S Arora1, V Venkataraman2, A Zhan3, S Donohue3, K M Biglan4, E R Dorsey5, M A Little6. 1. Nonlinearity and Complexity Research Group, Aston University, Birmingham, United Kingdom; Somerville College, University of Oxford, Oxford, United Kingdom. 2. Duke University School of Medicine, Durham, NC, United States. 3. Johns Hopkins University, Baltimore, MD, United States. 4. Department of Neurology, University of Rochester Medical Center, Rochester, NY, United States. 5. Department of Neurology, University of Rochester Medical Center, Rochester, NY, United States; CHET, University of Rochester Medical Center, Rochester, NY, United States. Electronic address: ray.dorsey@chet.rochester.edu. 6. Nonlinearity and Complexity Research Group, Aston University, Birmingham, United Kingdom; Media Lab, Massachusetts Institute of Technology, Boston, MA, United States.
Abstract
BACKGROUND: Remote, non-invasive and objective tests that can be used to support expert diagnosis for Parkinson's disease (PD) are lacking. METHODS: Participants underwent baseline in-clinic assessments, including the Unified Parkinson's Disease Rating Scale (UPDRS), and were provided smartphones with an Android operating system that contained a smartphone application that assessed voice, posture, gait, finger tapping, and response time. Participants then took the smart phones home to perform the five tasks four times a day for a month. Once a week participants had a remote (telemedicine) visit with a Parkinson disease specialist in which a modified (excluding assessments of rigidity and balance) UPDRS performed. Using statistical analyses of the five tasks recorded using the smartphone from 10 individuals with PD and 10 controls, we sought to: (1) discriminate whether the participant had PD and (2) predict the modified motor portion of the UPDRS. RESULTS: Twenty participants performed an average of 2.7 tests per day (68.9% adherence) for the study duration (average of 34.4 days) in a home and community setting. The analyses of the five tasks differed between those with Parkinson disease and those without. In discriminating participants with PD from controls, the mean sensitivity was 96.2% (SD 2%) and mean specificity was 96.9% (SD 1.9%). The mean error in predicting the modified motor component of the UPDRS (range 11-34) was 1.26 UPDRS points (SD 0.16). CONCLUSION: Measuring PD symptoms via a smartphone is feasible and has potential value as a diagnostic support tool.
BACKGROUND: Remote, non-invasive and objective tests that can be used to support expert diagnosis for Parkinson's disease (PD) are lacking. METHODS:Participants underwent baseline in-clinic assessments, including the Unified Parkinson's Disease Rating Scale (UPDRS), and were provided smartphones with an Android operating system that contained a smartphone application that assessed voice, posture, gait, finger tapping, and response time. Participants then took the smart phones home to perform the five tasks four times a day for a month. Once a week participants had a remote (telemedicine) visit with a Parkinson disease specialist in which a modified (excluding assessments of rigidity and balance) UPDRS performed. Using statistical analyses of the five tasks recorded using the smartphone from 10 individuals with PD and 10 controls, we sought to: (1) discriminate whether the participant had PD and (2) predict the modified motor portion of the UPDRS. RESULTS: Twenty participants performed an average of 2.7 tests per day (68.9% adherence) for the study duration (average of 34.4 days) in a home and community setting. The analyses of the five tasks differed between those with Parkinson disease and those without. In discriminating participants with PD from controls, the mean sensitivity was 96.2% (SD 2%) and mean specificity was 96.9% (SD 1.9%). The mean error in predicting the modified motor component of the UPDRS (range 11-34) was 1.26 UPDRS points (SD 0.16). CONCLUSION: Measuring PD symptoms via a smartphone is feasible and has potential value as a diagnostic support tool.
Authors: E Ray Dorsey; Floris P Vlaanderen; Lucien Jlpg Engelen; Karl Kieburtz; William Zhu; Kevin M Biglan; Marjan J Faber; Bastiaan R Bloem Journal: Mov Disord Date: 2016-08-08 Impact factor: 10.338