Using dynamic walking models to identify factors that contribute to increased risk of falling in older adults.
Using dynamic walking models to identify factors that contribute to increased risk of falling in older adults.
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使用动态步行模型来识别有助于增加老年人风险的因素。
DOI：
10.1016/j.humov.2013.07.001
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发表时间：
2013-10
影响因子：
2.1
通讯作者：
Dingwell, Jonathan B.
中科院分区：
文献类型：
--
作者：
Roos, Paulien E.;Dingwell, Jonathan B.
跌倒在老年人中很常见。跌倒最常见的原因是走路时被绊倒。模拟研究表明，老年人可能会受到下肢力量和运动速度的限制，在一次绊倒后恢复平衡。这篇综述考察了如何使用建模方法来确定不同的测量方法如何预测实际的跌倒风险，以及跌倒风险的一些因果机制是什么。尽管步态可变性的增加在实验上预测了跌倒风险的增加，但目前还不清楚哪种可变性测量方法是最好的，也不清楚与跌倒风险增加相对应的变化幅度是多少。通过模拟研究，我们发现步态变异性增加时摔倒风险的增加受初始变异性水平的影响很大。因此，步态变异性不能很容易地用来预测跌倒的风险。因此，我们探索了其他可能与跌倒风险相关的指标，并在动态步行模型中调查了稳定性指标(如Floquet乘数和局部发散指数)与实际跌倒风险的关系。我们证明，短期局部发散指数是跌倒风险的良好早期预测指标。神经元噪音随着年龄的增长而增加。然而，神经元噪音增加是否会导致跌倒风险的增加，目前还没有完全了解。在我们的动态行走模型中，我们发现神经元噪音增加会增加摔倒的风险。尽管跌倒风险增加的人会减慢步行速度，但人们质疑这种较慢的速度是否真的会降低跌倒的风险。通过我们的模型，我们证明了步行速度的降低会降低跌倒的风险。这可能是由于速度较慢所需的较小肌力的信号相关噪声减少，从而降低了运动学的可变性。这些见解可用于制定跌倒预防计划，以便更好地识别那些跌倒风险增加的人，并针对那些对跌倒风险影响最大的因素。
Falls are common in older adults. The most common cause of falls is tripping while walking. Simulation studies demonstrated that older adults may be restricted by lower limb strength and movement speed to regain balance after a trip. This review examines how modeling approaches can be used to determine how different measures predict actual fall risk and what some of the causal mechanisms of fall risk are. Although increased gait variability predicts increased fall risk experimentally, it is not clear which variability measures could best be used, or what magnitude of change corresponded with increased fall risk. With a simulation study we showed that the increase in fall risk with a certain increase in gait variability was greatly influenced by the initial level of variability. Gait variability can therefore not easily be used to predict fall risk. We therefore explored other measures that may be related to fall risk and investigated the relationship between stability measures such as Floquet multipliers and local divergence exponents and actual fall risk in a dynamic walking model. We demonstrated that short-term local divergence exponents were a good early predictor for fall risk. Neuronal noise increases with age. It has however not been fully understood if increased neuronal noise would cause an increased fall risk. With our dynamic walking model we showed that increased neuronal noise caused increased fall risk. Although people who are at increased risk of falling reduce their walking speed it had been questioned whether this slower speed would actually cause a reduced fall risk. With our model we demonstrated that a reduced walking speed caused a reduction in fall risk. This may be due to the decreased kinematic variability as a result of the reduced signal-dependent noise of the smaller muscle forces that are required for slower. These insights may be used in the development of fall prevention programs in order to better identify those at increased risk of falling and to target those factors that influence fall risk most.
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影响因子：
2.4
作者：
Dingwell, Jonathan B.;Kang Hyun Gu;Marin, Laura C.
通讯作者：
Marin, Laura C.
影响因子：
9.2
作者：
Hobbelen, D. G. E.;Wisse, M.
通讯作者：
Wisse, M.
影响因子：
2.4
作者：
Donelan, JM;Shipman, DW;Kuo, AD
通讯作者：
Kuo, AD
影响因子：
2.4
作者：
Dingwell, JB;Marin, LC
通讯作者：
Marin, LC
影响因子：
64.8
作者：
Harris, CM;Wolpert, DM
通讯作者：
Wolpert, DM