Development of a Gait Feature–Based Model for Classifying Cognitive Disorders Using a Single Wearable Inertial Sensor

 I will never have a normal gait as long as my spasticity exists, but I'm damned sure I have zero cognitive disorders. Arrogance is not a disorder if you know what you're talking about.

Development of a Gait Feature–Based Model for Classifying Cognitive Disorders Using a Single Wearable Inertial Sensor

View ORCID ProfileJeongbin Park, Hyang Jun Lee, Ji Sun Park, View ORCID ProfileChae Hyun Kim, Woo Jin Jung, Seunghyun Won, Jong Bin Bae, Ji Won Han, Ki Woong Kim

First published May 15, 2023, DOI: https://doi.org/10.1212/WNL.0000000000207372

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Abstract

Background and Objectives: Gait changes are potential markers of cognitive disorders (CD). We developed a model for classifying older adults with CD from those with normal cognition using gait speed and variability captured from a wearable inertia sensor and compared its diagnostic performance for CD with that of the model using the Mini-Mental State Examination (MMSE).

Methods: We enrolled community-dwelling older adults with normal gait from the Korean Longitudinal Study on Cognitive Aging and Dementia and measured their gait features using a wearable inertia sensor placed at the center of body mass while they walked on a 14-m long walkway thrice at comfortable paces. We randomly split our entire dataset into the development (80%) and validation (20%) datasets. We developed a model for classifying CD using logistic regression analysis from the development dataset and validated it in the validation dataset. In both datasets, we compared the diagnostic performance of the model with that using the MMSE. We estimated optimal cutoff score of our model using receiver operator characteristics analysis.

Results: In total, 595 participants were enrolled, 101 of them had CD. Our model included both gait speed and temporal gait variability and exhibited good diagnostic performance for classifying CD from normal cognition in both the development (area under the receiver operator characteristic curve [AUC] = 0.788, 95% confidence interval [CI] = 0.748–0.823, p < 0.001) and validation datasets (AUC = 0.811, 95% CI = 0.729–0.877, p < 0.001). Our model showed comparable diagnostic performance for CD to that of the model using the MMSE in both the development (difference in AUC = 0.026, standard error [SE] = 0.043, z statistic = 0.610, p = 0.542) and validation datasets (difference in AUC = 0.070, SE = 0.073, z statistic = 0.956, p = 0.330). The optimal cutoff score of the gait-based model was > -1.56.

Discussion: Our gait-based model using a wearable inertia sensor may be a promising diagnostic marker of CD in older adults.

Classification of Evidence: This study provides Class III evidence that gait analysis can accurately distinguish cognitive disorders from healthy controls in older adults.

• Received August 25, 2022.
• Accepted in final form March 17, 2023.

• © 2023 American Academy of Neurology