Lateral balance factors predict future falls in community-living older adults
Lateral balance factors predict future falls in community-living older adults
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DOI：
10.1016/j.apmr.2008.01.023
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发表时间：
2008-09-01
影响因子：
4.3
通讯作者：
Rogers, Mark W.
中科院分区：
文献类型：
--
作者：
Hilliard, Marjorie Johnson;Martinez, Katherine M.;Rogers, Mark W.
目的：前瞻性地确定中外侧（ML）保护性步进性能、最大髋关节外展扭矩和躯干活动能力的测量能力，以预测社区生活老年人跌倒的风险。设计：横断面研究。设置：平衡和跌倒研究实验室。参与者：医学筛选和功能独立的社区生活老年志愿者（N = 51）。干预措施：不适用。主要结果指标：指标包括：(1)保护性步伐反应：采用多种平衡恢复步骤和侧步/交叉步骤恢复模式的试验百分比，以及电机驱动腰拉摄动ML站立平衡后的第一步长度；(2)髋关节外展强度和轴向活动度；(3)髋关节外展关节峰值等速扭矩和躯干功能性轴向旋转（FAR）运动范围；(4)跌倒发生率：每月邮寄报告跌倒发生率，测试后随访1年。单变量和双变量logistic回归分析模型确定了哪种单一措施和组合措施最能预测跌倒状况。结果：所有试验中使用多步（100%多步）是预测跌倒最有价值的单变量模型（优势比，6.2；P= 0.005）。双变量模型，包括100%多步和髋关节外旋扭矩或FAR变量，比100%多步显著提高跌倒预测。髋关节外展和FAR logistic回归最能预测跌倒状态。结论：研究结果确定了跌倒风险的新预测变量，强调了通过ML步动平衡恢复性能与有助于侧平衡稳定性的神经肌肉骨骼因素的重要性。结果还强调了跌倒的主要危险因素，这些因素可以通过临床干预来增强侧平衡功能和预防跌倒。
Objective: 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.Design: Cross-sectional study.Setting: A balance and falls research laboratory.Participants: Medically screened and functionally independent community-living older adult volunteers (N = 5 1).Interventions: Not applicable.Main Outcome Measures: 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.Results: 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.Conclusions: 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.