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Barbosa R, Mendonça M, Bastos P, Pita Lobo P, Valadas A, Correia Guedes L, Ferreira JJ, Rosa MM, Matias R, Coelho M. 3D Kinematics Quantifies Gait Response to Levodopa earlier and to a more Comprehensive Extent than the MDS-Unified Parkinson's Disease Rating Scale in Patients with Motor Complications. Mov Disord Clin Pract 2024; 11:795-807. [PMID: 38610081 PMCID: PMC11233852 DOI: 10.1002/mdc3.14016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2023] [Revised: 01/20/2024] [Accepted: 02/13/2024] [Indexed: 04/14/2024] Open
Abstract
BACKGROUND Quantitative 3D movement analysis using inertial measurement units (IMUs) allows for a more detailed characterization of motor patterns than clinical assessment alone. It is essential to discriminate between gait features that are responsive or unresponsive to current therapies to better understand the underlying pathophysiological basis and identify potential therapeutic strategies. OBJECTIVES This study aims to characterize the responsiveness and temporal evolution of different gait subcomponents in Parkinson's disease (PD) patients in their OFF and various ON states following levodopa administration, utilizing both wearable sensors and the gold-standard MDS-UPDRS motor part III. METHODS Seventeen PD patients were assessed while wearing a full-body set of 15 IMUs in their OFF state and at 20-minute intervals following the administration of a supra-threshold levodopa dose. Gait was reconstructed using a biomechanical model of the human body to quantify how each feature was modulated. Comparisons with non-PD control subjects were conducted in parallel. RESULTS Significant motor changes were observed in both the upper and lower limbs according to the MDS-UPDRS III, 40 minutes after levodopa intake. IMU-assisted 3D kinematics detected significant motor alterations as early as 20 minutes after levodopa administration, particularly in upper limbs metrics. Although all "pace-domain" gait features showed significant improvement in the Best-ON state, most rhythmicity, asymmetry, and variability features did not. CONCLUSION IMUs are capable of detecting motor alterations earlier and in a more comprehensive manner than the MDS-UPDRS III. The upper limbs respond more rapidly to levodopa, possibly reflecting distinct thresholds to levodopa across striatal regions.
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Affiliation(s)
- Raquel Barbosa
- Neurology DeparmentCentre Hospitalier Universitaire ToulouseToulouseFrance
- Nova Medical School, Faculdade de Ciências MedicasUniversidade Nova de LisboaLisbonPortugal
| | - Marcelo Mendonça
- Nova Medical School, Faculdade de Ciências MedicasUniversidade Nova de LisboaLisbonPortugal
- Champalimaud Research and Clinical Centre, Champalimaud Centre for the UnknownLisbonPortugal
| | - Paulo Bastos
- Neurology DeparmentCentre Hospitalier Universitaire ToulouseToulouseFrance
- Nova Medical School, Faculdade de Ciências MedicasUniversidade Nova de LisboaLisbonPortugal
| | - Patrícia Pita Lobo
- Department of Neurosciences and Mental HealthNeurology Hospital Santa Maria, CHLUNLisbonPortugal
- Instituto de Medicina Molecular João Lobo Antunes, Faculty of MedicineUniversity of LisbonLisbonPortugal
| | - Anabela Valadas
- Department of Neurosciences and Mental HealthNeurology Hospital Santa Maria, CHLUNLisbonPortugal
- Instituto de Medicina Molecular João Lobo Antunes, Faculty of MedicineUniversity of LisbonLisbonPortugal
| | - Leonor Correia Guedes
- Department of Neurosciences and Mental HealthNeurology Hospital Santa Maria, CHLUNLisbonPortugal
- Instituto de Medicina Molecular João Lobo Antunes, Faculty of MedicineUniversity of LisbonLisbonPortugal
| | - Joaquim J. Ferreira
- Instituto de Medicina Molecular João Lobo Antunes, Faculty of MedicineUniversity of LisbonLisbonPortugal
- Laboratory of Clinical Pharmacology and Therapeutics, Faculdade de MedicinaUniversidade de LisboaLisbonPortugal
- CNS‐ Campus Neurológico SeniorTorres VedrasPortugal
| | - Mário Miguel Rosa
