Feasibility of wearable technology for ‘real‐world’ gait analysis in children with Prader–Willi and Angelman syndromes

Abstract

Background Prader–Willi syndrome (PWS) and Angelman syndrome (AS) are neurodevelopmental disorders in need of innovative ‘real‐world’ outcome measures to evaluate treatment effects. Instrumented gait analysis (IGA) using wearable technology offers a potentially feasible solution to measure “real‐world’ neurological and motor dysfunction in these groups. Methods Children (50% female; 6–16 years) diagnosed with PWS (n = 9) and AS (n = 5) completed ‘real‐world’ IGA assessments using the Physilog®5 wearable. PWS participants completed a laboratory assessment and a ‘real‐world’ long walk. The AS group completed ‘real‐world’ caregiver‐assisted assessments. Mean and variability results for stride time, cadence, stance percentage (%) and stride length were extracted and compared across three different data reduction protocols. Results The wearables approach was found to be feasible, with all participants able to complete at least one assessment. This study also demonstrated significant agreement, using Lin's concordance correlation coefficient (CCC), between laboratory and ‘real‐world’ assessments in the PWS group for mean stride length, mean stance % and stance % CV (n = 7, CCC: 0.782–0.847, P = 0.011–0.009). Conclusion ‘Real‐world’ gait analysis using the Physilog®5 wearable was feasible to efficiently assess neurological and motor dysfunction in children affected with PWS and AS.

Promoting child and adolescent health through wearable technology: A systematic review

“… 56 In addition, it was found to be feasible in patients with ADHD, 53 autism spectrum disorder, 46 congenital heart disease, 64 Prader–Willi syndrome, and Angelman syndrome. 30 The investigation conducted in patients with cystic fibrosis pulmonary disease showed good adherence. 66 …”

“…The goal of these studies was to identify a reliable reference for selecting wearable technology tailored to specific purposes. For example, Physilog®5 29 , 30 and G-Walk 31 were considered reliable in gait data monitoring, while Meizu Bong 2s 32 and Garmin Vivofit Jr 2 33 were found to have good accuracy in counting users’ steps. Fitbit Ace 2 33 and Fitbit Ace 34 were found to have less than ideal accuracy.…”

The use of digital outcome measures in clinical trials in rare neurological diseases: a systematic literature review

“…Patients with PWS were included in eight different studies using accelerometers and/or gyroscopes. One study simply demonstrated significant agreement between mean stride length, mean stance percentage, and stance percentage coefficients of variation measured during two different walking tasks [ 108 ]. One study showed that because of the poor gait symmetry in PWS, upper body accelerations and the harmonic ratio (i.e., measure of step-to-step symmetry based on trunk acceleration) can be used as innovative parameters for gait analysis, providing information that cannot be extracted from spatiotemporal parameters only [ 109 ].…”

Risk of using smartphones while walking for digital natives in realistic environments: Effects of cognitive–motor interference

“…In a previous study, the concurrent validity between the Physilog5® sensor and GAITRite was highly consistent for stride length (concordance correlation coefficient [CCC] = 0.975), stride velocity (CCC = 0.979), and stride time (CCC >0.996) [ 43 ]. IMUs have been proposed as a valuable tool for assessing children with ambulatory impairments in real-world settings [ 43 , 44 ]. The alignment and calibration of IMU sensors prior to measurement are not necessary because the Physilog® algorithm is capable of immediately estimating their values while walking [ 45 ].…”