Comparison of Gait Parameters Collected Across Two Commercially Available Gait Systems in Older Adults

Background/Objectives: Research examining mobility in older adults often utilizes spatiotemporal gait parameters obtained from computerized walkway systems like GAITRite (Franklin, NY, USA) and Zenometrics, LLC (Peekskill, NY, USA). However, such parameters can vary across these widely used software applications due to differences in algorithms and post-processing techniques, making it potentially unsuitable to pool parameters acquired from different applications. We aim to determine whether gait parameters obtained from GAITRite and processed using two software systems can be reliably pooled for use in studies with older adults. Methods: Baseline gait data from 193 older adults aged 64–94 years old were examined. The gait protocol consisted of normal walking (NW) and walk while talking (WWT) conditions in which participants were instructed to walk on computerized walkways containing embedded pressure sensors. The resulting walks were processed using both software applications to attain eight gait parameters recorded from the right foot (velocity, stride length, double support percentage, cadence, swing time, stance time, stride length standard deviation, and swing time standard deviation). Linear regressions adjusted for age and gender between GAITRite (version 4.7) and their respective PKMAS (ProtoKinetics Movement Analysis Software version 6.0; Zenometrics, LLC.) gait variables were run to determine agreement between variables across the two pieces of software. Results: Adjusted linear regression models revealed that gait parameters across software systems were significantly correlated in both the NW (β ranged from 0.87 to 1.02, p ≤ 0.01) and WWT conditions (β ranged from 0.94 to 1.01, p ≤ 0.01). Swing time variability in the NW condition showed a lower correlation (β = 0.87). Furthermore, intercepts for all parameters except for the double support percentage in the NW condition (intercept = 2.63, p ≤ 0.01) and WWT condition (intercept = 2.75, p = 0.02) and stance time in the WWT condition (intercept = 0.05, p = 0.04) were not significantly different from 0. Conclusions: The results provide support that commonly examined gait parameters from GAITRite and PKMAS can be pooled and analyzed for use in studies with older adults. However, caution should be taken when pooling swing time variability and double support percentage data.