Objective: To objectively evaluate gait asymmetry and asymmetry index as a measure of gait in a sample of patients with PD compared to a control group.

Background: Gait asymmetry is a crucial feature for patients with Parkinson disease (PD) because it is related to a loss of functional independence that is one of the most important indicators of quality of life. Gait speed, stance time and postural instability are associated with falls. Gait impairments in PD affect functional mobility that requires changes in balance and gait strategies.

Methods: Fifteen PD patients and 15 age and sex matched healthy subjects were recruited (Table 1.). Lower limbs spatiotemporal parameters were acquired by using eMotion capture system (that uses a motion sensing camera). Descriptive analysis was performed for gait variables. Parametric and non-parametric analysis of Gait Asymmetry (GA) and Asymmetry Index (AI) between PD patients and control group was performed. The following are the equations required for the calculation of GA and AI:  GA= {100 X [ln(Vmin/Vmax)]} , AI= Standard deviation _{left & right} / Mean _{left & right}

Results: Analysis of GA and AI were similar in the distribution and the two measures did not appear to distinguish patients with PD. For GA only stance time was significantly different between patients and the control group (Table 2.)

Conclusions: Gait asymmetry has been considered previously as a characteristic of PD gait and now its presence has been associated to enhanced risk of falls. Objective evaluation of gait continues to be a challenge in the daily clinic neurological examination. Portable motion sensing devices may be an alternative for objectively analyzing gait parameters in clinic. We found a significant difference in stance time between PD patients and controls using the GA equation (p=0.0488). Differences in stance time indicate temporal dysregulation of gait, that include compensatory responses for regulating step length, and it is considered a risk factor for falls. We didn’t find other significant differences for the rest of the variables.

References: Lord S, Galna B, Yarnall AJ, Colleman S, Burn D, Rochester L (2016) Predicting first fall in newly diagnosed Parkinson´s disease: insights for a fall naïve cohort. Mov Disord. doi: 10.1002/mds.26742.

Roemmich RT, Nocera JR, Vallabhajosula S, Amano S, Naugle KM, Stegemöller EL, Hass CJ (2012) Spatiotemporal variability during gait initiation in Parkinson´s disease. Gait Posture. doi: 10.1016/j.gaitpost.2012.01.018.

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MDS Abstracts - https://www.mdsabstracts.org/abstract/gait-asymmetry-and-asymmetry-index-in-a-sample-of-patients-with-pd-and-control-group-in-cali-colombia/