Multicomponent analysis of a digital Trail Making Test

R. Fellows, Jessamyn Dahmen, D. Cook, M. Schmitter-Edgecombe
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引用次数: 78

Abstract

Abstract Objective: 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.Method: 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: Results revealed significant correlations between paper and digital variants of Part A (rs = .541, p < .001) and paper and digital versions of Part B (rs = .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.Conclusions: Taken together, these results elucidate specific cognitive abilities underlying TMT performance, as well as the utility of isolating digital components.
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数字造径试验的多分量分析
摘要目的:本研究的目的是使用新开发的基于数字平板的TMT变体来分离TMT表现背后的组件认知过程。方法:与纸质试道测试类似,该数字版本由A部分和b部分两个条件组成,但该数字版本自动收集额外数据来创建组件子测试分数,以隔离认知能力。具体来说,除了总完成时间和错误次数外,数字轨迹制作测试(dTMT)还记录了几个独特的组件,包括暂停次数、暂停持续时间、提升、提升持续时间、每个圈内的时间以及圈间的时间。参与者是居住在社区的老年人,他们完成了神经心理学评估,包括处理速度、抑制控制、视觉工作记忆/排序和集合转换。通过传统的神经心理学测量方法对dTMT的成分得分进行回归分析,评估了TMT表现的能力。结果:结果显示纸质版和电子版A部分(rs = .541, p < .001)和纸质版和电子版B部分(rs = .799, p < .001)之间存在显著相关性。传统神经心理学测量的回归分析显示,快速加工最能预测A部分成分,而抑制控制和视觉/空间测序是b部分特定成分的预测因子。探索性分析显示,特定dTMT-B成分与基于绩效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,以及隔离数字组件的效用。
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