A wearable cardiac ultrasound imager.

Continuous imaging of cardiac functions is highly desirable for the assessment of long-term cardiovascular health, detection of acute cardiac dysfunction and clinical management of critically ill or surgical patients1-4. However, conventional non-invasive approaches to image the cardiac function cannot provide continuous measurements owing to device bulkiness5-11, and existing wearable cardiac devices can only capture signals on the skin12-16. Here we report a wearable ultrasonic device for continuous, real-time and direct cardiac function assessment. We introduce innovations in device design and material fabrication that improve the mechanical coupling between the device and human skin, allowing the left ventricle to be examined from different views during motion. We also develop a deep learning model that automatically extracts the left ventricular volume from the continuous image recording, yielding waveforms of key cardiac performance indices such as stroke volume, cardiac output and ejection fraction. This technology enables dynamic wearable monitoring of cardiac performance with substantially improved accuracy in various environments.

Emerging Technologies for Identifying Atrial Fibrillation.

Atrial fibrillation (AF) is a major cause of morbidity and mortality globally, and much of this is driven by challenges in its timely diagnosis and treatment. Existing and emerging mobile technologies have been used to successfully identify AF in a variety of clinical and community settings, and while these technologies offer great promise for revolutionizing AF detection and screening, several major barriers may impede their effectiveness. The unclear clinical significance of device-detected AF, potential challenges in integrating patient-generated data into existing healthcare systems and clinical workflows, harm resulting from potential false positives, and identifying the appropriate scope of population-based screening efforts are all potential concerns that warrant further investigation. It is crucial for stakeholders such as healthcare providers, researchers, funding agencies, insurers, and engineers to actively work together in fulfilling the tremendous potential of mobile technologies to improve AF identification and management on a population level.

Automated seizure detection using wearable devices: A clinical practice guideline of the International League Against Epilepsy and the International Federation of Clinical Neurophysiology.

The objective of this clinical practice guideline (CPG) is to provide recommendations for healthcare personnel working with patients with epilepsy on the use of wearable devices for automated seizure detection in patients with epilepsy, in outpatient, ambulatory settings. The Working Group of the International League Against Epilepsy (ILAE) and the International Federation of Clinical Neurophysiology (IFCN) developed the CPG according to the methodology proposed by the ILAE Epilepsy Guidelines Working Group. We reviewed the published evidence using The Preferred Reporting Items for Systematic Review and Meta-Analysis (PRISMA) statement and evaluated the evidence and formulated the recommendations following the Grading of Recommendations Assessment, Development and Evaluation (GRADE) system. We found high level of evidence for the accuracy of automated detection of generalized tonic-clonic seizures (GTCS) and focal-to-bilateral tonic-clonic seizures (FBTCS) and recommend the use of wearable automated seizure detection devices for selected patients when accurate detection of GTCS and FBTCS is recommended as a clinical adjunct. We also found a moderate level of evidence for seizure types without GTCS or FBTCS. However, it was uncertain whether the detected alarms resulted in meaningful clinical outcomes for the patients. We recommend using clinically validated devices for automated detection of GTCS and FBTCS, especially in unsupervised patients, where alarms can result in rapid intervention (weak/conditional recommendation). At present, we do not recommend clinical use of the currently available devices for other seizure types (weak/conditional recommendation). Further research and development are needed to improve the performance of automated seizure detection and to document their accuracy and clinical utility.

A catalog of validity indices for step counting wearable technologies during treadmill walking: the CADENCE-Kids study.

