients (d=-0.22, p=0.001), but mean activity (mesor) did not differ (d=0.03, p=0.31), suggesting differences in rest-activity patterns rather than overall activity levels. A shape naïve approach utilizing fPCA indicated that increased activity during the night may explain overall rhythm differences observed in iRBD. Conclusion Multiple metrics of rest-activity rhythms support the hypothesis that 24-hour rhythms are disrupted in iRBD. It remains to be determined whether rhythm fragmentation in iRBD reflects higher activity levels during REM sleep, or if dysfunctional rhythms represent the direct effects of degenerating sleep-wake regulating circuits, indicating the early stages of Lewy body disease. Support (If Any) NIA F32AG074625 to JW; NIA P30AG066515 to JW, KP, and EM

Combining passive eating monitoring and ecological momentary assessment to characterize dietary lapses from a lifestyle modification intervention

Dietary lapses (i.e., specific instances of nonadherence to recommended dietary goals) contribute to suboptimal weight loss outcomes during lifestyle modification programs. Passive eating monitoring could enhance lapse measurement via objective assessment of eating characteristics that could be markers for lapse (e.g., more bites consumed). The purpose of this study was to evaluate if passively-inferred eating characteristics (i.e., bites, eating duration, and eating rate), measured via wrist-worn device, could distinguish dietary lapses from non-lapse eating. Adults (n = 25) with overweight/obesity received a 24-week lifestyle modification intervention. Participants completed ecological momentary assessment (EMA; repeated smartphone surveys) biweekly to self-report on dietary lapses and non-lapse eating episodes. Participants wore a wrist device that captured continuous wrist motion. Previously-validated algorithms inferred eating episodes from wrist data, and calculated bite count, duration, and rate (seconds per bite). Mixed effects logistic regressions revealed no simple effects of bite count, duration, or eating rate on the likelihood of dietary lapse. Moderation analyses revealed that eating episodes in the evening were more likely to be lapses if they involved fewer bites (B = −0.16, p < .05), were shorter (B = −0.54, p < .05), or had a slower rate (B = 1.27, p < .001). Statistically significant interactions between eating characteristics (Bs = −0.30 to −0.08, ps < .001) revealed two distinct patterns. Eating episodes that were 1. smaller, slower, and shorter than average, or 2. larger, quicker, and longer than average were associated with increased probability of lapse. This study is the first to use objective eating monitoring to characterize dietary lapses throughout a lifestyle modification intervention. Results demonstrate the potential of sensors to identify non-adherence using only patterns of passively-sensed eating characteristics, thereby minimizing the need for self-report in future studies. Clinical trials registry numberNCT03739151.

Head-to-head comparison of single-lead and six-lead electrocardiography of a wrist-worn device for atrial fibrillation detection

