Saenen Leen, Orban de Xivry Jean-Jacques, Verheyden Geert
Department of Rehabilitation Sciences, KU Leuven, 3001 Leuven, Belgium.
Department of Movement Sciences, KU Leuven, 3001 Leuven, Belgium.
Brain Sci. 2022 Jul 29;12(8):1005. doi: 10.3390/brainsci12081005.
Upper limb sensory processing deficits are common in the chronic phase after stroke and are associated with decreased functional performance. Yet, current clinical assessments show suboptimal psychometric properties. Our aim was to develop and validate a novel robot-based assessment of sensory processing. We assessed 60 healthy participants and 20 participants with chronic stroke using existing clinical and robot-based assessments of sensorimotor function. In addition, sensory processing was evaluated with a new evaluation protocol, using a bimanual planar robot, through passive or active exploration, reproduction and identification of 15 geometrical shapes. The discriminative validity of this novel assessment was evaluated by comparing the performance between healthy participants and participants with stroke, and the convergent validity was evaluated by calculating the correlation coefficients with existing assessments for people with stroke. The results showed that participants with stroke showed a significantly worse sensory processing ability than healthy participants (passive condition: = 0.028, Hedges' g = 0.58; active condition: = 0.012, Hedges' g = 0.73), as shown by the less accurate reproduction and identification of shapes. The novel assessment showed moderate to high correlations with the tactile discrimination test: a sensitive clinical assessment of sensory processing (r = 0.52-0.71). We conclude that the novel robot-based sensory processing assessment shows good discriminant and convergent validity for use in participants with chronic stroke.
上肢感觉处理缺陷在中风后的慢性期很常见，并且与功能表现下降有关。然而，目前的临床评估显示其心理测量特性并不理想。我们的目的是开发并验证一种基于机器人的新型感觉处理评估方法。我们使用现有的临床和基于机器人的感觉运动功能评估方法，对60名健康参与者和20名慢性中风参与者进行了评估。此外，通过一种新的评估方案，使用双臂平面机器人，通过被动或主动探索、复制和识别15种几何形状来评估感觉处理。通过比较健康参与者和中风参与者之间的表现来评估这种新型评估的区分效度，通过计算与中风患者现有评估的相关系数来评估收敛效度。结果显示，中风参与者的感觉处理能力明显比健康参与者差（被动条件： = 0.028，Hedges' g = 0.58；主动条件： = 0.012，Hedges' g = 0.73），形状的复制和识别准确性较低即表明了这一点。这种新型评估与触觉辨别测试显示出中度到高度的相关性：触觉辨别测试是一种对感觉处理敏感的临床评估（r = 0.52 - 0.71）。我们得出结论，这种新型的基于机器人的感觉处理评估在慢性中风参与者中显示出良好的区分效度和收敛效度。
