Dual-task walking for early detection of Alzheimer’s disease: comparative analysis of tasks using whole-body gait variables

The worldwide rise in dementia creates an urgent need for screening methods that are both sensitive and easy to administer. Dual-task walking—requiring people to walk while performing a second cognitive or motor task—meets these criteria because it stresses gait and cognition simultaneously, revealing deficits that emerge early in Alzheimer’s disease and Mild Cognitive Impairment (MCI). Although recent studies have explored integrating various gait variables from dual-task assessment with classification models, there remains uncertainty regarding the effective gait variables for inclusion in these models and the selection of the most effective tasks. This study aims to investigate whether incorporating gait variables derived from whole-body movement characteristics improves the performance of classification models and to identify the most effective tasks for inclusion in these models.

We analyzed data from 36 participants, including 18 cognitively normal individuals and 18 with MCI. Using motion capture technology, gait variables encompassing whole-body movements, including upper body dynamics, were recorded under both normal walking conditions and during dual-task performance. The dual tasks included: (1) Subtracting threes from a given number, (2) Carrying a cup on a tray without moving it, (3) Holding a cup filled with water without spilling it, and (4) Answering verbal questions. Classification models utilized were k-nearest neighbors, random forest, and support vector machines, with performance evaluated by the area under the curve (AUC).

First, we observed that variables related to upper-body motion (i.e., Anterior–Posterior and Medial–Lateral sway) while walking played an important role in the classification models for detecting MCI, particularly during cognitively demanding tasks (subtracting numbers and answering verbal questions) while walking. Second, the tasks carrying a cup on a tray and holding a cup filled with water while walking yielded superior classification model performance to other tasks especially in considering multiple features (AUC = 0.79).

This study underscores the benefits of incorporating gait variables of the upper body to enhance the performance of classification models for MCI detection. These insights could contribute to the development of more precise and practical screening tools for MCI.