Fluid Cognitive Abilities Are Important for Learning and Retention of a New, Explicitly Learned Walking Pattern in Individuals After Stroke.

Fluid Cognitive Abilities Are Important for Learning and Retention of a New, Explicitly Learned Walking Pattern in Individuals After Stroke.
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DOI:
10.1177/15459683211001025
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发表时间:
2021-05
影响因子:
4.2
通讯作者:
Reisman DS
Reisman DS
中科院分区:
医学1区
文献类型:
--
作者:
French MA;Cohen ML;Pohlig RT;Reisman DS

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脑卒中后运动学习有显著的可变性,这一可变性对个体对康复干预的反应了解甚少。认知能力与神经完整成人的上肢运动学习有关,但尚未对中风后运动学习进行研究。目的了解脑卒中后运动学习与脑卒中后记忆保留和认知的关系。慢性中风患者(6个月)参加了三次测试。在第一个阶段,参与者在跑步机上行走,并通过步数的视觉反馈学习一种新的行走模式。在第二阶段,参与者在跑步机上行走,并评估24小时的记忆力。身体和认知测试,包括Fugl-Meyer-Lower Extremity (FM-LE),来自NIH工具箱-认知电池®的流体认知综合评分(FCCS),以及来自韦氏记忆量表- iv®的空间添加,在第三期完成。完成了两个序列回归模型:一个以学习为因变量,一个以保留为因变量。年龄、身体缺陷(即FM-LE)和认知测量(即FCCS和空间加法)是自变量。学习模型和记忆模型分别包括49名和34名参与者。在考虑了年龄和FM-LE后,认知测量解释了学习(ΔR2=0.17, p=0.008;总体模型R2=0.31, p=0.002)和保留(ΔR2=0.17, p=0.023;总体模型R2=0.44, p=0.002)的变异性的重要部分。认知能力似乎是理解中风后运动学习和保持的重要因素。这对于将运动学习原理纳入中风后个性化康复干预的发展具有重要意义。
There is significant variability in post-stroke locomotor learning that is poorly understood and impacts individual responses to rehabilitation interventions. Cognitive abilities relate to upper extremity motor learning in neurologically intact adults, but have not been studied in post-stroke locomotor learning. To understand the relationship between locomotor learning and retention and cognition after stroke. Participants with chronic (>6 months) stroke participated in three testing sessions. During the first session, participants walked on a treadmill and learned a new walking pattern through visual feedback about their step length. During the second session, participants walked on a treadmill and 24-hour retention was assessed. Physical and cognitive tests, including the Fugl-Meyer-Lower Extremity (FM-LE), Fluid Cognition Composite Score (FCCS) from the NIH Toolbox -Cognition Battery®, and Spatial Addition from the Wechsler Memory Scale-IV®, were completed in the third session. Two sequential regression models were completed: one with learning and one with retention as the dependent variables. Age, physical impairment (i.e., FM-LE), and cognitive measures (i.e., FCCS and Spatial Addition) were the independent variables. Forty-nine and thirty-four participants were included in the learning and retention models, respectively. After accounting for age and FM-LE, cognitive measures explained a significant portion of variability in learning (ΔR2=0.17, p=0.008; overall model R2=0.31, p=0.002) and retention (ΔR2=0.17, p=0.023; overall model R2=0.44, p=0.002). Cognitive abilities appear to be an important factor for understanding locomotor learning and retention after stroke. This has significant implications for incorporating locomotor learning principles into the development of personalized rehabilitation interventions after stroke.
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