Faculty Advisor

Dawson, Nicole

Publication Date

Spring 2018


Background and Purpose: Falls amongst older adults are a significant health concern, often resulting in injury. Prior research on falls has largely focused on analysis of gait parameters during comfortable walking speed, with inconsistent findings. This study examined data on gait parameters in fast gait speed in non- or single-fallers (falls1). The purpose of this study was to determine if increased variability in step length, step time, or double limb support time during fast gait speed correlates with recurrent falls.

Methods: This was a retrospective analysis on data collected on community-dwelling older adults. 59 participants were included (men=19). Inclusion criteria included age greater than sixty and ability to complete testing protocol. Exclusion criteria included neurological disorders, Mini Mental State Examination (MMSE) score of less than 24 out of 30, use of psychoactive medications, or significant visual impairment. Fall history was determined via questionnaire. Gait was analyzed using the GAITRite® system to collect data on variability. Participants were instructed to walk at a fast speed. Multivariate correlation was performed to analyze the relationship between gait variability and recurrent fallers. Logistic regression was utilized to analyze potential variables that may be predictive of recurrent falls while also controlling for toe in and out angle, sex, height, and weight.

Results and Discussion: Recurrent fallers demonstrated a significant positive association with variability in double limb support time (r=0.378, p < 0.003) and step time ( r=0.320, p=0.013). There was no significant association between variability and step length. Logistic regression analysis revealed that double support time variability was no longer significant (p

Conclusion: Increased double limb support time and step time variability in fast gait speed significantly correlate with recurrent falls. However, they were no longer significant when controlling for other variables. Results of logistic regression reveal that it is a combination of factors that likely contribute to falls.

Access Status

UCF Only