BACKGROUND: Wearable technologies play an important role in measuring physical activity (PA) and promoting health. Standardized validation indices (i.e., accuracy, bias, and precision) compare performance of step counting wearable technologies in young people. PURPOSE: To produce a catalog of validity indices for step counting wearable technologies assessed during different treadmill speeds (slow [0.8-3.2 km/h], normal [4.0-6.4 km/h], fast [7.2-8.0 km/h]), wear locations (waist, wrist/arm, thigh, and ankle), and age groups (children, 6-12 years; adolescents, 13-17 years; young adults, 18-20 years). METHODS: One hundred seventeen individuals (13.1 ± 4.2 years, 50.4% female) participated in this cross-sectional study and completed 5-min treadmill bouts (0.8 km/h to 8.0 km/h) while wearing eight devices (Waist: Actical, ActiGraph GT3X+, NL-1000, SW-200; Wrist: ActiGraph GT3X+; Arm: SenseWear; Thigh: activPAL; Ankle: StepWatch). Directly observed steps served as the criterion measure. Accuracy (mean absolute percentage error, MAPE), bias (mean percentage error, MPE), and precision (correlation coefficient, r; standard deviation, SD; coefficient of variation, CoV) were computed. RESULTS: Five of the eight tested wearable technologies (i.e., Actical, waist-worn ActiGraph GT3X+, activPAL, StepWatch, and SW-200) performed at < 5% MAPE over the range of normal speeds. More generally, waist (MAPE = 4%), thigh (4%) and ankle (5%) locations displayed higher accuracy than the wrist location (23%) at normal speeds. On average, all wearable technologies displayed the lowest accuracy across slow speeds (MAPE = 50.1 ± 35.5%), and the highest accuracy across normal speeds (MAPE = 15.9 ± 21.7%). Speed and wear location had a significant effect on accuracy and bias (P < 0.001), but not on precision (P > 0.05). Age did not have any effect (P > 0.05). CONCLUSIONS: Standardized validation indices focused on accuracy, bias, and precision were cataloged by speed, wear location, and age group to serve as important reference points when selecting and/or evaluating device performance in young people moving forward. Reduced performance can be expected at very slow walking speeds (0.8 to 3.2 km/h) for all devices. Ankle-worn and thigh-worn devices demonstrated the highest accuracy. Speed and wear location had a significant effect on accuracy and bias, but not precision. TRIAL REGISTRATION: Clinicaltrials.gov NCT01989104 . Registered November 14, 2013.

Feasibility of using "SMARTER" methodology for monitoring precipitating conditions of pediatric migraine episodes.

OBJECTIVE: To evaluate the feasibility in children of an intensive prospective data monitoring methodology for identifying precipitating conditions for migraine occurrence. BACKGROUND: Migraine headaches are a common pain condition in childhood and can become increasingly chronic and disabling with repeated episodes. Identifying conditions that forecast when a child's migraine is likely to occur may facilitate next-generation adaptive treatments to prevent future migraine attacks. METHODS: In this cohort study of a sample of 30 youth (ages 10-17) with migraine recruited through a pediatric headache clinic, smartphones supplemented with wearable biosensors were used over a period of 28 days to collect contextual data thought to be potentially relevant to headache occurrence. Self-reported data on headache occurrence, lifestyle, and perceptions of the environment were collected in 4 epochs per day using custom real-time reporting software. Data derived from the wearable biosensor included information on autonomic arousal and physical activity. Built-in sensors on participants' own phones also were used to indicate location and to quantify the sensory environment (e.g., ambient noise and light levels). Data fidelity was monitored to evaluate feasibility of the methods, and participant acceptability was assessed via an end-of-study survey. RESULTS: Self-report data were obtained on a mean of 88.9% (24.9/28) of assigned days (SD = 22.4%) and at a mean of 68.9% (77.2/112) of assigned moments (SD = 24.5%). Data from the wearable biosensor were obtained for a mean of 18.7 hours per day worn (SD = 2.3 hours), with participants on average wearing the sensor on 20.3 days (SD = 9.9). Fidelity of obtaining objective data from phone sensors on the sensory environment and other environmental conditions was highly variable, with these data obtainable from 5 to 22/30 (16.7%-73.3%) of participants' own phones. Most participants (63.3%-100%) responded with at least "somewhat agree" to questions about acceptability of the study methods. However, 5 to 7/30 (16.7%-23.3%) patients indicated difficulties with burden and remembering to wear the sensor. Almost all participants (29/30, 96.7%) agreed that they would want information about when a migraine might occur. CONCLUSIONS: A contemporary data sampling approach comprising ambulatory sensors and real-time reporting appears to be acceptable to most youth with migraine in this study. Reliability of acquiring some data sources from participants' own phones, however, was suboptimal. Further refining these data sampling methods may enable a novel means of predicting and preventing recurrences of migraine episodes in youth.

