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Kamo H, Oyama G, Yamasaki Y, Nagayama T, Nawashiro R, Hattori N. A proof of concept: digital diary using 24-hour monitoring using wearable device for patients with Parkinson's disease in nursing homes. Front Neurol 2024; 15:1356042. [PMID: 38660090 PMCID: PMC11041395 DOI: 10.3389/fneur.2024.1356042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/14/2023] [Accepted: 02/26/2024] [Indexed: 04/26/2024] Open
Abstract
Introduction In the advanced stages of Parkinson's disease (PD), motor complications such as wearing-off and dyskinesia are problematic and vary daily. These symptoms need to be monitored precisely to provide adequate care for patients with advanced PD. Methods This study used wearable devices to explore biomarkers for motor complications by measuring multiple biomarkers in patients with PD residing in facilities and combining them with lifestyle and clinical assessments. Data on the pulse rate and activity index (metabolic equivalents) were collected from 12 patients over 30 days. Results The pulse rate and activity index during the off- and on-periods and dyskinesia were analyzed for two participants; the pulse rate and activity index did not show any particular trend in each participant; however, the pulse rate/activity index was significantly greater in the off-state compared to that in the dyskinesia and on-states, and this index in the dyskinesia state was significantly greater than that in the on-state in both participants. Conclusion These results suggest the pulse rate and activity index combination would be a useful indicator of wearing-off and dyskinesia and that biometric information from wearable devices may function as a digital diary. Accumulating more cases and collecting additional data are necessary to verify our findings.
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Affiliation(s)
- Hikaru Kamo
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
| | - Genko Oyama
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
- Department of Home Medical Care System, Based on Information and Communication Technology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Drug Development for Parkinson's Disease, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of PRO-Based Integrated Data Analysis in Neurological Disorders, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Research and Therapeutics for Movement Disorders, Juntendo University Graduate School of Medicine, Tokyo, Japan
| | - Yui Yamasaki
- Sunwels Company Limited, Chiyoda-ku, Tokyo, Japan
| | | | | | - Nobutaka Hattori
- Department of Neurology, Juntendo University School of Medicine, Tokyo, Japan
- Department of Home Medical Care System, Based on Information and Communication Technology, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of Drug Development for Parkinson's Disease, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Department of PRO-Based Integrated Data Analysis in Neurological Disorders, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Research and Therapeutics for Movement Disorders, Juntendo University Graduate School of Medicine, Tokyo, Japan
- Research Institute of Disease of Old Age, Graduate School of Medicine, Juntendo University, Tokyo, Japan
- Neurodegenerative Disorders Collaborative Laboratory, RIKEN Center for Brain Science, Saitama, Japan
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Maetzler W, Mirelman A, Pilotto A, Bhidayasiri R. Identifying Subtle Motor Deficits Before Parkinson's Disease is Diagnosed: What to Look for? JOURNAL OF PARKINSON'S DISEASE 2024:JPD230350. [PMID: 38363620 DOI: 10.3233/jpd-230350] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/17/2024]
Abstract
Motor deficits typical of Parkinson's disease (PD), such as gait and balance disturbances, tremor, reduced arm swing and finger movement, and voice and breathing changes, are believed to manifest several years prior to clinical diagnosis. Here we describe the evidence for the presence and progression of motor deficits in this pre-diagnostic phase in order to provide suggestions for the design of future observational studies for an effective, quantitatively oriented investigation. On the one hand, these future studies must detect these motor deficits in as large (potentially, population-based) cohorts as possible with high sensitivity and specificity. On the other hand, they must describe the progression of these motor deficits in the pre-diagnostic phase as accurately as possible, to support the testing of the effect of pharmacological and non-pharmacological interventions. Digital technologies and artificial intelligence can substantially accelerate this process.
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Affiliation(s)
- Walter Maetzler
- Department of Neurology University Hospital Schleswig-Holstein and Kiel University, Kiel, Germany
| | - Anat Mirelman
- Laboratory for Early Markers of Neurodegeneration, Neurological Institute, Tel Aviv Sourasky Medical Center, Tel Aviv, Israel
- Sagol School of Neuroscience and Faculty of Medicine, Tel Aviv University, Tel Aviv, Israel
| | - Andrea Pilotto
- Neurology Unit, Department of Clinical and Experimental Sciences, University of Brescia, Brescia, Italy
- Laboratory of Digital Neurology and Biosensors, University of Brescia, Brescia, Italy
- Neurology Unit, Department of Continuity of Care and Frailty, ASST Spedali Civili Brescia Hospital, Brescia, Italy
| | - Roongroj Bhidayasiri
- Chulalongkorn Centre of Excellence for Parkinson's Disease & Related Disorders, Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Thai Red Cross Society, Bangkok, Thailand
- The Academy of Science, The Royal Society of Thailand, Bangkok, Thailand
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Sigcha L, Polvorinos-Fernández C, Costa N, Costa S, Arezes P, Gago M, Lee C, López JM, de Arcas G, Pavón I. Monipar: movement data collection tool to monitor motor symptoms in Parkinson's disease using smartwatches and smartphones. Front Neurol 2023; 14:1326640. [PMID: 38148984 PMCID: PMC10750794 DOI: 10.3389/fneur.2023.1326640] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/23/2023] [Accepted: 11/21/2023] [Indexed: 12/28/2023] Open
Abstract
Introduction Parkinson's disease (PD) is a neurodegenerative disorder commonly characterized by motor impairments. The development of mobile health (m-health) technologies, such as wearable and smart devices, presents an opportunity for the implementation of clinical tools that can support tasks such as early diagnosis and objective quantification of symptoms. Objective This study evaluates a framework to monitor motor symptoms of PD patients based on the performance of standardized exercises such as those performed during clinic evaluation. To implement this framework, an m-health tool named Monipar was developed that uses off-the-shelf smart devices. Methods An experimental protocol was conducted with the participation of 21 early-stage PD patients and 7 healthy controls who used Monipar installed in off-the-shelf smartwatches and smartphones. Movement data collected using the built-in acceleration sensors were used to extract relevant digital indicators (features). These indicators were then compared with clinical evaluations performed using the MDS-UPDRS scale. Results The results showed moderate to strong (significant) correlations between the clinical evaluations (MDS-UPDRS scale) and features extracted from the movement data used to assess resting tremor (i.e., the standard deviation of the time series: r = 0.772, p < 0.001) and data from the pronation and supination movements (i.e., power in the band of 1-4 Hz: r = -0.662, p < 0.001). Conclusion These results suggest that the proposed framework could be used as a complementary tool for the evaluation of motor symptoms in early-stage PD patients, providing a feasible and cost-effective solution for remote and ambulatory monitoring of specific motor symptoms such as resting tremor or bradykinesia.
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Affiliation(s)
- Luis Sigcha
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Madrid, Spain
- ALGORITMI Research Center, School of Engineering, University of Minho, Guimarães, Portugal
| | - Carlos Polvorinos-Fernández
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Madrid, Spain
| | - Nélson Costa
- ALGORITMI Research Center, School of Engineering, University of Minho, Guimarães, Portugal
| | - Susana Costa
- ALGORITMI Research Center, School of Engineering, University of Minho, Guimarães, Portugal
| | - Pedro Arezes
- ALGORITMI Research Center, School of Engineering, University of Minho, Guimarães, Portugal
| | - Miguel Gago
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, Braga, Portugal
| | - Chaiwoo Lee
- AgeLab, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Juan Manuel López
- Escuela Técnica Superior de Ingeniería y Sistemas de Telecomunicación (ETSIT), Universidad Politécnica de Madrid, Madrid, Spain
| | - Guillermo de Arcas
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Madrid, Spain
| | - Ignacio Pavón
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Madrid, Spain
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López-Blanco R, Sorrentino Rodriguez A, Cubo E, Gabilondo Í, Ezpeleta D, Labrador-Espinosa MÁ, Sánchez-Ferro Á, Tejero C, Matarazzo M. Impact of new technologies on neurology in Spain. Review by the New Technologies Ad-Hoc Committee of the Spanish Society of Neurology. Neurologia 2023; 38:591-598. [PMID: 35842132 DOI: 10.1016/j.nrleng.2020.10.011] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 10/10/2020] [Indexed: 10/17/2022] Open
Abstract
INTRODUCTION New technologies are increasingly widespread in biomedicine. Using the consensus definition of new technologies established by the New Technologies Ad-Hoc Committee of the Spanish Society of Neurology (SEN), we evaluated the impact of these technologies on Spanish neurology, based on communications presented at Annual Meetings of the SEN. MATERIAL AND METHODS We defined the concept of new technology in neurology as a novel technology or novel application of an existing technology, characterised by a certain degree of coherence persisting over time, with the potential to have an impact on the present and/or future of neurology. We conducted a descriptive study of scientific communications presented at the SEN's annual meetings from 2012 to 2018, analysing the type of technology, the field of neurology, and the geographical provenance of the studies. RESULTS We identified 299 communications related with new technologies from a total of 8139 (3.7%), including 120 posters and 179 oral communications, ranging from 1.6% of all communications in 2012 to 6.8% in 2018. The technologies most commonly addressed were advanced neuroimaging (24.7%), biosensors (17.1%), electrophysiology and neurostimulation (14.7%), and telemedicine (13.7%). The neurological fields where new technologies were most widely employed were movement disorders (18.4%), cerebrovascular diseases (15.7%), and dementia (13.4%). Madrid was the region presenting the highest number of communications related to new technologies (32.8%), followed by Catalonia (26.8%) and Andalusia (9.0%). CONCLUSIONS The number of communications addressing new technologies follows an upward trend. The number of technologies used in neurology has increased in parallel with their availability. We found scientific communications in all neurological subspecialties, with a heterogeneous geographical distribution.
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Affiliation(s)
- R López-Blanco
- Servicio Integrado de Neurología, Hospital Universitario Rey Juan Carlos (Móstoles), Hospital General de Villalba, Hospital Universitario Infanta Elena (Valdemoro), Madrid, Spain
| | | | - E Cubo
- Hospital Universitario Burgos, Burgos, Spain
| | - Í Gabilondo
- Hospital Universitario de Cruces, Barakaldo, Spain
| | - D Ezpeleta
- Hospital Universitario Quirónsalud Madrid, Pozuelo de Alarcón, Madrid, Spain
| | - M Á Labrador-Espinosa
- Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío, Sevilla, Spain
| | - Á Sánchez-Ferro
- HM CINAC, Hospital Universitario HM Puerta del Sur, Móstoles, Madrid, Spain
| | - C Tejero
- Hospital Clinico Universitario Lozano Blesa, Zaragoza, Spain
| | - M Matarazzo
- HM CINAC, Hospital Universitario HM Puerta del Sur, Móstoles, Madrid, Spain; Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, BC, Canada.
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Gupta R, Kumari S, Senapati A, Ambasta RK, Kumar P. New era of artificial intelligence and machine learning-based detection, diagnosis, and therapeutics in Parkinson's disease. Ageing Res Rev 2023; 90:102013. [PMID: 37429545 DOI: 10.1016/j.arr.2023.102013] [Citation(s) in RCA: 6] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/29/2023] [Revised: 06/26/2023] [Accepted: 07/06/2023] [Indexed: 07/12/2023]
Abstract
Parkinson's disease (PD) is characterized by the loss of neuronal cells, which leads to synaptic dysfunction and cognitive defects. Despite the advancements in treatment strategies, the management of PD is still a challenging event. Early prediction and diagnosis of PD are of utmost importance for effective management of PD. In addition, the classification of patients with PD as compared to normal healthy individuals also imposes drawbacks in the early diagnosis of PD. To address these challenges, artificial intelligence (AI) and machine learning (ML) models have been implicated in the diagnosis, prediction, and treatment of PD. Recent times have also demonstrated the implication of AI and ML models in the classification of PD based on neuroimaging methods, speech recording, gait abnormalities, and others. Herein, we have briefly discussed the role of AI and ML in the diagnosis, treatment, and identification of novel biomarkers in the progression of PD. We have also highlighted the role of AI and ML in PD management through altered lipidomics and gut-brain axis. We briefly explain the role of early PD detection through AI and ML algorithms based on speech recordings, handwriting patterns, gait abnormalities, and neuroimaging techniques. Further, the review discuss the potential role of the metaverse, the Internet of Things, and electronic health records in the effective management of PD to improve the quality of life. Lastly, we also focused on the implementation of AI and ML-algorithms in neurosurgical process and drug discovery.
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Affiliation(s)
- Rohan Gupta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA.
| | - Smita Kumari
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA
| | | | - Rashmi K Ambasta
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA
| | - Pravir Kumar
- Molecular Neuroscience and Functional Genomics Laboratory, Department of Biotechnology, Delhi Technological, University, USA.
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Alberts JL, Shuaib U, Fernandez H, Walter BL, Schindler D, Miller Koop M, Rosenfeldt AB. The Parkinson's disease waiting room of the future: measurements, not magazines. Front Neurol 2023; 14:1212113. [PMID: 37670776 PMCID: PMC10475536 DOI: 10.3389/fneur.2023.1212113] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/25/2023] [Accepted: 08/08/2023] [Indexed: 09/07/2023] Open
Abstract
Utilizing technology to precisely quantify Parkinson's disease motor symptoms has evolved over the past 50 years from single point in time assessments using traditional biomechanical approaches to continuous monitoring of performance with wearables. Despite advances in the precision, usability, availability and affordability of technology, the "gold standard" for assessing Parkinson's motor symptoms continues to be a subjective clinical assessment as none of these technologies have been fully integrated into routine clinical care of Parkinson's disease patients. To facilitate the integration of technology into routine clinical care, the Develop with Clinical Intent (DCI) model was created. The DCI model takes a unique approach to the development and integration of technology into clinical practice by focusing on the clinical problem to be solved by technology rather than focusing on the technology and then contemplating how it could be integrated into clinical care. The DCI model was successfully used to develop the Parkinson's disease Waiting Room of the Future (WROTF) within the Center for Neurological Restoration at the Cleveland Clinic. Within the WROTF, Parkinson's disease patients complete the self-directed PD-Optimize application on an iPad. The PD-Optimize platform contains cognitive and motor assessments to quantify PD symptoms that are difficult and time-consuming to evaluate clinically. PD-Optimize is completed by the patient prior to their medical appointment and the results are immediately integrated into the electronic health record for discussion with the movement disorder neurologist. Insights from the clinical use of PD-Optimize has spurred the development of a virtual reality technology to evaluate instrumental activities of daily living in PD patients. This new technology will undergo rigorous assessment and validation as dictated by the DCI model. The DCI model is intended to serve as a health enablement roadmap to formalize and accelerate the process of bringing the advantages of cutting-edge technology to those who could benefit the most: the patient.
