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

Aoyu Li1, Jingwen Li2, Jiali Chai3

  • 1School of Software, Taiyuan University of Technology, Jinzhong, China.

JMIR Mhealth and Uhealth
|June 26, 2024
PubMed
Summary

This study introduces a tablet-based system using drawing tasks to detect mild cognitive impairment (MCI). The system effectively identifies MCI by analyzing movement kinetics, offering a user-friendly screening tool for cognitive decline.

Keywords:
digital cognitive testdual taskmild cognitive impairmentmobile phonemovement kinetics

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Area of Science:

  • Neuroscience
  • Gerontology
  • Human-Computer Interaction

Background:

  • Early detection of cognitive impairment or dementia is crucial for managing neurodegenerative diseases.
  • Current diagnostic tools for mild cognitive impairment (MCI) are often inaccessible or time-consuming.
  • Novel, efficient methods are needed to aid clinicians in MCI detection.

Purpose of the Study:

  • To assess the feasibility and efficiency of using tablet-based "drawing and dragging" tasks to detect MCI.
  • To analyze movement kinetics as a biomarker for cognitive function.
  • To evaluate user experience with a novel cognitive screening system.

Main Methods:

  • Iterative design of "drawing and dragging" tasks involving stakeholders.
  • Evaluation of stroke patterns and movement kinetics in healthy and MCI groups.
  • Analysis of hand motor function features (time, stroke, frequency, score, sequence).
  • User experience assessment via questionnaires and interviews.

Main Results:

  • The system achieved 85% accuracy in detecting MCI.
  • Time- and score-based movement kinetics were most effective in differentiating groups.
  • MCI patients exhibited increased stroke transition times, longer drawing durations, slower dragging, and lower scores.
  • MCI patients showed impaired decision-making and visual-spatial sequencing.

Conclusions:

  • Tablet-based system quantitatively assesses hand motor function for MCI detection.
  • Identifies digital biomarkers for MCI and Alzheimer's dementia.
  • Monitors cognitive and behavioral decline, including executive function and visual perception.