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运动学在评估脑卒中后早期上肢运动功能恢复中的作用。

The contribution of kinematics in the assessment of upper limb motor recovery early after stroke.

机构信息

1M2H, Euromov, Montpellier-1 University, Montpellier, France.

出版信息

Neurorehabil Neural Repair. 2014 Jan;28(1):4-12. doi: 10.1177/1545968313498514. Epub 2013 Aug 1.

Abstract

BACKGROUND

Kinematic assessment of upper limb motor recovery after stroke may be related to clinical scores while being more sensitive and reliable than clinical evaluation alone.

OBJECTIVE

To identify the potential of kinematics in assessing upper limb recovery early poststroke.

METHODS

Thirteen patients were included within 1 month poststroke and evaluated once a week for 6 weeks and at 3 months with (a) the Fugl-Meyer Assessment (FMA) and (b) kinematic analysis of reach-to-grasp movements. The link between clinical and kinematic data was identified using mixed model with random coefficient analysis.

RESULTS

Movement time, trajectory length, directness, smoothness, mean and maximum velocity of the hand were sensitive to change over time and distinguished between movements of paretic, nonparetic, and healthy control limbs. The FMA score increased with movement smoothness over time, explaining 62.5% of FMA variability.

CONCLUSION

Kinematic analysis of reach-to-grasp movements is relevant to assess upper limb recovery early poststroke, and is linked to the FMA. Kinematics could provide more accurate real-time indicators of patients' recovery as compared with the sole use of clinical scores, although it remains challenging to establish the universality of the reaching model in relation to motor recovery after stroke.

摘要

背景

脑卒中后上肢运动功能恢复的运动学评估可能与临床评分相关,且比单独的临床评估更敏感、更可靠。

目的

确定运动学在脑卒中后早期上肢恢复评估中的潜力。

方法

13 例患者在脑卒中后 1 个月内被纳入研究,在 6 周内每周评估一次,在 3 个月时进行(a)Fugl-Meyer 评估(FMA)和(b)伸手抓握运动的运动学分析。使用混合模型随机系数分析确定临床和运动学数据之间的关系。

结果

运动时间、轨迹长度、直接性、平滑度、手的平均速度和最大速度随时间的变化而敏感,并区分了患侧、非患侧和健康对照组肢体的运动。FMA 评分随运动平滑度的增加而增加,解释了 FMA 变异性的 62.5%。

结论

伸手抓握运动的运动学分析与脑卒中后早期上肢恢复的评估相关,与 FMA 相关。与单独使用临床评分相比,运动学可以提供更准确的实时患者恢复指标,但建立与脑卒中后运动恢复相关的伸手模型的普遍性仍然具有挑战性。

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