Smart wearable devices as a psychological intervention for healthy lifestyle and quality of life: a randomized controlled trial.

PURPOSE: Creating a healthy lifestyle is important across different life stages. Commercial smart wearable devices are an innovative and interesting approach as an early psychological intervention for modifying health-related behaviors. Therefore, the purpose of this study was to explore the effects of smart wearable devices on health-promoting lifestyles and quality of life. METHODS: The study design was a three-parallel randomized controlled trial with a 3-month intervention. Two commercial smart wearable devices (smartwatches and smart bracelets) with different levels of complicated functions were applied as a psychological intervention in comparison with a smartphone app as the control group. Participants were healthy young adults with a median age of 26 years. Outcome measurements were conducted by self-administered questionnaires. Chi-square tests and ANOVA were performed for testing the difference of participants at baseline, and generalized estimating equations were performed for testing the effect of the intervention. RESULTS: At the beginning, 81 participants were recruited and 73 participants completed the study. Results of a healthy lifestyle demonstrated significant group effects of exercise and a significant effect of the interaction for self-actualization and stress management in the experimental group with a smartwatch (Self-actualization: MD = 0.35[- 0.10,0.80]; Exercise: MD = 0.21[- 0.33 0.75]; Stress management: MD = 0.36[- 0.04,0.76]) by comparing with only using mobile app (Self-actualization: MD = - 0.03[- 0.25,0.18]; Exercise: MD = - 0.12[- 0.38,0.14]; Stress management, MD = - 0.28[- 0.55,0.00]). The significant effect of group-by-time interaction for self-actualization was found in the experimental group with a smart bracelet (MD = 0.05[- 0.30,0.20]) by comparing with the control group. The GEE-adjusted model indicated significant effects of the interaction on the comprehensive, physical, and mental quality of life in the experimental group with the smartwatch (Comprehensive: MD = 0.24[- 0.04,0.52]; Physical: MD = 0.67[0.26,1.09]; Mental: MD = 0.72[0.29,1.16]) by comparing with the control group (Comprehensive: MD = - 1.57[- 2.55, - 0.59]; Physical: MD = 0.25[0.00,0.50]; Mental: MD = 0.08[- 0.11,0.27]). CONCLUSION: From a psychological perspective, smart wearable devices have potential benefits of shaping a healthy lifestyle and improving the quality of life. Enhancing the utility of commercial well-designed smart wearable devices is an innovative and effective strategy for promoting public health.

Reliability and validity of the COSMED K5 portable metabolic system during walking.

PURPOSE: Portable methods for assessing energy expenditure outside the laboratory and clinical environments are becoming more widely used. As such, it is important to understand the accuracy of such devices. Therefore, the purpose was to determine the reliability and validity of the COSMED K5 portable metabolic system. METHODS: Reliability and validity were assessed in 27 adults (age: 27 ± 5 years; n = 15 women) using a walking protocol. The protocol consisted of a 5-min walk/2-min rest cycle starting at 1.5 mph and increasing in 0.5-mph increments to 4.0 mph. During visit one, participants wore the K5 to assess oxygen consumption ([Formula: see text]O2), carbon dioxide production ([Formula: see text]CO2), and other metabolic variables. Two to seven days later, the protocol was repeated twice with the COSMED K5 and K4b2 systems in a randomized, counterbalanced order. RESULTS: Intraclass correlation coefficients (ICC) revealed that the K5 reliably measured [Formula: see text]O2 (ICC 0.64-0.85) and [Formula: see text]CO2 across all walking speeds (ICC 0.50-0.80), with stronger reliability at faster walking speeds compared with slower speeds. Moderate-to-strong relationships were observed for measured gases between the K5 and K4b2. Specifically, [Formula: see text]O2 exhibited a moderately high-to-high relationship between devices (r = 0.72-0.82), and a similarly moderately high-to-high relationship was observed for [Formula: see text]CO2 (r = 0.68-0.82). While there were no differences in [Formula: see text]O2 measured between devices (p ≥ 0.10), the K5 provided lower [Formula: see text]CO2 readings than the K4b2 during the 3.0, 3.5, and 4.0 mph walking speeds (p ≤ 0.02). CONCLUSIONS: The K5 provided reliable and valid measures of metabolic variables, with greater reliability and validity at faster walking speeds.

