Potential indicators of cognitive problems include alterations in gait. Using gait speed and variability data from a wearable inertia sensor, Jeongbin Park and colleagues created a model for separating older adults with CD from those with normal cognition. They then compared the model's CD diagnostic performance to that of the model using the Mini-Mental State Examination (MMSE).
In order to measure the gait characteristics of community-dwelling older adults with normal gait, researchers recruited them from the Korean Longitudinal Study on Cognitive Ageing and Dementia. They walked on a 14-m-long walkway three times at comfortable paces while wearing a wearable inertia sensor placed at the center of their body mass. The dataset was divided at random into the validation (20%) and development (80%) datasets. Using logistic regression analysis on the development dataset, a model was created for categorizing CD, and it was then verified on the validation dataset. The model's diagnostic performance was compared to that of the MMSE in both datasets. Utilizing receiver operator characteristics research, we derived the optimal cutoff score for our model.
The key findings of this study were:
1. There were 595 participants altogether, and 101 of them had CD.
2. In both the development and validation datasets, this model distinguished CD from normal cognition with high diagnostic performance using both gait speed and temporal gait variability.
3. In both the development and validation datasets, it demonstrated equivalent diagnostic performance for CD to that of the model utilizing the MMSE.
4. The gait-based model's ideal cutoff score was > -1.56.
Reference:
Park, J., Lee, H. J., Park, J. S., Kim, C. H., Jung, W. J., Won, S., Bae, J. B., Han, J. W., & Kim, K. W. (2023). Development of a Gait Feature–Based Model for Classifying Cognitive Disorders Using a Single Wearable Inertial Sensor. In Neurology (p. 10.1212/WNL.0000000000207372). Ovid Technologies (Wolters Kluwer Health). https://doi.org/10.1212/wnl.0000000000207372
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