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Affiliation(s)
- Jay L. Alberts
- Department of Biomedical Engineering, Cleveland Clinic, Lerner Research Institute, Cleveland, OH, United States
- Cleveland Clinic, Neurological Institute, Center for Neurological Restoration, Cleveland, OH, United States
| | - Umar Shuaib
- Cleveland Clinic, Neurological Institute, Center for Neurological Restoration, Cleveland, OH, United States
| | - Hubert Fernandez
- Cleveland Clinic, Neurological Institute, Center for Neurological Restoration, Cleveland, OH, United States
| | - Benjamin L. Walter
- Cleveland Clinic, Neurological Institute, Center for Neurological Restoration, Cleveland, OH, United States
| | - David Schindler
- Department of Biomedical Engineering, Cleveland Clinic, Lerner Research Institute, Cleveland, OH, United States
| | - Mandy Miller Koop
- Department of Biomedical Engineering, Cleveland Clinic, Lerner Research Institute, Cleveland, OH, United States
| | - Anson B. Rosenfeldt
- Department of Biomedical Engineering, Cleveland Clinic, Lerner Research Institute, Cleveland, OH, United States
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di Biase L, Pecoraro PM, Carbone SP, Caminiti ML, Di Lazzaro V. Levodopa-Induced Dyskinesias in Parkinson's Disease: An Overview on Pathophysiology, Clinical Manifestations, Therapy Management Strategies and Future Directions. J Clin Med 2023; 12:4427. [PMID: 37445461 DOI: 10.3390/jcm12134427] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/21/2023] [Revised: 06/18/2023] [Accepted: 06/27/2023] [Indexed: 07/15/2023] Open
Abstract
Since its first introduction, levodopa has become the cornerstone for the treatment of Parkinson's disease and remains the leading therapeutic choice for motor control therapy so far. Unfortunately, the subsequent appearance of abnormal involuntary movements, known as dyskinesias, is a frequent drawback. Despite the deep knowledge of this complication, in terms of clinical phenomenology and the temporal relationship during a levodopa regimen, less is clear about the pathophysiological mechanisms underpinning it. As the disease progresses, specific oscillatory activities of both motor cortical and basal ganglia neurons and variation in levodopa metabolism, in terms of the dopamine receptor stimulation pattern and turnover rate, underlie dyskinesia onset. This review aims to provide a global overview on levodopa-induced dyskinesias, focusing on pathophysiology, clinical manifestations, therapy management strategies and future directions.
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Affiliation(s)
- Lazzaro di Biase
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Brain Innovations Lab, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo, 21, 00128 Rome, Italy
| | - Pasquale Maria Pecoraro
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
| | - Simona Paola Carbone
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
| | - Maria Letizia Caminiti
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
| | - Vincenzo Di Lazzaro
- Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo, 200, 00128 Roma, Italy
- Unit of Neurology, Neurophysiology, Neurobiology and Psichiatry, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo, 21, 00128 Roma, Italy
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Chahine LM, Simuni T. Role of novel endpoints and evaluations of response in Parkinson disease. HANDBOOK OF CLINICAL NEUROLOGY 2023; 193:325-345. [PMID: 36803820 DOI: 10.1016/b978-0-323-85555-6.00010-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/18/2023]
Abstract
With progress in our understanding of Parkinson disease (PD) and other neurodegenerative disorders, from clinical features to imaging, genetic, and molecular characterization comes the opportunity to refine and revise how we measure these diseases and what outcome measures are used as endpoints in clinical trials. While several rater-, patient-, and milestone-based outcomes for PD exist that may serve as clinical trial endpoints, there remains an unmet need for endpoints that are clinically meaningful, patient centric while also being more objective and quantitative, less susceptible to effects of symptomatic therapy (for disease-modification trials), and that can be measured over a short period and yet accurately represent longer-term outcomes. Several novel outcomes that may be used as endpoints in PD clinical trials are in development, including digital measures of signs and symptoms, as well a growing array of imaging and biospecimen biomarkers. This chapter provides an overview of the state of PD outcome measures as of 2022, including considerations for selection of clinical trial endpoints in PD, advantages and limitations of existing measures, and emerging potential novel endpoints.
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Affiliation(s)
- Lana M Chahine
- Department of Neurology, University of Pittsburgh, Pittsburgh, PA, United States
| | - Tanya Simuni
- Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, United States.
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Quantitative High Density EEG Brain Connectivity Evaluation in Parkinson's Disease: The Phase Locking Value (PLV). J Clin Med 2023; 12:jcm12041450. [PMID: 36835985 PMCID: PMC9967371 DOI: 10.3390/jcm12041450] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/03/2023] [Revised: 02/01/2023] [Accepted: 02/07/2023] [Indexed: 02/17/2023] Open
Abstract
INTRODUCTION The present study explores brain connectivity in Parkinson's disease (PD) and in age matched healthy controls (HC), using quantitative EEG analysis, at rest and during a motor tasks. We also evaluated the diagnostic performance of the phase locking value (PLV), a measure of functional connectivity, in differentiating PD patients from HCs. METHODS High-density, 64-channels, EEG data from 26 PD patients and 13 HC were analyzed. EEG signals were recorded at rest and during a motor task. Phase locking value (PLV), as a measure of functional connectivity, was evaluated for each group in a resting state and during a motor task for the following frequency bands: (i) delta: 2-4 Hz; (ii) theta: 5-7 Hz; (iii) alpha: 8-12 Hz; beta: 13-29 Hz; and gamma: 30-60 Hz. The diagnostic performance in PD vs. HC discrimination was evaluated. RESULTS Results showed no significant differences in PLV connectivity between the two groups during the resting state, but a higher PLV connectivity in the delta band during the motor task, in HC compared to PD. Comparing the resting state versus the motor task for each group, only HCs showed a higher PLV connectivity in the delta band during motor task. A ROC curve analysis for HC vs. PD discrimination, showed an area under the ROC curve (AUC) of 0.75, a sensitivity of 100%, and a negative predictive value (NPV) of 100%. CONCLUSIONS The present study evaluated the brain connectivity through quantitative EEG analysis in Parkinson's disease versus healthy controls, showing a higher PLV connectivity in the delta band during the motor task, in HC compared to PD. This neurophysiology biomarkers showed the potentiality to be explored in future studies as a potential screening biomarker for PD patients.
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10
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Feasibility of a wearable inertial sensor to assess motor complications and treatment in Parkinson's disease. PLoS One 2023; 18:e0279910. [PMID: 36730238 PMCID: PMC9894418 DOI: 10.1371/journal.pone.0279910] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/02/2022] [Accepted: 12/18/2022] [Indexed: 02/03/2023] Open
Abstract
BACKGROUND Wearable sensors-based systems have emerged as a potential tool to continuously monitor Parkinson's Disease (PD) motor features in free-living environments. OBJECTIVES To analyse the responsivity of wearable inertial sensor (WIS) measures (On/Off-Time, dyskinesia, freezing of gait (FoG) and gait parameters) after treatment adjustments. We also aim to study the ability of the sensor in the detection of MF, dyskinesia, FoG and the percentage of Off-Time, under ambulatory conditions of use. METHODS We conducted an observational, open-label study. PD patients wore a validated WIS (STAT-ONTM) for one week (before treatment), and one week, three months after therapeutic changes. The patients were analyzed into two groups according to whether treatment changes had been indicated or not. RESULTS Thirty-nine PD patients were included in the study (PD duration 8 ± 3.5 years). Treatment changes were made in 29 patients (85%). When comparing the two groups (treatment intervention vs no intervention), the WIS detected significant changes in the mean percentage of Off-Time (p = 0.007), the mean percentage of On-Time (p = 0.002), the number of steps (p = 0.008) and the gait fluidity (p = 0.004). The mean percentage of Off-Time among the patients who decreased their Off-Time (79% of patients) was -7.54 ± 5.26. The mean percentage of On-Time among the patients that increased their On-Time (59% of patients) was 8.9 ± 6.46. The Spearman correlation between the mean fluidity of the stride and the UPDRS-III- Factor I was 0.6 (p = <0.001). The system detected motor fluctuations (MF) in thirty-seven patients (95%), whilst dyskinesia and FoG were detected in fifteen (41%), and nine PD patients (23%), respectively. However, the kappa agreement analysis between the UPDRS-IV/clinical interview and the sensor was 0.089 for MF, 0.318 for dyskinesia and 0.481 for FoG. CONCLUSIONS It's feasible to use this sensor for monitoring PD treatment under ambulatory conditions. This system could serve as a complementary tool to assess PD motor complications and treatment adjustments, although more studies are required.
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di Biase L, Raiano L, Caminiti ML, Pecoraro PM, Di Lazzaro V. Parkinson's Disease Wearable Gait Analysis: Kinematic and Dynamic Markers for Diagnosis. SENSORS (BASEL, SWITZERLAND) 2022; 22:8773. [PMID: 36433372 PMCID: PMC9693970 DOI: 10.3390/s22228773] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 08/29/2022] [Revised: 11/02/2022] [Accepted: 11/07/2022] [Indexed: 06/16/2023]
Abstract
Introduction: Gait features differ between Parkinson's disease (PD) and healthy subjects (HS). Kinematic alterations of gait include reduced gait speed, swing time, and stride length between PD patients and HS. Stride time and swing time variability are increased in PD patients with respect to HS. Additionally, dynamic parameters of asymmetry of gait are significantly different among the two groups. The aim of the present study is to evaluate which kind of gait analysis (dynamic or kinematic) is more informative to discriminate PD and HS gait features. Methods: In the present study, we analyzed gait dynamic and kinematic features of 108 PD patients and 88 HS from four cohorts of two datasets. Results: Kinematic features showed statistically significant differences among PD patients and HS for gait speed and time Up and Go test and for selected kinematic dispersion indices (standard deviation and interquartile range of swing, stance, and double support time). Dynamic features did not show any statistically significant difference between PD patients and HS. Discussion: Despite kinematics features like acceleration being directly proportional to dynamic features like ground reaction force, the results of this study showed the so-called force/rhythm dichotomy since kinematic features were more informative than dynamic ones.
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Affiliation(s)
- Lazzaro di Biase
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
- Brain Innovations Lab, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo 21, 00128 Rome, Italy
| | - Luigi Raiano
- NeXT: Neurophysiology and Neuroengineering of Human-Technology Interaction Research Unit, Campus Bio-Medico University, 00128 Rome, Italy
| | - Maria Letizia Caminiti
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
| | - Pasquale Maria Pecoraro
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
| | - Vincenzo Di Lazzaro
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Roma, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
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di Biase L, Pecoraro PM, Pecoraro G, Caminiti ML, Di Lazzaro V. Markerless Radio Frequency Indoor Monitoring for Telemedicine: Gait Analysis, Indoor Positioning, Fall Detection, Tremor Analysis, Vital Signs and Sleep Monitoring. SENSORS (BASEL, SWITZERLAND) 2022; 22:8486. [PMID: 36366187 PMCID: PMC9656920 DOI: 10.3390/s22218486] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/05/2022] [Revised: 10/20/2022] [Accepted: 11/01/2022] [Indexed: 06/16/2023]
Abstract
Quantitative indoor monitoring, in a low-invasive and accurate way, is still an unmet need in clinical practice. Indoor environments are more challenging than outdoor environments, and are where patients experience difficulty in performing activities of daily living (ADLs). In line with the recent trends of telemedicine, there is an ongoing positive impulse in moving medical assistance and management from hospitals to home settings. Different technologies have been proposed for indoor monitoring over the past decades, with different degrees of invasiveness, complexity, and capabilities in full-body monitoring. The major classes of devices proposed are inertial-based sensors (IMU), vision-based devices, and geomagnetic and radiofrequency (RF) based sensors. In recent years, among all available technologies, there has been an increasing interest in using RF-based technology because it can provide a more accurate and reliable method of tracking patients' movements compared to other methods, such as camera-based systems or wearable sensors. Indeed, RF technology compared to the other two techniques has higher compliance, low energy consumption, does not need to be worn, is less susceptible to noise, is not affected by lighting or other physical obstacles, has a high temporal resolution without a limited angle of view, and fewer privacy issues. The aim of the present narrative review was to describe the potential applications of RF-based indoor monitoring techniques and highlight their differences compared to other monitoring technologies.
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Affiliation(s)
- Lazzaro di Biase
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
- Brain Innovations Lab, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo 21, 00128 Rome, Italy
| | - Pasquale Maria Pecoraro
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
| | - Giovanni Pecoraro
- Department of Electronics Engineering, University of Rome Tor Vergata, 00133 Rome, Italy
| | - Maria Letizia Caminiti
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
| | - Vincenzo Di Lazzaro
- Research Unit of Neurology, Neurophysiology and Neurobiology, Department of Medicine and Surgery, Università Campus Bio-Medico di Roma, Via Alvaro del Portillo 21, 00128 Rome, Italy
- Operative Research Unit of Neurology, Fondazione Policlinico Universitario Campus Bio-Medico, Via Alvaro del Portillo 200, 00128 Rome, Italy
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Nóbrega LR, Cabral AM, Oliveira FHM, de Oliveira Andrade A, Krishnan S, Pereira AA. Wrist Movement Variability Assessment in Individuals with Parkinson's Disease. Healthcare (Basel) 2022; 10:healthcare10091656. [PMID: 36141268 PMCID: PMC9498573 DOI: 10.3390/healthcare10091656] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/27/2022] [Revised: 08/24/2022] [Accepted: 08/24/2022] [Indexed: 12/05/2022] Open
Abstract
(1) Background: Parkinson’s disease (PD) is a neurodegenerative disorder represented by the progressive loss of dopamine-producing neurons, it decreases the individual’s motor functions and affects the execution of movements. There is a real need to include quantitative techniques and reliable methods to assess the evolution of PD. (2) Methods: This cross-sectional study assessed the variability of wrist RUD (radial and ulnar deviation) and FE (flexion and extension) movements measured by two pairs of capacitive sensors (PS25454 EPIC). The hypothesis was that PD patients have less variability in wrist movement execution than healthy individuals. The data was collected from 29 participants (age: 62.13 ± 9.7) with PD and 29 healthy individuals (60.70 ± 8). Subjects performed the experimental tasks at normal and fast speeds. Six features that captured the amplitude of the hand movements around two axes were estimated from the collected signals. (3) Results: The movement variability was greater for healthy individuals than for PD patients (p < 0.05). (4) Conclusion: The low variability seen in the PD group may indicate they execute wrist RUD and FE in a more restricted way. The variability analysis proposed here could be used as an indicator of patient progress in therapeutic programs and required changes in medication dosage.
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Affiliation(s)
- Lígia Reis Nóbrega
- Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38400-902, MG, Brazil
| | - Ariana Moura Cabral
- Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38400-902, MG, Brazil
| | | | | | - Sridhar Krishnan
- Electrical and Computer Engineering, Toronto Metropolitan University, Toronto, ON M5B 2K3, Canada
| | - Adriano Alves Pereira
- Faculty of Electrical Engineering, Federal University of Uberlândia, Uberlândia 38400-902, MG, Brazil
- Correspondence: ; Tel.: +55-34-3239-4711
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Deb R, An S, Bhat G, Shill H, Ogras UY. A Systematic Survey of Research Trends in Technology Usage for Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2022; 22:5491. [PMID: 35897995 PMCID: PMC9371095 DOI: 10.3390/s22155491] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 04/27/2022] [Revised: 07/17/2022] [Accepted: 07/20/2022] [Indexed: 06/15/2023]
Abstract
Parkinson's disease (PD) is a neurological disorder with complicated and disabling motor and non-motor symptoms. The complexity of PD pathology is amplified due to its dependency on patient diaries and the neurologist's subjective assessment of clinical scales. A significant amount of recent research has explored new cost-effective and subjective assessment methods pertaining to PD symptoms to address this challenge. This article analyzes the application areas and use of mobile and wearable technology in PD research using the PRISMA methodology. Based on the published papers, we identify four significant fields of research: diagnosis, prognosis and monitoring, predicting response to treatment, and rehabilitation. Between January 2008 and December 2021, 31,718 articles were published in four databases: PubMed Central, Science Direct, IEEE Xplore, and MDPI. After removing unrelated articles, duplicate entries, non-English publications, and other articles that did not fulfill the selection criteria, we manually investigated 1559 articles in this review. Most of the articles (45%) were published during a recent four-year stretch (2018-2021), and 19% of the articles were published in 2021 alone. This trend reflects the research community's growing interest in assessing PD with wearable devices, particularly in the last four years of the period under study. We conclude that there is a substantial and steady growth in the use of mobile technology in the PD contexts. We share our automated script and the detailed results with the public, making the review reproducible for future publications.
