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Evaluation of a sensor algorithm for motor state rating in Parkinson's disease
Dalarna University, School of Technology and Business Studies, Microdata Analysis.
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Number of Authors: 122019 (English)In: Parkinsonism & Related Disorders, ISSN 1353-8020, E-ISSN 1873-5126Article in journal (Refereed) Epub ahead of print
Abstract [en]

INTRODUCTION: A treatment response objective index (TRIS) was previously developed based on sensor data from pronation-supination tests. This study aimed to examine the performance of TRIS for medication effects in a new population sample with Parkinson's disease (PD) and its usefulness for constructing individual dose-response models.

METHODS: Twenty-five patients with PD performed a series of tasks throughout a levodopa challenge while wearing sensors. TRIS was used to determine motor changes in pronation-supination tests following a single levodopa dose, and was compared to clinical ratings including the Treatment Response Scale (TRS) and six sub-items of the UPDRS part III.

RESULTS: As expected, correlations between TRIS and clinical ratings were lower in the new population than in the initial study. TRIS was still significantly correlated to TRS (rs = 0.23, P < 0.001) with a root mean square error (RMSE) of 1.33. For the patients (n = 17) with a good levodopa response and clear motor fluctuations, a stronger correlation was found (rs = 0.38, RMSE = 1.29, P < 0.001). The mean TRIS increased significantly when patients went from the practically defined off to their best on state (P = 0.024). Individual dose-response models could be fitted for more participants when TRIS was used for modelling than when TRS ratings were used.

CONCLUSION: The objective sensor index shows promise for constructing individual dose-response models, but further evaluations and retraining of the TRIS algorithm are desirable to improve its performance and to ensure its clinical effectiveness.

Place, publisher, year, edition, pages
2019.
Keywords [en]
Independent evaluation, Levodopa challenge test, Machine learning algorithms, Parkinson's disease, Wearable sensors
National Category
Other Medical Engineering
Research subject
Complex Systems – Microdata Analysis; Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-29857DOI: 10.1016/j.parkreldis.2019.03.022PubMedID: 30935826Scopus ID: 2-s2.0-85063430752OAI: oai:DiVA.org:du-29857DiVA, id: diva2:1302557
Available from: 2019-04-05 Created: 2019-04-05 Last updated: 2019-04-08Bibliographically approved

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Thomas, IliasWestin, Jerker

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