Spatiotemporal Gait Parameters That Predict the Tinetti Performance-Oriented Mobility Assessment in People With Stroke

The purpose of this study was to find which spatiotemporal gait parameters gained from stroke patients could be predictive factors for the gait part of Tinetti Performance-Oriented Mobility Assessment (POMA-G). Two hundred forty-six stroke patients were recruited for this study. They participated in two assessments, the POMA-G and computerized spatiotemporal gait analysis. To analyze the relationship between the POMA-G and spatiotemporal parameters, we used Pearson's correlation coefficients. In addition, multiple linear regression analyses (stepwise method) were used to predict the spatiotemporal gait parameters that correlated most with the POMA-G. The results show that the gait velocity (r=.67, p<.01), cadence (r=.66, p<.01), step length of the affected side (r=.49, p<.01), step length of the non-affected side (r=.53, p<.01), swing percentage of the non-affected side (r=.47, p<.01), and single support percentage of the affected side (r=.53, p<.01) as well as the double support percentage of the non-affected side (r=-.42, p<.01) and the step-length asymmetry (r=-.64, p<.01) correlated with POMA-G. The gait velocity, step-length asymmetry, cadence, and single support percentage of the affected side explained 67%, 2%, 2%, and 1% of the variance in the POMA-G, respectively. In conclusion, gait velocity would be the most predictive factor for the POMA-G.