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Affiliation(s)
| | - Sizhe An
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53705, USA;
| | - Ganapati Bhat
- School of Electrical Engineering & Computer Science, Washington State University, Pullman, WA 99164, USA;
| | - Holly Shill
- Lonnie and Muhammad Ali Movement Disorder Center, Phoenix, AZ 85013, USA;
| | - Umit Y. Ogras
- Department of Electrical and Computer Engineering, University of Wisconsin-Madison, Madison, WI 53705, USA;
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Bernardes RA, Ventura F, Neves H, Fernandes MI, Sousa P. Wearable Walking Assistant for Freezing of Gait With Environmental IoT Monitoring: A Contribution to the Discussion. Front Public Health 2022; 10:861621. [PMID: 35795702 PMCID: PMC9251205 DOI: 10.3389/fpubh.2022.861621] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/24/2022] [Accepted: 05/30/2022] [Indexed: 01/14/2023] Open
Abstract
Parkinson's disease (PD) is the second most common neurodegenerative disease, significantly increasing in the last three decades. Worldwide, seven to ten million people are affected by PD. In people living with PD, freezing of gait (FoG) significantly impacts activities of daily living, potentially leading to falls, injuries, and loss of autonomy. FoG prevalence rates vary widely, reaching at least 50% of patients with PD. Current therapeutic options have limited effectiveness, and their complement with innovative technology-based solutions in the real world is demanded to enhance daily functioning for people living with PD. This article provides a narrative review of current technological developments for people living with PD and, derived from that evidence, presents a perspective on integrating wearable technology and IoT to support telemonitoring and self-management of people living with PD in their daily living environment. Complementing current therapeutic options with technology-based solutions in PD patients' real-world environment is crucial to enhancing the quality of life of people living with PD. In that way, wearable technology and IoT might constitute resources of excellence in seamless monitoring and self-management in people's home environments.
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Alfalahi H, Khandoker AH, Chowdhury N, Iakovakis D, Dias SB, Chaudhuri KR, Hadjileontiadis LJ. Diagnostic accuracy of keystroke dynamics as digital biomarkers for fine motor decline in neuropsychiatric disorders: a systematic review and meta-analysis. Sci Rep 2022; 12:7690. [PMID: 35546606 PMCID: PMC9095860 DOI: 10.1038/s41598-022-11865-7] [Citation(s) in RCA: 21] [Impact Index Per Article: 10.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/24/2021] [Accepted: 04/25/2022] [Indexed: 12/12/2022] Open
Abstract
The unmet timely diagnosis requirements, that take place years after substantial neural loss and neuroperturbations in neuropsychiatric disorders, affirm the dire need for biomarkers with proven efficacy. In Parkinson's disease (PD), Mild Cognitive impairment (MCI), Alzheimers disease (AD) and psychiatric disorders, it is difficult to detect early symptoms given their mild nature. We hypothesize that employing fine motor patterns, derived from natural interactions with keyboards, also knwon as keystroke dynamics, could translate classic finger dexterity tests from clinics to populations in-the-wild for timely diagnosis, yet, further evidence is required to prove this efficiency. We have searched PubMED, Medline, IEEEXplore, EBSCO and Web of Science for eligible diagnostic accuracy studies employing keystroke dynamics as an index test for the detection of neuropsychiatric disorders as the main target condition. We evaluated the diagnostic performance of keystroke dynamics across 41 studies published between 2014 and March 2022, comprising 3791 PD patients, 254 MCI patients, and 374 psychiatric disease patients. Of these, 25 studies were included in univariate random-effect meta-analysis models for diagnostic performance assessment. Pooled sensitivity and specificity are 0.86 (95% Confidence Interval (CI) 0.82-0.90, I2 = 79.49%) and 0.83 (CI 0.79-0.87, I2 = 83.45%) for PD, 0.83 (95% CI 0.65-1.00, I2 = 79.10%) and 0.87 (95% CI 0.80-0.93, I2 = 0%) for psychomotor impairment, and 0.85 (95% CI 0.74-0.96, I2 = 50.39%) and 0.82 (95% CI 0.70-0.94, I2 = 87.73%) for MCI and early AD, respectively. Our subgroup analyses conveyed the diagnosis efficiency of keystroke dynamics for naturalistic self-reported data, and the promising performance of multimodal analysis of naturalistic behavioral data and deep learning methods in detecting disease-induced phenotypes. The meta-regression models showed the increase in diagnostic accuracy and fine motor impairment severity index with age and disease duration for PD and MCI. The risk of bias, based on the QUADAS-2 tool, is deemed low to moderate and overall, we rated the quality of evidence to be moderate. We conveyed the feasibility of keystroke dynamics as digital biomarkers for fine motor decline in naturalistic environments. Future work to evaluate their performance for longitudinal disease monitoring and therapeutic implications is yet to be performed. We eventually propose a partnership strategy based on a "co-creation" approach that stems from mechanistic explanations of patients' characteristics derived from data obtained in-clinics and under ecologically valid settings. The protocol of this systematic review and meta-analysis is registered in PROSPERO; identifier CRD42021278707. The presented work is supported by the KU-KAIST joint research center.
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Affiliation(s)
- Hessa Alfalahi
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates.
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates.
| | - Ahsan H Khandoker
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
| | - Nayeefa Chowdhury
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
| | - Dimitrios Iakovakis
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
| | - Sofia B Dias
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- CIPER, Faculdade de Motricidade Humana, Universidade de Lisboa, Cruz Quebrada, 1499-002, Lisbon, Portugal
| | - K Ray Chaudhuri
- Parkinson's Foundation Centre of Excellence, King's College Hospital NHS Foundation Trust, Denmark Hill, London, SE5 9RS, United Kingdom
- Department of Basic and Clinical Neurosciences, Institute of Psychiatry, Psychology & Neuroscience, King's College London, De Crespigny Park, London, SE5 8AF, United Kingdom
| | - Leontios J Hadjileontiadis
- Department of Biomedical Engineering, Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Healthcare Engineering Innovation Center (HEIC), Khalifa University of Science and Technology, P O Box 127788, Abu Dhabi, United Arab Emirates
- Department of Electrical and Computer Engineering, Aristotle University of Thessaloniki, 54124, Thessaloniki, Greece
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Ko YF, Kuo PH, Wang CF, Chen YJ, Chuang PC, Li SZ, Chen BW, Yang FC, Lo YC, Yang Y, Ro SCV, Jaw FS, Lin SH, Chen YY. Quantification Analysis of Sleep Based on Smartwatch Sensors for Parkinson's Disease. BIOSENSORS 2022; 12:bios12020074. [PMID: 35200335 PMCID: PMC8869576 DOI: 10.3390/bios12020074] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 12/24/2021] [Revised: 01/24/2022] [Accepted: 01/25/2022] [Indexed: 05/15/2023]
Abstract
Rapid eye movement (REM) sleep behavior disorder (RBD) is associated with Parkinson's disease (PD). In this study, a smartwatch-based sensor is utilized as a convenient tool to detect the abnormal RBD phenomenon in PD patients. Instead, a questionnaire with sleep quality assessment and sleep physiological indices, such as sleep stage, activity level, and heart rate, were measured in the smartwatch sensors. Therefore, this device can record comprehensive sleep physiological data, offering several advantages such as ubiquity, long-term monitoring, and wearable convenience. In addition, it can provide the clinical doctor with sufficient information on the patient's sleeping patterns with individualized treatment. In this study, a three-stage sleep staging method (i.e., comprising sleep/awake detection, sleep-stage detection, and REM-stage detection) based on an accelerometer and heart-rate data is implemented using machine learning (ML) techniques. The ML-based algorithms used here for sleep/awake detection, sleep-stage detection, and REM-stage detection were a Cole-Kripke algorithm, a stepwise clustering algorithm, and a k-means clustering algorithm with predefined criteria, respectively. The sleep staging method was validated in a clinical trial. The results showed a statistically significant difference in the percentage of abnormal REM between the control group (1.6 ± 1.3; n = 18) and the PD group (3.8 ± 5.0; n = 20) (p = 0.04). The percentage of deep sleep stage in our results presented a significant difference between the control group (38.1 ± 24.3; n = 18) and PD group (22.0 ± 15.0, n = 20) (p = 0.011) as well. Further, our results suggested that the smartwatch-based sensor was able to detect the difference of an abnormal REM percentage in the control group (1.6 ± 1.3; n = 18), PD patient with clonazepam (2.0 ± 1.7; n = 10), and without clonazepam (5.7 ± 7.1; n = 10) (p = 0.007). Our results confirmed the effectiveness of our sensor in investigating the sleep stage in PD patients. The sensor also successfully determined the effect of clonazepam on reducing abnormal REM in PD patients. In conclusion, our smartwatch sensor is a convenient and effective tool for sleep quantification analysis in PD patients.
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Affiliation(s)
- Yi-Feng Ko
- Department of Biomedical Engineering, National Taiwan University, Taipei 10617, Taiwan; (Y.-F.K.); (F.-S.J.)
| | - Pei-Hsin Kuo
- Department of Neurology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97002, Taiwan;
- Department of Neurology, School of Medicine, Tzu Chi University, Hualien 97004, Taiwan
| | - Ching-Fu Wang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (C.-F.W.); (S.-Z.L.); (B.-W.C.); (Y.Y.)
- Biomedical Engineering Research and Development Center, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan
| | - Yu-Jen Chen
- Department of Healthcare Solution FW R&D, ASUSTeK Computer Incrporation, Taipei 11259, Taiwan; (Y.-J.C.); (P.-C.C.)
| | - Pei-Chi Chuang
- Department of Healthcare Solution FW R&D, ASUSTeK Computer Incrporation, Taipei 11259, Taiwan; (Y.-J.C.); (P.-C.C.)
| | - Shih-Zhang Li
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (C.-F.W.); (S.-Z.L.); (B.-W.C.); (Y.Y.)
| | - Bo-Wei Chen
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (C.-F.W.); (S.-Z.L.); (B.-W.C.); (Y.Y.)
| | - Fu-Chi Yang
- School of Health Care Administration, Taipei Medical University, Taipei 11031, Taiwan;
| | - Yu-Chun Lo
- The Ph.D. Program for Neural Regenerative Medicine, Taipei Medical University, Taipei 11031, Taiwan;
| | - Yi Yang
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (C.-F.W.); (S.-Z.L.); (B.-W.C.); (Y.Y.)
| | - Shuan-Chu Vina Ro
- Department of Biomedical Engineering, Johns Hopkins School of Medicine, Baltimore, MD 21205, USA;
| | - Fu-Shan Jaw
- Department of Biomedical Engineering, National Taiwan University, Taipei 10617, Taiwan; (Y.-F.K.); (F.-S.J.)
| | - Sheng-Huang Lin
- Department of Neurology, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Hualien 97002, Taiwan;
- Department of Neurology, School of Medicine, Tzu Chi University, Hualien 97004, Taiwan
- Correspondence: (S.-H.L.); (Y.-Y.C.)
| | - You-Yin Chen
- Department of Biomedical Engineering, National Yang Ming Chiao Tung University, Taipei 11221, Taiwan; (C.-F.W.); (S.-Z.L.); (B.-W.C.); (Y.Y.)
- The Ph.D. Program for Neural Regenerative Medicine, Taipei Medical University, Taipei 11031, Taiwan;
- Correspondence: (S.-H.L.); (Y.-Y.C.)
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di Biase L, Tinkhauser G, Martin Moraud E, Caminiti ML, Pecoraro PM, Di Lazzaro V. Adaptive, personalized closed-loop therapy for Parkinson's disease: biochemical, neurophysiological, and wearable sensing systems. Expert Rev Neurother 2021; 21:1371-1388. [PMID: 34736368 DOI: 10.1080/14737175.2021.2000392] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
INTRODUCTION Motor complication management is one of the main unmet needs in Parkinson's disease patients. AREAS COVERED Among the most promising emerging approaches for handling motor complications in Parkinson's disease, adaptive deep brain stimulation strategies operating in closed-loop have emerged as pivotal to deliver sustained, near-to-physiological inputs to dysfunctional basal ganglia-cortical circuits over time. Existing sensing systems that can provide feedback signals to close the loop include biochemical-, neurophysiological- or wearable-sensors. Biochemical sensing allows to directly monitor the pharmacokinetic and pharmacodynamic of antiparkinsonian drugs and metabolites. Neurophysiological sensing relies on neurotechnologies to sense cortical or subcortical brain activity and extract real-time correlates of symptom intensity or symptom control during DBS. A more direct representation of the symptom state, particularly the phenomenological differentiation and quantification of motor symptoms, can be realized via wearable sensor technology. EXPERT OPINION Biochemical, neurophysiologic, and wearable-based biomarkers are promising technological tools that either individually or in combination could guide adaptive therapy for Parkinson's disease motor symptoms in the future.
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Affiliation(s)
- Lazzaro di Biase
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico Di Roma, Rome, Italy.,Brain Innovations Lab, Università Campus Bio-Medico Di Roma, Rome, Italy
| | - Gerd Tinkhauser
- Department of Neurology, Bern University Hospital and University of Bern, Bern, Switzerland
| | - Eduardo Martin Moraud
- Department of Clinical Neurosciences, Lausanne University Hospital (Chuv) and University of Lausanne (Unil), Lausanne, Switzerland.,Defitech Center for Interventional Neurotherapies (.neurorestore), Lausanne University Hospital and Swiss Federal Institute of Technology (Epfl), Lausanne, Switzerland
| | - Maria Letizia Caminiti
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico Di Roma, Rome, Italy
| | - Pasquale Maria Pecoraro
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico Di Roma, Rome, Italy
| | - Vincenzo Di Lazzaro
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico Di Roma, Rome, Italy
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Ashfaque Mostafa T, Soltaninejad S, McIsaac TL, Cheng I. A Comparative Study of Time Frequency Representation Techniques for Freeze of Gait Detection and Prediction. SENSORS (BASEL, SWITZERLAND) 2021; 21:6446. [PMID: 34640763 PMCID: PMC8512068 DOI: 10.3390/s21196446] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 08/13/2021] [Revised: 09/13/2021] [Accepted: 09/24/2021] [Indexed: 11/30/2022]
Abstract
Freezing of Gait (FOG) is an impairment that affects the majority of patients in the advanced stages of Parkinson's Disease (PD). FOG can lead to sudden falls and injuries, negatively impacting the quality of life for the patients and their families. Rhythmic Auditory Stimulation (RAS) can be used to help patients recover from FOG and resume normal gait. RAS might be ineffective due to the latency between the start of a FOG event, its detection and initialization of RAS. We propose a system capable of both FOG prediction and detection using signals from tri-axial accelerometer sensors that will be useful in initializing RAS with minimal latency. We compared the performance of several time frequency analysis techniques, including moving windows extracted from the signals, handcrafted features, Recurrence Plots (RP), Short Time Fourier Transform (STFT), Discreet Wavelet Transform (DWT) and Pseudo Wigner Ville Distribution (PWVD) with Deep Learning (DL) based Long Short Term Memory (LSTM) and Convolutional Neural Networks (CNN). We also propose three Ensemble Network Architectures that combine all the time frequency representations and DL architectures. Experimental results show that our ensemble architectures significantly improve the performance compared with existing techniques. We also present the results of applying our method trained on a publicly available dataset to data collected from patients using wearable sensors in collaboration with A.T. Still University.
