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Jiménez-Barrios M, González-Bernal J, Cubo E, Gabriel-Galán JM, García-López B, Berardi A, Tofani M, Galeoto G, Matthews MJA, Santamaría-Peláez M, González-Santos J. Functionality and Quality of Life with Parkinson's Disease after Use of a Dynamic Upper Limb Orthosis: A Pilot Study. INTERNATIONAL JOURNAL OF ENVIRONMENTAL RESEARCH AND PUBLIC HEALTH 2023; 20:4995. [PMID: 36981905 PMCID: PMC10049252 DOI: 10.3390/ijerph20064995] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 11/20/2022] [Revised: 03/04/2023] [Accepted: 03/07/2023] [Indexed: 06/18/2023]
Abstract
Parkinson's disease (PD) is a chronic, neurodegenerative movement disorder, whose symptoms have a negative impact on quality of life and functionality. Although its main treatment is pharmacological, non-pharmacological aids such as the dynamic elastomeric fabric orthosis (DEFO) merit an evaluation. Our objective is to assess the DEFO in upper limb (UL) functional mobility and in the quality of life of PD patients. A total of 40 patients with PD participated in a randomized controlled crossover study, and were assigned to a control group (CG) and to an experimental group (EG). Both groups used the DEFO for two months, the experimental group the first two months of the study and the control group the last two. Motor variables were measured in the ON and OFF states at the baseline assessment and at two months. Differences from the baseline assessment were observed in some motor items of the Kinesia assessment, such as rest tremor, amplitude, rhythm or alternating movements in the ON and OFF states with and without orthosis. No differences were found in the unified Parkinson's disease rating scale (UPDRS) or the PD quality-of-life questionnaire. The DEFO improves some motor aspects of the UL in PD patients but this does not translate to the amelioration of the standard of functional and quality-of-life scales.
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Affiliation(s)
| | | | - Esther Cubo
- Neurology Service, Burgos University Hospital, 09006 Burgos, Spain
| | | | | | - Anna Berardi
- Department of Human Neurosciences, University of la Sapienza, 00188 Rome, Italy
| | - Marco Tofani
- Department of Human Neurosciences, University of la Sapienza, 00188 Rome, Italy
| | - Giovanni Galeoto
- Department of Human Neurosciences, University of la Sapienza, 00188 Rome, Italy
| | - Martin J. A. Matthews
- Faculty of Health, School of Health Professions Peninsula Allied Health Centre, University of Plymouth, Derriford Rd., Plymouth PL6 8BH, UK
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Towards Real-Time Prediction of Freezing of Gait in Patients With Parkinson's Disease: Addressing the Class Imbalance Problem. SENSORS 2019; 19:s19183898. [PMID: 31509999 PMCID: PMC6767263 DOI: 10.3390/s19183898] [Citation(s) in RCA: 17] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/15/2019] [Revised: 09/05/2019] [Accepted: 09/08/2019] [Indexed: 01/06/2023]
Abstract
Freezing of gait (FoG) is a common motor symptom in patients with Parkinson's disease (PD). FoG impairs gait initiation and walking and increases fall risk. Intelligent external cueing systems implementing FoG detection algorithms have been developed to help patients recover gait after freezing. However, predicting FoG before its occurrence enables preemptive cueing and may prevent FoG. Such prediction remains challenging given the relative infrequency of freezing compared to non-freezing events. In this study, we investigated the ability of individual and ensemble classifiers to predict FoG. We also studied the effect of the ADAptive SYNthetic (ADASYN) sampling algorithm and classification cost on classifier performance. Eighteen PD patients performed a series of daily walking tasks wearing accelerometers on their ankles, with nine experiencing FoG. The ensemble classifier formed by Support Vector Machines, K-Nearest Neighbors, and Multi-Layer Perceptron using bagging techniques demonstrated highest performance (F1 = 90.7) when synthetic FoG samples were added to the training set and class cost was set as twice that of normal gait. The model identified 97.4% of the events, with 66.7% being predicted. This study demonstrates our algorithm's potential for accurate prediction of gait events and the provision of preventive cueing in spite of limited event frequency.
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