Abstract Funding Acknowledgements Type of funding sources: Public grant(s) – EU funding. Main funding source(s): Funded by the European Regional Development Fund under agreement with the Research Council of Lithuania. Background The increasing numbers of available mHealth tools for electrocardiography (ECG)-based atrial fibrillation (AF) detection promote long-term screening. A common feature of smartwatches is lead-I-like ECG. However, only limited data exists directly comparing the performance of single-lead and six-lead wearable-recorded ECGs. Purpose To compare the accuracy of single-lead and six-lead ECGs of the same wrist-worn device for AF detection. Methods We included patients with AF which represent the main group for testing the diagnostic ability of wearable. In addition, authors selected control groups of stable sinus rhythm (SR) and SR with frequent premature contractions. Cardiac rhythm was monitored using an investigational wrist-worn device which provides six-lead ECG, similar to standard limb leads. To display a single-lead wearable-recorded ECGs, the same six-lead ECG tracings were trimmed to a width of lead-I-like ECGs. A validated Holter ECG device constituted a gold standard test for rhythm verification. Two independent diagnosis-blinded cardiologists evaluated reference, six-lead and single-lead ECGs as "AF", "SR", or "Inconclusive". A third cardiologist evaluated ECGs only in cases of physician disagreement. Results A total of 344 adult patients were enrolled in this study including AF group (121 patients) and control group of SR with or without frequent premature contractions (223 patients). Patients with missing (11/420; 2.62%) or insufficient quality (43/420; 10.24%) of wearable-recorded ECGs were excluded. AF detection based on single-lead and six-lead ECGs yielded sensitivity of 95.73% (95% CI 90.31–98.6%) and 99.16% (95% CI 95.41–99.98%), respectively. Specificity was 100% (95% CI 96.19–100%) for both single-lead and six-lead ECGs when differentiating between AF and stable SR. If patients with frequent premature beats were included in the control group, the specificity of single-lead and six-lead ECGs dropped to 95.81% (95% CI 92.31–98.07%) and 99.1% (95% CI 96.78–99.89%), respectively. False positive cases were more common for single-lead ECG (9/332) compared to six-lead ECG (2/341) (P=0.02). There was a strong association between reference ECGs and wearable-recorded ECGs (P&amp;lt;0.001): Cramer‘s V 0.91, (95% CI 0.82–1.0) for single-lead ECG and 0.98 (95% CI 0.89–1.0) for six-lead ECG. After including a control group of frequent premature contractions, single-lead ECG (12/344) was more frequently labelled "Inconclusive" than six-lead ECG (3/344) (P=0.01). Inter-rater agreement, measured as Cohen’s kappa, indicated great concordance in both methods but was higher for six-lead ECG (0.945, P&amp;lt;0.001) than single-lead ECG (0.887, P&amp;lt;0.001). Conclusions Six-lead ECG of a wearable device demonstrated higher diagnostic accuracy of AF detection than single-lead ECG when controlled by patients with frequent premature contractions. The performance of both methods was equivalent when controlled by patients with stable SR.

Virtual Breathalyzer: Towards the Detection of Intoxication Using Motion Sensors of Commercial Wearable Devices.

Driving under the influence of alcohol is a widespread phenomenon in the US where it is considered a major cause of fatal accidents. In this research, we present Virtual Breathalyzer, a novel approach for detecting intoxication from the measurements obtained by the sensors of smartphones and wrist-worn devices. We formalize the problem of intoxication detection as the supervised machine learning task of binary classification (drunk or sober). In order to evaluate our approach, we conducted a field experiment and collected 60 free gait samples from 30 patrons of three bars using a Microsoft Band and Samsung Galaxy S4. We validated our results against an admissible breathalyzer used by the police. A system based on this concept successfully detected intoxication and achieved the following results: 0.97 AUC and 0.04 FPR, given a fixed TPR of 1.0. Our approach can be used to analyze the free gait of drinkers when they walk from the car to the bar and vice versa, using wearable devices which are ubiquitous and more widespread than admissible breathalyzers. This approach can be utilized to alert people, or even a connected car, and prevent people from driving under the influence of alcohol.

The Usefulness of Assessing Heart Rate Variability in Patients with Acute Myocardial Infarction (HeaRt-V-AMI).

Background: Heart rate variability (HRV) could have independent and critical prognostic values in patients admitted for ST segment elevation myocardial infarction (STEMI). There are limited data in the literature regarding HRV assessment in STEMI setting. Thus, we aim to investigate the potential correlations between HRV and adverse outcomes in a contemporary cohort of patients presenting with STEMI undergoing primary percutaneous coronary intervention (PCI). Methods: We will perform a prospective, observational cohort study in a single healthcare center. Adult patients aged ≥18 years presenting with STEMI in sinus rhythm will be enrolled for primary PCI within 12 h from symptoms onset. Time domain, frequency domain, and nonlinear HRV parameters will be measured using a medically approved wrist-wearable device for 5 min segments during myocardial revascularization by primary PCI. Additional HRV measurements will be performed one and six months from the index event. The primary composite outcome will include all-cause mortality and major adverse cardiovascular events (during the hospital stay, one month, and one year following admission). Several secondary outcomes will be analyzed: individual components of the primary composite outcome, target lesion revascularization, hospitalizations for heart failure, ventricular arrhythmias, left ventricular ejection fraction, and left ventricular diastolic function. Conclusions: Our study will enlighten the reliability and usefulness of HRV evaluation as a prognostic marker in contemporary patients with STEMI. The potential validation of HRV as a risk marker for adverse outcomes following STEMI will ensure a background for including HRV parameters in future risk scores and guidelines.

PO-661-07 A PHOTOPLETHYMOGRAPHY-BASED APPROACH TO ATRIAL FIBRILLATION-BURDEN ESTIMATION IN HIGH-RISK PATIENTS

Atrial fibrillation (AF) is the most common arrhythmia leading to hospital admission. Its incidence is linked to increased risk of stroke, congestive heart failure, and overall mortality. AF burden is a key determinant of outcomes, but accurate quantification can be limited by the cardiac monitoring period (typically days). Alternatively, longer-term monitoring comes at higher cost and often requires an invasive approach. The introduction of reliable wrist-worn devices may improve access to long-term monitoring of AF burden.

Intra- and Inter-Subject Perspectives on the Detection of Focal Onset Motor Seizures in Epilepsy Patients.

Focal onset epileptic seizures are highly heterogeneous in their clinical manifestations, and a robust seizure detection across patient cohorts has to date not been achieved. Here, we assess and discuss the potential of supervised machine learning models for the detection of focal onset motor seizures by means of a wrist-worn wearable device, both in a personalized context as well as across patients. Wearable data were recorded in-hospital from patients with epilepsy at two epilepsy centers. Accelerometry, electrodermal activity, and blood volume pulse data were processed and features for each of the biosignal modalities were calculated. Following a leave-one-out approach, a gradient tree boosting machine learning model was optimized and tested in an intra-subject and inter-subject evaluation. In total, 20 seizures from 9 patients were included and we report sensitivities of 67% to 100% and false alarm rates of down to 0.85 per 24 h in the individualized assessment. Conversely, for an inter-subject seizure detection methodology tested on an out-of-sample data set, an optimized model could only achieve a sensitivity of 75% at a false alarm rate of 13.4 per 24 h. We demonstrate that robustly detecting focal onset motor seizures with tonic or clonic movements from wearable data may be possible for individuals, depending on specific seizure manifestations.

Are Machine Learning Models on Wrist Accelerometry Robust against Differences in Physical Performance among Older Adults?

Sufficient physical activity (PA) reduces the risk of a myriad of diseases and preserves physical capabilities in later life. While there have been significant achievements in mapping accelerations to real-life movements using machine learning (ML), errors continue to be common, particularly for wrist-worn devices. It remains unknown whether ML models are robust for estimating age-related loss of physical function. In this study, we evaluated the performance of ML models (XGBoost and LASSO) to estimate the hallmark measures of PA in low physical performance (LPP) and high physical performance (HPP) groups. Our models were built to recognize PA types and intensities, identify each individual activity, and estimate energy expenditure (EE) using wrist-worn accelerometer data (33 activities per participant) from a large sample of participants (n = 247, 57% females, aged 60+ years). Results indicated that the ML models were accurate in recognizing PA by type and intensity while also estimating EE accurately. However, the models built to recognize individual activities were less robust. Across all tasks, XGBoost outperformed LASSO. XGBoost obtained F1-Scores for sedentary (0.932 0.005), locomotion (0.946 0.003), lifestyle (0.927 0.006), and strength flexibility exercise (0.915 0.017) activity type recognition tasks. The F1-Scores for recognizing low, light, and moderate activity intensity were (0.9320.005), (0.840 0.004), and (0.869 0.005), respectively. The root mean square error for EE estimation was 0.836 0.059 METs. There was no evidence showing that splitting the participants into the LPP and HPP groups improved the models’ performance on estimating the hallmark measures of physical activities. In conclusion, using features derived from wrist-worn accelerometer data, machine learning models can accurately recognize PA types and intensities and estimate EE for older adults with high and low physical function.

Comparison of Wrist- and Hip-Worn Activity Monitors When Meeting Step Guidelines.

