Longitudinal assessment of gait variability to predict falls in Parkinson's disease

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Project Summary The broad goal of this research project is to improve the prediction of falls in patients with Parkinson’s disease (PD) through a comprehensive multidisciplinary approach that includes longitudinal measurements of walking, cognitive and functional performances. PD is a life-changing disorders affecting one million Americans, with more than 60,000 new cases reported every year. Within 3 years of diagnosis, more than 85% of people with clinically probable PD develop gait problems, which in turn lead to falls resulting in serious injury and reduced quality of life. There is a pressing need to identify fall risk factors before the occurrence of the first fall, and to better understand behavioral and cognitive changes leading to falls in PD patients. Gait variability during steady-state walking gives insights into the ability (and inability) of the central neuromuscular system to sustain stable but adaptive locomotion. Gait variability is related to PD severity, to the degree of functional impairments, and can retrospectively distinguish PD fallers and non-fallers. However, the evolution of gait variability with PD progression and the relationship between (the magnitude and the ordering of) stride-to-stride fluctuations and future falls in PD are unclear. In addition, large individual variations exist at the level of gait performances corresponding to specific levels of disease severity. Deficits in cognitive and sensory-motor functions associated with PD also impair the ability to walk while doing another task (i.e., dual-tasking). When attention resources in PD patients are allocated to more than one task, gait abnormalities increase. This suggests that gait variability during cognitive dual-task may present a higher sensitivity to predict future falls in PD patients compared to regular, single-task walking. The potential of gait variability to predict future falls in PD patients has been suggested, but prospective evidence is lacking to identify how individual changes in gait variability correspond to changes in cognition and functional performances, and to future falls. Our central hypothesis is that PD-related cognitive dysfunctions affecting gait variability during dual-task walking will predict future falls and near falls in PD patients. Our specific aims are 1) to determine the effects of an attention-demanding task (i.e., phoneme monitoring) on the temporal ordering of gait variability in PD patients and age-matched controls, 2) to characterize within-participant changes of gait variability over six-month intervals and their relationship to changes in cognition and functional performance, and 3) to predict near falls, falls and mobility impairments occurring during a one-year follow-up period in PD patients based on baseline gait variability. The longitudinal design of this study will consist in collecting gait variability during single and dual-task over-ground walking every six months, in PD patients and controls, and to collect information about falls and near falls. This project will improve the objective assessment of fall risk in PD patients using gait parameters during cognitively challenging conditions, similar to those experienced in patients’ daily lives.