Recommendations for determining the validity of consumer wearable heart rate devices: expert statement and checklist of the INTERLIVE Network.

Assessing vital signs such as heart rate (HR) by wearable devices in a lifestyle-related environment provides widespread opportunities for public health related research and applications. Commonly, consumer wearable devices assessing HR are based on photoplethysmography (PPG), where HR is determined by absorption and reflection of emitted light by the blood. However, methodological differences and shortcomings in the validation process hamper the comparability of the validity of various wearable devices assessing HR. Towards Intelligent Health and Well-Being: Network of Physical Activity Assessment (INTERLIVE) is a joint European initiative of six universities and one industrial partner. The consortium was founded in 2019 and strives towards developing best-practice recommendations for evaluating the validity of consumer wearables and smartphones. This expert statement presents a best-practice validation protocol for consumer wearables assessing HR by PPG. The recommendations were developed through the following multi-stage process: (1) a systematic literature review based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses, (2) an unstructured review of the wider literature pertaining to factors that may introduce bias during the validation of these devices and (3) evidence-informed expert opinions of the INTERLIVE Network. A total of 44 articles were deemed eligible and retrieved through our systematic literature review. Based on these studies, a wider literature review and our evidence-informed expert opinions, we propose a validation framework with standardised recommendations using six domains: considerations for the target population, criterion measure, index measure, testing conditions, data processing and the statistical analysis. As such, this paper presents recommendations to standardise the validity testing and reporting of PPG-based HR wearables used by consumers. Moreover, checklists are provided to guide the validation protocol development and reporting. This will ensure that manufacturers, consumers, healthcare providers and researchers use wearables safely and to its full potential.

Reliability of wearable sensors to assess impact metrics during sport-specific tasks.

There is little information on the reliability of inertial measurement units for capturing impact load metrics during sport-specific movements. The purpose of this study is to determine the reliability of the Blue Trident IMU sensors in measuring impact load, step count and cumulative bone stimulus during a series of soccer-related tasks. Ten healthy recreational soccer players (age: 27.9 ± 2.18; height: 1.77 ± 0.10 m; mass: 79.02 ± 13.07 kg) volunteered for a 3-visit study and performed 4 tasks. Bilateral impact load, total number of steps and cumulative bone stimulus during the tasks were collected. Data were sampled using a dual-g sensor. Intraclass correlation coefficients (ICC3,1) with 95% confidence intervals assessed between-day reliability. Impact load (0.58-0.89) and cumulative bone stimulus (0.90-0.97) had good to excellent reliability across tasks. ICC values for right/left step count were good to excellent during acceleration-deceleration (0.728-0.837), change direction (0.734-0.955) and plant/cut manoeuvres (0.701-0.866) and fair to good during the ball kick (0.588-0.683). This suggests that wearable sensors can reliably measure the cumulative impact load during outdoor functional movements; however, kicking manoeuvres are less reliable. Measuring impact load in the field expands the ability to capture more ecologically valid data.

Reliability and Validity of the Polar V800 Sports Watch for Estimating Vertical Jump Height.

