Cognitive-Motor Processes of Volitional Stepping

About This Grant

Falls are the leading cause of injury in older adults, leading to costly injury, high stress on the healthcare system, reduced quality of life and fatality. With a rapidly aging population, fall rates are growing exponentially. Thus, there is a critical need for high-efficacy fall prevention interventions. Among various strategies, volitional stepping-based exercise interventions are promising due to their low-cost, accessibility, and fall rate reduction. Despite the demonstrated benefits of volitional step training in reducing fall risk and improving cognitive and motor functions, not all older adults benefit uniformly. This variability is linked to heterogeneity in the age-ability and task complexity of these training paradigms. Regardless of an individual’s age or the complexity of the stepping task, effective stepping relies on cognitive-motor processes. Currently, there is a critical gap in the knowledge of these processes in the context of effective stepping. This is limiting our ability to optimize fall prevention interventions to greater efficacy rates. Today, this gap can be overcome with innovations in mobile electroencephalography (EEG). Thus, this study will quantify the cognitive-motor processes of effective stepping across age groups (younger and older adults) and task complexity (simple and complex cues). This knowledge will provide essential data to guide the optimization of volitional step training design parameters, making them population-specific and more effective in reducing fall rates. We will recruit 30 healthy younger and 30 healthy older adults to complete a clinically validated step training paradigm. This will involve volitional stepping in response to visual cues while standing in place, with electrocortical activity recorded via high- density mobile EEG and stepping performance monitored via motion capture. Our first aim is to characterize the electrocortical activity of effective stepping by age group and task complexity. This will provide the fundamental knowledge to aid the optimization of intervention design parameters to elicit necessary cognitive- motor processes for training benefits across diverse age populations. Our second aim is to discover new biomarkers for fall prevention in older adults via cognitive-motor processes of effective stepping. Identifying individual cognitive-motor processes of effective stepping has the potential to provide objective biomarkers of fall risk and forecast individual benefits from step training interventions. Success in these aims will lead to transformative knowledge for fall prevention research and clinical practices for older adults.