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Biometric Digital Health Technology for Measuring Motor Function in Parkinson’s Disease: Results from a Feasibility and Patient Satisfaction Study

OBJECTIVES: To assess the feasibility, predictive value, and user satisfaction of objectively quantifying motor function in Parkinson’s disease (PD) through a tablet-based application (iMotor) using self-administered tests. METHODS: PD and healthy controls (HCs) performed finger tapping, hand pronat...

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Autores principales: Mitsi, Georgia, Mendoza, Enrique Urrea, Wissel, Benjamin D., Barbopoulou, Elena, Dwivedi, Alok K., Tsoulos, Ioannis, Stavrakoudis, Athanassios, Espay, Alberto J., Papapetropoulos, Spyros
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Frontiers Media S.A. 2017
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5468407/
https://www.ncbi.nlm.nih.gov/pubmed/28659858
http://dx.doi.org/10.3389/fneur.2017.00273
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author Mitsi, Georgia
Mendoza, Enrique Urrea
Wissel, Benjamin D.
Barbopoulou, Elena
Dwivedi, Alok K.
Tsoulos, Ioannis
Stavrakoudis, Athanassios
Espay, Alberto J.
Papapetropoulos, Spyros
author_facet Mitsi, Georgia
Mendoza, Enrique Urrea
Wissel, Benjamin D.
Barbopoulou, Elena
Dwivedi, Alok K.
Tsoulos, Ioannis
Stavrakoudis, Athanassios
Espay, Alberto J.
Papapetropoulos, Spyros
author_sort Mitsi, Georgia
collection PubMed
description OBJECTIVES: To assess the feasibility, predictive value, and user satisfaction of objectively quantifying motor function in Parkinson’s disease (PD) through a tablet-based application (iMotor) using self-administered tests. METHODS: PD and healthy controls (HCs) performed finger tapping, hand pronation–supination and reaction time tasks using the iMotor application. RESULTS: Thirty-eight participants (19 with PD and 17 HCs) were recruited in the study. PD subjects were 53% male, with a mean age of 67.8 years (±8.8), mean disease duration of 6.5 years (±4.6), Movement Disorders Society version of the Unified Parkinson Disease Rating Scale III score 26.3 (±6.7), and Hoehn & Yahr stage 2. In the univariate analysis, most tapping variables were significantly different in PD compared to HC. Tap interval provided the highest predictive ability (90%). In the multivariable logistic regression model reaction time (reaction time test) (p = 0.021) and total taps (two-target test) (p = 0.026) were associated with PD. A combined model with two-target (total taps and accuracy) and reaction time produced maximum discriminatory performance between HC and PD. The overall accuracy of the combined model was 0.98 (95% confidence interval: 0.93–1). iMotor use achieved high rates of patients’ satisfaction as evaluated by a patient satisfaction survey. CONCLUSION: iMotor differentiated PD subjects from HCs using simple alternating tasks of motor function. Results of this feasibility study should be replicated in larger, longitudinal, appropriately designed, controlled studies. The impact on patient care of at-home iMotor-assisted remote monitoring also deserves further evaluation.
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spelling pubmed-54684072017-06-28 Biometric Digital Health Technology for Measuring Motor Function in Parkinson’s Disease: Results from a Feasibility and Patient Satisfaction Study Mitsi, Georgia Mendoza, Enrique Urrea Wissel, Benjamin D. Barbopoulou, Elena Dwivedi, Alok K. Tsoulos, Ioannis Stavrakoudis, Athanassios Espay, Alberto J. Papapetropoulos, Spyros Front Neurol Neuroscience OBJECTIVES: To assess the feasibility, predictive value, and user satisfaction of objectively quantifying motor function in Parkinson’s disease (PD) through a tablet-based application (iMotor) using self-administered tests. METHODS: PD and healthy controls (HCs) performed finger tapping, hand pronation–supination and reaction time tasks using the iMotor application. RESULTS: Thirty-eight participants (19 with PD and 17 HCs) were recruited in the study. PD subjects were 53% male, with a mean age of 67.8 years (±8.8), mean disease duration of 6.5 years (±4.6), Movement Disorders Society version of the Unified Parkinson Disease Rating Scale III score 26.3 (±6.7), and Hoehn & Yahr stage 2. In the univariate analysis, most tapping variables were significantly different in PD compared to HC. Tap interval provided the highest predictive ability (90%). In the multivariable logistic regression model reaction time (reaction time test) (p = 0.021) and total taps (two-target test) (p = 0.026) were associated with PD. A combined model with two-target (total taps and accuracy) and reaction time produced maximum discriminatory performance between HC and PD. The overall accuracy of the combined model was 0.98 (95% confidence interval: 0.93–1). iMotor use achieved high rates of patients’ satisfaction as evaluated by a patient satisfaction survey. CONCLUSION: iMotor differentiated PD subjects from HCs using simple alternating tasks of motor function. Results of this feasibility study should be replicated in larger, longitudinal, appropriately designed, controlled studies. The impact on patient care of at-home iMotor-assisted remote monitoring also deserves further evaluation. Frontiers Media S.A. 2017-06-13 /pmc/articles/PMC5468407/ /pubmed/28659858 http://dx.doi.org/10.3389/fneur.2017.00273 Text en Copyright © 2017 Mitsi, Mendoza, Wissel, Barbopoulou, Dwivedi, Tsoulos, Stavrakoudis, Espay and Papapetropoulos. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
spellingShingle Neuroscience
Mitsi, Georgia
Mendoza, Enrique Urrea
Wissel, Benjamin D.
Barbopoulou, Elena
Dwivedi, Alok K.
Tsoulos, Ioannis
Stavrakoudis, Athanassios
Espay, Alberto J.
Papapetropoulos, Spyros
Biometric Digital Health Technology for Measuring Motor Function in Parkinson’s Disease: Results from a Feasibility and Patient Satisfaction Study
title Biometric Digital Health Technology for Measuring Motor Function in Parkinson’s Disease: Results from a Feasibility and Patient Satisfaction Study
title_full Biometric Digital Health Technology for Measuring Motor Function in Parkinson’s Disease: Results from a Feasibility and Patient Satisfaction Study
title_fullStr Biometric Digital Health Technology for Measuring Motor Function in Parkinson’s Disease: Results from a Feasibility and Patient Satisfaction Study
title_full_unstemmed Biometric Digital Health Technology for Measuring Motor Function in Parkinson’s Disease: Results from a Feasibility and Patient Satisfaction Study
title_short Biometric Digital Health Technology for Measuring Motor Function in Parkinson’s Disease: Results from a Feasibility and Patient Satisfaction Study
title_sort biometric digital health technology for measuring motor function in parkinson’s disease: results from a feasibility and patient satisfaction study
topic Neuroscience
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5468407/
https://www.ncbi.nlm.nih.gov/pubmed/28659858
http://dx.doi.org/10.3389/fneur.2017.00273
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