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Affiliation(s)
- Tahjid Ashfaque Mostafa
- Multimedia Research Center, Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada;
| | - Sara Soltaninejad
- Multimedia Research Center, Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada;
| | - Tara L. McIsaac
- Arizona School of Health Sciences, A.T. Still University, 5850 E. Still Circle, Mesa, AZ 85206, USA;
- School of Pharmacy and Health Professions, Creighton University Health Sciences, 3100 N. Central Ave., Phoenix, AZ 85013, USA
| | - Irene Cheng
- Multimedia Research Center, Department of Computing Science, University of Alberta, Edmonton, AB T6G 2E8, Canada;
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Hou Y, Zheng H, Li J, Wang S, Zhang D, Tang T, Xu M, Zhou H. A New Method for Postural Misalignment of a 6-Year-Old Girl With Cerebral Palsy: A Case Report. Arch Rehabil Res Clin Transl 2021; 3:100116. [PMID: 34179752 PMCID: PMC8212001 DOI: 10.1016/j.arrct.2021.100116] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022] Open
Abstract
Objective To demonstrate the effects of a newly designed postural alignment relearning system on postural control dysfunction in a typical patient with cerebral palsy (CP). Design Evaluation before and after 8 weeks of Constraint Standing Training 3-dimensional postural alignment relearning system. Setting Department of Rehabilitation Medicine. Participant A 6-year-old girl with CP and postural misalignment on Gross Motor Function Classification System level I. Interventions Constraint Standing Training for 8 weeks to correct postural misalignment. Main Outcome Measures Parameters of lateral plain radiographs in static standing, posturography measurements in standing and walking, motor ability (Gross Motor Function Measure-88 [GMFM-88] scores, manual muscle testing [MMT] scores, muscle architecture), and gait kinematic parameters (40 3-dimensional parameters of arms, trunk, waist, and lower limbs). Results Knee hyperextension angle in static standing; peaks of knee flexion angle (KFA) when walking, hip flexion angle and ankle flexion angle in dynamic standing; and the KFA at initial contact in gait cycle all decreased significantly (P<.01). Scores of GMFM-88 sections D and E and MMT of 5 core stability muscles improved (P<.01). The velocities and range of motion of the arms, the 3-dimensinoal range of motion of the trunk and waist, and most of the parameters of the lower limbs showed statistically significant change (P<.01). Bilateral muscle thickness did not change significantly after the treatment (P=.738 left, P=.978 right), but the gluteus maximus morphology was changed: the muscle fibers became rounder, the interfiber space decreased, and the border lines of the muscle fibers got clearer. Conclusions Postural alignment, motor ability, and gait may be homologous external manifestations of more fundamental core abilities, referring to correct standing posture cognition, muscle activation, and postural unconsciousness. Constraint Standing Training 3-dimensional postural alignment relearning system aimed to improve the static and dynamic standing control ability, may fix postural misalignment and improve motor ability and flexed-knee gait. Future work should use Constraint Standing Training with patients with different kinds of misalignment, choose sensitive indicators, observe the duration of each step, and reveal the mechanism causes postural misalignment.
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Affiliation(s)
- Ying Hou
- Department of Rehabilitation Medicine, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou
| | - Huitian Zheng
- Department of Rehabilitation Medicine, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou
| | - Jinping Li
- Department of Rehabilitation Medicine, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou
| | - Shujia Wang
- Department of Rehabilitation Medicine, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou
| | - Dongmei Zhang
- Department of Rehabilitation Medicine, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou
| | - Tong Tang
- Department of Rehabilitation Medicine, Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou
| | - Mindan Xu
- Neurological Rehabilitation Department, Zhangjiagang Gangcheng Rehabilitation Hospital, Zhangjiagang
| | - Hong Zhou
- Children Rehabilitation Department, Kunshan Rehabilitation Hospital, Kunshan, China
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21
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Albán-Cadena AC, Villalba-Meneses F, Pila-Varela KO, Moreno-Calvo A, Villalba-Meneses CP, Almeida-Galárraga DA. Wearable sensors in the diagnosis and study of Parkinson's disease symptoms: a systematic review. J Med Eng Technol 2021; 45:532-545. [PMID: 34060967 DOI: 10.1080/03091902.2021.1922528] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.7] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Nowadays, there are several diseases which affect different systems of the body, producing changes in the correct functioning of the organism and the people lifestyles. One of them is Parkinson's disease (PD), which is defined as a neurodegenerative disorder provoked by the destruction of dopaminergic neurons in the brain, resulting in a set of motor and non-motor symptoms. As this disease affects principally to ancient people, several researchers have studied different treatments and therapies for stopping neurodegeneration and diminishing symptoms, to improve the quality patients' lives. The most common therapies created for PD are based on pharmacological treatment for controlling the degeneration advance and the physical ones which do not reveal the progress of patients. For this reason, this review paper opens the possibility for using wearable motion capture systems as an option for the control and study of PD. Therefore, it aims to (1) study the different wearable systems used for capture the movements of PD patients and (2) determine which of them bring better results for monitoring and assess PD people. For the analysis, it uses papers based on experiments that prove the functioning of several motion systems in different aspects as monitoring, treatment and diagnose of the disease. As a result, it works with 30 papers which describe the factors mentioned before. Additionally, the paper uses journals and literature review about the pathology, its characteristics and the function of wearable sensors for the correct understanding of the topic.
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Affiliation(s)
- Andrea C Albán-Cadena
- School of Biological Sciences & Engineering, Universidad Yachay Tech, Urcuquí, Ecuador
| | - Fernando Villalba-Meneses
- School of Biological Sciences & Engineering, Universidad Yachay Tech, Urcuquí, Ecuador.,University of Zaragoza, Zaragoza, Spain
| | - Kevin O Pila-Varela
- School of Biological Sciences & Engineering, Universidad Yachay Tech, Urcuquí, Ecuador
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22
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Quantitative mobility measures complement the MDS-UPDRS for characterization of Parkinson's disease heterogeneity. Parkinsonism Relat Disord 2021; 84:105-111. [PMID: 33607526 DOI: 10.1016/j.parkreldis.2021.02.006] [Citation(s) in RCA: 10] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Revised: 12/28/2020] [Accepted: 02/03/2021] [Indexed: 11/20/2022]
Abstract
INTRODUCTION Emerging technologies show promise for enhanced characterization of Parkinson's Disease (PD) motor manifestations. We evaluated quantitative mobility measures from a wearable device compared to the conventional motor assessment, the Movement Disorders Society-Unified PD Rating Scale part III (motor MDS-UPDRS). METHODS We evaluated 176 PD subjects (mean age 65, 65% male, 66% H&Y stage 2) during routine clinic visits using the motor MDS-UPDRS and a 10-min motor protocol with a body-fixed sensor (DynaPort MT, McRoberts BV), including the 32-ft walk, Timed Up and Go (TUG), and standing posture with eyes closed. Regression models examined 12 quantitative mobility measures for associations with (i) motor MDS-UPDRS, (ii) motor subtype (tremor dominant vs. postural instability/gait difficulty), (iii) Montreal Cognitive Assessment (MoCA), and (iv) physical functioning disability (PROMIS-29). All analyses included age, gender, and disease duration as covariates. Models iii-iv were secondarily adjusted for motor MDS-UPDRS. RESULTS Quantitative mobility measures from gait, TUG transitions, turning, and posture were significantly associated with motor MDS-UPDRS (7 of 12 measures, p < 0.05) and motor subtype (6 of 12 measures, p < 0.05). Compared with motor MDS-UPDRS, several quantitative mobility measures accounted for a 1.5- or 1.9-fold increased variance in either cognition or physical functioning disability, respectively. Among minimally-impaired subjects in the bottom quartile of motor MDS-UPDRS, including subjects with normal gait exam, the measures captured substantial residual motor heterogeneity. CONCLUSION Clinic-based quantitative mobility assessments using a wearable sensor captured features of motor performance beyond those obtained with the motor MDS-UPDRS and may offer enhanced characterization of disease heterogeneity.
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23
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Tortelli R, Rodrigues FB, Wild EJ. The use of wearable/portable digital sensors in Huntington's disease: A systematic review. Parkinsonism Relat Disord 2021; 83:93-104. [PMID: 33493786 PMCID: PMC7957324 DOI: 10.1016/j.parkreldis.2021.01.006] [Citation(s) in RCA: 19] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 10/13/2020] [Accepted: 01/08/2021] [Indexed: 01/26/2023]
Abstract
In chronic neurological conditions, wearable/portable devices have potential as innovative tools to detect subtle early disease manifestations and disease fluctuations for the purpose of clinical diagnosis, care and therapeutic development. Huntington's disease (HD) has a unique combination of motor and non-motor features which, combined with recent and anticipated therapeutic progress, gives great potential for such devices to prove useful. The present work aims to provide a comprehensive account of the use of wearable/portable devices in HD and of what they have contributed so far. We conducted a systematic review searching MEDLINE, Embase, and IEEE Xplore. Thirty references were identified. Our results revealed large variability in the types of sensors used, study design, and the measured outcomes. Digital technologies show considerable promise for therapeutic research and clinical management of HD. However, more studies with standardized devices and harmonized protocols are needed to optimize the potential applicability of wearable/portable devices in HD. Wearable/portable sensors have been proposed to detect and quantify manifestations of many neurodegenerative diseases. No systematic review so far has examined their use in Huntington's disease (HD). This work draws a broad picture of the digital wearable-based landscape in HD. The utility of wearables in clinical practice and therapeutic research still needs to be proved. Collaborative efforts are needed to further investigate their clinical use in HD.
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Affiliation(s)
- Rosanna Tortelli
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Filipe B Rodrigues
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK
| | - Edward J Wild
- UCL Huntington's Disease Centre, UCL Queen Square Institute of Neurology, University College London, London, UK.
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24
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Sigcha L, Pavón I, Costa N, Costa S, Gago M, Arezes P, López JM, De Arcas G. Automatic Resting Tremor Assessment in Parkinson's Disease Using Smartwatches and Multitask Convolutional Neural Networks. SENSORS 2021; 21:s21010291. [PMID: 33406692 PMCID: PMC7794726 DOI: 10.3390/s21010291] [Citation(s) in RCA: 20] [Impact Index Per Article: 6.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 11/26/2020] [Revised: 12/22/2020] [Accepted: 12/29/2020] [Indexed: 12/28/2022]
Abstract
Resting tremor in Parkinson's disease (PD) is one of the most distinctive motor symptoms. Appropriate symptom monitoring can help to improve management and medical treatments and improve the patients' quality of life. Currently, tremor is evaluated by physical examinations during clinical appointments; however, this method could be subjective and does not represent the full spectrum of the symptom in the patients' daily lives. In recent years, sensor-based systems have been used to obtain objective information about the disease. However, most of these systems require the use of multiple devices, which makes it difficult to use them in an ambulatory setting. This paper presents a novel approach to evaluate the amplitude and constancy of resting tremor using triaxial accelerometers from consumer smartwatches and multitask classification models. These approaches are used to develop a system for an automated and accurate symptom assessment without interfering with the patients' daily lives. Results show a high agreement between the amplitude and constancy measurements obtained from the smartwatch in comparison with those obtained in a clinical assessment. This indicates that consumer smartwatches in combination with multitask convolutional neural networks are suitable for providing accurate and relevant information about tremor in patients in the early stages of the disease, which can contribute to the improvement of PD clinical evaluation, early detection of the disease, and continuous monitoring.
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Affiliation(s)
- Luis Sigcha
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7, 28031 Madrid, Spain; (L.S.); (J.M.L.); (G.D.A.)
- ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimarães, Portugal; (N.C.); (S.C.); (P.A.)
| | - Ignacio Pavón
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7, 28031 Madrid, Spain; (L.S.); (J.M.L.); (G.D.A.)
- Correspondence: ; Tel.: +34-91-067-7222
| | - Nélson Costa
- ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimarães, Portugal; (N.C.); (S.C.); (P.A.)
| | - Susana Costa
- ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimarães, Portugal; (N.C.); (S.C.); (P.A.)
| | - Miguel Gago
- Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057 Braga, Portugal;
| | - Pedro Arezes
- ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimarães, Portugal; (N.C.); (S.C.); (P.A.)
| | - Juan Manuel López
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7, 28031 Madrid, Spain; (L.S.); (J.M.L.); (G.D.A.)
| | - Guillermo De Arcas
- Instrumentation and Applied Acoustics Research Group (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7, 28031 Madrid, Spain; (L.S.); (J.M.L.); (G.D.A.)
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25
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Monteiro Oliveira FH, Fernandes da Cunha D, Gomes Rabelo A, David Luiz LM, Fraga Vieira M, Alves Pereira A, de Oliveira Andrade A. A non-contact system for the assessment of hand motor tasks in people with Parkinson’s disease. SN APPLIED SCIENCES 2021. [DOI: 10.1007/s42452-020-04001-5] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/30/2022] Open
Abstract
AbstractClinical diagnosis of Parkinson’s disease (PD) motor symptoms remains a problem. Most of the current studies focus on objective evaluations to make the evaluation more reliable. Most of these systems are based on the use of inertial and electromyographic sensors that require contact with the body part being assessed. Contact sensors restrict natural movement, may be uncomfortable and may require preparation of the body, which may cause irritation. As an alternative to contact sensors for the study of hand motor tasks performed by subjects with and without PD, electrical potential sensing technology is used in this research. A custom hardware has been designed to enable data collection by hand movement. A micro-machine system validated the developed system, and a relationship model was established between hand displacement and non-contact capacitive (NCC) sensor response. An experiment was conducted, including 57 subjects, 30 with PD (experimental group) and 27 healthy control group, followed by an analysis of statistical features extracted from the instantaneous mean frequency (IMNF) of NCC sensor. These results were compared with those obtained from gyroscope signals that are considered in the field to be the gold standard. As a result, NCC responses were correlated linearly with hand displacement (R2 = 0.7692 and $${\text{R}}_{\text{adj}}^{2}$$
R
adj
2
= 0.7631). The statistical evaluation of IMNF features showed, that both, contact and non-contact sensors, were able to discriminate movement patterns of the control group from the experimental one. The results confirm statistical similarity between features extracted from NCC and gyroscope signals.
