Fellows Robert P, Dahmen Jessamyn, Cook Diane, Schmitter-Edgecombe Maureen
a Department of Psychology , Washington State University , Pullman , WA , USA.
b School of Electrical Engineering and Computer Science , Washington State University , Pullman , WA , USA.
Clin Neuropsychol. 2017 Jan;31(1):154-167. doi: 10.1080/13854046.2016.1238510. Epub 2016 Oct 3.
The purpose of the current study was to use a newly developed digital tablet-based variant of the TMT to isolate component cognitive processes underlying TMT performance.
Similar to the paper-based trail making test, this digital variant consists of two conditions, Part A and Part B. However, this digital version automatically collects additional data to create component subtest scores to isolate cognitive abilities. Specifically, in addition to the total time to completion and number of errors, the digital Trail Making Test (dTMT) records several unique components including the number of pauses, pause duration, lifts, lift duration, time inside each circle, and time between circles. Participants were community-dwelling older adults who completed a neuropsychological evaluation including measures of processing speed, inhibitory control, visual working memory/sequencing, and set-switching. The abilities underlying TMT performance were assessed through regression analyses of component scores from the dTMT with traditional neuropsychological measures.
Results revealed significant correlations between paper and digital variants of Part A (r = .541, p < .001) and paper and digital versions of Part B (r = .799, p < .001). Regression analyses with traditional neuropsychological measures revealed that Part A components were best predicted by speeded processing, while inhibitory control and visual/spatial sequencing were predictors of specific components of Part B. Exploratory analyses revealed that specific dTMT-B components were associated with a performance-based medication management task.
Taken together, these results elucidate specific cognitive abilities underlying TMT performance, as well as the utility of isolating digital components.
本研究的目的是使用新开发的基于数字平板电脑的连线测验(TMT)变体,以分离出TMT表现背后的认知过程组成部分。
与纸质连线测验类似,这种数字变体包括A部分和B部分两个条件。然而,这个数字版本会自动收集额外的数据,以创建组成子测验分数,从而分离认知能力。具体而言,除了完成的总时间和错误数量外,数字连线测验(dTMT)还记录了几个独特的组成部分,包括停顿次数、停顿持续时间、提笔次数、提笔持续时间、每个圆圈内的时间以及圆圈之间的时间。参与者是居住在社区的老年人,他们完成了一项神经心理学评估,包括处理速度、抑制控制、视觉工作记忆/序列以及任务切换的测量。通过对dTMT的组成分数与传统神经心理学测量进行回归分析,评估了TMT表现背后的能力。
结果显示,A部分的纸质版和数字版之间存在显著相关性(r = 0.541,p < 0.001),B部分的纸质版和数字版之间也存在显著相关性(r = 0.799,p < 0.001)。与传统神经心理学测量的回归分析表明,A部分的组成部分最好由快速处理来预测,而抑制控制和视觉/空间序列是B部分特定组成部分的预测因素。探索性分析表明,特定的dTMT - B组成部分与基于表现的药物管理任务相关。
综上所述,这些结果阐明了TMT表现背后的特定认知能力,以及分离数字组成部分的效用。