The importance of physiological data variability in wearable devices for digital health applications

This paper aims at characterizing the variability of physiological data collected through a wearable device (Empatica E4), given that both intra- and inter-subject variability play a pivotal role in digital health applications, where Artificial Intelligence (AI) techniques have become popular. Inter-beat intervals (IBIs), ElectroDermal Activity (EDA) and Skin Temperature (SKT) signals have been considered and variability has been evaluated in terms of general statistics (mean and standard deviation) and coefficient of variation. Results show that both intra- and inter-subject variability values are significant, especially when considering those parameters describing how the signals vary over time. Moreover, EDA seems to be the signal characterized by the highest variability, followed by IBIs, contrary to SKT that results more stable. This variability could affect AI algorithms in classifying signals according to particular discriminants (e.g. emotions, daily activities, etc.), taking into account the dual role of variability: hindering a net distinction between classes, but also making algorithms more robust for deep learning purposes thanks to the consideration of a wide test population. Indeed, it is worthy to note that variability plays a fundamental role in the whole measurement chain, characterizing data reliability and impacting on the final results accuracy and consequently on decision-making processes

Validity of the Empatica E4 wristband to measure heart rate variability (HRV) parameters:A comparison to electrocardiography (ECG)

Wearable monitoring devices are an innovative way to measure heart rate (HR) and heart rate variability (HRV), however, there is still debate about the validity of these wearables. This study aimed to validate the accuracy and predictive value of the Empatica E4 wristband against the VU University Ambulatory Monitoring System (VU-AMS) in a clinical population of traumatized adolescents in residential care. A sample of 345 recordings of both the Empatica E4 wristband and the VU-AMS was derived from a feasibility study that included fifteen participants. They wore both devices during two experimental testing and twelve intervention sessions. We used correlations, cross-correlations, Mann-Whitney tests, difference factors, Bland-Altman plots, and Limits of Agreement to evaluate differences in outcomes between devices. Significant correlations were found between Empatica E4 and VU-AMS recordings for HR, SDNN, RMSSD, and HF recordings. There was a significant difference between the devices for all parameters but HR, although effect sizes were small for SDNN, LF, and HF. For all parameters but RMSSD, testing outcomes of the two devices led to the same conclusions regarding significance. The Empatica E4 wristband provides a new opportunity to measure HRV in an unobtrusive way. Results of this study indicate the potential of the Empatica E4 as a practical and valid tool for research on HR and HRV under non-movement conditions. While more research needs to be conducted, this study could be considered as a first step to support the use of HRV recordings provided by wearables

Mobile devices for the remote acquisition of physiological and behavioral biomarkers in psychiatric clinical research

Psychiatric disorders are linked to a variety of biological, psychological, and contextual causes and consequences. Laboratory studies have elucidated the importance of several key physiological and behavioral biomarkers in the study of psychiatric disorders, but much less is known about the role of these biomarkers in naturalistic settings. These gaps are largely driven by methodological barriers to assessing biomarker data rapidly, reliably, and frequently outside the clinic or laboratory. Mobile health (mHealth) tools offer new opportunities to study relevant biomarkers in concert with other types of data (e.g., self-reports, global positioning system data). This review provides an overview on the state of this emerging field and describes examples from the literature where mHealth tools have been used to measure a wide array of biomarkers in the context of psychiatric functioning (e.g., psychological stress, anxiety, autism, substance use). We also outline advantages and special considerations for incorporating mHealth tools for remote biomarker measurement into studies of psychiatric illness and treatment and identify several specific opportunities for expanding this promising methodology. Integrating mHealth tools into this area may dramatically improve psychiatric science and facilitate highly personalized clinical care of psychiatric disorders

Associations Between Heart Rate Variability Measured With a Wrist-Worn Sensor and Older Adults' Physical Function: Observational Study.

