Student Scholar Symposium Abstracts and Posters

Document Type

Poster

Publication Date

Spring 5-6-2026

Faculty Advisor(s)

Rahul Soangra

Abstract

Falls are a common and serious complication in post-stroke patients, with 37–73% of individuals falling within the first 6 months post-stroke and up to 73% within the first year (Verheyden et al., 2013). This study is part of a broader investigation examining whether gait characteristics and impaired cognitive function can predict fall risk when used alongside artificial intelligence. Prior to strength testing and gait analysis, multiple surveys were administered to assess the participants' cognitive function, including the Montreal Cognitive Assessment (MoCA). The MoCA is a cognitive screening tool developed to detect mild cognitive impairment and assess multiple cognitive domains, including memory, language, executive function, visuospatial skills, calculation, abstraction, attention, concentration, and orientation (Nasreddine et al., 2005). This study aimed to assess the cognitive function, as measured by MoCA, in stroke survivors. The average MoCA score across all study participants was 24.7 ± 5.5 (n = 17), which falls below the commonly used cutoff of 26/30 and suggests cognitive impairment in this population. These findings are consistent with prior research showing lower MoCA scores in stroke populations compared to healthy individuals, indicating reduced cognitive performance following stroke. However, recent studies suggest that lower cutoffs than the standard may improve the accuracy of detecting cognitive impairment in stroke populations (Salvadori et al., 2022). The small sample size limits the generalizability of these findings. In the future, we will collect additional data to strengthen our hypothesis.

Comments

Presented at the Spring 2026 Student Scholar Symposium at Chapman University.

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