IntroductionPhysical activity (PA) guidelines aimed at accumulating 10,000 steps per day have become increasingly common with the advent of wristband PA monitors. However, accumulated steps measured with wristband PA monitors may not be equal to steps measured with validated, hip-worn pedometers. Consequently, evaluating and developing guidelines for step counts using wristband PA monitors for the general population is needed. We compared step counts accumulated with hip-worn pedometers with those accumulated with wrist-worn activity monitors during 1) treadmill exercise, 2) treadmill walking, and 3) activities of daily living (ADL) to determine their accuracy in meeting step count guidelines (ie, 10,000 steps/d).MethodsEighty-six adults (aged 18–65 y; body mass index, 19–45 kg/m2) completed 30 minutes of treadmill exercise while simultaneously using a hip-worn pedometer and wrist-worn PA monitor. Remaining steps needed to reach 10,000 steps (ie, 10,000 steps minus the number of pedometer steps recorded from treadmill exercise = remainder) were completed via treadmill walking or ADL. Steps were recorded for both devices after treadmill exercise, treadmill walking, and ADL for both devices.ResultsFewer steps were accumulated via wrist-worn PA monitors than via hip-worn pedometers during treadmill exercise (3,552 [SD, 63] steps vs 3,790 [SD, 55] steps, P < .01) and treadmill walking (5,877 [SD, 83] steps vs 6,243 [SD, 49] steps, P < .01). More steps were accumulated via wrist-worn PA monitors than hip-worn pedometers during ADL (7,695 [SD, 207] steps vs 6,309 [SD, 57] steps, P < .01). Consequently, total steps were significantly higher for wristband PA monitors than hip-worn pedometers (11,247 [SD, 210] steps vs 10,099 [SD, 39] steps; P < .01).ConclusionThe widely used 10,000-step recommendation may not be accurate for all users of all activity monitors, given the discrepancy in daily step count among wrist-worn and hip-worn devices. Having a more accurate indication of number of steps taken per day based on the device used could have positive effects on health.

High Specificity Wearable Device With Photoplethysmography and Six-Lead Electrocardiography for Atrial Fibrillation Detection Challenged by Frequent Premature Contractions: DoubleCheck-AF.

BackgroundConsumer smartwatches have gained attention as mobile health (mHealth) tools able to detect atrial fibrillation (AF) using photoplethysmography (PPG) or a short strip of electrocardiogram (ECG). PPG has limited accuracy due to the movement artifacts, whereas ECG cannot be used continuously, is usually displayed as a single-lead signal and is limited in asymptomatic cases.ObjectiveDoubleCheck-AF is a validation study of a wrist-worn device dedicated to providing both continuous PPG-based rhythm monitoring and instant 6-lead ECG with no wires. We evaluated its ability to differentiate between AF and sinus rhythm (SR) with particular emphasis on the challenge of frequent premature beats.Methods and ResultsWe performed a prospective, non-randomized study of 344 participants including 121 patients in AF. To challenge the specificity of the device two control groups were selected: 95 patients in stable SR and 128 patients in SR with frequent premature ventricular or atrial contractions (PVCs/PACs). All ECG tracings were labeled by two independent diagnosis-blinded cardiologists as “AF,” “SR” or “Cannot be concluded.” In case of disagreement, a third cardiologist was consulted. A simultaneously recorded ECG of Holter monitor served as a reference. It revealed a high burden of ectopy in the corresponding control group: 6.2 PVCs/PACs per minute, bigeminy/trigeminy episodes in 24.2% (31/128) and runs of ≥3 beats in 9.4% (12/128) of patients. AF detection with PPG-based algorithm, ECG of the wearable and combination of both yielded sensitivity and specificity of 94.2 and 96.9%; 99.2 and 99.1%; 94.2 and 99.6%, respectively. All seven false-positive PPG-based cases were from the frequent PVCs/PACs group compared to none from the stable SR group (P < 0.001). In the majority of these cases (6/7) cardiologists were able to correct the diagnosis to SR with the help of the ECG of the device (P = 0.012).ConclusionsThis is the first wearable combining PPG-based AF detection algorithm for screening of AF together with an instant 6-lead ECG with no wires for manual rhythm confirmation. The system maintained high specificity despite a remarkable amount of frequent single or multiple premature contractions.

