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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 1873-5126, Vol. 64, p. 112-117Article in journal (Refereed) Published
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. Vol. 64, p. 112-117
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.2019.03.022ISI: 000487567800016PubMedID: 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: 2019-10-18Bibliographically approved

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Thomas, IliasWestin, Jerker

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