This study aimed to assess the reliability and validity of the Polar V800 to measure vertical jump height. Twenty-two physically active healthy men (age: 22.89 ± 4.23 years; body mass: 70.74 ± 8.04 kg; height: 1.74 ± 0.76 m) were recruited for the study. The reliability was evaluated by comparing measurements acquired by the Polar V800 in two identical testing sessions one week apart. Validity was assessed by comparing measurements simultaneously obtained using a force platform (gold standard), high-speed camera and the Polar V800 during squat jump (SJ) and countermovement jump (CMJ) tests. In the test-retest reliability, high intraclass correlation coefficients (ICCs) were observed (mean: 0.90, SJ and CMJ) in the Polar V800. There was no significant systematic bias ± random errors (p > 0.05) between test-retest. Low coefficients of variation (<5%) were detected in both jumps in the Polar V800. In the validity assessment, similar jump height was detected among devices (p > 0.05). There was almost perfect agreement between the Polar V800 compared to a force platform for the SJ and CMJ tests (Mean ICCs = 0.95; no systematic bias ± random errors in SJ mean: -0.38 ± 2.10 cm, p > 0.05). Mean ICC between the Polar V800 versus high-speed camera was 0.91 for the SJ and CMJ tests, however, a significant systematic bias ± random error (0.97 ± 2.60 cm; p = 0.01) was detected in CMJ test. The Polar V800 offers valid, compared to force platform, and reliable information about vertical jump height performance in physically active healthy young men.

Subclinical and Device-Detected Atrial Fibrillation: Pondering the Knowledge Gap: A Scientific Statement From the American Heart Association.

The widespread use of cardiac implantable electronic devices and wearable monitors has led to the detection of subclinical atrial fibrillation in a substantial proportion of patients. There is evidence that these asymptomatic arrhythmias are associated with increased risk of stroke. Thus, detection of subclinical atrial fibrillation may offer an opportunity to reduce stroke risk by initiating anticoagulation. However, it is unknown whether long-term anticoagulation is warranted and in what populations. This scientific statement explores the existing data on the prevalence, clinical significance, and management of subclinical atrial fibrillation and identifies current gaps in knowledge and areas of controversy and consensus.

Biosensors to monitor MS activity.

Advances in wearable and wireless biosensing technology pave the way for a brave new world of novel multiple sclerosis (MS) outcome measures. Our current tools for examining patients date back to the 19th century and while invaluable to the neurologist invite accompaniment from these new technologies and artificial intelligence (AI) analytical methods. While the most common biosensor tool used in MS publications to date is the accelerometer, the landscape is changing quickly with multi-sensor applications, electrodermal sensors, and wireless radiofrequency waves. Some caution is warranted to ensure novel outcomes have clear clinical relevance and stand-up to the rigors of reliability, reproducibility, and precision, but the ultimate implementation of biosensing in the MS clinical setting is inevitable.

Can commercially available wearable EEG devices be used for diagnostic purposes? An explorative pilot study.

Electroencephalography (EEG) is a core element in the diagnosis of epilepsy syndromes and can help to monitor antiseizure treatment. Mobile EEG (mEEG) devices are increasingly available on the consumer market and may offer easier access to EEG recordings especially in rural or resource-poor areas. The usefulness of consumer-grade devices for clinical purposes is still underinvestigated. Here, we compared EEG traces of a commercially available mEEG device (Emotiv EPOC) to a simultaneously recorded clinical video EEG (vEEG). Twenty-two adult patients (11 female, mean age 40.2 years) undergoing noninvasive vEEG monitoring for clinical purposes were prospectively enrolled. The EEG recordings were evaluated by 10 independent raters with unmodifiable view settings. The individual evaluations were compared with respect to the presence of abnormal EEG findings (regional slowing, epileptiform potentials, seizure pattern). Video EEG yielded a sensitivity of 56% and specificity of 88% for abnormal EEG findings, whereas mEEG reached 39% and 85%, respectively. Interrater reliability coefficients were better in vEEG as compared to mEEG (ϰ = 0.50 vs. 0.30), corresponding to a moderate and fair agreement. Intrarater reliability between mEEG and vEEG evaluations of simultaneous recordings of a given participant was moderate (ϰ = 0.48). Given the limitations of our exploratory pilot study, our results suggest that vEEG is superior to mEEG, but that mEEG can be helpful for diagnostic purposes. We present the first quantitative comparison of simultaneously acquired clinical and mobile consumer-grade EEG for a clinical use-case.

Telehealth in the time of Corona: 'doctor in the house'.

