Rispens Sietse M, van Schooten Kimberley S, Pijnappels Mirjam, Daffertshofer Andreas, Beek Peter J, van Dieën Jaap H
MOVE Research Institute Amsterdam, Faculty of Human Movement Sciences, VU University Amsterdam, Amsterdam, Netherlands.
MOVE Research Institute Amsterdam, Faculty of Human Movement Sciences, VU University Amsterdam, Amsterdam, Netherlands
Neurorehabil Neural Repair. 2015 Jan;29(1):54-61. doi: 10.1177/1545968314532031. Epub 2014 Apr 23.
Background. Gait characteristics extracted from trunk accelerations during daily life locomotion are complementary to questionnaire- or laboratory-based gait and balance assessments and may help to improve fall risk prediction. Objective. The aim of this study was to identify gait characteristics that are associated with self-reported fall history and that can be reliably assessed based on ambulatory data collected during a single week. Methods. We analyzed 2 weeks of trunk acceleration data (DynaPort MoveMonitor, McRoberts) collected among 113 older adults (age range, 65-97 years). During episodes of locomotion, various gait characteristics were determined, including local dynamic stability, interstride variability, and several spectral features. For each characteristic, we performed a negative binomial regression analysis with the participants' self-reported number of falls in the preceding year as outcome. Reliability of gait characteristics was assessed in terms of intraclass correlations between both measurement weeks. Results. The percentages of spectral power below 0.7 Hz along the vertical and anteroposterior axes and below 10 Hz along the mediolateral axis, as well as local dynamic stability, local dynamic stability per stride, gait smoothness, and the amplitude and slope of the dominant frequency along the vertical axis, were associated with the number of falls in the preceding year and could be reliably assessed (all P < .05, intraclass correlation > 0.75). Conclusions. Daily life gait characteristics are associated with fall history in older adults and can be reliably estimated from a week of ambulatory trunk acceleration measurements.
背景。从日常生活行走过程中的躯干加速度提取的步态特征，是基于问卷或实验室的步态及平衡评估的补充，可能有助于改善跌倒风险预测。目的。本研究的目的是确定与自我报告的跌倒史相关且可根据一周内收集的动态数据可靠评估的步态特征。方法。我们分析了113名老年人（年龄范围65 - 97岁）收集的两周躯干加速度数据（DynaPort MoveMonitor，McRoberts）。在行走过程中，确定了各种步态特征，包括局部动态稳定性、步幅间变异性和几个频谱特征。对于每个特征，我们以前一年参与者自我报告的跌倒次数作为结果进行负二项回归分析。步态特征的可靠性根据两个测量周之间的组内相关性进行评估。结果。垂直轴和前后轴低于0.7 Hz以及内外侧轴低于10 Hz的频谱功率百分比，以及局部动态稳定性、每步局部动态稳定性、步态平滑度以及垂直轴上主导频率的幅度和斜率，与前一年的跌倒次数相关且可可靠评估（所有P < .05，组内相关性> 0.75）。结论。日常生活步态特征与老年人的跌倒史相关，并且可以从一周的动态躯干加速度测量中可靠地估计出来。
