Swing time as a predictive variable for Parkinson’s disease

ABSTRACT Currently, Parkinson’s Disease (PD) is diagnosed based only on the clinical observation of a symptom combination, which can lead to late diagnosis, since some individuals have the disease for 5 to 10 years before diagnosis. The aim of this study was to identify temporal kinematic variables of gait, capable of discriminating older adults with or without PD. Forty individuals were divided into two groups: older adults without PD (n=21) and with PD (n=19). Ten consecutive gait cycles were obtained during gait at a preferred speed and then used in data analysis. Discriminative analysis was performed to determine a predictor model of gait changes, characteristic of PD, estimated based on the specificity and sensitivity of each analyzed variable, with temporal kinematic variables. The variable with discriminative value of sensitivity and specificity was swing time, which can be classified as the variable with most predictive potential of PD, and the cut-off point found for this variable was 0.48 seconds. Kinematic gait analysis allows discriminating a group of individuals with PD from a group of healthy individuals, with high sensitivity and specificity, through the swing time, which is lower in the group affected by the disease (cut-off=0.48 seconds).