The COVID-19 pandemic has led to many physicians working from home whenever possible. Although the concept of 'remote' patient care has been around for decades, present circumstances have provided a grand impetus in that direction with a view to protecting both patient and caregiver. In this article, we discuss some of the various challenges to moving forward with telemedicine, drawing in part on our own experiences in dealing with the COVID-19 pandemic. Clinical, technical, financial and cultural barriers to telemedicine are identified, along with a discussion concerning anticipated benefits. We conclude that the COVID-19 pandemic will likely forever change how healthcare is conducted as telemedicine figures increasingly prominently in the clinical landscape.

Effectiveness of Upper Limb Wearable Technology for Improving Activity and Participation in Adult Stroke Survivors: Systematic Review.

BACKGROUND: With advances in technology, the adoption of wearable devices has become a viable adjunct in poststroke rehabilitation. Upper limb (UL) impairment affects up to 77% of stroke survivors impacting on their ability to carry out everyday activities. However, despite an increase in research exploring these devices for UL rehabilitation, little is known of their effectiveness. OBJECTIVE: This review aimed to assess the effectiveness of UL wearable technology for improving activity and participation in adult stroke survivors. METHODS: Randomized controlled trials (RCTs) and randomized comparable trials of UL wearable technology for poststroke rehabilitation were included. Primary outcome measures were validated measures of activity and participation as defined by the International Classification of Functioning, Disability, and Health. Databases searched were MEDLINE, Web of Science (Core collection), CINAHL, and the Cochrane Library. The Cochrane Risk of Bias Tool was used to assess the methodological quality of the RCTs and the Downs and Black Instrument for the quality of non RCTs. RESULTS: In the review, we included 11 studies with collectively 354 participants at baseline and 323 participants at final follow-up including control groups and participants poststroke. Participants' stroke type and severity varied. Only 1 study found significant between-group differences for systems functioning and activity (P≤.02). The 11 included studies in this review had small sample sizes ranging from 5 to 99 participants at an average (mean) age of 57 years. CONCLUSIONS: This review has highlighted a number of reasons for insignificant findings in this area including low sample sizes and the appropriateness of the methodology for complex interventions. However, technology has the potential to measure outcomes, provide feedback, and engage users outside of clinical sessions. This could provide a platform for motivating stroke survivors to carry out more rehabilitation in the absence of a therapist, which could maximize recovery. TRIAL REGISTRATION: PROSPERO CRD42017057715; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=57715.

Social Network Analysis of Intensive Care Unit Health Care Professionals Measured by Wearable Sociometric Badges: Longitudinal Observational Study.

BACKGROUND: Use of wearable sensor technology for studying human teamwork behavior is expected to generate a better understanding of the interprofessional interactions between health care professionals. OBJECTIVE: We used wearable sociometric sensor badges to study how intensive care unit (ICU) health care professionals interact and are socially connected. METHODS: We studied the face-to-face interaction data of 76 healthcare professionals in the ICU at Mie University Hospital collected over 4 weeks via wearable sensors. RESULTS: We detail the spatiotemporal distributions of staff members' inter- and intraprofessional active face-to-face interactions, thereby generating a comprehensive visualization of who met whom, when, where, and for how long in the ICU. Social network analysis of these active interactions, concomitant with centrality measurements, revealed that nurses constitute the core members of the network, while doctors remain in the periphery. CONCLUSIONS: Our social network analysis using the comprehensive ICU interaction data obtained by wearable sensors has revealed the leading roles played by nurses within the professional communication network.

Patterns of Use and Key Predictors for the Use of Wearable Health Care Devices by US Adults: Insights from a National Survey.

