uu.seUppsala University Publications
Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Evaluation of a sensor algorithm for motor state rating in Parkinson's disease
Univ Gothenburg, Sahlgrenska Acad, Inst Neurosci & Physiol, Dept Clin Neurosci, Gothenburg, Sweden.
Dalarna Univ, Dept Microdata Anal, Falun, Sweden.
RISE Acreo, Gothenburg, Sweden;Karolinska Inst, Dept Clin Neurosci, Neurol, Stockholm, Sweden.
Show others and affiliations
2019 (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 (r(s) = 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 (r(s) = 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
ELSEVIER SCI LTD , 2019. Vol. 64, p. 112-117
Keywords [en]
Levodopa challenge test, Independent evaluation, Wearable sensors, Parkinson's disease, Machine learning algorithms
National Category
Neurology Other Medical Engineering
Identifiers
URN: urn:nbn:se:uu:diva-395925DOI: 10.1016/j.parkreldis.2019.03.022ISI: 000487567800016PubMedID: 30935826OAI: oai:DiVA.org:uu-395925DiVA, id: diva2:1366653
Funder
Swedish Foundation for Strategic Research , SBE 13-0086Vinnova, 2014-03727Available from: 2019-10-30 Created: 2019-10-30 Last updated: 2019-10-30Bibliographically approved

Open Access in DiVA

No full text in DiVA

Other links

Publisher's full textPubMed

Authority records BETA

Medvedev, AlexanderNyholm, DagSenek, Marina

Search in DiVA

By author/editor
Medvedev, AlexanderNyholm, DagSenek, MarinaWestin, Jerker
By organisation
Automatic controlDivision of Systems and ControlLandtblom: NeurologyDepartment of Neuroscience
In the same journal
Parkinsonism & Related Disorders
NeurologyOther Medical Engineering

Search outside of DiVA

GoogleGoogle Scholar

doi
pubmed
urn-nbn

Altmetric score

doi
pubmed
urn-nbn
Total: 9 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf