Rispens Sietse M, Van Dieën Jaap H, Van Schooten Kimberley S, Cofré Lizama L Eduardo, Daffertshofer Andreas, Beek Peter J, Pijnappels Mirjam
Department of Human Movement Sciences, MOVE Research Institute Amsterdam, Vrije Universiteit Amsterdam, van der Boechorststraat 9, Amsterdam, 1081 BT, The Netherlands.
Department of Medicine (Royal Melbourne Hospital), The University of Melbourne, Melbourne, Australia.
J Neuroeng Rehabil. 2016 Feb 2;13:12. doi: 10.1186/s12984-016-0118-9.
Body-worn sensors allow assessment of gait characteristics that are predictive of fall risk, both when measured during treadmill walking and in daily life. The present study aimed to assess differences as well as associations between fall-related gait characteristics measured on a treadmill and in daily life.
In a cross-sectional study, trunk accelerations of 18 older adults (72.3 ± 4.5 years) were recorded during walking on a treadmill (Dynaport Hybrid sensor) and during daily life (Dynaport MoveMonitor). A comprehensive set of 32 fall-risk-related gait characteristics was estimated and compared between both settings.
For 25 gait characteristics, a systematic difference between treadmill and daily-life measurements was found. Gait was more variable, less symmetric, and less stable during daily life. Fourteen characteristics showed a significant correlation between treadmill and daily-life measurements, including stride time and regularity (0.48 < r < 0.73; p < 0.022). No correlation between treadmill and daily-life measurements was found for stride-time variability, acceleration range and sample entropy in vertical and mediolateral direction, gait symmetry in vertical direction, and stability estimated as the local divergence exponent by Rosenstein's method in mediolateral direction (r < 0.16; p > 0.25).
Gait characteristics revealed less stable, less symmetric, and more variable gait during daily life than on a treadmill, yet about half of the characteristics were significantly correlated between conditions. These results suggest that daily-life gait analysis is sensitive to static personal factors (i.e., physical and cognitive capacity) as well as dynamic situational factors (i.e., behavior and environment), which may both represent determinants of fall risk.
可穿戴式传感器能够评估步态特征，这些特征在跑步机行走测试以及日常生活中均能预测跌倒风险。本研究旨在评估在跑步机上和日常生活中测量的与跌倒相关的步态特征之间的差异及关联。
在一项横断面研究中，记录了18名老年人（72.3±4.5岁）在跑步机上行走（使用Dynaport Hybrid传感器）和日常生活中（使用Dynaport MoveMonitor）时的躯干加速度。估计并比较了两种环境下32项与跌倒风险相关的综合步态特征。
对于25项步态特征，发现跑步机测量和日常生活测量之间存在系统性差异。在日常生活中，步态的变异性更大、对称性更低且稳定性更差。14项特征在跑步机测量和日常生活测量之间显示出显著相关性，包括步幅时间和规律性（0.48＜r＜0.73；p＜0.022）。在步幅时间变异性、垂直和内外侧方向的加速度范围以及样本熵、垂直方向的步态对称性以及通过Rosenstein方法在内外侧方向估计的作为局部分散指数的稳定性方面，未发现跑步机测量和日常生活测量之间存在相关性（r＜0.16；p＞0.25）。
与在跑步机上相比，日常生活中的步态特征显示出更不稳定、对称性更低且变异性更大，但约一半的特征在不同条件之间存在显著相关性。这些结果表明，日常生活步态分析对静态个人因素（即身体和认知能力）以及动态情境因素（即行为和环境）敏感，这两者都可能是跌倒风险的决定因素。
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