An analysis of a digital variant of the Trail Making Test using machine learning techniques

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 paperbased TMT. Furthermore, the dTMT's additional data may be able to help monitor other cognitive processes not captured by the paper-based TMT alone.