Validation of a Smartphone Application Measuring Motor Function in Parkinson's Disease
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Background: Measurement of motor function is critical to the assessment and management of Parkinson's disease. Ambulatory motor assessment has the potential to provide a glimpse of the patient's clinical state beyond the consultation. We custom-designed a smartphone application that quantitatively measures hand dexterity and hypothesized that this can give an indication of a patient's overall motor function.
Objective: The aims of this study were to (i) validate this smartphone application against MDS-UPDRS motor assessment (MDS-UPDRS-III) and the two-target tapping test; (ii) generate a prediction model for MDS-UPDRS-III; (iii) assess repeatability of our smartphone application and (iv) examine compliance and user-satisfaction of this application.
Methods: 103 patients with Parkinson's disease were recruited from two movement disorders clinics. After initial assessment, a group of patients underwent repeat assessment within two weeks. Patients were invited to use the smartphone application at home over three days, followed by a survey to assess their experience.
Results: Significant correlation between key smartphone application test parameters and MDS-UPDRS-III (r = 0.281-0.608, p < 0.0001) was demonstrated. A prediction model based on these parameters accounted for 52.3% of variation in MDS-UPDRS-III (R2 = 0.523, F(4,93) = 25.48, p < 0.0001). Forty-eight patients underwent repeat assessment under identical clinical conditions. Repeatability of key smartphone application tests parameters and predicted MDS-UPDRS-III was moderate to strong (intraclass correlation coefficient 0.584-0.763, p < 0.0001). The follow-up survey identified that our patients were very comfortable with the smartphone application and mobile technology.
Conclusions: Our smartphone application demonstrated satisfactory repeatability and validity when measured against MDS-UPDRS-III. Its performance is acceptable considering our smartphone application measures hand dexterity only.
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Zhang Y, Zeng Z, Mirian M, Yen K, Park K, Doo M Sci Rep. 2024; 14(1):5307.
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Austin D, Dixon M, Tulimieri D, Cashaback J, Semrau J J Neuroeng Rehabil. 2023; 20(1):114.
PMID: 37658432 PMC: 10474703. DOI: 10.1186/s12984-023-01240-6.
Broeder S, Roussos G, De Vleeschhauwer J, DCruz N, Orban de Xivry J, Nieuwboer A J Neural Transm (Vienna). 2023; 130(7):937-947.
PMID: 37268772 PMC: 10237522. DOI: 10.1007/s00702-023-02659-w.
Deep Clinical Phenotyping of Parkinson's Disease: Towards a New Era of Research and Clinical Care.
Xu Z, Shen B, Tang Y, Wu J, Wang J Phenomics. 2023; 2(5):349-361.
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