Combination of Paper and Electronic Trail Making Tests for Automatic Analysis of Cognitive Impairment: Development and Validation Study

Abstract

Background Computer-aided detection, used in the screening and diagnosing of cognitive impairment, provides an objective, valid, and convenient assessment. Particularly, digital sensor technology is a promising detection method. Objective This study aimed to develop and validate a novel Trail Making Test (TMT) using a combination of paper and electronic devices. Methods This study included community-dwelling older adult individuals (n=297), who were classified into (1) cognitively healthy controls (HC; n=100 participants), (2) participants diagnosed with mild cognitive impairment (MCI; n=98 participants), and (3) participants with Alzheimer disease (AD; n=99 participants). An electromagnetic tablet was used to record each participant’s hand-drawn stroke. A sheet of A4 paper was placed on top of the tablet to maintain the traditional interaction style for participants who were not familiar or comfortable with electronic devices (such as touchscreens). In this way, all participants were instructed to perform the TMT-square and circle. Furthermore, we developed an efficient and interpretable cognitive impairment–screening model to automatically analyze cognitive impairment levels that were dependent on demographic characteristics and time-, pressure-, jerk-, and template-related features. Among these features, novel template-based features were based on a vector quantization algorithm. First, the model identified a candidate trajectory as the standard answer (template) from the HC group. The distance between the recorded trajectories and reference was computed as an important evaluation index. To verify the effectiveness of our method, we compared the performance of a well-trained machine learning model using the extracted evaluation index with conventional demographic characteristics and time-related features. The well-trained model was validated using follow-up data (HC group: n=38; MCI group: n=32; and AD group: n=22). Results We compared 5 candidate machine learning methods and selected random forest as the ideal model with the best performance (accuracy: 0.726 for HC vs MCI, 0.929 for HC vs AD, and 0.815 for AD vs MCI). Meanwhile, the well-trained classifier achieved better performance than the conventional assessment method, with high stability and accuracy of the follow-up data. Conclusions The study demonstrated that a model combining both paper and electronic TMTs increases the accuracy of evaluating participants’ cognitive impairment compared to conventional paper-based feature assessment.

Comprehensive assessment of fine motor movement and cognitive function among older adults in China: a cross-sectional study

“…In our research, we discovered a negative correlation between the amount of two-handed coverage and MoCA-BJ scores. This finding aligns with previous studies [ 35 , 36 ], Dahdal executed the Motor Performance Series (MLS) to analyze fine hand movements and found that Parkinson’s patients with MCI had poorer dynamic stability compared to those without MCI [ 37 ]. An important feature of human fine motor is the minimal jerk, which represents a swing and pause throughout a continuous movement [ 17 ], cognitive functions play a crucial role in the control of the jerk.…”

Detection of Mild Cognitive Impairment Through Hand Motor Function Under Digital Cognitive Test: Mixed Methods Study

“…This analysis considered an expected effect size of 0.3, a significance level of .05, and a statistical power of 0.8, requiring at least 82 participants per group [37]. Similar research in the field also informed our sample size, which typically used participant numbers within this range for comparable outcomes [38][39][40][41].…”