Stability of sensor-based gait parameters reassessed after a period of one year in people with multiple sclerosis

Abstract

Background Currently, there are several studies showing that wearable inertial sensors are highly sensitive in the detection of gait disturbances in people with multiple sclerosis (PwMS), showing excellent reliability within one or 7–14 days. However, it is not known how stable these gait parameters remain over a longer period of time. This is surprising, because many treatments last longer than two weeks. Thus, the purpose of the current study was to examine gait parameters obtained by means of wearable inertial sensors during a 6-min walk and to reassess these parameters after a period of one year. Methods Fifty PwMS (without a relapse or a recent change in the Expanded Disability Status Scale (EDSS) or treatment) and 20 healthy participants were examined at two assessment points (interval between assessments: 14.4 ± 6.6 months). At each assessment point, all participants had to complete a 6-min walking test, an observer-rater test (Berg Balance Scale, BBS) and a Timed-up and Go Test (TUG). To measure mean gait parameters (i.e. walking speed, stride length, stride time, the duration of the stance and swing phase and minimum toe-to-floor distance), as well as the intraindividual standard deviation of each mean gait parameter, wearable inertial sensors were utilized. Results We found that even after one year all mean gait parameters showed excellent Intraclass Correlation Coefficients (ICC between 0.75 and 0.95) in PwMS. Looking at MS subgroups, the ICCs were slightly higher in MS subgroup 2 (EDSS 2.0–5.0) than those in MS subgroup 1 (EDSS 0.0–1.5) and healthy controls. Compared to the mean gait parameters, parameters of gait variability showed only good-to-fair ICC values in PwMS. Concerning BBS and TUG, the ICC values after one year were close to the ICC values of the measured mean gait parameters. Conclusions Due to the excellent stability of mean gait parameters after one year, these sensor-based gait parameters can be identified as clinically relevant markers to evaluate treatment effects over a longer (several months) period of time in MS.

Progression risk stratification with six-minute walk gait speed trajectory in multiple sclerosis

“…In addition, HRPs trended towards worsening on the PASAT, MSIS-29, and activity counts when compared to LRPs. In review of the literature, we note two studies that have looked at gait speed and its relationship with progression in MS with mixed results ( 33 , 34 ). Muller et al ( 34 ) utilizing wearable sensor technology during 6 MW reported no change in gait speed over a 12-month follow-up period in 50 MS and 20 healthy control study participants.…”

“…In review of the literature, we note two studies that have looked at gait speed and its relationship with progression in MS with mixed results ( 33 , 34 ). Muller et al ( 34 ) utilizing wearable sensor technology during 6 MW reported no change in gait speed over a 12-month follow-up period in 50 MS and 20 healthy control study participants. In another 12-month study, Galea et al ( 33 ) measured 6 MW gait speed using wearable sensor technology and found a significant decreased in gait speed over 12 months, but these changes were not reflected in the EDSS, which remained stable for most participants over the 12-month period.…”

Validity of an inertial sensor-based system for the assessment of spatio-temporal parameters in people with multiple sclerosis

“…Even in early stages without clinical signs of walking disability, gait and balance impairments were observed ( 4 – 9 ). Previous findings in motor impaired people with MS (PwMS) were reduced stride length and gait speed as well as an elevated stride time and stance time ( 4 – 7 , 9 , 10 ). Those gait parameters correlated with the disease-associated fatigue and may be used to define the outcome of pharmaceutical trials ( 6 , 7 , 9 , 11 ).…”

A Computer Vision-Based System to Help Health Professionals to Apply Tests for Fall Risk Assessment

“…Therefore, there exists a need to automate diagnosis and treatments (even in the context of rehabilitation and fall risk monitoring, and this is what some investigations propose. The vast majority of them have chosen to integrate IMUs (Inertial Measurement Units), to obtain signals and associate them with the score of one of the previously described tests [ 35 , 36 , 37 , 38 , 39 , 40 , 41 , 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 ]. This solution has some drawbacks and limitations, since it is required that people wear the equipment on different parts of the body.…”