BACKGROUND: Despite the growing popularity of wearable health care devices (from fitness trackes such as Fitbit to smartwatches such as Apple Watch and more sophisticated devices that can collect information on metrics such as blood pressure, glucose levels, and oxygen levels), we have a limited understanding about the actual use and key factors affecting the use of these devices by US adults. OBJECTIVE: The main objective of this study was to examine the use of wearable health care devices and the key predictors of wearable use by US adults. METHODS: Using a national survey of 4551 respondents, we examined the usage patterns of wearable health care devices (use of wearables, frequency of their use, and willingness to share health data from a wearable with a provider) and a set of predictors that pertain to personal demographics (age, gender, race, education, marital status, and household income), individual health (general health, presence of chronic conditions, weight perceptions, frequency of provider visits, and attitude towards exercise), and technology self-efficacy using logistic regression analysis. RESULTS: About 30% (1266/4551) of US adults use wearable health care devices. Among the users, nearly half (47.33%) use the devices every day, with a majority (82.38% weighted) willing to share the health data from wearables with their care providers. Women (16.25%), White individuals (19.74%), adults aged 18-50 years (19.52%), those with some level of college education or college graduates (25.60%), and those with annual household incomes greater than US $75,000 (17.66%) were most likely to report using wearable health care devices. We found that the use of wearables declines with age: Adults aged >50 years were less likely to use wearables compared to those aged 18-34 years (odds ratios [OR] 0.46-0.57). Women (OR 1.26, 95% CI 0.96-1.65), White individuals (OR 1.65, 95% CI 0.97-2.79), college graduates (OR 1.05, 95% CI 0.31-3.51), and those with annual household incomes greater than US $75,000 (OR 2.6, 95% CI 1.39-4.86) were more likely to use wearables. US adults who reported feeling healthier (OR 1.17, 95% CI 0.98-1.39), were overweight (OR 1.16, 95% CI 1.06-1.27), enjoyed exercise (OR 1.23, 95% CI 1.06-1.43), and reported higher levels of technology self-efficacy (OR 1.33, 95% CI 1.21-1.46) were more likely to adopt and use wearables for tracking or monitoring their health. CONCLUSIONS: The potential of wearable health care devices is under-realized, with less than one-third of US adults actively using these devices. With only younger, healthier, wealthier, more educated, technoliterate adults using wearables, other groups have been left behind. More concentrated efforts by clinicians, device makers, and health care policy makers are needed to bridge this divide and improve the use of wearable devices among larger sections of American society.

Opportunities and Challenges Surrounding the Use of Data From Wearable Sensor Devices in Health Care: Qualitative Interview Study.

BACKGROUND: Wearable sensors connected via networked devices have the potential to generate data that may help to automate processes of care, engage patients, and increase health care efficiency. The evidence of effectiveness of such technologies is, however, nascent and little is known about unintended consequences. OBJECTIVE: Our objective was to explore the opportunities and challenges surrounding the use of data from wearable sensor devices in health care. METHODS: We conducted a qualitative, theoretically informed, interview-based study to purposefully sample international experts in health care, technology, business, innovation, and social sciences, drawing on sociotechnical systems theory. We used in-depth interviews to capture perspectives on development, design, and use of data from wearable sensor devices in health care, and employed thematic analysis of interview transcripts with NVivo to facilitate coding. RESULTS: We interviewed 16 experts. Although the use of data from wearable sensor devices in health and care has significant potential in improving patient engagement, there are a number of issues that stakeholders need to negotiate to realize these benefits. These issues include the current gap between data created and meaningful interpretation in health and care contexts, integration of data into health care professional decision making, negotiation of blurring lines between consumer and medical care, and pervasive monitoring of health across previously disconnected contexts. CONCLUSIONS: Stakeholders need to actively negotiate existing challenges to realize the integration of data from wearable sensor devices into electronic health records. Viewing wearables as active parts of a connected digital health and care infrastructure, in which various business, personal, professional, and health system interests align, may help to achieve this.

Toward Continuous Social Phenotyping: Analyzing Gaze Patterns in an Emotion Recognition Task for Children With Autism Through Wearable Smart Glasses.

