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Wearable Movement‐Tracking for Prodromal Parkinson's Disease Detection: A Cross‐Country Validation Study

Abstract:
Background: Models trained on accelerometer data have been proposed for detecting prodromal Parkinson's disease (PD). However, uncertainties in diagnosis timing in the UK Biobank (UKBB) may affect generalizability to other cohorts. Objectives: The aim of the study was to evaluate the performance of previously published models for prodromal PD detection in other international cohorts. Methods: We applied the models to data from German and British cohorts of individuals with isolated or idiopathic rapid eye movement sleep behavior disorder and healthy controls. We compared hourly acceleration patterns and classification performance across cohorts. Results: The British cohort exhibited visually similar activity patterns to UK Biobank but weaker statistical differences and reduced model performance. The German cohort showed no significant group differences and lower performance. No pair of cohorts demonstrated statistical equivalence. Conclusions: Models trained on UK Biobank data may capture early clinical disease rather than universal prodromal markers. Prospective validation in well‐characterized cohorts is essential before clinical translation. © 2026 The Author(s). Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.
Publication status:
Published
Peer review status:
Peer reviewed

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Publisher copy:
10.1002/mds.70329

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Role:
Author
ORCID:
0009-0003-2741-2611
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Institution:
University of Oxford
Role:
Author
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Role:
Author
ORCID:
0000-0001-5858-7762


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Funder identifier:
https://ror.org/0472cxd90
Grant:
10116958
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Funder identifier:
https://ror.org/02wedp412
Grant:
UK DRI‐5209


Publisher:
Wiley
Journal:
Movement Disorders More from this journal
Article number:
mds.70329
Publication date:
2026-05-15
Acceptance date:
2026-03-30
DOI:
EISSN:
1531-8257
ISSN:
0885-3185


Language:
English
Source identifiers:
4052780
Deposit date:
2026-05-16
ARK identifier:
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