Abstract
BACKGROUND: Gait impairments and fatigue are the most common and disabling symptoms in people with multiple sclerosis (PwMS). Objective 6-min walk test (6MWT) gait testing can be improved through body-worn accelerometers, but its association to subjective fatigue and objective fatigability is contradictory. This study aims to validate an algorithm using smartphone sensor data for spatial-temporal gait parameters in PwMS and healthy controls, and evaluate its accuracy in detecting fatigability, and quantify its association with fatigue in PwMS. METHODS: We recruited PwMS with mild to moderate disability (EDSS 0.0-6.5) and healthy controls in a supervised, lab-based cohort study. All participants performed the 6MWT while wearing a smartphone at the hip, which collected acceleration data of step count, cadence and walking speed. Algorithm validation included the mean absolute percentage error (MAPE) and Bland-Altman analysis. Fatigability and fatigue were measured in PwMS, with fatigability defined as a 10% decline in gait performance, and fatigue using the fatigue scale for motor and cognitive functions (FSMC). Further, correlations between gait parameters and FSMC were assessed. RESULTS: A total of 38 PwMS and 24 healthy controls were included. The algorithm demonstrated high validity for step count (MAPE < 3%) and cadence (MAPE < 10%). Gait analyses revealed fatigability in between 2.6 and 15.8% of PwMS, with large differences between the gait parameter assessed. Significant correlations were found especially between FSMC motor fatigue scores and step count (r = - 0.50), cadence (r = 0.51) and walking speed (r = 0.50). CONCLUSION: Smartphone-based gait analysis provides an accessible and valid method for detecting steps and cadence. There are major differences in the assessment of fatigability, but an allover association to subjective motor fatigue.