Hilliard Marjorie Johnson, Martinez Katherine M, Janssen Imke, Edwards Beatrice, Mille Marie-Laure, Zhang Yunhui, Rogers Mark W
Department of Physical Therapy, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA.
Arch Phys Med Rehabil. 2008 Sep;89(9):1708-13. doi: 10.1016/j.apmr.2008.01.023.
To prospectively determine the capacity of measures of mediolateral (ML) protective stepping performance, maximum hip abduction torque, and trunk mobility, in order to predict the risk of falls among community-living older people.
Cross-sectional study.
A balance and falls research laboratory.
Medically screened and functionally independent community-living older adult volunteers (N=51).
Not applicable.
Measures included: (1) protective stepping responses: percentage of trials with multiple balance recovery steps and sidestep/crossover step recovery patterns, and first step length following motor-driven waist-pull perturbations of ML standing balance; (2) hip abduction strength and axial mobility: (3) peak isokinetic hip abduction joint torque and trunk functional axial rotation (FAR) range of motion; and (4) fall incidence: monthly mail-in reporting of fall occurrences with follow-up contact for 1 year post-testing. One- and 2-variable logistic regression analysis models determined which single and combined measures optimally predicted fall status.
The single variable model with the strongest predictive value for falls was the use of multiple steps in all trials (100% multiple steps) (odds ratio, 6.2; P=.005). Two-variable models, including 100% multiple steps and either hip abduction torque or FAR variables, significantly improved fall prediction over 100% multiple steps alone. The hip abduction and FAR logistic regression optimally predicted fall status.
The findings identify new predictor variables for risk of falling that underscore the importance of dynamic balance recovery performance through ML stepping in relation to neuromusculoskeletal factors contributing to lateral balance stability. The results also highlight focused risk factors for falling that are amenable to clinical interventions for enhancing lateral balance function and preventing falls.
前瞻性地确定内外侧（ML）保护性跨步表现、最大髋关节外展扭矩和躯干活动度的测量指标预测社区居住老年人跌倒风险的能力。
横断面研究。
一个平衡与跌倒研究实验室。
经过医学筛查且功能独立的社区居住老年志愿者（N = 51）。
不适用。
测量指标包括：（1）保护性跨步反应：在ML站立平衡的电动腰部牵拉扰动后，具有多个平衡恢复步和侧步/交叉步恢复模式的试验百分比，以及第一步长度；（2）髋关节外展力量和轴向活动度：（3）等速髋关节外展关节扭矩峰值和躯干功能性轴向旋转（FAR）活动范围；（4）跌倒发生率：通过每月邮寄报告跌倒事件，并在测试后进行1年的随访联系。单变量和双变量逻辑回归分析模型确定哪些单一和组合测量指标能最佳预测跌倒状态。
对跌倒预测价值最强的单变量模型是所有试验中使用多个步骤（100%多个步骤）（优势比，6.2；P = 0.005）。双变量模型，包括100%多个步骤以及髋关节外展扭矩或FAR变量，比单独的100%多个步骤显著改善了跌倒预测。髋关节外展和FAR逻辑回归能最佳预测跌倒状态。
研究结果确定了跌倒风险的新预测变量，强调了通过ML跨步实现动态平衡恢复表现相对于有助于侧向平衡稳定性的神经肌肉骨骼因素的重要性。结果还突出了可通过临床干预改善侧向平衡功能和预防跌倒的跌倒重点风险因素。
