Gait and balance characteristics after a non-cerebellar stroke

UNCG Author/Contributor (non-UNCG co-authors, if there are any, appear on document)
Ruth D. Stout (Creator)
The University of North Carolina at Greensboro (UNCG )
Web Site:
Christopher Rhea

Abstract: One of the most common neurological injuries in the elderly is a stroke event, affecting nearly 800,000 adults in the U.S. alone every year. Since falls occur at a rate of 73% per year with people who are more than six months past the stroke event compared to approximately 30% with aged matched healthy, the potential consequences for injury are devastating. Current literature does not completely address the specific deficits in gait and balance after a stroke. To resolve this problem, the purpose of this thesis was to compare gait mechanics to clinical tests that indicate fall risks in 20 healthy elderly adults (63.4±8.9) and 7 non-cerebellar/non-brain stem post-stroke survivors (57.6±7.7). The dependent variables for gait were step width, step length, stride length, step time and stride time. The metrics of mean, standard deviation (SD), coefficient of variation (CoV), detrended fluctuation analysis alpha (DFA α) and sample entropy (SampEn) were calculated for each dependent variable. Further, the Timed Up and Go (TUG), Berg balance assessment (Berg), Functional Gait Assessment (FGA), Activities-Specific Balance Confidence Scale (ABC), lower extremity strength, and lower extremity flexibility were all taken as clinical assessments. The data showed that most dependent variables for mean, SD, and CoV were different between groups, whereas DFA α and SampEn generally were not. The TUG, Berg, FGA, and ABC showed group differences. No differences in strength or flexibility were observed between the unaffected limbs of the stroke survivor group and matched limbs of the healthy elderly group. However, significant differences were observed in strength and flexibility between the affected and matched limbs between groups. Sixty-four out of a possible 200 correlations between the gait and clinical metrics were significant. These data suggest that summary metrics (mean, SD, and CoV) may be the strongest indicators of gait dysfunction after a stroke.

Additional Information

Language: English
Date: 2014
Balance testing, Clinical correlations, Detrended Fluctuation Analysis (DFA), Fall risk, Healthy elderly, Stroke
Cerebrovascular disease $x Patients $x Rehabilitation
Gait in humans
Gait disorders in old age
Equilibrium (Physiology)
Falls (Accidents) in old age $x Prevention
Falls (Accidents) in old age $x Risk factors

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