Remote collection of physical performance measures for older people: a systematic review

Remotely collected physical performance measures could improve inclusion of under-served groups in clinical research as well as enabling continuation of research in pandemic conditions. It is unclear whether remote collection is feasible and acceptable to older patients, or whether results are comparable to face-to-face measures. We conducted a systematic review according to a prespecified protocol. We included studies with mean participant age ≥ 60 years, with no language restriction. Studies examining the gait speed, Short Physical Performance Battery, distance walk tests, grip strength, Tinetti score, Berg balance test, sit-to-stand test and timed up and go were included. Reports of feasibility, acceptability, correlation between remote and face-to-face assessments and absolute differences between remote and face-to-face assessments were sought. Data were synthesised using Synthesis Without Meta-analysis methodology; 30 analyses from 17 publications were included. Study size ranged from 10 to 300 participants, with a mean age ranging from 61 to >80 years. Studies included a broad range of participants and conditions. Most studies had a moderate or high risk of bias. Only two studies undertook assessment of acceptability or feasibility, reporting good results. Correlation between face-to-face and remote measures was variable across studies, with no measure showing consistently good correlation. Only nine studies examined the accuracy of remote measures; in six studies, accuracy was rated as good (<5% mean difference between face-to-face and remote measures). There is a lack of robust evidence that remote collection of physical performance measures is acceptable to patients, feasible or provides comparable results to face-to-face measures.

Human-centered approaches that integrate sensor technology across the lifespan: Opportunities and challenges

Children, parents, older adults, and caregivers routinely use sensor technology as a source of health information and health monitoring. The purpose of this paper is to describe three exemplars of research that used a human-centered approach to engage participants in the development, design, and usability of interventions that integrate technology to promote health. The exemplars are based on current research studies that integrate sensor technology into pediatric, adult, and older adult populations living with a chronic health condition. Lessons learned and considerations for future studies are discussed. Nurses have successfully implemented interventions that use technology to improve health and detect, prevent, and manage diseases in children, families, individuals and communities. Nurses are key stakeholders to inform clinically relevant health monitoring that can support timely and personalized intervention and recommendations.

A Comparison of Four Approaches to Evaluate the Sit-to-Stand Movement

The sit-to-stand test (STS) is a simple test of function in older people that can identify people at risk of falls. The aim of this study was to develop two novel methods of evaluating performance in the STS using a low-cost RGB camera and another an instrumented chair containing load cells in the seat of the chair to detect center of pressure movements and ground reaction forces. The two systems were compared to a Kinect and a force plate. Twenty-one younger subjects were tested when performing two 5STS movements at self-selected slow and normal speeds while 16 older fallers were tested when performing one 5STS at a self-selected pace. All methods had acceptable limits of agreement with an expert for total STS time for younger subjects and older fallers, with smaller errors observed for the chair (-0.18 ± 0.17 s) and force plate (-0.19 ± 0.79 s) than for the RGB camera (-0.30 ± 0.51 s) and the Kinect (-0.38 ± 0.50 s) for older fallers. The chair had the smallest limits of agreement compared to the expert for both younger and older participants. The new device was also able to estimate movement velocity, which could be used to estimate muscle power during the STS movement. Subsequent studies will test the device against opto-electronic systems, incorporate additional sensors, and then develop predictive equations for measures of physical function.