Department of Neurorehabilitation Sciences, Ospedale San Luca, IRCCS, Istituto Auxologico Italiano, Milan, 20149, Italy.
Department of Biomedical Sciences for Health, Università degli Studi di Milano, Milan, 20133, Italy.
J Neuroeng Rehabil. 2024 Apr 2;21(1):44. doi: 10.1186/s12984-024-01339-4.
Tracking gait and balance impairment in time is paramount in the care of older neurological patients. The Minimal Detectable Change (MDC), built upon the Standard Error of the Measurement (SEM), is the smallest modification of a measure exceeding the measurement error. Here, a novel method based on linear mixed-effects models (LMMs) is applied to estimate the standard error of the measurement from data collected before and after rehabilitation and calculate the MDC of gait and balance measures.
One hundred nine older adults with a gait impairment due to neurological disease (66 stroke patients) completed two assessment sessions before and after inpatient rehabilitation. In each session, two trials of the 10-meter walking test and the Timed Up and Go (TUG) test, instrumented with inertial sensors, have been collected. The 95% MDC was calculated for the gait speed, TUG test duration (TTD) and other measures from the TUG test, including the angular velocity peak (ω) in the TUG test's turning phase. Random intercepts and slopes LMMs with sessions as fixed effects were used to estimate SEM. LMMs assumptions (residuals normality and homoscedasticity) were checked, and the predictor variable ln-transformed if needed.
The MDC of gait speed was 0.13 m/s. The TTD MDC, ln-transformed and then expressed as a percentage of the baseline value to meet LMMs' assumptions, was 15%, i.e. TTD should be < 85% of the baseline value to conclude the patient's improvement. ω MDC, also ln-transformed and expressed as the baseline percentage change, was 25%.
LMMs allowed calculating the MDC of gait and balance measures even if the test-retest steady-state assumption did not hold. The MDC of gait speed, TTD and ω from the TUG test with an inertial sensor have been provided. These indices allow monitoring of the gait and balance impairment, which is central for patients with an increased falling risk, such as neurological old persons.
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跟踪老年人神经科患者的步态和平衡障碍随时间的变化至关重要。基于测量标准误差 (SEM) 的最小可检测变化 (MDC) 是超过测量误差的测量值的最小修正。在这里，应用一种基于线性混合效应模型 (LMM) 的新方法来估计康复前后数据的测量标准误差，并计算步态和平衡测量的 MDC。
109 名因神经疾病导致步态障碍的老年人（66 名脑卒中患者）在住院康复前后完成了两次评估。在每次评估中，使用惯性传感器收集了 10 米步行测试和计时起立行走测试 (TUG) 的两次试验。计算了步态速度、TUG 测试持续时间 (TTD) 和 TUG 测试其他指标（TUG 测试转弯阶段的角速度峰值 (ω)）的 95% MDC。使用具有会话作为固定效应的随机截距和斜率 LMM 来估计 SEM。检查了 LMM 假设（残差正态性和同方差性），并在必要时对预测变量进行了自然对数转换。
步态速度的 MDC 为 0.13 m/s。TTD 的 MDC 经过自然对数转换，然后表示为基线值的百分比，以满足 LMMs 的假设，为 15%，即 TTD 应<85%的基线值，以得出患者改善的结论。ω的 MDC 也经过自然对数转换，并表示为基线百分比变化，为 25%。
即使测试-重测稳态假设不成立，LMM 也允许计算步态和平衡测量的 MDC。提供了使用惯性传感器的 TUG 测试的步态速度、TTD 和 ω 的 MDC。这些指标可用于监测步态和平衡障碍，这对有跌倒风险增加的患者（如神经科老年人）至关重要。
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