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26
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Monje MHG, Fuller RLM, Cubo E, Mestre TA, Tan AH, Stout JC, Ali S, Chahine L, Dujardin K, Fitzer-Attas CJ, Youn J, Bloem BR, Horak FB, Merola A, Reilmann R, Paul SS, Dorsey ER, Maetzler W, Espay AJ, Martinez-Martin P, Stebbins GT, Sánchez-Ferro Á. Toward e-Scales: Digital Administration of the International Parkinson and Movement Disorder Society Rating Scales. Mov Disord Clin Pract 2020; 8:208-214. [PMID: 33553489 DOI: 10.1002/mdc3.13135] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/09/2020] [Revised: 09/30/2020] [Accepted: 11/14/2020] [Indexed: 12/17/2022] Open
Affiliation(s)
- Mariana H G Monje
- HM CINAC, Hospital Universitario HM Puerta del Sur Madrid Spain.,Department of Anatomy, Histology and Neuroscience, School of Medicine Universidad Autónoma de Madrid Madrid Spain
| | | | - Esther Cubo
- Neurology Department Hospital Universitario Burgos Burgos Spain
| | - Tiago A Mestre
- Parkinson's Disease and Movement Disorders Center, Division of Neurology, Department of Medicine The Ottawa Hospital Research Institute, University of Ottawa Ottawa Ontario Canada
| | - Ai Huey Tan
- Division of Neurology and the Mah Pooi Soo & Tan Chin Nam Centre for Parkinson's & Related Disorders, Faculty of Medicine University of Malaya Kuala Lumpur Malaysia
| | - Julie C Stout
- Turner Institute for Brain and Mental Health, School of Psychological Sciences Monash University Clayton Victoria Australia
| | - Shazia Ali
- International Movement Disorders Society Milwaukee Illinois USA
| | - Lana Chahine
- Neurology Department, School of Medicine University of Pittsburgh Pittsburgh Pennsylvania USA
| | - Kathy Dujardin
- Movement Disorders Department Lille University Medical Center Lille France
| | | | - Jinyoung Youn
- Neurology Department Samsung Medical Center School of Medicine Seoul South Korea
| | - Bastiaan R Bloem
- Radboud University Medical Centre, Donders Institute for Brain, Cognition and Behaviour, Department of Neurology Centre of Expertise for Parkinson & Movement Disorders Nijmegen The Netherlands
| | - Fay B Horak
- Department of Neurology Oregon Health and Science University Portland Oregon USA
| | - Aristide Merola
- Department of Neurology Ohio State Wexner Medical Center Columbus Ohio USA
| | - Ralf Reilmann
- George-Huntingon-Institute & Department of Clinical Radiology University of Muenster Münster Germany.,Department of Neurodegeneration, Hertie Institute for Clinical Brain Research University of Tuebingen Tübingen Germany
| | - Serene S Paul
- Discipline of Physiotherapy, Sydney School of Health Sciences, Faculty of Medicine and Health University of Sydney Sydney New South Wales Australia
| | - Earl Ray Dorsey
- Center for Health + Technology and Department of Neurology University of Rochester Medical Center Rochester New York USA
| | - Walter Maetzler
- Department of Neurology University Hospital Schleswig-Holstein, Kiel University Kiel Germany
| | - Alberto J Espay
- Gardner Family Center for Parkinson's Disease and Movement Disorders and Neurology Department University of Cincinnati Cincinnati Ohio USA
| | - Pablo Martinez-Martin
- Center for Networked Biomedical Research in Neurodegenerative Diseases (CIBERNED) Carlos III Institute of Health Madrid Spain
| | - Glenn T Stebbins
- Department of Neurological Sciences Rush University Medical Center Chicago Illinois USA
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27
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Santos García D, López Ariztegui N, Cubo E, Vinagre Aragón A, García-Ramos R, Borrué C, Fernández-Pajarín G, Caballol N, Cabo I, Barrios-López JM, Hernández Vara J, Ávila Rivera MA, Gasca-Salas C, Escalante S, Manrique de Lara P, Pérez Noguera R, Álvarez Sauco M, Sierra M, Monje MHG, Sánchez Ferro A, Novo Ponte S, Alonso-Frech F, Macías-García D, Legarda I, Rojo A, Álvarez Fernández I, Buongiorno MT, Pastor P, García Ruíz P. Clinical utility of a personalized and long-term monitoring device for Parkinson's disease in a real clinical practice setting: An expert opinion survey on STAT-ON™. Neurologia 2020; 38:S0213-4853(20)30339-X. [PMID: 33358530 DOI: 10.1016/j.nrl.2020.10.013] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/06/2020] [Revised: 10/01/2020] [Accepted: 10/05/2020] [Indexed: 10/22/2022] Open
Abstract
BACKGROUND STAT-ON™ is an objective tool that registers ON-OFF fluctuations making possible to know the state of the patient at every moment of the day in normal life. Our aim was to analyze the opinion of different Parkinson's disease experts about the STAT-ON™ tool after using the device in a real clinical practice setting (RCPS). METHODS STAT-ON™ was provided by the Company Sense4Care to Spanish neurologists for using it in a RCPS. Each neurologist had the device for at least three months and could use it in PD patients at his/her own discretion. In February 2020, a survey with 30 questions was sent to all participants. RESULTS Two thirds of neurologists (53.8% females; mean age 44.9±9 years old) worked in a Movement Disorders Unit, the average experience in PD was 16±6.9 years, and 40.7% of them had previously used other devices. A total of 119 evaluations were performed in 114 patients (range 2-9 by neurologist; mean 4.5±2.3). STAT-ON™ was considered "quite" to "very useful" by 74% of the neurologists with an overall opinion of 6.9±1.7 (0, worst; 10, best). STAT-ON™ was considered better than diaries by 70.3% of neurologists and a useful tool for the identification of patients with advanced PD by 81.5%. Proper identification of freezing of gait episodes and falls were frequent limitations reported. CONCLUSION STAT-ON™ could be a useful device for using in PD patients in clinical practice.
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Affiliation(s)
- D Santos García
- CHUAC, Complejo Hospitalario Universitario de A Coruña, Spain.
| | | | - E Cubo
- Complejo Asistencial Universitario de Burgos, Burgos, Spain
| | | | | | - C Borrué
- Hospital Infanta Sofía, Madrid, Spain
| | | | - N Caballol
- Consorci Sanitari Integral, Hospital Moisés Broggi, Sant Joan Despí, Barcelona, Spain
| | - I Cabo
- Complejo Hospitalario Universitario de Pontevedra (CHOP), Pontevedra, Spain
| | | | | | - M A Ávila Rivera
- Consorci Sanitari Integral, Hospital General de L'Hospitalet, L'Hospitalet de Llobregat, Barcelona, Spain
| | | | - S Escalante
- Hospital de Tortosa Verge de la Cinta (HTVC), Tortosa, Tarragona, Spain
| | | | | | | | - M Sierra
- Hospital Universitario Marqués de Valdecilla, Santander, Spain
| | - M H G Monje
- CINAC, Hospital Puerta del Sur, Madrid, Spain
| | | | | | | | | | - I Legarda
- Hospital Universitario Son Espases, Palma de Mallorca, Spain
| | - A Rojo
- Hospital Universitario Príncipe de Asturias, Alcalá de Henares, Madrid, Spain
| | | | - M T Buongiorno
- Hospital Universitari Mutua de Terrassa, Terrassa, Barcelona, Spain
| | - P Pastor
- Hospital Universitari Mutua de Terrassa, Terrassa, Barcelona, Spain
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28
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López-Blanco R, Sorrentino Rodriguez A, Cubo E, Gabilondo Í, Ezpeleta D, Labrador-Espinosa MA, Sánchez-Ferro Á, Tejero C, Matarazzo M. Impact of new technologies on neurology in Spain. Review by the New Technologies Ad-Hoc Committee of the Spanish Society of Neurology. Neurologia 2020; 38:S0213-4853(20)30429-1. [PMID: 33358062 DOI: 10.1016/j.nrl.2020.10.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/30/2020] [Accepted: 10/10/2020] [Indexed: 11/22/2022] Open
Abstract
INTRODUCTION New technologies (NT) are increasingly widespread in biomedicine. Using the consensus definition of NT established by the New Technologies Ad-Hoc Committee of the Spanish Society of Neurology (SEN), we evaluated the impact of these technologies on Spanish neurology, based on communications presented at Annual Meetings of the SEN. MATERIAL AND METHODS We defined the concept of NT in neurology as a novel technology or novel application of an existing technology, characterised by a certain degree of coherence persisting over time, with the potential to have an impact on the present and/or future of neurology. We conducted a descriptive study of scientific communications presented at the SEN's annual meetings from 2012 to 2018, analysing the type of NT, the field of neurology, and the geographical provenance of the studies. RESULTS We identified 299 communications related with NT from a total of 8,139 (3.7%), including 120 posters and 179 oral communications, ranging from 1.6% of all communications in 2012 to 6.8% in 2018. The technologies most commonly addressed were advanced neuroimaging (24.7%), biosensors (17.1%), electrophysiology and neurostimulation (14.7%), and telemedicine (13.7%). The neurological fields where NT were most widely employed were movement disorders (18.4%), cerebrovascular diseases (15.7%), and dementia (13.4%). Madrid was the region presenting the highest number of communications related to NT (32.8%), followed by Catalonia (26.8%) and Andalusia (9.0%). CONCLUSIONS The number of communications addressing NT follows an upward trend. The number of NT used in neurology has increased in parallel with their availability. We found scientific communications in all neurological subspecialties, with a heterogeneous geographical distribution.
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Affiliation(s)
- R López-Blanco
- Servicio Integrado de Neurología. Hospital Universitario Rey Juan Carlos (Móstoles), Hospital General de Villalba, Hospital Universitario Infanta Elena (Valdemoro), Madrid, España
| | | | - E Cubo
- Hospital Universitario de Burgos, Burgos, España
| | - Í Gabilondo
- Hospital Universitario de Cruces, Barakaldo, España
| | - D Ezpeleta
- Hospital Universitario Quirónsalud Madrid, Pozuelo de Alarcón, Madrid, España
| | - M A Labrador-Espinosa
- Instituto de Biomedicina de Sevilla, Hospital Universitario Virgen del Rocío, Sevilla, España
| | - Á Sánchez-Ferro
- HM CINAC, Hospital Universitario HM Puerta del Sur, Móstoles, Madrid, España
| | - C Tejero
- Hospital Clínico Universitario Lozano Blesa, Zaragoza, España
| | - M Matarazzo
- HM CINAC, Hospital Universitario HM Puerta del Sur, Móstoles, Madrid, España; Pacific Parkinson's Research Centre, University of British Columbia, Vancouver, BC, Canadá.
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29
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Bhidayasiri R, Mari Z. Digital phenotyping in Parkinson's disease: Empowering neurologists for measurement-based care. Parkinsonism Relat Disord 2020; 80:35-40. [DOI: 10.1016/j.parkreldis.2020.08.038] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2020] [Revised: 08/26/2020] [Accepted: 08/28/2020] [Indexed: 12/24/2022]
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30
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Luis-Martínez R, Monje MHG, Antonini A, Sánchez-Ferro Á, Mestre TA. Technology-Enabled Care: Integrating Multidisciplinary Care in Parkinson's Disease Through Digital Technology. Front Neurol 2020; 11:575975. [PMID: 33250846 PMCID: PMC7673441 DOI: 10.3389/fneur.2020.575975] [Citation(s) in RCA: 25] [Impact Index Per Article: 6.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2020] [Accepted: 08/24/2020] [Indexed: 11/13/2022] Open
Abstract
Parkinson's disease (PD) management requires the involvement of movement disorders experts, other medical specialists, and allied health professionals. Traditionally, multispecialty care has been implemented in the form of a multidisciplinary center, with an inconsistent clinical benefit and health economic impact. With the current capabilities of digital technologies, multispecialty care can be reshaped to reach a broader community of people with PD in their home and community. Digital technologies have the potential to connect patients with the care team beyond the traditional sparse clinical visit, fostering care continuity and accessibility. For example, video conferencing systems can enable the remote delivery of multispecialty care. With big data analyses, wearable and non-wearable technologies using artificial intelligence can enable the remote assessment of patients' conditions in their natural home environment, promoting a more comprehensive clinical evaluation and empowering patients to monitor their disease. These advances have been defined as technology-enabled care (TEC). We present examples of TEC under development and describe the potential challenges to achieve a full integration of technology to address complex care needs in PD.
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Affiliation(s)
- Raquel Luis-Martínez
- Department of Neurosciences, University of Basque Country (UPV/EHU), Leioa, Spain
- Department of Neurosciences (DNS), Padova University, Padova, Italy
| | - Mariana H G Monje
- HM CINAC, Hospital Universitario HM Puerta del Sur, Universidad CEU-San Pablo, Madrid, Spain
| | - Angelo Antonini
- Department of Neurosciences (DNS), Padova University, Padova, Italy
| | - Álvaro Sánchez-Ferro
- HM CINAC, Hospital Universitario HM Puerta del Sur, Universidad CEU-San Pablo, Madrid, Spain
| | - Tiago A Mestre
- Division of Neurology, Department of Medicine, The Ottawa Hospital Research Institute, Parkinson's Disease and Movement Disorders Center, The University of Ottawa Brain Research Institute, Ottawa, ON, Canada
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31
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Evers LJ, Raykov YP, Krijthe JH, Silva de Lima AL, Badawy R, Claes K, Heskes TM, Little MA, Meinders MJ, Bloem BR. Real-Life Gait Performance as a Digital Biomarker for Motor Fluctuations: The Parkinson@Home Validation Study. J Med Internet Res 2020; 22:e19068. [PMID: 33034562 PMCID: PMC7584982 DOI: 10.2196/19068] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/02/2020] [Revised: 08/10/2020] [Accepted: 08/21/2020] [Indexed: 12/16/2022] Open
Abstract
Background Wearable sensors have been used successfully to characterize bradykinetic gait in patients with Parkinson disease (PD), but most studies to date have been conducted in highly controlled laboratory environments. Objective This paper aims to assess whether sensor-based analysis of real-life gait can be used to objectively and remotely monitor motor fluctuations in PD. Methods The Parkinson@Home validation study provides a new reference data set for the development of digital biomarkers to monitor persons with PD in daily life. Specifically, a group of 25 patients with PD with motor fluctuations and 25 age-matched controls performed unscripted daily activities in and around their homes for at least one hour while being recorded on video. Patients with PD did this twice: once after overnight withdrawal of dopaminergic medication and again 1 hour after medication intake. Participants wore sensors on both wrists and ankles, on the lower back, and in the front pants pocket, capturing movement and contextual data. Gait segments of 25 seconds were extracted from accelerometer signals based on manual video annotations. The power spectral density of each segment and device was estimated using Welch’s method, from which the total power in the 0.5- to 10-Hz band, width of the dominant frequency, and cadence were derived. The ability to discriminate between before and after medication intake and between patients with PD and controls was evaluated using leave-one-subject-out nested cross-validation. Results From 18 patients with PD (11 men; median age 65 years) and 24 controls (13 men; median age 68 years), ≥10 gait segments were available. Using logistic LASSO (least absolute shrinkage and selection operator) regression, we classified whether the unscripted gait segments occurred before or after medication intake, with mean area under the receiver operator curves (AUCs) varying between 0.70 (ankle of least affected side, 95% CI 0.60-0.81) and 0.82 (ankle of most affected side, 95% CI 0.72-0.92) across sensor locations. Combining all sensor locations did not significantly improve classification (AUC 0.84, 95% CI 0.75-0.93). Of all signal properties, the total power in the 0.5- to 10-Hz band was most responsive to dopaminergic medication. Discriminating between patients with PD and controls was generally more difficult (AUC of all sensor locations combined: 0.76, 95% CI 0.62-0.90). The video recordings revealed that the positioning of the hands during real-life gait had a substantial impact on the power spectral density of both the wrist and pants pocket sensor. Conclusions We present a new video-referenced data set that includes unscripted activities in and around the participants’ homes. Using this data set, we show the feasibility of using sensor-based analysis of real-life gait to monitor motor fluctuations with a single sensor location. Future work may assess the value of contextual sensors to control for real-world confounders.
