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

Area of Science:

• Neurology and Cognitive Science
• Biomedical Data Science
• Gerontology and Aging Research

Background:

• Traditional Parkinson's disease (PD) assessments face accessibility and scalability issues.
• Remote cognitive testing presents a solution for improved accessibility and reduced participant burden.
• The utility of remote cognitive measures for assessing PD severity remains under-explored.

Purpose of the Study:

• To determine if remote cognitive assessments can serve as informative indicators of PD severity.
• To compare motor-cognitive associations from remote assessments with traditional in-person findings.
• To establish a benchmark for the interpretability and adoption of remote cognitive measures in PD.

Main Methods:

• Analyzed a large in-person dataset (PPMI, n=1,417) correlating cognitive (MoCA) and motor (MDS-UPDRS III, H&Y) scores.
• Administered remote assessments (videoconference MoCA-VC, online MDS-UPDRS III, H&Y) to 152 individuals with PD across diverse locations.
• Utilized multivariable regression to examine the relationship between disease stage (H&Y) and cognitive scores (MoCA, MoCA-VC).

Main Results:

• Small, negative correlations were found between MoCA and motor scores in both in-person (ρ=-0.17 to -0.12) and remote (ρ=-0.32 to -0.16) settings.
• Each one-stage increase in H&Y was associated with a 0.65-point MoCA decrease (in-person) and a 1.16-point MoCA-VC decrease (remote).
• Remote assessments demonstrated motor-cognitive associations comparable to in-person evaluations.

Conclusions:

• Remote, home-based cognitive assessments effectively capture motor-cognitive relationships in Parkinson's disease.
• These findings support the use of remote cognitive measures as accessible and scalable indicators of PD severity.
• Remote testing holds significant potential for broader adoption in PD research and clinical practice.