Grant: Travel Awards - Institute Community Support

Introduction: Parkinson's disease (PD) is an incurable neurodegenerative disorder that affects 5 out of 1000 Canadians over 65. Current conventional clinical scales (CCS) used to monitor PD progression and estimate its severity lack sensitivity. As a more direct and practical substitute, the adoption of quantitative gait analysis (QGA) has been proposed. However, the complex interpretation of multiple QGA features (i.e., step length, step width, etc) has highlighted the need for a better definition of gait features representative of the PD gait pattern. To address the poor interpretability of QGA, Galna et al.'s (2013, Mov. Dis.) model emerged as the first holistic attempt summarizing PD gait into 16 features. Despite its validation, no study has yet explored the capacity of Galna et al.'s model features to serve as predictors of PD severity and progression. Question: Do gait features derived from Galna et al.'s model predict disease severity and progression in elderly people with PD (PwP) ? Method: An individual patient data meta-analysis will be performed, compiling QGA and CCS (including baseline and up to 6 years of follow-up) data from 17 studies (953 PwP and 336 controls). The analysis will identify specific features from Galna et al.'s model that: (1) discriminate PwP from controls; (2) predict CCS scores (indicating severity); (3) predict CCS score changes (indicating progression). Expected Results: It is anticipated that all features from Galna et al.'s model will effectively differentiate controls from PwP and significantly predict both CCS scores and changes in CCS scores. Potential Impact: By identifying gait features capable of predicting PD severity and progression, this project has the potential to establish gait analyses as reliable alternatives to the limitations of CCS. Ultimately, this project paves the way for significant improvements in monitoring PwP, thereby promoting the health and well-being of elderly Canadians affected by this disease.