ArticleNPJ digital medicine2026
Accessible assessment of motor and cognitive symptoms in Parkinson's disease: integrating large datasets, machine-learning, and videoconferencing.
Article in NPJ digital medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
2 citing papers in PubMed.
- Integrating remote testing and machine learning to identify markers of cerebellar ataxia at home.Communications medicine · 2026Article
- Characterizing motor-cognitive associations in Parkinson's disease using digital assessments.Frontiers in aging neuroscience · 2026Article
Corrections and comments
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Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In-person motor and cognitive assessments for Parkinson's disease(PD) face accessibility, scalability, and geographical diversity challenges. We aimed to address these by integrating large datasets, machine learning (ML), and videoconferencing. We developed the Motor and Cognitive Videoconference(MaC-VC) protocol, allowing non-experts to remotely administer the MDS-UPDRS III and MoCA tests. In this cross-sectional study, we administered MaC-VC to 145 participants from 60+ geographical locations and compared the results with a large (n = 1264), expert-rated, in-person assessments from the PPMI dataset. The abridged, online-feasible MDS-UPDRS III accounted for 95% of the variance in complete MDS-UPDRS III scores. When comparing early-versus-advanced PD in each dataset (In-Person/Online), we observed consistent significant trends in four measures: MDS-UPDRS-III, MoCA, disease-duration, and sex. Using a bidirectional cross-dataset-validation technique, ML classifiers yielded high classification performance both within-dataset and between-datasets(AUCs>0.9), demonstrating predictive power across diverse populations. These findings support the feasibility and generalizability of MaC-VC, paving the path for accessibility, scalability, and geographical diversity in PD assessments.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.