JMIR Publications

ABSTRACT

Background:

Early detection of cognitive impairment or dementia is essential to reduce the incidence of severe neurodegenerative diseases. However, currently available diagnostic tools for detecting mild cognitive impairment (MCI) or dementia are time-consuming, expensive, or not widely accessible. Hence, exploring more effective methods to assist clinicians in detecting MCI is necessary.

Objective:

This study explores the feasibility and efficiency of assessing MCI through movement kinetics under tablet-based “drawing & dragging” tasks.

Methods:

We iteratively designed “drawing & dragging” tasks by conducting symposiums, programming and interviews with stakeholders (neurologists, nurses, engineers, MCI patients, healthy older adults and caregivers). Subsequently, stroke patterns and movement kinetics were evaluated in healthy control (HC) and MCI groups by comparing five categories of features related to hand motor function (i.e., time, stroke, frequency, score, and sequence). Finally, user experience with the overall cognitive screening system was investigated using structured questionnaires and unstructured interviews, and their suggestions were recorded.

Results:

The “drawing & dragging” tasks can detect MCI effectively, with an average accuracy of 85.2%. Through statistical comparison of movement kinetics, we discover that the time- and score-based features are the most effective among all the features. Specifically, compared with the HC group, the MCI group significantly increased the time it took for the hand to switch from one stroke to the next, with longer drawing times, slow dragging and lower scores (all p<0.05). In addition, MCI patients had poorer decision-making strategies and visual perception of drawing sequence features, as evidenced by adding auxiliary information and losing more local details in the drawing. Feedback from user experience indicates that our system is user-friendly and facilitates screening for deficits in self-perception.

Conclusions:

The tablet-based MCI detection system quantitatively assesses hand motor function in older adults and further elucidates the cognitive and behavioral decline phenomenon in MCI patients. This innovative approach serves to identify and measure digital biomarkers associated with MCI or AD, enabling the monitoring of changes in patients' executive function and visual perceptual abilities as the disease advances.