- Department of Neurosciences and Mental HealthNeurology Hospital Santa Maria, CHLUNLisbonPortugal
- Instituto de Medicina Molecular João Lobo Antunes, Faculty of MedicineUniversity of LisbonLisbonPortugal
- Laboratory of Clinical Pharmacology and Therapeutics, Faculdade de MedicinaUniversidade de LisboaLisbonPortugal
| | - Ricardo Matias
- Physics Department & Institute of Biophysics and Biomedical Engineering (IBEB), Faculty of SciencesUniversity of LisbonLisbonPortugal
- KinetikosCoimbraPortugal
| | - Miguel Coelho
- Department of Neurosciences and Mental HealthNeurology Hospital Santa Maria, CHLUNLisbonPortugal
- Instituto de Medicina Molecular João Lobo Antunes, Faculty of MedicineUniversity of LisbonLisbonPortugal
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Liu W, Bai J. Meta-analysis of the quantitative assessment of lower extremity motor function in elderly individuals based on objective detection. J Neuroeng Rehabil 2024; 21:111. [PMID: 38926890 PMCID: PMC11202321 DOI: 10.1186/s12984-024-01409-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2023] [Accepted: 06/20/2024] [Indexed: 06/28/2024] Open
Abstract
OBJECTIVE To avoid deviation caused by the traditional scale method, the present study explored the accuracy, advantages, and disadvantages of different objective detection methods in evaluating lower extremity motor function in elderly individuals. METHODS Studies on lower extremity motor function assessment in elderly individuals published in the PubMed, Web of Science, Cochrane Library and EMBASE databases in the past five years were searched. The methodological quality of the included trials was assessed using RevMan 5.4.1 and Stata, followed by statistical analyses. RESULTS In total, 19 randomized controlled trials with a total of 2626 participants, were included. The results of the meta-analysis showed that inertial measurement units (IMUs), motion sensors, 3D motion capture systems, and observational gait analysis had statistical significance in evaluating the changes in step velocity and step length of lower extremity movement in elderly individuals (P < 0.00001), which can be used as a standardized basis for the assessment of motor function in elderly individuals. Subgroup analysis showed that there was significant heterogeneity in the assessment of step velocity [SMD=-0.98, 95%CI(-1.23, -0.72), I2 = 91.3%, P < 0.00001] and step length [SMD=-1.40, 95%CI(-1.77, -1.02), I2 = 86.4%, P < 0.00001] in elderly individuals. However, the sensors (I2 = 9%, I2 = 0%) and 3D motion capture systems (I2 = 0%) showed low heterogeneity in terms of step velocity and step length. The sensitivity analysis and publication bias test demonstrated that the results were stable and reliable. CONCLUSION observational gait analysis, motion sensors, 3D motion capture systems, and IMUs, as evaluation means, play a certain role in evaluating the characteristic parameters of step velocity and step length in lower extremity motor function of elderly individuals, which has good accuracy and clinical value in preventing motor injury. However, the high heterogeneity of observational gait analysis and IMUs suggested that different evaluation methods use different calculation formulas and indicators, resulting in the failure to obtain standardized indicators in clinical applications. Thus, multimodal quantitative evaluation should be integrated.
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Affiliation(s)
- Wen Liu
- Rehabilitation Medicine Center, The Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, China
- Department of Spine and Spinal Cord Surgery, Beijing Boai Hospital, China Rehabilitation Research Centre, Beijing, China
| | - Jinzhu Bai
- Rehabilitation Medicine Center, The Second Affiliated Hospital and Yuying Children's Hospital, Wenzhou Medical University, Wenzhou, China.
- Department of Spine and Spinal Cord Surgery, Beijing Boai Hospital, China Rehabilitation Research Centre, Beijing, China.
- School of Rehabilitation Medicine, Capital Medical University, Beijing, China.
