Validity of a Novel Research-Grade Physical Activity and Sleep Monitor for Continuous Remote Patient Monitoring

In the midst of the COVID-19 pandemic, Remote Patient Monitoring technologies are highly important for clinicians and researchers. These connected-health technologies enable monitoring of patients and facilitate remote clinical trial research while reducing the potential for the spread of the novel coronavirus. There is a growing requirement for monitoring of the full 24 h spectrum of behaviours with a single research-grade sensor. This research describes a free-living and supervised protocol comparison study of the Verisense inertial measurement unit to assess physical activity and sleep parameters and compares it with the Actiwatch 2 actigraph. Fifteen adults (11 males, 23.4 ± 3.4 years and 4 females, 29 ± 12.6 years) wore both monitors for 2 consecutive days and nights in the free-living study while twelve adults (11 males, 23.4 ± 3.4 years and 1 female, 22 ± 0 years) wore both monitors for the duration of a gym-based supervised protocol study. Agreement of physical activity epoch-by-epoch data with activity classification of sedentary, light and moderate-to-vigorous activity and sleep metrics were evaluated using Spearman’s rank-order correlation coefficients and Bland–Altman plots. For all activity, Verisense showed high agreement for both free-living and supervised protocol of r = 0.85 and r = 0.78, respectively. For physical activity classification, Verisense showed high agreement of sedentary activity of r = 0.72 for free-living but low agreement of r = 0.36 for supervised protocol; low agreement of light activity of r = 0.42 for free-living and negligible agreement of r = −0.04 for supervised protocol; and moderate agreement of moderate-to-vigorous activity of r = 0.52 for free-living with low agreement of r = 0.49 for supervised protocol. For sleep metrics, Verisense showed moderate agreement for sleep time and total sleep time of r = 0.66 and 0.54, respectively, but demonstrated high agreement for determination of wake time of r = 0.83. Overall, our results showed moderate-high agreement of Verisense with Actiwatch 2 for assessing epoch-by-epoch physical activity and sleep, but a lack of agreement for activity classifications. Future validation work of Verisense for activity cut-point potentially holds promise for 24 h continuous remote patient monitoring

Physical and Motor Fitness Tests for Older Adults Living in Nursing Homes: A Systematic Review

Abstract: This systematic review aimed to identify the physical/motor fitness tests for nursing home residents and to examine their psychometric properties. Electronic databases were searched for articles published between January 2005 and October 2021 using MeSh terms and relevant keywords. Of the total of 4196 studies identified, 3914 were excluded based on title, abstracts, or because they were duplicates. The remaining 282 studies were full-text analyzed, and 41 were excluded, resulting in 241 studies included in the review. The most common physical component assessed was muscle strength; 174 (72.2%) studies assessed this component. Balance (138 studies, 57.3%) and agility (102 studies, 42.3%) were the second and third components, respectively, most widely assessed. In this review, we also describe the most used assessment tests for each physical/motor component. Some potentially relevant components such as manual dexterity and proprioception have been little considered. There are few studies assessing the psychometric properties of the tests for nursing home residents, although the data show that, in general, they are reliable. This review provides valuable information to researchers and health-care professionals regarding the physical/motor tests used in nursing home residences, helping them select the screening tools that could most closely fit their study objectives

Body-worn accelerometer-based health assessment algorithms for independent living older adults

The mainstream smart wearable products used for activity trackers have experienced significant growth recently. Among the older population, collecting long periods of activity data in a real-life setting is challenging even with wearable devices. Studies have found inconsistent and lower accuracies when older adults use these smart devices [1], [2],[2],[3]. As a person ages, many have lower daily levels of activity and their dynamic functional patterns, such as gaits and sit-to-stand transitional movements vary throughout the day. This thesis explores wearable health-tracking applications by evaluating daytime and nighttime pattern metrics calculated from continuous accelerometer signals. These signals were collected externally from the upper trunk of the body in an independent-living environment of 30 elderly volunteers. Our gold standard to validate the metrics from the accelerometer signals were similar metrics calculated from an in-home sensor network [4]. This thesis first developed an algorithm to count steps and another algorithm to detect stand-to-sit and sit-to-stand (STS) to demonstrate the importance of considering differences in daily functional health patterns when creating algorithms. Next, this thesis validates that accelerometer data can show similar motion density results as motion sensor data. And thirdly, this thesis proposes an updated vacancy algorithm using a new motion sensor system that detects when no one is in the living space, compared against the current algorithm.Includes bibliographical references (pages 108-111)

Classification of Frailty among Community Dwelling Older Adults Using Parameters of Physical Activity Obtained Independently and Unsupervised

The global population is ageing at an unprecedented rate, with the percentage of those aged over 65 years expected to double and those aged over 80 years expected to treble by the year 2050. With ageing comes biological and physiological changes that affect functional capacity. Frailty is a potentially avoidable, reversible biopsychosocial condition associated with biological but not chronological age, affecting a quarter of all community-dwelling older adults. Frailty results in disability, increased dependency and institutionalisation. Screening for frailty could help reduce its prevalence and mitigate the adverse outcomes however, traditional screening tools are time-consuming to perform, require clinician input and by their subjective nature are flawed. The use of wearable sensors has been proposed as a means of screening for frailty and parameters of mobility and physical activity have been identified as being associated with frailty. The goal of this thesis was to examine if community-dwelling older adults could capture parameters of mobility and physical activity independently in their own home and if these parameters could discriminate between frail and non-frail status. This work provides evidence that a single parameter of mobility and physical activity obtained from a single body-worn sensor correlates with frailty. It also provides evidence that community-dwelling older adults can independently capture parameters of mobility and physical activity, unsupervised in their own home using a consumer-grade wearable device, and that these data can predict pre-frailty and frailty with acceptable accuracy. Thresholds for parameters of physical activity predictive of frailty have been identified. The results of this thesis will guide future work to focus community-dwelling older adults on the importance of frailty screening and guide the development of a user-friendly device or sensor system suitable for use by older adults for continuous data collection relevant to frailty