Remote Motor and Cognitive Assessment in Parkinson’s Disease Using MaC-VC Protocol

Overview

The study demonstrates the feasibility and accuracy of the Motor and Cognitive Videoconference (MaC-VC) protocol for remote assessment of Parkinson’s disease (PD). Utilizing large datasets and machine learning, MaC-VC reliably replicates in-person motor and cognitive evaluations across diverse geographic locations.

Background

Parkinson’s disease assessment traditionally relies on in-person motor and cognitive testing, which faces challenges related to accessibility, scalability, and geographic diversity. The Movement Disorder Society Unified Parkinson’s Disease Rating Scale (MDS-UPDRS III) and Montreal Cognitive Assessment (MoCA) are standard tools for evaluating motor and cognitive function in PD. Remote assessment methods leveraging telehealth and machine learning have the potential to overcome these barriers, enabling broader and more frequent patient monitoring.

Data Highlights

Key Findings

• The abridged MDS-UPDRS III used in MaC-VC accounts for 95% of the variance in full in-person scores, supporting its validity.
• Consistent significant trends were observed between early and advanced PD stages across both datasets in MDS-UPDRS III, MoCA scores, disease duration, and sex distribution.
• Machine learning classifiers trained on either dataset achieved high classification accuracy (AUC > 0.9) within and across datasets, demonstrating robust predictive power.
• MaC-VC protocol enables remote administration of motor and cognitive assessments by non-experts via videoconferencing.
• The study included participants from over 60 geographical locations, highlighting the protocol’s scalability and accessibility.

Clinical Implications

The MaC-VC protocol offers a validated, scalable approach to remotely assess motor and cognitive functions in Parkinson’s disease, potentially increasing patient access to regular monitoring regardless of location. Integration of machine learning enhances diagnostic accuracy and supports clinical decision-making. This approach may reduce barriers related to travel and specialist availability, facilitating timely interventions.

Conclusion

The MaC-VC protocol effectively replicates expert in-person assessments of Parkinson’s disease motor and cognitive symptoms remotely, supported by robust machine learning validation. This innovation paves the way for accessible, scalable, and geographically diverse PD patient evaluations.

References

1. Binoy et al. 2024 -- Online cognitive testing in Parkinson’s disease: advantages and challenges
2. Goetz et al. 2008 -- Movement disorder society-sponsored revision of the Unified Parkinson’s disease rating scale (MDS-UPDRS)
3. Nasreddine et al. 2005 -- The Montreal Cognitive Assessment, MoCA: a brief screening tool for mild cognitive impairment
4. Hewitt et al. 2020 -- Transitioning to telehealth neuropsychology service