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Xie H, Zhang N, Xia C, Ding Y, Zhao H, Huang Y. The clinical characteristics of cerebral small vessel disease patients with motoric cognitive risk syndrome during single- and dual-task walking. Heliyon 2024; 10:e30007. [PMID: 38742083 PMCID: PMC11089308 DOI: 10.1016/j.heliyon.2024.e30007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/15/2024] [Revised: 04/10/2024] [Accepted: 04/18/2024] [Indexed: 05/16/2024] Open
Abstract
Objective We aimed to (1) identify neuroimaging biomarkers of distinguishing motoric cognitive risk syndrome (MCRS) risk among older Chinese adults with cerebral small vessel disease (CSVD) and (2) detect differences in gait parameters and neuroimaging biomarkers between CSVD individual with and without MCRS, especially during dual-task walking (DTW). Methods We enrolled 126 inpatients with CSVD who were divided into two groups according to MCRS status. Data on basic parameters, variability, asymmetry, and coordination were collected during single-task walking (STW) and DTW. Neuroimaging features (white matter hyperintensities, lacunes, and microbleeds) and total disease burden were calculated. Analysis of variance and logistic regression analyses were applied to assess the role of STW, DTW, and neuroimaging biomarkers in MCRS. Results In total, 126 consecutive inpatients with CSVD were included (84 and 42 patients were classified as MCRS-negative and MCRS-positive, respectively). The MCRS-positive group showed poorer performance for nearly all gait parameters compared with the MCRS-negative group during cognitive DTW. Meanwhile, all gait parameters except asymmetry were assessed in participants with MCRS for significant deterioration during cognitive DTW compared with that during STW. However, only basic parameters differed between STW and cognitive DTW in participants without MCRS. A significant independent association between total CSVD scores and MCRS was also detected. Conclusions For CSVD patients, with higher total CSVD burden rather than any single neuroimaging marker, was linked to a greater risk of MCRS. In addition, CSVD individuals with MCRS had higher variability and phase coordination index (PCI), especially in cognitive DTW. Thus, they should concentrate more on their gait variability or coordination and reduce secondary task loads while walking in daily life, especially in cognitive secondary tasks.
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Affiliation(s)
- Hongyang Xie
- Department of Neurology, Chinese PLA General Hospital, Beijing, China
| | - Nan Zhang
- Department of Neurology, Chinese PLA General Hospital, Beijing, China
| | - Cuiqiao Xia
- Department of Neurology, Chinese PLA General Hospital, Beijing, China
| | - Yu Ding
- Department of Neurology, Chinese PLA General Hospital, Beijing, China
| | - Hongyi Zhao
- Department of Neurology, Chinese PLA General Hospital, Beijing, China
- Department of Neurology, Number 984 Hospital of the PLA, Beijing, China
| | - Yonghua Huang
- Department of Neurology, Chinese PLA General Hospital, Beijing, China
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Sotirakis C, Su Z, Brzezicki MA, Conway N, Tarassenko L, FitzGerald JJ, Antoniades CA. Identification of motor progression in Parkinson's disease using wearable sensors and machine learning. NPJ Parkinsons Dis 2023; 9:142. [PMID: 37805655 PMCID: PMC10560243 DOI: 10.1038/s41531-023-00581-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Accepted: 09/20/2023] [Indexed: 10/09/2023] Open
Abstract
Wearable devices offer the potential to track motor symptoms in neurological disorders. Kinematic data used together with machine learning algorithms can accurately identify people living with movement disorders and the severity of their motor symptoms. In this study we aimed to establish whether a combination of wearable sensor data and machine learning algorithms with automatic feature selection can estimate the clinical rating scale and whether it is possible to monitor the motor symptom progression longitudinally, for people with Parkinson's Disease. Seventy-four patients visited the lab seven times at 3-month intervals. Their walking (2-minutes) and postural sway (30-seconds,eyes-closed) were recorded using six Inertial Measurement Unit sensors. Simple linear regression and Random Forest algorithms were utilised together with different routines of automatic feature selection or factorisation, resulting in seven different machine learning algorithms to estimate the clinical rating scale (Movement Disorder Society- Unified Parkinson's Disease Rating Scale part III; MDS-UPDRS-III). Twenty-nine features were found to significantly progress with time at group level. The Random Forest model revealed the most accurate estimation of the MDS-UPDRS-III among the seven models. The model estimations detected a statistically significant progression of the motor symptoms within 15 months when compared to the first visit, whereas the MDS-UPDRS-III did not capture any change. Wearable sensors and machine learning can track the motor symptom progression in people with PD better than the conventionally used clinical rating scales. The methods described in this study can be utilised complimentary to the clinical rating scales to improve the diagnostic and prognostic accuracy.