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Affiliation(s)
- Luc Jw Evers
- Center of Expertise for Parkinson and Movement Disorders, department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands.,Institute for Computing and Information Sciences, Radboud University, Nijmegen, Netherlands
| | - Yordan P Raykov
- Department of Mathematics, School of Engineering and Applied Sciences, Aston University, Birmingham, United Kingdom
| | - Jesse H Krijthe
- Institute for Computing and Information Sciences, Radboud University, Nijmegen, Netherlands
| | - Ana Lígia Silva de Lima
- Center of Expertise for Parkinson and Movement Disorders, department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
| | - Reham Badawy
- School of Computer Science, University of Birmingham, Birmingham, United Kingdom
| | | | - Tom M Heskes
- Institute for Computing and Information Sciences, Radboud University, Nijmegen, Netherlands
| | - Max A Little
- School of Computer Science, University of Birmingham, Birmingham, United Kingdom
| | - Marjan J Meinders
- Scientific Center for Quality of Healthcare (IQ healthcare), Radboud Institute for Health Sciences, Radboud University Medical Center, Nijmegen, Netherlands
| | - Bastiaan R Bloem
- Center of Expertise for Parkinson and Movement Disorders, department of Neurology, Donders Institute for Brain, Cognition and Behaviour, Radboud University Medical Center, Nijmegen, Netherlands
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Zhang A, De la Torre F, Hodgins J. Comparing laboratory and in-the-wild data for continuous Parkinson's Disease tremor detection. ANNUAL INTERNATIONAL CONFERENCE OF THE IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. IEEE ENGINEERING IN MEDICINE AND BIOLOGY SOCIETY. ANNUAL INTERNATIONAL CONFERENCE 2020; 2020:5436-5441. [PMID: 33019210 DOI: 10.1109/embc44109.2020.9176255] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/08/2022]
Abstract
Passive, continuous monitoring of Parkinson's Disease (PD) symptoms in the wild (i.e., in home environments) could improve disease management, thereby improving a patient's quality of life. We envision a system that uses machine learning to automatically detect PD symptoms from accelerometer data collected in the wild. Building such systems, however, is challenging because it is difficult to obtain labels of symptom occurrences in the wild. Many researchers therefore train machine learning algorithms on laboratory data with the assumption that findings will translate to the wild. This paper assesses how well laboratory data represents wild data by comparing PD symptom (tremor) detection performance of three models on both lab and wild data. Findings indicate that, for this application, laboratory data is not a good representation of wild data. Results also show that training on wild data, even though labels are less precise, leads to better performance on wild data than training on accurate labels from laboratory data.
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Otte K, Ellermeyer T, Vater TS, Voigt M, Kroneberg D, Rasche L, Krüger T, Röhling HM, Kayser B, Mansow-Model S, Klostermann F, Brandt AU, Paul F, Lipp A, Schmitz-Hübsch T. Instrumental Assessment of Stepping in Place Captures Clinically Relevant Motor Symptoms of Parkinson's Disease. SENSORS (BASEL, SWITZERLAND) 2020; 20:E5465. [PMID: 32977647 PMCID: PMC7582555 DOI: 10.3390/s20195465] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Revised: 09/10/2020] [Accepted: 09/21/2020] [Indexed: 12/13/2022]
Abstract
Fluctuations of motor symptoms make clinical assessment in Parkinson's disease a complex task. New technologies aim to quantify motor symptoms, and their remote application holds potential for a closer monitoring of treatment effects. The focus of this study was to explore the potential of a stepping in place task using RGB-Depth (RGBD) camera technology to assess motor symptoms of people with Parkinson's disease. In total, 25 persons performed a 40 s stepping in place task in front of a single RGBD camera (Kinect for Xbox One) in up to two different therapeutic states. Eight kinematic parameters were derived from knee movements to describe features of hypokinesia, asymmetry, and arrhythmicity of stepping. To explore their potential clinical utility, these parameters were analyzed for their Spearman's Rho rank correlation to clinical ratings, and for intraindividual changes between treatment conditions using standard response mean and paired t-test. Test performance not only differed between ON and OFF treatment conditions, but showed moderate correlations to clinical ratings, specifically ratings of postural instability (pull test). Furthermore, the test elicited freezing in some subjects. Results suggest that this single standardized motor task is a promising candidate to assess an array of relevant motor symptoms of Parkinson's disease. The simple technical test setup would allow future use by patients themselves.
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Affiliation(s)
- Karen Otte
- NeuroCure Clinical Research Center, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany; (T.K.); (H.M.R.); (A.U.B.); (F.P.)
- Motognosis GmbH, 10119 Berlin, Germany; (B.K.); (S.M.-M.)
| | - Tobias Ellermeyer
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany; (T.E.); (T.-S.V.); (M.V.); (D.K.); (F.K.); (A.L.)
- Department of Neurology, Vivantes Auguste-Viktoria-Klinikum, 12157 Berlin, Germany
| | - Tim-Sebastian Vater
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany; (T.E.); (T.-S.V.); (M.V.); (D.K.); (F.K.); (A.L.)
| | - Marlen Voigt
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany; (T.E.); (T.-S.V.); (M.V.); (D.K.); (F.K.); (A.L.)
| | - Daniel Kroneberg
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany; (T.E.); (T.-S.V.); (M.V.); (D.K.); (F.K.); (A.L.)
| | - Ludwig Rasche
- Experimental and Clinical Research Center, Charité-Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health and Max Delbrück Center for Molecular Medicine, 13125 Berlin, Germany;
- Department of Neurology, Park-Klinik Weißensee, 13086 Berlin, Germany
| | - Theresa Krüger
- NeuroCure Clinical Research Center, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany; (T.K.); (H.M.R.); (A.U.B.); (F.P.)
| | - Hanna Maria Röhling
- NeuroCure Clinical Research Center, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany; (T.K.); (H.M.R.); (A.U.B.); (F.P.)
- Motognosis GmbH, 10119 Berlin, Germany; (B.K.); (S.M.-M.)
| | - Bastian Kayser
- Motognosis GmbH, 10119 Berlin, Germany; (B.K.); (S.M.-M.)
| | | | - Fabian Klostermann
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany; (T.E.); (T.-S.V.); (M.V.); (D.K.); (F.K.); (A.L.)
| | - Alexander Ulrich Brandt
- NeuroCure Clinical Research Center, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany; (T.K.); (H.M.R.); (A.U.B.); (F.P.)
- Department of Neurology, University of California, Irvine, CA 92868, USA
| | - Friedemann Paul
- NeuroCure Clinical Research Center, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany; (T.K.); (H.M.R.); (A.U.B.); (F.P.)
- Experimental and Clinical Research Center, Charité-Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health and Max Delbrück Center for Molecular Medicine, 13125 Berlin, Germany;
- Einstein Center for Neuroscience, 10117 Berlin, Germany
| | - Axel Lipp
- Movement Disorders and Neuromodulation Unit, Department of Neurology, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany; (T.E.); (T.-S.V.); (M.V.); (D.K.); (F.K.); (A.L.)
- Department of Neurology, Park-Klinik Weißensee, 13086 Berlin, Germany
| | - Tanja Schmitz-Hübsch
- NeuroCure Clinical Research Center, Charité–Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health, 10117 Berlin, Germany; (T.K.); (H.M.R.); (A.U.B.); (F.P.)
- Experimental and Clinical Research Center, Charité-Universitätsmedizin Berlin Corporate Member of Freie Universität Berlin, Humboldt-Universität zu Berlin, and Berlin Institute of Health and Max Delbrück Center for Molecular Medicine, 13125 Berlin, Germany;
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Estimation of Parkinson's disease severity using speech features and extreme gradient boosting. Med Biol Eng Comput 2020; 58:2757-2773. [PMID: 32910301 DOI: 10.1007/s11517-020-02250-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/28/2020] [Accepted: 08/20/2020] [Indexed: 10/23/2022]
Abstract
In recent years, there is an increasing interest in building e-health systems. The systems built to deliver the health services with the use of internet and communication technologies aim to reduce the costs arising from outpatient visits of patients. Some of the related recent studies propose machine learning-based telediagnosis and telemonitoring systems for Parkinson's disease (PD). Motivated from the studies showing the potential of speech disorders in PD telemonitoring systems, in this study, we aim to estimate the severity of PD from voice recordings of the patients using motor Unified Parkinson's Disease Rating Scale (UPDRS) as the evaluation metric. For this purpose, we apply various speech processing algorithms to the voice signals of the patients and then use these features as input to a two-stage estimation model. The first step is to apply a wrapper-based feature selection algorithm, called Boruta, and select the most informative speech features. The second step is to feed the selected set of features to a decision tree-based boosting algorithm, extreme gradient boosting, which has been recently applied successfully in many machine learning tasks due to its generalization ability and speed. The feature selection analysis showed that the vibration pattern of the vocal fold is an important indicator of PD severity. Besides, we also investigate the effectiveness of using age and years passed since diagnosis as covariates together with speech features. The lowest mean absolute error with 3.87 was obtained by combining these covariates and speech features with prediction level fusion. Graphical Abstract Framework for the proposed UPDRS estimation model.
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Borzì L, Olmo G, Artusi CA, Fabbri M, Rizzone MG, Romagnolo A, Zibetti M, Lopiano L. A new index to assess turning quality and postural stability in patients with Parkinson's disease. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.102059] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/26/2022]
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Wolters AF, Heijmans M, Michielse S, Leentjens AFG, Postma AA, Jansen JFA, Ivanov D, Duits AA, Temel Y, Kuijf ML. The TRACK-PD study: protocol of a longitudinal ultra-high field imaging study in Parkinson's disease. BMC Neurol 2020; 20:292. [PMID: 32758176 PMCID: PMC7409458 DOI: 10.1186/s12883-020-01874-2] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/23/2020] [Accepted: 07/29/2020] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The diagnosis of Parkinson's Disease (PD) remains a challenge and is currently based on the assessment of clinical symptoms. PD is also a heterogeneous disease with great variability in symptoms, disease course, and response to therapy. There is a general need for a better understanding of this heterogeneity and the interlinked long-term changes in brain function and structure in PD. Over the past years there is increasing interest in the value of new paradigms in Magnetic Resonance Imaging (MRI) and the potential of ultra-high field strength imaging in the diagnostic work-up of PD. With this multimodal 7 T MRI study, our objectives are: 1) To identify distinctive MRI characteristics in PD patients and to create a diagnostic tool based on these differences. 2) To correlate MRI characteristics to clinical phenotype, genetics and progression of symptoms. 3) To detect future imaging biomarkers for disease progression that could be valuable for the evaluation of new therapies. METHODS The TRACK-PD study is a longitudinal observational study in a cohort of 130 recently diagnosed (≤ 3 years after diagnosis) PD patients and 60 age-matched healthy controls (HC). A 7 T MRI of the brain will be performed at baseline and repeated after 2 and 4 years. Complete assessment of motor, cognitive, neuropsychiatric and autonomic symptoms will be performed at baseline and follow-up visits with wearable sensors, validated questionnaires and rating scales. At baseline a blood DNA sample will also be collected. DISCUSSION This is the first longitudinal, observational, 7 T MRI study in PD patients. With this study, an important contribution can be made to the improvement of the current diagnostic process in PD. Moreover, this study will be able to provide valuable information related to the different clinical phenotypes of PD and their correlating MRI characteristics. The long-term aim of this study is to better understand PD and develop new biomarkers for disease progression which may help new therapy development. Eventually, this may lead to predictive models for individual PD patients and towards personalized medicine in the future. TRIAL REGISTRATION Dutch Trial Register, NL7558 . Registered March 11, 2019.
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Affiliation(s)
- A F Wolters
- Department of Neurology, Maastricht University Medical Centre, P.O. Box 5800, 6202 AZ, Maastricht, The Netherlands.
- School for Mental Health and Neuroscience, EURON, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands.
| | - M Heijmans
- School for Mental Health and Neuroscience, EURON, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands
| | - S Michielse
- School for Mental Health and Neuroscience, EURON, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands
| | - A F G Leentjens
- School for Mental Health and Neuroscience, EURON, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands
- Department of Psychiatry, Maastricht University Medical Centre, P.O. Box 5800, 6202 AZ, Maastricht, The Netherlands
| | - A A Postma
- School for Mental Health and Neuroscience, EURON, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, P.O. Box 5800, 6202 AZ, Maastricht, The Netherlands
| | - J F A Jansen
- School for Mental Health and Neuroscience, EURON, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands
- Department of Radiology and Nuclear Medicine, Maastricht University Medical Centre, P.O. Box 5800, 6202 AZ, Maastricht, The Netherlands
| | - D Ivanov
- Department of Cognitive Neuroscience, Faculty of Psychology and Neuroscience, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands
| | - A A Duits
- School for Mental Health and Neuroscience, EURON, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands
- Department of Medical Psychology, Maastricht University Medical Centre, P.O. Box 5800, 6202 AZ, Maastricht, The Netherlands
| | - Y Temel
- School for Mental Health and Neuroscience, EURON, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands
- Department of Neurosurgery, Maastricht University Medical Centre, P.O. Box 5800, 6202 AZ, Maastricht, The Netherlands
| | - M L Kuijf
- Department of Neurology, Maastricht University Medical Centre, P.O. Box 5800, 6202 AZ, Maastricht, The Netherlands
- School for Mental Health and Neuroscience, EURON, Maastricht University, P.O. Box 616, 6200 MD, Maastricht, The Netherlands
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Zajki-Zechmeister T, Kögl M, Kalsberger K, Franthal S, Homayoon N, Katschnig-Winter P, Wenzel K, Zajki-Zechmeister L, Schwingenschuh P. Quantification of tremor severity with a mobile tremor pen. Heliyon 2020; 6:e04702. [PMID: 32904326 PMCID: PMC7452531 DOI: 10.1016/j.heliyon.2020.e04702] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 06/26/2020] [Accepted: 08/11/2020] [Indexed: 11/23/2022] Open
Abstract
BACKGROUND An objective evaluation of tremor severity is necessary to document the course of disease, the efficacy of treatment, or interventions in clinical trials. Most available objective quantification devices are complex, immobile, or not validated. NEW METHOD We used the TREMITAS-System that comprises a pen-shaped sensor for tremor quantification. The Power of Main Peak and the Total Power were used as surrogate markers for tremor amplitude. Tremor severity was assessed by the TREMITAS-System and relevant subscores of the MDS-UPDRS and TETRAS rating scales in 14 patients with Parkinson's disease (PD) and 16 patients with Essential tremor (ET) off and on therapy. We compared tremor amplitudes assessed during wearable and hand-held constellations. RESULTS We found significant correlations between tremor amplitudes captured by TREM and tremor severity assessed by the MDS-UPDRS in PD (r = 0.638-0.779) and the TETRAS in ET (r = 0.597-0. 704) off and on therapy. The TREMITAS-System captured the L-Dopa-induced improvement of tremor in PD patients (p = 0.027). Tremor amplitudes did not differ between the handheld and wearable constellation (p > 0.05). COMPARISON WITH EXISTING METHODS We confirm the results of previous studies using inertial based sensors that tremor severity and drug-induced changes of tremor severity can be quantified using inertial based sensors. The assessment of tremor amplitudes was not influenced by using a handheld or wearable constellation. CONCLUSIONS The TREMITAS-System can be used to quantify rest tremor in PD and postural tremor in ET and is capable of detecting clinically relevant changes in tremor in clinical and research settings.
