Characterizing motor-cognitive associations in Parkinson's disease using digital assessments.

Research overview

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Traditional motor and cognitive assessments for people with Parkinson's disease (PD) have long faced challenges related to accessibility, scalability, and geographical diversity when administered in-person. Remote cognitive testing offers the promise of improved accessibility, reduced participant burden, and greater scalability, particularly for individuals with mobility limitations or limited access to specialized care. While remote cognitive assessments are increasingly used in PD research and clinical settings, it remains unclear whether these measures provide an informative index of PD severity. These gaps limit the interpretability and broader adoption of remote cognitive measures as accessible and scalable indicators of PD severity. To establish a benchmark, we first analyzed a large in-person dataset (PPMI; n = 1,417). We found significant, negative, and small correlations between a cognitive measure (MoCA) scores and two motor measures (MDS-UPDRS III, ρ = -0.17; and H&Y scores, ρ = -0.12). Second, we administered remote assessments to 152 individuals with PD across 60+ geographical locations, including a videoconference-based MoCA (MoCA-VC) and online versions of the MDS-UPDRS III and H&Y. Consistent with in-person findings, we found significant, negative, and small correlations between the MoCA-VC scores and the two motor measures (MDS-UPDRS III, ρ = -0.32; and H&Y scores, ρ = -0.16). Additionally, we examined the decrease in the MoCA score as a function of disease stage. A multivariable regression model demonstrated that each one disease stage increase in H&Y was associated with a 0.65-point decrease in MoCA (in-person settings) and a 1.16-point decrease in MoCA-VC (online settings). Together, these findings demonstrate that a remote, home-based cognitive assessment captures motor-cognitive associations comparable to those observed in large in-person settings, supporting its potential utility as an accessible and scalable cognitive index of PD severity.

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