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Affiliation(s)
- Charalampos Sotirakis
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Zi Su
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Maksymilian A Brzezicki
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Niall Conway
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
| | - Lionel Tarassenko
- Institute of Biomedical Engineering, Department of Engineering Science, University of Oxford, Oxford, UK
| | - James J FitzGerald
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK
- Nuffield Department of Surgical Sciences, University of Oxford, Oxford, UK
| | - Chrystalina A Antoniades
- NeuroMetrology Lab, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, UK.
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Liuzzi P, Carpinella I, Anastasi D, Gervasoni E, Lencioni T, Bertoni R, Carrozza MC, Cattaneo D, Ferrarin M, Mannini A. Machine learning based estimation of dynamic balance and gait adaptability in persons with neurological diseases using inertial sensors. Sci Rep 2023; 13:8640. [PMID: 37244933 PMCID: PMC10224964 DOI: 10.1038/s41598-023-35744-x] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2023] [Accepted: 05/23/2023] [Indexed: 05/29/2023] Open
Abstract
Poor dynamic balance and impaired gait adaptation to different contexts are hallmarks of people with neurological disorders (PwND), leading to difficulties in daily life and increased fall risk. Frequent assessment of dynamic balance and gait adaptability is therefore essential for monitoring the evolution of these impairments and/or the long-term effects of rehabilitation. The modified dynamic gait index (mDGI) is a validated clinical test specifically devoted to evaluating gait facets in clinical settings under a physiotherapist's supervision. The need of a clinical environment, consequently, limits the number of assessments. Wearable sensors are increasingly used to measure balance and locomotion in real-world contexts and may permit an increase in monitoring frequency. This study aims to provide a preliminary test of this opportunity by using nested cross-validated machine learning regressors to predict the mDGI scores of 95 PwND via inertial signals collected from short steady-state walking bouts derived from the 6-minute walk test. Four different models were compared, one for each pathology (multiple sclerosis, Parkinson's disease, and stroke) and one for the pooled multipathological cohort. Model explanations were computed on the best-performing solution; the model trained on the multipathological cohort yielded a median (interquartile range) absolute test error of 3.58 (5.38) points. In total, 76% of the predictions were within the mDGI's minimal detectable change of 5 points. These results confirm that steady-state walking measurements provide information about dynamic balance and gait adaptability and can help clinicians identify important features to improve upon during rehabilitation. Future developments will include training of the method using short steady-state walking bouts in real-world settings, analysing the feasibility of this solution to intensify performance monitoring, providing prompt detection of worsening/improvements, and complementing clinical assessments.
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Affiliation(s)
- Piergiuseppe Liuzzi
- AIRLab, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 50143, Florence, Italy
- Scuola Superiore Sant'Anna, Istituto di BioRobotica, 56025, Pontedera, Italy
| | - Ilaria Carpinella
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy.
