Abstract
BackgroundWearable sensors offer the potential to bring new knowledge to inform interventions in patients affected by multiple sclerosis (MS) by thoroughly quantifying gait characteristics and gait deficits from prolonged daily living measurements. The aim of this study was to characterise gait in both laboratory and daily life conditions for a group of patients with moderate to severe ambulatory impairment due to MS. To this purpose, algorithms to detect and characterise gait from wearable inertial sensors data were also validated.MethodsFourteen patients with MS were divided into two groups according to their disability level (EDSS 6.5–6.0 and EDSS 5.5–5.0, respectively). They performed both intermittent and continuous walking bouts (WBs) in a gait laboratory wearing waist and shank mounted inertial sensors. An algorithm (W-CWT) to estimate gait events and temporal parameters (mean and variability values) using data recorded from the waist mounted sensor (Dynaport, Mc Roberts) was tested against a reference algorithm (S-REF) based on the shank-worn sensors (OPAL, APDM). Subsequently, the accuracy of another algorithm (W-PAM) to detect and classify WBs was also tested. The validated algorithms were then used to quantify gait characteristics during short (sWB, 5–50 steps), intermediate (iWB, 51–100 steps) and long (lWB, >100 steps) daily living WBs and laboratory walking. Group means were compared using a two-way ANOVA.ResultsW-CWT compared to S-REF showed good gait event accuracy (0.05–0.10 s absolute error) and was not influenced by disability level. It slightly overestimated stride time in intermittent walking (0.012 s) and overestimated highly variability of temporal parameters in both intermittent (17.5%–58.2%) and continuous walking (11.2%–76.7%). The accuracy of W-PAM was speed-dependent and decreased with increasing disability. The ANOVA analysis showed that patients walked at a slower pace in daily living than in the laboratory. In daily living gait, all mean temporal parameters decreased as the WB duration increased. In the sWB, the patients with a lower disability score showed, on average, lower values of the temporal parameters. Variability decreased as the WB duration increased.ConclusionsThis study validated a method to quantify walking in real life in people with MS and showed how gait characteristics estimated from short walking bouts during daily living may be the most informative to quantify level of disability and effects of interventions in patients moderately affected by MS. The study provides a robust approach for the quantification of recognised clinically relevant outcomes and an innovative perspective in the study of real life walking.
Highlights
Multiple sclerosis (MS) is a chronic autoimmune inflammatory disease of the central nervous system
W-CWT compared to S-REF showed good gait event accuracy (0.05–0.10 s absolute error) and was not influenced by disability level
The study provides a robust approach for the quantification of recognised clinically relevant outcomes and an innovative perspective in the study of real life walking
Summary
Multiple sclerosis (MS) is a chronic autoimmune inflammatory disease of the central nervous system. Gait deficits in persons affected by MS have been traditionally identified using specialized equipment in confined gait laboratories, such as motion capture systems and pressure sensitive walkways [5] These studies showed that patients with MS have slower walking speed, prolonged stride time and longer double support compared to controls [6]. Leveraging on the body of knowledge coming from laboratory-based observations and on the availability of wearable sensors able to monitor gait unobtrusively for long periods of time, the attention of the research is moving toward the investigation of gait in real-life scenarios [7,8] Thanks to these tools, it has been recently shown that gait variability in patients with MS is an early sign of disease progression [9,10] and is associated to higher risk of falls [11]. Algorithms to detect and characterise gait from wearable inertial sensors data were validated
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