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A smartphone-based system to quantify dexterity in Parkinson's disease patients
Computer Engineering, School of Technology and Business Studies, Dalarna University, Sweden.
Dept. of Neuroscience, Neurology, Uppsala University, Sweden.
Dept. of Neuroscience, Neurology, Uppsala University, Sweden.
Dept. of Pharmacology, University of Gothenburg, Sweden.
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2017 (English)In: Informatics in Medicine Unlocked, ISSN 2352-9148Article in journal (Refereed) Published
Abstract [en]

Objectives

The aim of this paper is to investigate whether a smartphone-based system can be used to quantify dexterity in Parkinson's disease (PD). More specifically, the aim was to develop data-driven methods to quantify and characterize dexterity in PD.

Methods

Nineteen advanced PD patients and 22 healthy controls participated in a clinical trial in Uppsala, Sweden. The subjects were asked to perform tapping and spiral drawing tests using a smartphone. Patients performed the tests before, and at pre-specified time points after they received 150% of their usual levodopa morning dose. Patients were video recorded and their motor symptoms were assessed by three movement disorder specialists using three Unified PD Rating Scale (UPDRS) motor items from part III, the dyskinesia scoring and the treatment response scale (TRS). The raw tapping and spiral data were processed and analyzed with time series analysis techniques to extract 37 spatiotemporal features. For each of the five scales, separate machine learning models were built and tested by using principal components of the features as predictors and mean ratings of the three specialists as target variables.

Results

There were weak to moderate correlations between smartphone-based scores and mean ratings of UPDRS item #23 (0.52; finger tapping), UPDRS #25 (0.47; rapid alternating movements of hands), UPDRS #31 (0.57; body bradykinesia and hypokinesia), sum of the three UPDRS items (0.46), dyskinesia (0.64), and TRS (0.59). When assessing the test-retest reliability of the scores it was found that, in general, the clinical scores had better test-retest reliability than the smartphone-based scores. Only the smartphone-based predicted scores on the TRS and dyskinesia scales had good repeatability with intra-class correlation coefficients of 0.51 and 0.84, respectively. Clinician-based scores had higher effect sizes than smartphone-based scores indicating a better responsiveness in detecting changes in relation to treatment interventions. However, the first principal component of the 37 features was able to capture changes throughout the levodopa cycle and had trends similar to the clinical TRS and dyskinesia scales. Smartphone-based scores differed significantly between patients and healthy controls.

Conclusions

Quantifying PD motor symptoms via instrumented, dexterity tests employed in a smartphone is feasible and data from such tests can also be used for measuring treatment-related changes in patients.

Place, publisher, year, edition, pages
2017.
Keyword [en]
Parkinson's disease; Motor assessment; Spiral tests; Tapping tests; Smartphone; Dyskinesia; Bradykinesia; Objective measures; Telemedicine
National Category
Computer and Information Science
Research subject
Informatics
Identifiers
URN: urn:nbn:se:oru:diva-57657DOI: 10.1016/j.imu.2017.05.005OAI: oai:DiVA.org:oru-57657DiVA: diva2:1095470
Available from: 2017-05-15 Created: 2017-05-15 Last updated: 2017-05-16Bibliographically approved

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Publisher's full texthttp://www.sciencedirect.com/science/article/pii/S2352914817300230

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Orebro University School of Business, Örebro University, Sweden
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CiteExportLink to record
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Citation style
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