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Affiliation(s)
| | - Mariella Kögl
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz, 8036, Austria
| | - Kerstin Kalsberger
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz, 8036, Austria
| | - Sebastian Franthal
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz, 8036, Austria
| | - Nina Homayoon
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz, 8036, Austria
| | - Petra Katschnig-Winter
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz, 8036, Austria
| | - Karoline Wenzel
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz, 8036, Austria
| | | | - Petra Schwingenschuh
- Department of Neurology, Medical University of Graz, Auenbruggerplatz 22, Graz, 8036, Austria
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Monje MHG, Foffani G, Obeso J, Sánchez-Ferro Á. New Sensor and Wearable Technologies to Aid in the Diagnosis and Treatment Monitoring of Parkinson's Disease. Annu Rev Biomed Eng 2020; 21:111-143. [PMID: 31167102 DOI: 10.1146/annurev-bioeng-062117-121036] [Citation(s) in RCA: 48] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/09/2022]
Abstract
Parkinson's disease (PD) is a degenerative disorder of the brain characterized by the impairment of the nigrostriatal system. This impairment leads to specific motor manifestations (i.e., bradykinesia, tremor, and rigidity) that are assessed through clinical examination, scales, and patient-reported outcomes. New sensor-based and wearable technologies are progressively revolutionizing PD care by objectively measuring these manifestations and improving PD diagnosis and treatment monitoring. However, their use is still limited in clinical practice, perhaps because of the absence of external validation and standards for their continuous use at home. In the near future, these systems will progressively complement traditional tools and revolutionize the way we diagnose and monitor patients with PD.
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Affiliation(s)
- Mariana H G Monje
- HM CINAC, Hospital Universitario HM Puerta del Sur, Universidad CEU-San Pablo, 28938 Móstoles, Madrid, Spain; , , , .,Department of Anatomy, Histology and Neuroscience, School of Medicine, Universidad Autónoma de Madrid, 28029 Madrid, Spain
| | - Guglielmo Foffani
- HM CINAC, Hospital Universitario HM Puerta del Sur, Universidad CEU-San Pablo, 28938 Móstoles, Madrid, Spain; , , , .,Hospital Nacional de Parapléjicos, Servicio de Salud de Castilla La Mancha, 45071 Toledo, Spain
| | - José Obeso
- HM CINAC, Hospital Universitario HM Puerta del Sur, Universidad CEU-San Pablo, 28938 Móstoles, Madrid, Spain; , , , .,Centro de Investigación Biomédica en Red, Enfermedades Neurodegenerativas, 28031 Madrid, Spain
| | - Álvaro Sánchez-Ferro
- HM CINAC, Hospital Universitario HM Puerta del Sur, Universidad CEU-San Pablo, 28938 Móstoles, Madrid, Spain; , , , .,Centro de Investigación Biomédica en Red, Enfermedades Neurodegenerativas, 28031 Madrid, Spain.,Research Laboratory of Electronics, Massachusetts Institute of Technology, Cambridge, Massachusetts 02139, USA
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Vaz PG, Reis AL, Cardoso J. Supination/pronation movement quantification using stereoscopic vision based system towards Parkinson’s Disease assessment – A pilot study. Biomed Signal Process Control 2020. [DOI: 10.1016/j.bspc.2020.101976] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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di Biase L, Di Santo A, Caminiti ML, De Liso A, Shah SA, Ricci L, Di Lazzaro V. Gait Analysis in Parkinson's Disease: An Overview of the Most Accurate Markers for Diagnosis and Symptoms Monitoring. SENSORS (BASEL, SWITZERLAND) 2020; 20:E3529. [PMID: 32580330 PMCID: PMC7349580 DOI: 10.3390/s20123529] [Citation(s) in RCA: 72] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 06/10/2020] [Revised: 06/14/2020] [Accepted: 06/17/2020] [Indexed: 12/15/2022]
Abstract
The aim of this review is to summarize that most relevant technologies used to evaluate gait features and the associated algorithms that have shown promise to aid diagnosis and symptom monitoring in Parkinson's disease (PD) patients. We searched PubMed for studies published between 1 January 2005, and 30 August 2019 on gait analysis in PD. We selected studies that have either used technologies to distinguish PD patients from healthy subjects or stratified PD patients according to motor status or disease stages. Only those studies that reported at least 80% sensitivity and specificity were included. Gait analysis algorithms used for diagnosis showed a balanced accuracy range of 83.5-100%, sensitivity of 83.3-100% and specificity of 82-100%. For motor status discrimination the gait analysis algorithms showed a balanced accuracy range of 90.8-100%, sensitivity of 92.5-100% and specificity of 88-100%. Despite a large number of studies on the topic of objective gait analysis in PD, only a limited number of studies reported algorithms that were accurate enough deemed to be useful for diagnosis and symptoms monitoring. In addition, none of the reported algorithms and technologies has been validated in large scale, independent studies.
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Affiliation(s)
- Lazzaro di Biase
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo 21, 00128 Rome, Italy; (A.D.S.); (M.L.C.); (A.D.L.); (L.R.); (V.D.L.)
| | - Alessandro Di Santo
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo 21, 00128 Rome, Italy; (A.D.S.); (M.L.C.); (A.D.L.); (L.R.); (V.D.L.)
| | - Maria Letizia Caminiti
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo 21, 00128 Rome, Italy; (A.D.S.); (M.L.C.); (A.D.L.); (L.R.); (V.D.L.)
| | - Alfredo De Liso
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo 21, 00128 Rome, Italy; (A.D.S.); (M.L.C.); (A.D.L.); (L.R.); (V.D.L.)
| | - Syed Ahmar Shah
- Usher Institute, Edinburgh Medical School: Molecular, Genetic and Population Health Sciences, The University of Edinburgh, EH16 4UX Edinburgh, UK;
| | - Lorenzo Ricci
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo 21, 00128 Rome, Italy; (A.D.S.); (M.L.C.); (A.D.L.); (L.R.); (V.D.L.)
| | - Vincenzo Di Lazzaro
- Unit of Neurology, Neurophysiology, Neurobiology, Department of Medicine, Università Campus Bio-Medico di Roma, Via Álvaro del Portillo 21, 00128 Rome, Italy; (A.D.S.); (M.L.C.); (A.D.L.); (L.R.); (V.D.L.)
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Santiago A, Langston JW, Gandhy R, Dhall R, Brillman S, Rees L, Barlow C. Qualitative Evaluation of the Personal KinetiGraphTM Movement Recording System in a Parkinson's Clinic. JOURNAL OF PARKINSONS DISEASE 2020; 9:207-219. [PMID: 30412506 PMCID: PMC6398558 DOI: 10.3233/jpd-181373] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/25/2022]
Abstract
Background: Wearable sensors provide accurate, continuous objective measurements, quantifying the variable motor states of patients with Parkinson’s disease (PD) in real time. Objectives: To evaluate the impact of using continuous objective measurement using the Personal KinetiGraph™ (PKG®) Movement Recording System in the routine clinical care of patients with PD (PwP). Methods: Physicians employed the use of the PKG in patients for whom they were seeking objective measurement. Patients wore a PKG data logger for ≥6 days during routine daily living activities. During the survey period of December 2015 through July 2016, physician surveys were completed by four Movement Disorder Specialists for whom measurements from the PKG were available during a subsequent routine clinic visit. Results: Of 112 completed physician surveys, 46 (41%) indicated the PKG provided relevant additional information sufficient to consider adjusting their therapeutic management plan; 66 (59%) indicated the PKG provided no further information to support a therapeutic decision differing from that made during a routine clinical evaluation. Upon further review of these 46 surveys, 36 surveys (78%) revealed the information provided by the PKG ultimately resulted in adjusting the patient’s medical management. Conclusions: The PKG provided novel additional information beyond that captured during a routine clinic visit sufficient to change the medical management of PwP. Physicians adjusted treatment nearly a third of the time based on data provided by real-time, remote monitoring outside the clinic setting. The use of the PKG may provide for better informed therapeutic decisions, improving the quality of life for PwP.
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Affiliation(s)
- Anthony Santiago
- Formerly of Parkinson's Institute and Clinical Center, Sunnyvale, CA, USA
| | - James W Langston
- Formerly of Parkinson's Institute and Clinical Center, Sunnyvale, CA, USA
| | - Rita Gandhy
- Formerly of Parkinson's Institute and Clinical Center, Sunnyvale, CA, USA
| | - Rohit Dhall
- Formerly of Parkinson's Institute and Clinical Center, Sunnyvale, CA, USA.,Department of Neurology, University of Arkansas, Little Rock, AR, USA
| | - Salima Brillman
- Formerly of Parkinson's Institute and Clinical Center, Sunnyvale, CA, USA
| | - Linda Rees
- Formerly of Parkinson's Institute and Clinical Center, Sunnyvale, CA, USA
| | - Carrolee Barlow
- Parkinson's Institute and Clinical Center, Sunnyvale, CA, USA
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Merchant KM, Cedarbaum JM, Brundin P, Dave KD, Eberling J, Espay AJ, Hutten SJ, Javidnia M, Luthman J, Maetzler W, Menalled L, Reimer AN, Stoessl AJ, Weiner DM. A Proposed Roadmap for Parkinson's Disease Proof of Concept Clinical Trials Investigating Compounds Targeting Alpha-Synuclein. JOURNAL OF PARKINSONS DISEASE 2020; 9:31-61. [PMID: 30400107 PMCID: PMC6398545 DOI: 10.3233/jpd-181471] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 12/26/2022]
Abstract
The convergence of human molecular genetics and Lewy pathology of Parkinson's disease (PD) have led to a robust, clinical-stage pipeline of alpha-synuclein (α-syn)-targeted therapies that have the potential to slow or stop the progression of PD and other synucleinopathies. To facilitate the development of these and earlier stage investigational molecules, the Michael J. Fox Foundation for Parkinson's Research convened a group of leaders in the field of PD research from academia and industry, the Alpha-Synuclein Clinical Path Working Group. This group set out to develop recommendations on preclinical and clinical research that can de-risk the development of α-syn targeting therapies. This consensus white paper provides a translational framework, from the selection of animal models and associated end-points to decision-driving biomarkers as well as considerations for the design of clinical proof-of-concept studies. It also identifies current gaps in our biomarker toolkit and the status of the discovery and validation of α-syn-associated biomarkers that could help fill these gaps. Further, it highlights the importance of the emerging digital technology to supplement the capture and monitoring of clinical outcomes. Although the development of disease-modifying therapies targeting α-syn face profound challenges, we remain optimistic that meaningful strides will be made soon toward the identification and approval of disease-modifying therapeutics targeting α-syn.
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Affiliation(s)
- Kalpana M Merchant
- Vincere Biosciences, Inc., and Department of Neurology, Northwestern University Feinberg School of Medicine, Chicago, IL, USA
| | | | - Patrik Brundin
- Van Andel Research Institute, Center for Neurodegenerative Science, Grand Rapids, MI, USA
| | - Kuldip D Dave
- The Michael J. Fox Foundation for Parkinson's Research, New York, NY, USA
| | - Jamie Eberling
- The Michael J. Fox Foundation for Parkinson's Research, New York, NY, USA
| | - Alberto J Espay
- UC Gardner Center for Parkinson's Disease and Movement Disorders, University of Cincinnati, Cincinnati, OH, USA
| | - Samantha J Hutten
- The Michael J. Fox Foundation for Parkinson's Research, New York, NY, USA
| | - Monica Javidnia
- Center for Health and Technology, University of Rochester Medical Center, Rochester, New York, USA
| | | | - Walter Maetzler
- Department of Neurology, University Hospital Schleswig-Holstein, Campus Kiel, Germany
| | - Liliana Menalled
- The Michael J. Fox Foundation for Parkinson's Research, New York, NY, USA
| | - Alyssa N Reimer
- The Michael J. Fox Foundation for Parkinson's Research, New York, NY, USA
| | - A Jon Stoessl
- Djavad Mowafaghian Centre for Brain Health, Pacific Parkinson's Research Center, University of British Columbia, Vancouver, BC, Canada
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43
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Habets J, Heijmans M, Herff C, Simons C, Leentjens AF, Temel Y, Kuijf M, Kubben P. Mobile Health Daily Life Monitoring for Parkinson Disease: Development and Validation of Ecological Momentary Assessments. JMIR Mhealth Uhealth 2020; 8:e15628. [PMID: 32339999 PMCID: PMC7248801 DOI: 10.2196/15628] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/26/2019] [Revised: 11/25/2019] [Accepted: 12/15/2019] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND Parkinson disease monitoring is currently transitioning from periodic clinical assessments to continuous daily life monitoring in free-living conditions. Traditional Parkinson disease monitoring methods lack intraday fluctuation detection. Electronic diaries (eDiaries) hold the potential to collect subjective experiences on the severity and burden of motor and nonmotor symptoms in free-living conditions. OBJECTIVE This study aimed to develop a Parkinson disease-specific eDiary based on ecological momentary assessments (EMAs) and to explore its validation. METHODS An observational cohort of 20 patients with Parkinson disease used the smartphone-based EMA eDiary for 14 consecutive days without adjusting free-living routines. The eDiary app presented an identical questionnaire consisting of questions regarding affect, context, motor and nonmotor symptoms, and motor performance 7 times daily at semirandomized moments. In addition, patients were asked to complete a morning and an evening questionnaire. RESULTS Mean affect correlated moderate-to-strong and moderate with motor performance (R=0.38 to 0.75; P<.001) and motor symptom (R=0.34 to 0.50; P<.001) items, respectively. The motor performance showed a weak-to-moderate negative correlation with motor symptoms (R=-0.31 to -0.48; P<.001). Mean group answers given for on-medication conditions vs wearing-off-medication conditions differed significantly (P<.05); however, not enough questionnaires were completed for the wearing-off-medication condition to reproduce these findings on individual levels. CONCLUSIONS We presented a Parkinson disease-specific EMA eDiary. Correlations between given answers support the internal validity of the eDiary and underline EMA's potential in free-living Parkinson disease monitoring. Careful patient selection and EMA design adjustment to this targeted population and their fluctuations are necessary to generate robust proof of EMA validation in future work. Combining clinical Parkinson disease knowledge with practical EMA experience is inevitable to design and perform studies, which will lead to the successful integration of eDiaries in free-living Parkinson disease monitoring.