| | - Denise Anastasi
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | - Elisa Gervasoni
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | - Tiziana Lencioni
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | - Rita Bertoni
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | | | - Davide Cattaneo
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
- Dipartimento di Fisiopatologia Medico-Chirurgica e dei Trapianti, Università di Milano, 20122, Milan, Italy
| | - Maurizio Ferrarin
- LAMoBIR and LaRiCE, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 20148, Milan, Italy
| | - Andrea Mannini
- AIRLab, IRCCS Fondazione Don Carlo Gnocchi ONLUS, 50143, Florence, Italy
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d'Alencar MS, Santos GV, Helene AF, Roque AC, Miranda JGV, Piemonte MEP. A non-expensive bidimensional assessment can detect subtle alterations in gait performance in people in the early stages of Parkinson's disease. Front Neurol 2023; 14:1101650. [PMID: 37153678 PMCID: PMC10155096 DOI: 10.3389/fneur.2023.1101650] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2022] [Accepted: 03/15/2023] [Indexed: 05/10/2023] Open
Abstract
Background Gait is one of the activities most affected by the symptoms of Parkinson's disease and may show a linear decline as the disease progresses. Early assessment of its performance through clinically relevant tests is a key factor in designing efficient therapeutic plans and procedures, which can be enhanced using simple and low-cost technological instruments. Objective To investigate the effectiveness of a two-dimensional gait assessment to identify the decline in gait performance associated with Parkinson's disease progression. Methods One hundred and seventeen people with Parkinson's disease, classified between early and intermediate stages, performed three clinical gait tests (Timed Up and Go, Dynamic Gait Index, and item 29 of the Unified Parkinson's Disease Rating Scale), in addition to a six-meter gait test recorded by a two-dimensional movement analysis software. Based on variables generated by the software, a gait performance index was created, allowing a comparison between its results with the results obtained by clinical tests. Results There were differences between sociodemographic variables directly related to the evolution of Parkinson's disease. Compared to clinical tests, the index proposed to analyze gait showed greater sensitivity and was able to differentiate the first three stages of disease evolution (Hoehn and Yahr I and II: p = 0.03; Hoehn and Yahr I and III: p = 0.00001; Hoehn and Yahr II and III: p = 0.02). Conclusion Based on the index provided by a two-dimensional movement analysis software that uses kinematic gait variables, it was possible to differentiate the gait performance decline among the three first stages of Parkinson's disease evolution. This study offers a promising possibility of early identification of subtle changes in an essential function of people with Parkinson's disease.
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Affiliation(s)
- Matheus Silva d'Alencar
- Department of Physical Therapy, Speech Therapy and Occupational Therapy, Faculty of Medical Science, University of São Paulo, São Paulo, Brazil
| | - Gabriel Venas Santos
- Department of Physical Therapy, Speech Therapy and Occupational Therapy, Faculty of Medical Science, University of São Paulo, São Paulo, Brazil
| | - André Frazão Helene
- Department of Physiology, Institute of Biosciences, University of São Paulo, São Paulo, Brazil
| | - Antonio Carlos Roque
- Department of Physics, School of Philosophy, Sciences and Letters of Ribeirão Preto, University of São Paulo, Ribeirão Preto, Brazil
| | | | - Maria Elisa Pimentel Piemonte
- Department of Physical Therapy, Speech Therapy and Occupational Therapy, Faculty of Medical Science, University of São Paulo, São Paulo, Brazil
- *Correspondence: Maria Elisa Pimentel Piemonte,
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Troisi Lopez E, Sorrentino P, Liparoti M, Minino R, Polverino A, Romano A, Carotenuto A, Amico E, Sorrentino G. The kinectome: A comprehensive kinematic map of human motion in health and disease. Ann N Y Acad Sci 2022; 1516:247-261. [PMID: 35838306 PMCID: PMC9796708 DOI: 10.1111/nyas.14860] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Abstract
Human voluntary movement stems from the coordinated activations in space and time of many musculoskeletal segments. However, the current methodological approaches to study human movement are still limited to the evaluation of the synergies among a few body elements. Network science can be a useful approach to describe movement as a whole and to extract features that are relevant to understanding both its complex physiology and the pathophysiology of movement disorders. Here, we propose to represent human movement as a network (that we named the kinectome), where nodes represent body points, and edges are defined as the correlations of the accelerations between each pair of them. We applied this framework to healthy individuals and patients with Parkinson's disease, observing that the patients' kinectomes display less symmetrical patterns as compared to healthy controls. Furthermore, we used the kinectomes to successfully identify both healthy and diseased subjects using short gait recordings. Finally, we highlighted topological features that predict the individual clinical impairment in patients. Our results define a novel approach to study human movement. While deceptively simple, this approach is well-grounded, and represents a powerful tool that may be applied to a wide spectrum of frameworks.
