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Related Experiment Video

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Traditional Trail Making Test Modified into Brand-new Assessment Tools: Digital and Walking Trail Making Test
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Digitally generated Trail Making Test data: Analysis using hidden Markov modeling.

Mengtian Du1,2, Stacy L Andersen3, Stephanie Cosentino4,5

  • 1Department of Biostatistics Boston University Boston Massachusetts USA.

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|March 14, 2022
PubMed
Summary
This summary is machine-generated.

Digital versions of the Trail Making Test (TMT) capture more than just cognitive function. New metrics reveal insights into graphomotor processing, gait speed, memory, and grip strength.

Keywords:
Bayesian hidden Markov modelsTrail Making Testagingcognitive functiondigital biomarker

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Area of Science:

  • Neuropsychology
  • Digital Health
  • Biometrics

Background:

  • The Trail Making Test (TMT) is a standard neuropsychological tool for assessing cognitive dysfunction.
  • Traditional TMT administration relies on manual completion, limiting data capture to completion time.
  • Digital recording offers potential for richer data extraction beyond simple timing.

Purpose of the Study:

  • To explore the utility of digitally recorded TMT versions for assessing cognitive and physical functions.
  • To develop novel metrics from digital TMT data using advanced statistical modeling.
  • To correlate these digital metrics with established cognitive and physical performance measures.

Main Methods:

  • Digitally recorded versions of TMT-A and TMT-B were analyzed.
  • Time-based metrics were derived from digital recordings.
  • Bayesian hidden Markov models were employed to extract additional performance metrics.
  • Regression analyses correlated digital metrics with cognitive and physical function scores.

Main Results:

  • Digital TMT metrics from both TMT-A and TMT-B correlated with graphomotor processing speed and gait speed.
  • Digital TMT-B metrics showed additional associations with episodic memory scores and grip strength.
  • These findings suggest digital TMT metrics provide nuanced information on cognitive and physical states.

Conclusions:

  • Digitally recorded TMT offers enhanced insights into cognitive and physical function compared to traditional methods.
  • Novel digital metrics can differentiate between cognitive and physical factors influencing TMT performance.
  • This approach holds promise for more comprehensive neuropsychological assessments.