BACKGROUND:Heart rate variability (HRV), or variation in beat-to-beat intervals of the heart, is a quantitative measure of autonomic regulation of the cardiovascular system. Low HRV derived from electrocardiogram (ECG) recordings is reported to be related to physical frailty in older adults. Recent advances in wearable technology offer opportunities to more easily integrate monitoring of HRV into regular clinical geriatric health assessments. However, signals obtained from ECG versus wearable photoplethysmography (PPG) devices are different, and a critical first step preceding their widespread use is to determine whether HRV metrics derived from PPG devices also relate to older adults' physical function. OBJECTIVE:This study aimed to investigate associations between HRV measured with a wrist-worn PPG device, the Empatica E4 sensor, and validated clinical measures of both objective and self-reported physical function in a cohort of older adults living independently within a continuing care senior housing community. Our primary hypothesis was that lower HRV would be associated with lower physical function. In addition, we expected that HRV would explain a significant proportion of variance in measures of physical health status. METHODS:We evaluated 77 participants from an ongoing study of older adults aged between 65 and 95 years. The assessments encompassed a thorough examination of domains typically included in a geriatric health evaluation. We collected HRV data with the Empatica E4 device and examined bivariate correlations between HRV quantified with the triangular index (HRV TI) and 3 widely used and validated measures of physical functioning-the Short Physical Performance Battery (SPPB), Timed Up and Go (TUG), and Medical Outcomes Study Short Form 36 (SF-36) physical composite scores. We further investigated the additional predictive power of HRV TI on physical health status, as characterized by SF-36 physical composite scores and Cumulative Illness Rating Scale for Geriatrics (CIRS-G) scores, using generalized estimating equation regression analyses with backward elimination. RESULTS:We observed significant associations of HRV TI with SPPB (n=52; Spearman ρ=0.41; P=.003), TUG (n=51; ρ=-0.40; P=.004), SF-36 physical composite scores (n=49; ρ=0.37; P=.009), and CIRS-G scores (n=52, ρ=-0.43; P=.001). In addition, the HRV TI explained a significant proportion of variance in SF-36 physical composite scores (R2=0.28 vs 0.11 without HRV) and CIRS-G scores (R2=0.33 vs 0.17 without HRV). CONCLUSIONS:The HRV TI measured with a relatively novel wrist-worn PPG device was related to both objective (SPPB and TUG) and self-reported (SF-36 physical composite) measures of physical function. In addition, the HRV TI explained additional variance in self-reported physical function and cumulative illness severity beyond traditionally measured aspects of physical health. Future steps include longitudinal tracking of changes in both HRV and physical function, which will add important insights regarding the predictive value of HRV as a biomarker of physical health in older adults

General Conceptual Framework of Future Wearables in Healthcare: Unified, Unique, Ubiquitous, and Unobtrusive (U4) for Customized Quantified Output

We concentrate on the importance and future conceptual development of wearable devices as the major means of personalized healthcare. We discuss and address the role of wearables in the new era of healthcare in proactive medicine. This work addresses the behavioral, environmental, physiological, and psychological parameters as the most effective domains in personalized healthcare, and the wearables are categorized according to the range of measurements. The importance of multi-parameter, multi-domain monitoring and the respective interactions are further discussed and the generation of wearables based on the number of monitoring area(s) is consequently formulated

Protocol of the STRess at Work (STRAW) project : how to disentangle day-to-day occupational stress among academics based on EMA, physiological data, and smartphone sensor and usage data

Several studies have reported on increasing psychosocial stress in academia due to work environment risk factors like job insecurity, work-family conflict, research grant applications, and high workload. The STRAW project adds novel aspects to occupational stress research among academic staff by measuring day-to-day stress in their real-world work environments over 15 working days. Work environment risk factors, stress outcomes, health-related behaviors, and work activities were measured repeatedly via an ecological momentary assessment (EMA), specially developed for this project. These results were combined with continuously tracked physiological stress responses using wearable devices and smartphone sensor and usage data. These data provide information on workplace context using our self-developed Android smartphone app. The data were analyzed using two approaches: 1) multilevel statistical modelling for repeated data to analyze relations between work environment risk factors and stress outcomes on a within- and between-person level, based on EMA results and a baseline screening, and 2) machine-learning focusing on building prediction models to develop and evaluate acute stress detection models, based on physiological data and smartphone sensor and usage data. Linking these data collection and analysis approaches enabled us to disentangle and model sources, outcomes, and contexts of occupational stress in academia

Physiological and driving behaviour changes associated to different road intersections

Road traffic injuries claim more than 1.2 million lives each year in the world and have a huge impact on health and development. It is commonly acknowledged that the human factor and the interaction between the human factor and the road environment are among the most common causes of road accidents. Intersections are among the most complex road environments: their geometric and traffic characteristics weigh the driver workload, affecting the driving behaviour and consequently the risk of accident. This study intends therefore to contribute for a better understanding of the relationship between different types of intersection and the human factor. The ultimate aim is to understand how at grade intersections affect the driving behaviour by comparing the drivers’ stress level for roundabouts and standard intersections. Electrodermal activity can provide a real-time assessment of the driver's stress level. Electrodermal activity was therefore collected continuously during a driving study which took place on a test environment based at Cranfield University and surrounding roads. Twenty participants were involved within the study. The analysis focused on four crossing manoeuvres on three at grade intersections (two T-junctions and a roundabout) situated on the study location. Results showed that the number of SCR peaks as well as the amplitude of the peaks are overall higher for the two manoeuvres on the roundabout. The stress level induced by each type of intersection was evaluated through an Electrodermal Impact Index which takes into account both the number and the amplitude of SCR peaks. Results suggested that the stress level induced by roundabouts is more than double that induced by standard intersections

The potential of wearable technology for monitoring social interactions based on interpersonal synchrony

Sensing data from wearables have been extensively evaluated for fitness tracking, health monitoring or rehabilitation of individuals. However, we believe that wearable sensing can go beyond the individual and offer insights into social dynamics and interactions with other users by considering multi-user data. In this work, we present a new approach to using wrist-worn wearables for social monitoring and the detection of social interaction features based on interpersonal synchrony - an approach transferable to smartwatches and fitness trackers. We build up on related work in the field of psychology and present a study where we collected wearable sensing data during a social event with 24 participants. Our preliminary results indicate differences in wearable sensing data during a social interaction between two people