BACKGROUND: Several studies have shown that facial attention differs in children with autism. Measuring eye gaze and emotion recognition in children with autism is challenging, as standard clinical assessments must be delivered in clinical settings by a trained clinician. Wearable technologies may be able to bring eye gaze and emotion recognition into natural social interactions and settings. OBJECTIVE: This study aimed to test: (1) the feasibility of tracking gaze using wearable smart glasses during a facial expression recognition task and (2) the ability of these gaze-tracking data, together with facial expression recognition responses, to distinguish children with autism from neurotypical controls (NCs). METHODS: We compared the eye gaze and emotion recognition patterns of 16 children with autism spectrum disorder (ASD) and 17 children without ASD via wearable smart glasses fitted with a custom eye tracker. Children identified static facial expressions of images presented on a computer screen along with nonsocial distractors while wearing Google Glass and the eye tracker. Faces were presented in three trials, during one of which children received feedback in the form of the correct classification. We employed hybrid human-labeling and computer vision-enabled methods for pupil tracking and world-gaze translation calibration. We analyzed the impact of gaze and emotion recognition features in a prediction task aiming to distinguish children with ASD from NC participants. RESULTS: Gaze and emotion recognition patterns enabled the training of a classifier that distinguished ASD and NC groups. However, it was unable to significantly outperform other classifiers that used only age and gender features, suggesting that further work is necessary to disentangle these effects. CONCLUSIONS: Although wearable smart glasses show promise in identifying subtle differences in gaze tracking and emotion recognition patterns in children with and without ASD, the present form factor and data do not allow for these differences to be reliably exploited by machine learning systems. Resolving these challenges will be an important step toward continuous tracking of the ASD phenotype.

Wearable Technology Acceptance in Health Care Based on National Culture Differences: Cross-Country Analysis Between Chinese and Swiss Consumers.

BACKGROUND: The advancement of wearable devices and growing demand of consumers to monitor their own health have influenced the medical industry. Health care providers, insurers, and global technology companies intend to develop more wearable devices incorporating medical technology and to target consumers worldwide. However, acceptance of these devices varies considerably among consumers of different cultural backgrounds. Consumer willingness to use health care wearables is influenced by multiple factors that are of varying importance in various cultures. However, there is insufficient knowledge of the extent to which social and cultural factors affect wearable technology acceptance in health care. OBJECTIVE: The aims of this study were to examine the influential factors on the intention to adopt health care wearables, and the differences in the underlying motives and usage barriers between Chinese and Swiss consumers. METHODS: A new model for acceptance of health care wearables was conceptualized by incorporating predictors of different theories such as technology acceptance, health behavior, and privacy calculus based on an existing framework. To verify the model, a web-based survey in both the Chinese and German languages was conducted in China and Switzerland, resulting in 201 valid Chinese and 110 valid Swiss respondents. A multigroup partial least squares path analysis was applied to the survey data. RESULTS: Performance expectancy (ß=.361, P<.001), social influence (ß=.475, P<.001), and hedonic motivation (ß=.111, P=.01) all positively affected the behavioral intention of consumers to adopt wearables, whereas effort expectancy, functional congruence, health consciousness, and perceived privacy risk did not demonstrate a significant impact on behavioral intention. The group-specific path coefficients indicated health consciousness (ß=.150, P=.01) as a factor positively affecting only the behavior intention of the Chinese respondents, whereas the factors affecting only the behavioral intention of the Swiss respondents proved to be effort expectancy (ß=.165, P=.02) and hedonic motivation (ß=.212, P=.02). Performance expectancy asserted more of an influence on the behavioral intention of the Swiss (ß=.426, P<.001) than the Chinese (ß=.271, P<.001) respondents, whereas social influence had a greater influence on the behavioral intention of the Chinese (ß=.321, P<.001) than the Swiss (ß=.217, P=.004) respondents. Overall, the Chinese consumers displayed considerably higher behavioral intention (P<.001) than the Swiss. These discrepancies are explained by differences in national culture. CONCLUSIONS: This is one of the first studies to investigate consumers' intention to adopt wearables from a cross-cultural perspective. This provides a theoretical and methodological foundation for future research, as well as practical implications for global vendors and insurers developing and promoting health care wearables with appropriate features in different countries. The testimonials and support by physicians, evidence of measurement accuracy, and easy handling of health care wearables would be useful in promoting the acceptance of wearables in Switzerland. The opinions of in-group members, involvement of employers, and multifunctional apps providing credible health care advice and solutions in cooperation with health care institutions would increase acceptance among the Chinese.