Affiliations: [a] School of Electrical Engineering and Computer Sciences, Washington State University, Pullman, WA, USA | [b] Department of Psychology, Washington State University, Pullman, WA, USA
Correspondence:
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Corresponding author: Jessamyn Dahmen, School of Electrical Engineering and Computer Sciences, Washington State University, Pullman, WA, USA. E-mail:jb3dahmen@wsu.edu
Abstract: BACKGROUND: The goal of this work is to develop a digital version of a standard
cognitive assessment, the Trail Making Test (TMT), and assess its utility. OBJECTIVE: This paper introduces a novel digital version of the TMT and introduces a
machine learning based approach to assess its capabilities. METHODS: Using digital Trail Making Test (dTMT) data collected from (N = 54) older
adult participants as feature sets, we use machine learning techniques to
analyze the utility of the dTMT and evaluate the insights provided by the
digital features. RESULTS: Predicted TMT scores correlate well with clinical digital test scores
(r = 0.98) and paper time to completion scores (r = 0.65). Predicted TICS
exhibited a small correlation with clinically derived TICS scores (r = 0.12
Part A, r = 0.10 Part B). Predicted FAB scores exhibited a small correlation
with clinically derived FAB scores (r = 0.13 Part A, r = 0.29 for Part B).
Digitally derived features were also used to predict diagnosis (AUC of
0.65). CONCLUSION: Our findings indicate that the dTMT is capable of measuring the same aspects
of cognition as the paper-based TMT. Furthermore, the dTMT's additional data
may be able to help monitor other cognitive processes not captured by the
paper-based TMT alone.
Keywords: Computerized cognitive assessment, design and validation, machine learning, mobile application, Trail Making Test
