Shishov Nataliya, Melzer Itshak, Bar-Haim Simona
Department of Physical Therapy, Recanati School for Community Health Professions, Faculty of Health Sciences, Ben-Gurion University of the Negev Beer-Sheva, Israel.
Front Hum Neurosci. 2017 Feb 24;11:82. doi: 10.3389/fnhum.2017.00082. eCollection 2017.
Upper limb function, essential for daily life, is often impaired in individuals after stroke and cerebral palsy (CP). For an improved upper limb function, learning should occur, and therefore training with motor learning principles is included in many rehabilitation interventions. Despite accurate measurement being an important aspect for examination and optimization of treatment outcomes, there are no standard algorithms for outcome measures selection. Moreover, the ability of the chosen measures to identify learning is not well established. We aimed to review and categorize the parameters and measures utilized for identification of motor learning in stroke and CP populations. PubMed, Pedro, and Web of Science databases were systematically searched between January 2000 and March 2016 for studies assessing a form of motor learning following upper extremity training using motor control measures. Thirty-two studies in persons after stroke and 10 studies in CP of any methodological quality were included. Identified outcome measures were sorted into two categories, "parameters," defined as identifying a form of learning, and "measures," as tools measuring the parameter. Review's results were organized as a narrative synthesis focusing on the outcome measures. The included studies were heterogeneous in their study designs, parameters and measures. Parameters included adaptation ( = 6), anticipatory control ( = 2), after-effects ( = 3), de-adaptation ( = 4), performance ( = 24), acquisition ( = 8), retention ( = 8), and transfer ( = 14). Despite motor learning theory's emphasis on long-lasting changes and generalization, the majority of studies did not assess the retention and transfer parameters. Underlying measures included kinematic analyses in terms of speed, geometry or both ( = 39), dynamic metrics, measures of accuracy, consistency, and coordination. There is no exclusivity of measures to a specific parameter. Many factors affect task performance and the ability to measure it-necessitating the use of several metrics to examine different features of movement and learning. Motor learning measures' applicability to clinical setting can benefit from a treatment-focused approach, currently lacking. The complexity of motor learning results in various metrics, utilized to assess its occurrence, making it difficult to synthesize findings across studies. Further research is desirable for development of an outcome measures selection algorithm, while considering the quality of such measurements.
上肢功能对日常生活至关重要，但中风和脑瘫（CP）患者的上肢功能常常受损。为改善上肢功能，需要进行学习，因此许多康复干预措施都纳入了基于运动学习原则的训练。尽管准确测量是检查和优化治疗效果的重要方面，但目前尚无选择结果测量指标的标准算法。此外，所选测量指标识别学习的能力也尚未得到充分证实。我们旨在回顾和分类用于识别中风和CP患者运动学习的参数和测量指标。在2000年1月至2016年3月期间，我们系统检索了PubMed、Pedro和Web of Science数据库，以查找使用运动控制测量指标评估上肢训练后运动学习形式的研究。纳入了32项关于中风患者的研究和10项关于任何方法学质量的CP患者的研究。确定的结果测量指标分为两类，“参数”，定义为识别一种学习形式，以及“测量指标”，作为测量参数的工具。综述结果以叙述性综合的形式呈现，重点关注结果测量指标。纳入的研究在研究设计、参数和测量指标方面存在异质性。参数包括适应（=6）、预期控制（=2）、后效应（=3）、去适应（=4）、表现（=24）、习得（=8）、保持（=8）和迁移（=14）。尽管运动学习理论强调长期变化和泛化，但大多数研究并未评估保持和迁移参数。基础测量指标包括速度、几何形状或两者兼有的运动学分析（=39）、动力学指标、准确性、一致性和协调性测量。测量指标与特定参数之间不存在排他性。许多因素会影响任务表现及其测量能力，因此需要使用多种指标来检查运动和学习的不同特征。目前缺乏以治疗为重点的方法，运动学习测量指标在临床环境中的适用性可能会从中受益。运动学习的复杂性导致用于评估其发生的指标多种多样，这使得难以综合各项研究的结果。在考虑此类测量质量的同时，开发结果测量指标选择算法需要进一步的研究。
