A smartphone-based system to quantify dexterity in Parkinson's disease patients
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...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2017-01-01
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| Series: | Informatics in Medicine Unlocked |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352914817300230 |
| _version_ | 1818990683950678016 |
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| author | Somayeh Aghanavesi Dag Nyholm Marina Senek Filip Bergquist Mevludin Memedi |
| author_facet | Somayeh Aghanavesi Dag Nyholm Marina Senek Filip Bergquist Mevludin Memedi |
| author_sort | Somayeh Aghanavesi |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-12-20T19:58:17Z |
| format | Article |
| id | doaj.art-1436fb37ea9d4f608d588dc89060bf8b |
| institution | Directory Open Access Journal |
| issn | 2352-9148 |
| language | English |
| last_indexed | 2024-12-20T19:58:17Z |
| publishDate | 2017-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Informatics in Medicine Unlocked |
| spelling | doaj.art-1436fb37ea9d4f608d588dc89060bf8b2022-12-21T19:28:07ZengElsevierInformatics in Medicine Unlocked2352-91482017-01-019C111710.1016/j.imu.2017.05.005A smartphone-based system to quantify dexterity in Parkinson's disease patientsSomayeh Aghanavesi0Dag Nyholm1Marina Senek2Filip Bergquist3Mevludin Memedi4Computer Engineering, School of Technology and Business Studies, Dalarna University, SwedenDept. of Neuroscience, Neurology, Uppsala University, SwedenDept. of Neuroscience, Neurology, Uppsala University, SwedenDept. of Pharmacology, University of Gothenburg, SwedenComputer Engineering, School of Technology and Business Studies, Dalarna University, SwedenObjectives: 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.http://www.sciencedirect.com/science/article/pii/S2352914817300230Parkinson's diseaseMotor assessmentSpiral testsTapping testsSmartphoneDyskinesiaBradykinesiaObjective measuresTelemedicine |
| spellingShingle | Somayeh Aghanavesi Dag Nyholm Marina Senek Filip Bergquist Mevludin Memedi A smartphone-based system to quantify dexterity in Parkinson's disease patients Informatics in Medicine Unlocked Parkinson's disease Motor assessment Spiral tests Tapping tests Smartphone Dyskinesia Bradykinesia Objective measures Telemedicine |
| title | A smartphone-based system to quantify dexterity in Parkinson's disease patients |
| title_full | A smartphone-based system to quantify dexterity in Parkinson's disease patients |
| title_fullStr | A smartphone-based system to quantify dexterity in Parkinson's disease patients |
| title_full_unstemmed | A smartphone-based system to quantify dexterity in Parkinson's disease patients |
| title_short | A smartphone-based system to quantify dexterity in Parkinson's disease patients |
| title_sort | smartphone based system to quantify dexterity in parkinson s disease patients |
| topic | Parkinson's disease Motor assessment Spiral tests Tapping tests Smartphone Dyskinesia Bradykinesia Objective measures Telemedicine |
| url | http://www.sciencedirect.com/science/article/pii/S2352914817300230 |
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