Development and External Validation of a Gait Test Based Diagnostic Model for Detecting Mild Cognitive Impairment

To address the lack of large-scale screening tools for mild cognitive impairment (MCI), this study aimed to assess the discriminatory ability of several gait tests for MCI and develop a screening tool based on gait test for MCI.

A diagnostic case-control test.

The general community.

We recruited 134 older adults (≥65 years) for the derivation sample, comprising -69 individuals in the cognitively normal group and -65 in the MCI group (N=134). An additional 70 participants were enrolled for the validation sample.

All participants completed gait tests consisting of a single task (ST) and 3 dual tasks (DTs): counting backwards, serial subtractions 7, and naming animals.

Binary logistic regression analyses were used to develop models, and the efficacy of each model was assessed using receiver operating characteristic (ROC) curve and area under the curve (AUC). The best effective model was the final diagnostic model and validated using ROC curve and calibration curve.

The DT gait test incorporating serial subtractions 7 as the cognitive task demonstrated the highest efficacy with the AUC of 0.906 and the accuracy of 0.831 in detecting MCI with "years of education" being adjusted. Furthermore, the model exhibited consistent performance across different age and sex groups. In external validation, the model displayed robust discrimination (AUC=0.913) and calibration (calibrated intercept=-0.062, slope=1.039).

The DT gait test incorporating serial subtractions 7 as the cognitive task demonstrated robust discriminate ability for MCI. This test holds the potential to serve as a large-scale screening tool for MCI, aids in the early detection and intervention of cognitive impairment in older adults.