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一种基于挑战的中风后体重支持跑步机训练方法:随机对照试验方案

A Challenge-Based Approach to Body Weight-Supported Treadmill Training Poststroke: Protocol for a Randomized Controlled Trial.

作者信息

Naidu Avantika, Brown David, Roth Elliot

机构信息

Department of Physical Therapy and Occupational Therapy, University of Alabama at Birmingham, Birmingham, AL, United States.

Department of Physical Medicine and Rehabilitation, Northwestern University, Chicago, IL, United States.

出版信息

JMIR Res Protoc. 2018 May 3;7(5):e118. doi: 10.2196/resprot.9308.

Abstract

BACKGROUND

Body weight support treadmill training protocols in conjunction with other modalities are commonly used to improve poststroke balance and walking function. However, typical body weight support paradigms tend to use consistently stable balance conditions, often with handrail support and or manual assistance.

OBJECTIVE

In this paper, we describe our study protocol, which involved 2 unique body weight support treadmill training paradigms of similar training intensity that integrated dynamic balance challenges to help improve ambulatory function post stroke. The first paradigm emphasized walking without any handrails or manual assistance, that is, hands-free walking, and served as the control group, whereas the second paradigm incorporated practicing 9 essential challenging mobility skills, akin to environmental barriers encountered during community ambulation along with hands-free walking (ie hands-free + challenge walking).

METHODS

We recruited individuals with chronic poststroke hemiparesis and randomized them to either group. Participants trained for 6 weeks on a self-driven, robotic treadmill interface that provided body weight support and a safe gait-training environment. We assessed participants at pre-, mid- and post 6 weeks of intervention-training, with a 6-month follow-up. We hypothesized greater walking improvements in the hands-free + challenge walking group following training because of increased practice opportunity of essential mobility skills along with hands-free walking.

RESULTS

We assessed 77 individuals with chronic hemiparesis, and enrolled and randomized 30 individuals poststroke for our study (hands-free group=19 and hands-free + challenge walking group=20) from June 2012 to January 2015. Data collection along with 6-month follow-up continued until January 2016. Our primary outcome measure is change in comfortable walking speed from pre to post intervention for each group. We will also assess feasibility, adherence, postintervention efficacy, and changes in various exploratory secondary outcome measures. Additionally, we will also assess participant responses to a study survey, conducted at the end of training week, to gauge each group's training experiences.

CONCLUSIONS

Our treadmill training paradigms, and study protocol represent advances in standardized approaches to selecting body weight support levels without the necessity for using handrails or manual assistance, while progressively providing dynamic challenges for improving poststroke ambulatory function during rehabilitation.

TRIAL REGISTRATION

ClinicalTrials.gov NCT02787759; https://clinicaltrials.gov/ct2/show/NCT02787759 (Archived by Webcite at http://www.webcitation.org/6yJZCrIea).

摘要

背景

体重支持跑步机训练方案通常与其他方式结合使用,以改善中风后的平衡和步行功能。然而,典型的体重支持模式往往采用始终稳定的平衡条件,通常伴有扶手支持和/或人工辅助。

目的

在本文中,我们描述了我们的研究方案,该方案涉及两种独特的、训练强度相似的体重支持跑步机训练模式,这些模式整合了动态平衡挑战,以帮助改善中风后的步行功能。第一种模式强调在没有任何扶手或人工辅助的情况下行走,即无扶手行走,并作为对照组,而第二种模式则包括练习9项基本的具有挑战性的移动技能,类似于社区行走时遇到的环境障碍以及无扶手行走(即无扶手+挑战行走)。

方法

我们招募了患有慢性中风后偏瘫的个体,并将他们随机分为两组。参与者在一个自主驱动的机器人跑步机界面上训练6周,该界面提供体重支持和安全的步态训练环境。我们在干预训练的6周前、中期和后期对参与者进行评估,并进行6个月的随访。我们假设,由于基本移动技能与无扶手行走的练习机会增加,无扶手+挑战行走组在训练后步行改善更大。

结果

我们评估了77名患有慢性偏瘫的个体,并在2012年6月至2015年1月期间招募并随机分配了30名中风后个体参与我们的研究(无扶手组=19名,无扶手+挑战行走组=20名)。数据收集以及6个月的随访一直持续到2016年1月。我们的主要结局指标是每组干预前后舒适步行速度的变化。我们还将评估可行性、依从性、干预后疗效以及各种探索性次要结局指标的变化。此外,我们还将评估参与者对在训练周结束时进行的研究调查的反应,以衡量每组的训练体验。

结论

我们的跑步机训练模式和研究方案代表了在选择体重支持水平的标准化方法方面的进展,无需使用扶手或人工辅助,同时在康复过程中逐步提供动态挑战以改善中风后的步行功能。

试验注册

ClinicalTrials.gov NCT02787759;https://clinicaltrials.gov/ct2/show/NCT02787759(由Webcite存档于http://www.webcitation.org/6yJZCrIea)。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/5e8d/5958283/4dc7e744ae53/resprot_v7i5e118_fig1.jpg

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本文引用的文献

1
Collaborative robotic biomechanical interactions and gait adjustments in young, non-impaired individuals.
J Neuroeng Rehabil. 2016 Jun 16;13(1):57. doi: 10.1186/s12984-016-0166-1.
5
Limb contribution to increased self-selected walking speeds during body weight support in individuals poststroke.
Gait Posture. 2015 Mar;41(3):857-9. doi: 10.1016/j.gaitpost.2015.02.004. Epub 2015 Feb 25.
6
Science-based neurorehabilitation: recommendations for neurorehabilitation from basic science.
J Mot Behav. 2015;47(1):7-17. doi: 10.1080/00222895.2014.931273.
7
Characteristics of horizontal force generation for individuals post-stroke walking against progressive resistive forces.
Clin Biomech (Bristol). 2015 Jan;30(1):40-5. doi: 10.1016/j.clinbiomech.2014.11.006. Epub 2014 Nov 25.
8
An assistive control approach for a lower-limb exoskeleton to facilitate recovery of walking following stroke.
IEEE Trans Neural Syst Rehabil Eng. 2015 May;23(3):441-9. doi: 10.1109/TNSRE.2014.2346193. Epub 2014 Aug 12.
10
Longitudinal changes in poststroke spatiotemporal gait asymmetry over inpatient rehabilitation.
Neurorehabil Neural Repair. 2015 Feb;29(2):153-62. doi: 10.1177/1545968314533614. Epub 2014 May 13.

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