Preliminary Validation of an Objective Fall-Risk Assessment for Individuals with Stroke

Abstract

Stroke is one of the leading causes of motor deficits in adults, resulting in gait and balance impairments. Stroke-related deficits such as muscle weakness, sensory loss, reduced attention, and visual-spatial awareness deficits, contribute to gait and balance dysfunction and falls. Falls are a significant health concern for individuals with stroke. In order to develop effective strategies to prevent falls, it is important to be able to objectively assess fall-risk. The objective of this preliminary investigation was to evaluate the validity of a quantitative robotic assessment (Silver Index) to detect the risk of falls in individuals with stroke. Ten individuals with chronic stroke performed the fall-risk assessment on a commercial robotic platform for balance assessment and training. The fall-risk assessment considers multiple components associated with falls, including quantitative biomechanical factors and the participants' clinical information to predict the risk of falls. It relies on a machine learning approach to compute a composite score. The validity of Silver Index was assessed by its relationship with functional balance and gait (Berg balance scale, timed up and go, and dynamic gait index) outcomes and posturography outcomes (center of pressure, trunk range of motion, and limits of stability) that are associated with the fall-risk. Preliminary evidence shows its correlation with Berg balance scale and posturography outcomes, thus supporting the construct validity of Silver Index.Clinical Relevance - Preliminary study to show evidence for the validity of Silver Index in stroke for risk of falls.