Rinderknecht Mike D, Popp Werner L, Lambercy Olivier, Gassert Roger
Rehabilitation Engineering Laboratory, Department of Health Sciences and Technology, Institute of Robotics and Intelligent Systems, ETH Zurich Zurich, Switzerland.
Front Hum Neurosci. 2016 Jun 29;10:316. doi: 10.3389/fnhum.2016.00316. eCollection 2016.
Quantitative assessments of position sense are essential for the investigation of proprioception, as well as for diagnosis, prognosis and treatment planning for patients with somatosensory deficits. Despite the development and use of various paradigms and robotic tools, their clinimetric properties are often poorly evaluated and reported. A proper evaluation of the latter is essential to compare results between different studies and to identify the influence of possible confounds on outcome measures. The aim of the present study was to perform a comprehensive evaluation of a rapid robotic assessment of wrist proprioception using a passive gauge position matching task. Thirty-two healthy subjects undertook six test-retests of proprioception of the right wrist on two different days. The constant error (CE) was 0.87°, the absolute error (AE) was 5.87°, the variable error (VE) was 4.59° and the total variability (E) was 6.83° in average for the angles presented in the range from 10° to 30°. The intraclass correlation analysis provided an excellent reliability for CE (0.75), good reliability for AE (0.68) and E (0.68), and fair reliability for VE (0.54). Tripling the assessment length had negligible effects on the reliabilities. Additional analysis revealed significant trends of larger overestimation (constant errors), as well as larger absolute and variable errors with increased flexion angles. No proprioceptive learning occurred, despite increased familiarity with the task, which was reflected in significantly decreased assessment duration by 30%. In conclusion, the proposed automated assessment can provide sensitive and reliable information on proprioceptive function of the wrist with an administration time of around 2.5 min, demonstrating the potential for its application in research or clinical settings. Moreover, this study highlights the importance of reporting the complete set of errors (CE, AE, VE, and E) in a matching experiment for the identification of trends and subsequent interpretation of results.
位置觉的定量评估对于本体感觉的研究至关重要,对于体感缺陷患者的诊断、预后和治疗规划也同样重要。尽管已经开发并使用了各种范式和机器人工具,但它们的临床测量特性往往评估和报告得很差。对后者进行适当评估对于比较不同研究的结果以及确定可能的混杂因素对结果测量的影响至关重要。本研究的目的是使用被动量规位置匹配任务对腕关节本体感觉的快速机器人评估进行全面评估。32名健康受试者在两天内对右手腕的本体感觉进行了六次重测。对于10°至30°范围内呈现的角度,平均恒定误差(CE)为0.87°,绝对误差(AE)为5.87°,可变误差(VE)为4.59°,总变异性(E)为6.83°。组内相关分析显示CE具有出色的可靠性(0.75),AE和E具有良好的可靠性(0.68),VE具有中等可靠性(0.54)。将评估长度增加两倍对可靠性的影响可忽略不计。进一步分析显示,随着屈曲角度增加,存在明显的高估趋势(恒定误差)以及更大的绝对误差和可变误差。尽管对任务的熟悉程度增加,但并未发生本体感觉学习,这体现在评估持续时间显著缩短了30%。总之,所提出的自动化评估可以在约2.5分钟的给药时间内提供有关腕关节本体感觉功能的敏感且可靠的信息,证明了其在研究或临床环境中的应用潜力。此外,本研究强调了在匹配实验中报告完整的误差集(CE、AE、VE和E)对于识别趋势和后续结果解释的重要性。