Center for the Study of Movement, Cognition and Mobility, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel.
Department of General Medicine, Faculty of Medical & Health Sciences, Tel Aviv University, Tel Aviv, Israel.
J Neurol. 2024 Jul;271(7):4462-4472. doi: 10.1007/s00415-024-12339-8. Epub 2024 May 2.
Trait and state physical fatigue (trait-PF and state-PF) negatively impact many people with multiple sclerosis (pwMS) but are challenging symptoms to measure. In this observational study, we explored the role of specific gait and autonomic nervous system (ANS) measures (i.e., heart rate, HR, r-r interval, R-R, HR variability, HRV) in trait-PF and state-PF.
Forty-eight pwMS [42 ± 1.9 years, 65% female, EDSS 2 (IQR: 0-5.5)] completed the Timed Up and Go test (simple and with dual task, TUG-DT) and the 6-min walk test (6MWT). ANS measures were measured via a POLAR H10 strap. Gait was measured using inertial-measurement units (OPALs, APDM Inc). Trait-PF was evaluated via the Modified Fatigue Impact Scale (MFIS) motor component. State-PF was evaluated via a Visual Analog Scale (VAS) scale before and after the completion of the 6MWT. Multiple linear regression models identified trait-PF and state-PF predictors.
Both HR and gait metrics were associated with trait-PF and state-PF. HRV at rest was associated only with state-PF. In models based on the first 3 min of the 6MWT, double support (%) and cadence explained 47% of the trait-PF variance; % change in R-R explained 43% of the state-PF variance. Models based on resting R-R and TUG-DT explained 39% of the state-PF.
These findings demonstrate that specific gait measures better capture trait-PF, while ANS metrics better capture state-PF. To capture both physical fatigue aspects, the first 3 min of the 6MWT are sufficient. Alternatively, TUG-DT and ANS rest metrics can be used for state-PF prediction in pwMS when the 6MWT is not feasible.
特质性疲劳和状态性疲劳（特质疲劳和状态疲劳）会对许多多发性硬化症患者（pwMS）产生负面影响，但这些症状难以测量。在这项观察性研究中，我们探讨了特定步态和自主神经系统（ANS）测量指标（即心率、RR 间期、心率变异性、HRV）在特质性疲劳和状态性疲劳中的作用。
48 名 pwMS[42±1.9 岁，65%女性，EDSS 2（IQR：0-5.5）]完成了计时起立行走测试（简单和双重任务，TUG-DT）和 6 分钟步行测试（6MWT）。ANS 测量通过 Polar H10 带进行。步态通过惯性测量单元（APDM Inc 的 OPALs）进行测量。特质疲劳通过修改后的疲劳影响量表（MFIS）运动分量进行评估。状态疲劳通过 6MWT 完成前后的视觉模拟量表（VAS）进行评估。多元线性回归模型确定了特质疲劳和状态疲劳的预测因素。
心率和步态指标均与特质疲劳和状态疲劳相关。静息时的心率变异性仅与状态疲劳相关。在基于 6MWT 前 3 分钟的模型中，双支撑（%）和步频解释了特质疲劳方差的 47%；RR 变化解释了状态疲劳方差的 43%。基于静息时的 RR 和 TUG-DT 的模型解释了状态疲劳的 39%。
这些发现表明，特定的步态测量指标更好地捕捉特质疲劳，而 ANS 指标更好地捕捉状态疲劳。要同时捕捉这两个方面的身体疲劳，6MWT 的前 3 分钟就足够了。或者，当 6MWT 不可行时，TUG-DT 和 ANS 静息指标可用于预测 pwMS 的状态疲劳。