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Affiliation(s)
- Emahnuel Troisi Lopez
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly
| | | | - Marianna Liparoti
- Department of Developmental and Social PsychologyUniversity “La Sapienza” of RomeRomeItaly
| | - Roberta Minino
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly
| | - Arianna Polverino
- Institute for Diagnosis and TreatmentHermitage CapodimonteNaplesItaly
| | - Antonella Romano
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly
| | - Anna Carotenuto
- Alzheimer Unit and Movement Disorders ClinicDepartment of NeurologyCardarelli HospitalNaplesItaly
| | - Enrico Amico
- Institute of Bioengineering, Center for NeuroprostheticsEPFLGenevaSwitzerland,Department of Radiology and Medical InformaticsUniversity of Geneva (UNIGE)GenevaSwitzerland
| | - Giuseppe Sorrentino
- Department of Motor Sciences and WellnessUniversity of Naples “Parthenope”NaplesItaly,Institute for Diagnosis and TreatmentHermitage CapodimonteNaplesItaly,Institute of Applied Sciences and Intelligent SystemsCNRPozzuoliItaly
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Mollà-Casanova S, Pedrero-Sánchez J, Inglés M, López-Pascual J, Muñoz-Gómez E, Aguilar-Rodríguez M, Sempere-Rubio N, Serra-Añó P. Impact of Parkinson’s Disease on Functional Mobility at Different Stages. Front Aging Neurosci 2022; 14:935841. [PMID: 35783141 PMCID: PMC9249436 DOI: 10.3389/fnagi.2022.935841] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/04/2022] [Accepted: 05/23/2022] [Indexed: 11/13/2022] Open
Abstract
Introduction Specific functional assessments to determine the progression of Parkinson’s Disease (PD) are important to slow down such progression and better plan rehabilitation. This study aimed to explore possible differences in the performance of different functional tasks included in a mobility test using sensors embedded in an Android device, in people at different PD stages. Materials and Methods Eighty-seven participants with PD agreed to participate in this cross-sectional study. They were assessed once using an inertial sensor and variables related to functional status were recorded (i.e., MLDisp, APDisp, DispA, Vrange, MLRange, PTurnSit, PStand, TTime, and RTime). Results There was significant impairment of the vertical range during gait between stages I and II. Further, when stages II and III were compared, the sit-to-stand power was significantly impaired, and the total time required to complete the test increased significantly (p < 0.05). Even more significant differences were obtained when stages I and III were compared, in particular, dysfunction in postural control, vertical range, sit to stand power and total time. Finally, there were no significant differences between stages in the medial-lateral displacements and reaction time (p > 0.05). Conclusion Functional mobility becomes more significantly impaired in the PD population as the PD stages progress. This implies impaired postural control, decreased ability to sit down or stand up from a chair, increased metabolic cost during walking, and overall slowing-down of motor function.
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Affiliation(s)
- Sara Mollà-Casanova
- UBIC, Department of Physiotherapy, Faculty of Physiotherapy, Universitat de Valéncia, Valencia, Spain
| | - Jose Pedrero-Sánchez
- Instituto de Biomecánica de Valencia, Universidad Politécnica de Valencia, Valencia, Spain
| | - Marta Inglés
- UBIC, Department of Physiotherapy, Faculty of Physiotherapy, Universitat de Valéncia, Valencia, Spain
| | - Juan López-Pascual
- Instituto de Biomecánica de Valencia, Universidad Politécnica de Valencia, Valencia, Spain
| | - Elena Muñoz-Gómez
- UBIC, Department of Physiotherapy, Faculty of Physiotherapy, Universitat de Valéncia, Valencia, Spain
| | - Marta Aguilar-Rodríguez
- UBIC, Department of Physiotherapy, Faculty of Physiotherapy, Universitat de Valéncia, Valencia, Spain
| | - Nuria Sempere-Rubio
- UBIC, Department of Physiotherapy, Faculty of Physiotherapy, Universitat de Valéncia, Valencia, Spain
- *Correspondence: Nuria Sempere-Rubio,
| | - Pilar Serra-Añó
- UBIC, Department of Physiotherapy, Faculty of Physiotherapy, Universitat de Valéncia, Valencia, Spain
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