Journal article
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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(Preview, Version of record, pdf, 726.7KB, Terms of use)
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- Publisher copy:
- 10.1002/mds.70329
Authors
+ European Research Council
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- Funder identifier:
- https://ror.org/0472cxd90
- Grant:
- 10116958
+ UK Dementia Research Institute
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- Funder identifier:
- https://ror.org/02wedp412
- Grant:
- UK DRI‐5209
+ National Institute for Health and Care Research
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- Funder identifier:
- 10.13039/501100000272
- 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:
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1531-8257
- ISSN:
-
0885-3185
- Language:
-
English
- Source identifiers:
-
4052780
- Deposit date:
-
2026-05-16
- ARK identifier:
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Terms of use
- Copyright date:
- 2026
- Licence:
- CC Attribution (CC BY)
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