Lum Peter S, Mulroy Sara, Amdur Richard L, Requejo Philip, Prilutsky Boris I, Dromerick Alexander W
Catholic University of America, Center for Applied Biomechanics and Rehabilitation Research, National Rehabilitation Hospital, DC VA Medical Center, Washington, DC, USA.
Top Stroke Rehabil. 2009 Jul-Aug;16(4):237-53. doi: 10.1310/tsr1604-237.
In terms of integration of the paretic upper extremity in activities of daily living (ADLs), outcome is poor after stroke. Furthermore, amount of real-world arm use appears only weakly correlated with laboratory motor function scales. Therefore, amount of arm use may depend critically on the location, extent, and type of functional gains, which can be quantified with comprehensive kinematic and EMG analysis of ADL performance. Gains in upper extremity function can occur via compensation or recovery of premorbid movement and EMG patterns, and traditional treatment approaches encourage adoption of compensatory strategies early in the postacute period that can inhibit potential recovery. A new treatment approach called Accelerated Skill Acquisition Program (ASAP) focuses on impairment reduction coupled with repetitive, task-specific training of the paretic arm during ADLs. We present pilot data that show recovery in subjects who received the ASAP, while a usual care control subject showed increased use of compensation over the same period. Finally, we discuss the advantages of data reduction methods such as principal components analysis, confirmatory factor analysis, and structural equation modeling, which can potentially distill large kinematic and EMG data sets into the key latent variables that predict amount of real-world use.
就中风后患侧上肢在日常生活活动（ADL）中的整合情况而言，中风后的结果较差。此外，现实世界中手臂的使用量似乎与实验室运动功能量表的相关性较弱。因此，手臂的使用量可能严重取决于功能改善的位置、程度和类型，而这可以通过对ADL表现进行全面的运动学和肌电图分析来量化。上肢功能的改善可以通过病前运动和肌电图模式的代偿或恢复来实现，传统的治疗方法鼓励在急性后期早期采用代偿策略，而这可能会抑制潜在的恢复。一种名为加速技能习得计划（ASAP）的新治疗方法侧重于减少损伤，同时在ADL期间对患侧手臂进行重复的、特定任务的训练。我们展示的初步数据表明，接受ASAP治疗的受试者有所恢复，而一名接受常规护理的对照受试者在同一时期内代偿使用增加。最后，我们讨论了数据简化方法（如主成分分析、验证性因素分析和结构方程建模）的优势，这些方法有可能将大量的运动学和肌电图数据集提炼为预测现实世界使用量的关键潜在变量。
