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Multimodal Classification of Parkinson’s Disease in Home Environments with Resiliency to Missing Modalities
Article

Feasibility of a Mobile-Based System for Unsupervised Monitoring in Parkinson’s Disease

1
Instituto de Medicina Molecular, 1649-028 Lisbon, Portugal
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CNS—Campus Neurológico, 2560-280 Torres Vedras, Portugal
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Kinetikos, 3030-199 Coimbra, Portugal
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Champalimaud Research, Champalimaud Centre for the Unknown, 1400 Lisbon, Portugal
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Zuckerman Mind Brain Behavior Institute, Columbia University, New York, NY 10027, USA
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Champalimaud Clinical Centre, Champalimaud Centre for the Unknown, 1400 Lisbon, Portugal
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Human Movement Analysis Lab., Escola Superior Saúde—Instituto Politécnico de Setúbal, 2910-761 Setubal, Portugal
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Laboratory of Clinical Pharmacology and Therapeutics, Faculdade de Medicina, Universidade de Lisboa, 1649-028 Lisbon, Portugal
*
Author to whom correspondence should be addressed.
Academic Editor: Antonio Suppa
Sensors 2021, 21(15), 4972; https://doi.org/10.3390/s21154972
Received: 19 May 2021 / Revised: 9 July 2021 / Accepted: 19 July 2021 / Published: 21 July 2021
(This article belongs to the Special Issue Sensors and Sensing Technology Applied in Parkinson Disease)
Mobile health (mHealth) has emerged as a potential solution to providing valuable ecological information about the severity and burden of Parkinson’s disease (PD) symptoms in real-life conditions. Objective: The objective of our study was to explore the feasibility and usability of an mHealth system for continuous and objective real-life measures of patients’ health and functional mobility, in unsupervised settings. Methods: Patients with a clinical diagnosis of PD, who were able to walk unassisted, and had an Android smartphone were included. Patients were asked to answer a daily survey, to perform three weekly active tests, and to perform a monthly in-person clinical assessment. Feasibility and usability were explored as primary and secondary outcomes. An exploratory analysis was performed to investigate the correlation between data from the mKinetikos app and clinical assessments. Results: Seventeen participants (85%) completed the study. Sixteen participants (94.1%) showed a medium-to-high level of compliance with the mKinetikos system. A 6-point drop in the total score of the Post-Study System Usability Questionnaire was observed. Conclusions: Our results support the feasibility of the mKinetikos system for continuous and objective real-life measures of a patient’s health and functional mobility. The observed correlations of mKinetikos metrics with clinical data seem to suggest that this mHealth solution is a promising tool to support clinical decisions. View Full-Text
Keywords: Parkinson’s disease; digital health; remote monitoring; sensors; wearable technology Parkinson’s disease; digital health; remote monitoring; sensors; wearable technology
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MDPI and ACS Style

Bouça-Machado, R.; Pona-Ferreira, F.; Leitão, M.; Clemente, A.; Vila-Viçosa, D.; Kauppila, L.A.; Costa, R.M.; Matias, R.; Ferreira, J.J. Feasibility of a Mobile-Based System for Unsupervised Monitoring in Parkinson’s Disease. Sensors 2021, 21, 4972. https://doi.org/10.3390/s21154972

AMA Style

Bouça-Machado R, Pona-Ferreira F, Leitão M, Clemente A, Vila-Viçosa D, Kauppila LA, Costa RM, Matias R, Ferreira JJ. Feasibility of a Mobile-Based System for Unsupervised Monitoring in Parkinson’s Disease. Sensors. 2021; 21(15):4972. https://doi.org/10.3390/s21154972

Chicago/Turabian Style

Bouça-Machado, Raquel, Filipa Pona-Ferreira, Mariana Leitão, Ana Clemente, Diogo Vila-Viçosa, Linda A. Kauppila, Rui M. Costa, Ricardo Matias, and Joaquim J. Ferreira 2021. "Feasibility of a Mobile-Based System for Unsupervised Monitoring in Parkinson’s Disease" Sensors 21, no. 15: 4972. https://doi.org/10.3390/s21154972

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