Concomitant sensory stimulation during therapy to enhance hand functional recovery post stroke

BackgroundPost-stroke hand impairment is prevalent and persistent even after a full course of rehabilitation. Hand diminishes stroke survivors’ abilities for activities of daily living and independence. One way to improve treatment efficacy is to augment therapy with peripheral sensory stimulation. Recently, a novel sensory stimulation, TheraBracelet, has been developed in which imperceptible vibration is applied during task practice through a wrist-worn device. The objective of this trial is to determine if combining TheraBracelet with hand task practice is superior to hand task practice alone.MethodsA double-blind randomized controlled trial will be used. Chronic stroke survivors will undergo a standardized hand task practice therapy program (3 days/week for 6 weeks) while wearing a device on the paretic wrist. The device will deliver TheraBracelet vibration for the treatment group and no vibration for the control group. The primary outcome is hand function measured by the Wolf Motor Function Test. Other outcomes include the Box and Block Test, Action Research Arm Test, upper extremity use in daily living, biomechanical measure of the sensorimotor grip control, and EEG-based neural communication.DiscussionThis research will determine clinical utility of TheraBracelet to guide future translation. The TheraBracelet stimulation is delivered via a wrist-worn device, does not interfere with hand motion, and can be easily integrated into clinical practice. Enhancing hand function should substantially increase stroke survivors' independence and quality of life and reduce caregiver burden.Trial registrationNCT04569123. Registered on September 29, 2020

Long–Short Ensemble Network for Bipolar Manic-Euthymic State Recognition Based on Wrist-Worn Sensors

Manic episodes of bipolar disorder can lead to uncritical behavior and delusional psychosis, often with destructive consequences for those affected and their surroundings. Early detection and intervention of a manic episode are crucial to prevent escalation, hospital admission, and premature death. However, people with bipolar disorder may not recognize that they are experiencing a manic episode and symptoms, such as euphoria and increased productivity can also deter affected individuals from seeking help. This work proposes to perform user-independent, automatic mood-state detection based on actigraphy and electrodermal activity acquired from a wrist-worn device during mania and after recovery (euthymia). This article proposes a new deep learning-based ensemble method leveraging long (20 h) and short (5 min) time intervals to discriminate between the mood states. When tested on 47 bipolar patients, the proposed classification scheme achieves an average accuracy of 91.59% in euthymic/manic mood-state recognition.

Pilot trial of a technology assisted treatment for trichotillomania

The present study examined the usability, acceptability, feasibility, and preliminary efficacy of a prototype wrist-worn motion detection device and accompanying mobile app, developed by HabitAware®, as a system for delivering self-administered Habit Reversal Training (HRT). As an exploratory aim, the effect of the device and HRT app combination was compared to a reminder bracelet. The pilot trial included 15 adults with trichotillomania who interacted with the device and app system (n = 10) or reminder bracelet (n = 5) for 4 weeks. Participants in the device and app condition reported high usability, acceptability, and perceived efficacy of the system. The device and HRT app combination reduced hair pulling severity. Individuals in the reminder bracelet condition also showed a significant improvement in hair pulling. A future efficacy study with a larger sample size, longer timeframe, and improved gesture detection algorithm is warranted.

Low Light Exposure and Physical Activity in Older Adults With and Without Age-Related Macular Degeneration.

PurposeTo investigate the extent of low light exposure and associated physical activity in older adults with and without age-related macular degeneration (AMD).MethodsLight exposure (lux) and physical activity (counts per minute, CPM) were measured in 28 older adults (14 bilateral AMD and 14 normally sighted controls) using a wrist-worn actigraphy device (Actiwatch) for 7 days and nights. Exposure to low light levels (≤10 lux) and physical activity during waking hours were determined, as well as number of brief active periods during sleeping hours (e.g., going to the bathroom). Assessments included visual acuity and the Low Luminance Questionnaire (LLQ).ResultsNo significant differences were found in low light exposure (39 ± 14% vs. 34 ± 10%) or physical activity (200 ± 82 CPM vs. 226 ± 55 CPM) during waking hours between the AMD and control group. However, the AMD group had more brief active periods during sleeping hours than controls (1.8 ± 1.3 vs. 1.1 ± 0.4; P = 0.007). Reduced physical activity under low light levels was significantly associated with lower LLQ scores (P = 0.012).ConclusionsExposure to low light levels and associated physical activity were similar in older adults with and without AMD. This has important implications for older adults with AMD, given the impact of low light levels on visual function and mobility, suggesting the need for including lighting advice in rehabilitation programs for this population.Translational RelevanceOlder adults with and without AMD spend over a third of waking hours under low light levels, which are an environmental falls hazard. Findings suggest the need for interventions to improve lighting levels for older adults.