# Gait-Based Screening for Cognitive Impairment in Older Adults: A Fast and Objective Approach

## TL;DR

This study shows that analyzing walking patterns can quickly and accurately detect cognitive impairment in older adults.

## Contribution

The study introduces new algorithms using gait analysis for rapid and objective cognitive impairment screening.

## Key findings

- Algorithms using stride length and double support time achieved high classification performance in detecting cognitive impairment.
- The best model had an AUC-ROC of 0.91 in training and 0.84 in verification, with high sensitivity and specificity.
- Alternative models also showed acceptable to excellent predictive power for cognitive impairment screening.

## Abstract

Background/Objectives: Cognitive impairment in older adults is a growing public health concern due to global population aging. Early detection is crucial, yet current screening methods are time-consuming and require clinical expertise. Gait analysis has emerged as a promising alternative for cognitive screening. The aim of the study was to identify gait variables associated with cognitive impairment and to develop predictive algorithms capable of distinguishing between cognitively impaired and non-impaired older adults using gold-standard gait analysis. Methods: A cross-sectional study was conducted with 42 adults aged > 60 years. Cognitive function was assessed using the Mini-Mental State Examination (MMSE), and participants were divided into high (MMSE > 24) and low (MMSE ≤ 24) cognitive function groups. Spatiotemporal gait parameters were recorded at participants’ usual pace using the Optogait system. Logistic regression models were developed using half of the sample (training group) and validated in the remaining participants (verification group). Results: Algorithms based on stride length and double support time demonstrated high classification performance. In the training group, the best-performing model achieved an AUC-ROC of 0.91, with a sensitivity of 71.4% and specificity of 92.3%. Validation in the verification group yielded an AUC-ROC of 0.84 and accuracy of 81.0%. Alternative models showed acceptable to excellent predictive power. Conclusions: Gait analysis using gold-standard methods can effectively identify cognitive impairment in older adults. The developed algorithms offer a rapid, objective, and accurate screening alternative with strong potential for clinical application.