Schönherr Carolin, Ziegler Julian, Zentek Ton, Rashid Asarnush, Strauss Sebastian, Tallner Alexander, Grothe Matthias
Department of Neurology, University Medicine Greifswald, Greifswald, Germany.
Innovation Management, Zentrum Für Telemedizin, Bad Kissingen, Germany.
J Neurol. 2025 Feb 22;272(3):217. doi: 10.1007/s00415-025-12906-7.
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.
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.
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).
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.
步态障碍和疲劳是多发性硬化症患者（PwMS）最常见且致残的症状。客观6分钟步行试验（6MWT）步态测试可通过佩戴式加速度计得到改善，但其与主观疲劳和客观疲劳性的关联存在矛盾。本研究旨在验证一种利用智能手机传感器数据获取PwMS患者和健康对照者时空步态参数的算法，评估其检测疲劳性的准确性，并量化其与PwMS患者疲劳的关联。
在一项基于实验室的队列研究中，我们招募了轻度至中度残疾（扩展残疾状态量表评分0.0 - 6.5）的PwMS患者和健康对照者。所有参与者在髋部佩戴智能手机进行6MWT，智能手机收集步数、步频和步行速度的加速度数据。算法验证包括平均绝对百分比误差（MAPE）和布兰德 - 奥特曼分析。对PwMS患者测量疲劳性和疲劳，疲劳性定义为步态表现下降10%，疲劳采用运动和认知功能疲劳量表（FSMC）进行评估。此外，评估步态参数与FSMC之间的相关性。
共纳入38例PwMS患者和24名健康对照者。该算法在步数（MAPE < 3%）和步频（MAPE < 10%）方面显示出高有效性。步态分析显示，2.6%至15.8%的PwMS患者存在疲劳性，所评估的步态参数之间存在较大差异。尤其在FSMC运动疲劳评分与步数（r = - 0.50）、步频（r = 0.51）和步行速度（r = 0.50）之间发现了显著相关性。
基于智能手机的步态分析为检测步数和步频提供了一种可及且有效的方法。在疲劳性评估方面存在较大差异，但与主观运动疲劳存在整体关联。
