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Abstract
Background and Objectives Gait changes are potential markers of cognitive disorders (CDs). We developed a model for classifying older adults with CD from those with normal cognition using gait speed and variability captured from a wearable inertial 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 inertial 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 characteristic analysis.
Results In total, 595 participants were enrolled, of which 101 of them experienced 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% 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 with 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 inertial 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 older adults with CDs from healthy controls.
Glossary
- AD=
- Alzheimer disease;
- AUC=
- area under the curve;
- CD=
- cognitive disorder;
- CERAD=
- Consortium to Establish a Registry for Alzheimer Disease;
- CN=
- cognitively normal;
- CoM=
- center of body mass;
- CoV=
- coefficient of variance;
- DLB=
- dementia with Lewy bodies;
- KLOSCAD=
- Korean Longitudinal Study on Cognitive Aging and Dementia;
- MCI=
- mild cognitive impairment;
- MMSE=
- Mini-Mental State Examination;
- PD=
- Parkinson disease;
- SE=
- standard error;
- VaD=
- vascular dementia
Footnotes
Go to Neurology.org/N for full disclosures. Funding information and disclosures deemed relevant by the authors, if any, are provided at the end of the article.
Submitted and externally peer reviewed. The handling editor was Associate Editor Peter Hedera, MD, PhD.
Editorial, page 10
Class of Evidence: NPub.org/coe
- Received August 25, 2022.
- Accepted in final form March 17, 2023.
- © 2023 American Academy of Neurology
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