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Affiliation(s)
- Jeroen Habets
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Margot Heijmans
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Christian Herff
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Claudia Simons
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands.,GGzE, Institute for Mental Health Care Eindhoven, Eindhoven, Netherlands
| | - Albert Fg Leentjens
- Department of Psychiatry and Neuropsychology, School for Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Yasin Temel
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands
| | - Mark Kuijf
- Department of Neurology, Maastricht University Medical Center, Maastricht, Netherlands
| | - Pieter Kubben
- Department of Neurosurgery, School of Mental Health and Neuroscience, Maastricht University, Maastricht, Netherlands.,Department of Neurosurgery, Radboud University Medical Center, Nijmegen, Netherlands
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44
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Raiano L, di Pino G, di Biase L, Tombini M, Tagliamonte NL, Formica D. PDMeter: A Wrist Wearable Device for an at-Home Assessment of the Parkinson's Disease Rigidity. IEEE Trans Neural Syst Rehabil Eng 2020; 28:1325-1333. [PMID: 32286997 DOI: 10.1109/tnsre.2020.2987020] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
This work focuses on the design and the validation of a wearable mechatronic device for an at-home assessment of wrist stiffness in patients affected by Parkinson's Disease (PD). The device includes one actuated joint and four passive revolute joints with a high overall intrinsic backdriveability. In order to allow the user to freely move the wrist during activities of daily living, we implemented a transparent controller on the basis of the interaction force sensed by the embedded load cell. Conversely, in order to provide perturbations for estimating the wrist flexion-extension rigidity, we implemented a torque controller. Firstly, we report a pilot study that aimed at characterizing the device in terms of range of motion (ROM) allowed, transparency perceived and torque-tracking capability. Then, we present a case study in which we tested our device with seven PD patients in both drug-OFF and drug-ON conditions and we compared the measured stiffness with the one measured in fourteen healthy controls and with the outcome of the most used clinical scale (MDS-UPDRS). The device allowed to successfully estimate the stiffness as different depending on the movement direction. Indeed, extension stiffness was higher than the flexion one, accordingly to the literature. Moreover, the device allowed to discriminate both Healthy subjects from PD subjects, and PD subjects in OFF condition from PD subjects in ON condition. In conclusion, we demonstrate the feasibility of the device in measuring wrist rigidity, thus enabling the possibility to implement an at-home assessment of the PD rigidity.
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45
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Sigcha L, Costa N, Pavón I, Costa S, Arezes P, López JM, De Arcas G. Deep Learning Approaches for Detecting Freezing of Gait in Parkinson's Disease Patients through On-Body Acceleration Sensors. SENSORS (BASEL, SWITZERLAND) 2020; 20:E1895. [PMID: 32235373 PMCID: PMC7181252 DOI: 10.3390/s20071895] [Citation(s) in RCA: 33] [Impact Index Per Article: 8.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 02/28/2020] [Revised: 03/21/2020] [Accepted: 03/25/2020] [Indexed: 12/19/2022]
Abstract
Freezing of gait (FOG) is one of the most incapacitating motor symptoms in Parkinson's disease (PD). The occurrence of FOG reduces the patients' quality of live and leads to falls. FOG assessment has usually been made through questionnaires, however, this method can be subjective and could not provide an accurate representation of the severity of this symptom. The use of sensor-based systems can provide accurate and objective information to track the symptoms' evolution to optimize PD management and treatments. Several authors have proposed specific methods based on wearables and the analysis of inertial signals to detect FOG in laboratory conditions, however, its performance is usually lower when being used at patients' homes. This study presents a new approach based on a recurrent neural network (RNN) and a single waist-worn triaxial accelerometer to enhance the FOG detection performance to be used in real home-environments. Also, several machine and deep learning approaches for FOG detection are evaluated using a leave-one-subject-out (LOSO) cross-validation. Results show that modeling spectral information of adjacent windows through an RNN can bring a significant improvement in the performance of FOG detection without increasing the length of the analysis window (required to using it as a cue-system).
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Affiliation(s)
- Luis Sigcha
- Grupo de Investigación en Instrumentación y Acústica Aplicada (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7., 28031 Madrid, Spain; (L.S.); (J.M.L.); (G.D.A.)
- ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal; (N.C.); (S.C.); (P.A.)
| | - Nélson Costa
- ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal; (N.C.); (S.C.); (P.A.)
| | - Ignacio Pavón
- Grupo de Investigación en Instrumentación y Acústica Aplicada (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7., 28031 Madrid, Spain; (L.S.); (J.M.L.); (G.D.A.)
| | - Susana Costa
- ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal; (N.C.); (S.C.); (P.A.)
| | - Pedro Arezes
- ALGORITMI Research Center, School of Engineering, University of Minho, 4800-058 Guimaraes, Portugal; (N.C.); (S.C.); (P.A.)
| | - Juan Manuel López
- Grupo de Investigación en Instrumentación y Acústica Aplicada (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7., 28031 Madrid, Spain; (L.S.); (J.M.L.); (G.D.A.)
| | - Guillermo De Arcas
- Grupo de Investigación en Instrumentación y Acústica Aplicada (I2A2), ETSI Industriales, Universidad Politécnica de Madrid, Campus Sur UPM, Ctra. Valencia, Km 7., 28031 Madrid, Spain; (L.S.); (J.M.L.); (G.D.A.)
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46
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Vienne-Jumeau A, Quijoux F, Vidal PP, Ricard D. Wearable inertial sensors provide reliable biomarkers of disease severity in multiple sclerosis: A systematic review and meta-analysis. Ann Phys Rehabil Med 2020; 63:138-147. [DOI: 10.1016/j.rehab.2019.07.004] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2019] [Revised: 05/17/2019] [Accepted: 07/05/2019] [Indexed: 01/05/2023]
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Ferreira-Sánchez MDR, Moreno-Verdú M, Cano-de-la-Cuerda R. Quantitative Measurement of Rigidity in Parkinson´s Disease: A Systematic Review. SENSORS (BASEL, SWITZERLAND) 2020; 20:E880. [PMID: 32041374 PMCID: PMC7038663 DOI: 10.3390/s20030880] [Citation(s) in RCA: 13] [Impact Index Per Article: 3.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/11/2020] [Revised: 01/27/2020] [Accepted: 02/04/2020] [Indexed: 12/16/2022]
Abstract
Rigidity is one of the cardinal symptoms of Parkinson´s disease (PD). Present in up 89% of cases, it is typically assessed with clinical scales. However, these instruments show limitations due to their subjectivity and poor intra- and inter-rater reliability. To compile all of the objective quantitative methods used to assess rigidity in PD and to study their validity and reliability, a systematic review was conducted using the Web of Science, PubMed, and Scopus databases. Studies from January 1975 to June 2019 were included, all of which were written in English. The Strengthening the Reporting of observational studies in Epidemiology Statement (STROBE) checklist for observational studies was used to assess the methodological rigor of the included studies. Thirty-six studies were included. Rigidity was quantitatively assessed in three ways, using servomotors, inertial sensors, and biomechanical and neurophysiological study of muscles. All methods showed good validity and reliability, good correlation with clinical scales, and were useful for detecting rigidity and studying its evolution. People with PD exhibit higher values in terms of objective muscle stiffness than healthy controls. Rigidity depends on the angular velocity and articular amplitude of the mobilization applied. There are objective, valid, and reliable methods that can be used to quantitatively assess rigidity in people with PD.
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Affiliation(s)
| | - Marcos Moreno-Verdú
- Department of Radiology, Rehabilitation and Physiotherapy, Universidad Complutense de Madrid (UCM), 28040 Madrid, Spain;
- Asociación Parkinson Madrid, 28014 Madrid, Spain
| | - Roberto Cano-de-la-Cuerda
- Department of Physical Therapy, Occupational Therapy, Physical Medicine and Rehabilitation, Universidad Rey Juan Carlos (URJC), Alcorcón, 28922 Madrid, Spain;
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48
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Shin KJ, Park J, Ha S, Park KM, Kim SE, Lee BI, Lee DA, Kim HT, Yoon JY. Decreased foot height may be a subclinical shuffling gait in early stage of Parkinson's disease: A study of three-dimensional motion analysis. Gait Posture 2020; 76:64-67. [PMID: 31733627 DOI: 10.1016/j.gaitpost.2019.11.005] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/26/2019] [Revised: 09/25/2019] [Accepted: 11/06/2019] [Indexed: 02/02/2023]
Affiliation(s)
- Kyong Jin Shin
- Department of Neurology, Haeundae Paik Hospital, Inje University, Busan, Republic of Korea
| | - Jinse Park
- Department of Neurology, Haeundae Paik Hospital, Inje University, Busan, Republic of Korea.
| | - Samyeol Ha
- Department of Neurology, Haeundae Paik Hospital, Inje University, Busan, Republic of Korea
| | - Kang Min Park
- Department of Neurology, Haeundae Paik Hospital, Inje University, Busan, Republic of Korea
| | - Sung Eun Kim
- Department of Neurology, Haeundae Paik Hospital, Inje University, Busan, Republic of Korea
| | - Byung In Lee
- Department of Neurology, Haeundae Paik Hospital, Inje University, Busan, Republic of Korea
| | - Dong Ah Lee
- Department of Neurology, Haeundae Paik Hospital, Inje University, Busan, Republic of Korea
| | - Hee-Tae Kim
- Department of Neurology, Hanyang University College of Medicine, Seoul, Republic of Korea
| | - Ji-Yeon Yoon
- Department of Motion Analysis Laboratory, Haeundae Paik Hospital, Inje University, Busan, Republic of Korea
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Morgan C, Rolinski M, McNaney R, Jones B, Rochester L, Maetzler W, Craddock I, Whone AL. Systematic Review Looking at the Use of Technology to Measure Free-Living Symptom and Activity Outcomes in Parkinson's Disease in the Home or a Home-like Environment. JOURNAL OF PARKINSON'S DISEASE 2020; 10:429-454. [PMID: 32250314 PMCID: PMC7242826 DOI: 10.3233/jpd-191781] [Citation(s) in RCA: 29] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Accepted: 01/31/2020] [Indexed: 12/12/2022]
Abstract
BACKGROUND The emergence of new technologies measuring outcomes in Parkinson's disease (PD) to complement the existing clinical rating scales has introduced the possibility of measurement occurring in patients' own homes whilst they freely live and carry out normal day-to-day activities. OBJECTIVE This systematic review seeks to provide an overview of what technology is being used to test which outcomes in PD from free-living participant activity in the setting of the home environment. Additionally, this review seeks to form an impression of the nature of validation and clinimetric testing carried out on the technological device(s) being used. METHODS Five databases (Medline, Embase, PsycInfo, Cochrane and Web of Science) were systematically searched for papers dating from 2000. Study eligibility criteria included: adults with a PD diagnosis; the use of technology; the setting of a home or home-like environment; outcomes measuring any motor and non-motor aspect relevant to PD, as well as activities of daily living; unrestricted/unscripted activities undertaken by participants. RESULTS 65 studies were selected for data extraction. There were wide varieties of participant sample sizes (<10 up to hundreds) and study durations (<2 weeks up to a year). The metrics evaluated by technology, largely using inertial measurement units in wearable devices, included gait, tremor, physical activity, bradykinesia, dyskinesia and motor fluctuations, posture, falls, typing, sleep and activities of daily living. CONCLUSIONS Home-based free-living testing in PD is being conducted by multiple groups with diverse approaches, focussing mainly on motor symptoms and sleep.
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Affiliation(s)
- Catherine Morgan
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, Faculty of Engineering, University of Bristol, Bristol, UK
- Movement Disorders Group, Bristol Brain Centre, Southmead Hospital, North Bristol National Health Service Trust, Bristol, UK
| | - Michal Rolinski
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Movement Disorders Group, Bristol Brain Centre, Southmead Hospital, North Bristol National Health Service Trust, Bristol, UK
| | - Roisin McNaney
- School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, Faculty of Engineering, University of Bristol, Bristol, UK
| | - Bennet Jones
- Library and Knowledge Service, Learning and Research, Southmead Hospital, North Bristol National Health Service Trust, Bristol, UK
| | - Lynn Rochester
- Institute of Neuroscience, Newcastle University, Newcastle Upon Tyne, UK
- Newcastle Upon Tyne Hospitals National Health Service Foundation Trust, Newcastle Upon Tyne, UK
| | - Walter Maetzler
- Department of Neurology, Christian-Albrechts University, Kiel, Germany
| | - Ian Craddock
- School of Computer Science, Electrical and Electronic Engineering, and Engineering Mathematics, Faculty of Engineering, University of Bristol, Bristol, UK
| | - Alan L. Whone
- Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
- Movement Disorders Group, Bristol Brain Centre, Southmead Hospital, North Bristol National Health Service Trust, Bristol, UK
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50
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Gajos KZ, Reinecke K, Donovan M, Stephen CD, Hung AY, Schmahmann JD, Gupta AS. Computer mouse use captures ataxia and parkinsonism, enabling accurate measurement and detection. Mov Disord 2019; 35:354-358. [PMID: 31769069 PMCID: PMC7028247 DOI: 10.1002/mds.27915] [Citation(s) in RCA: 23] [Impact Index Per Article: 4.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/08/2019] [Revised: 10/14/2019] [Accepted: 10/15/2019] [Indexed: 12/25/2022] Open
Abstract
Background Objective assessments of movement impairment are needed to support clinical trials and facilitate diagnosis. The objective of the current study was to determine if a rapid web‐based computer mouse test (Hevelius) could detect and accurately measure ataxia and parkinsonism. Methods Ninety‐five ataxia, 46 parkinsonism, and 29 control participants and 229,017 online participants completed Hevelius. We trained machine‐learning models on age‐normalized Hevelius features to (1) measure severity and disease progression and (2) distinguish phenotypes from controls and from each other. Results Regression model estimates correlated strongly with clinical scores (from r = 0.66 for UPDRS dominant arm total to r = 0.83 for the Brief Ataxia Rating Scale). A disease change model identified ataxia progression with high sensitivity. Classification models distinguished ataxia or parkinsonism from healthy controls with high sensitivity (≥0.91) and specificity (≥0.90). Conclusions Hevelius produces a granular and accurate motor assessment in a few minutes of mouse use and may be useful as an outcome measure and screening tool. © 2019 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.
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Affiliation(s)
- Krzysztof Z Gajos
- Harvard John A. Paulson School of Engineering and Applied Sciences, Cambridge, Massachusetts, USA
| | - Katharina Reinecke
- Paul G. Allen School of Computer Science and Engineering, University of Washington, Seattle, Seattle, Washington, USA
| | - Mary Donovan
- Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Christopher D Stephen
- Ataxia Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Masssachusetts, USA.,Movement Disorders Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Albert Y Hung
- Movement Disorders Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
| | - Jeremy D Schmahmann
- Ataxia Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Masssachusetts, USA
| | - Anoopum S Gupta
- Ataxia Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Masssachusetts, USA.,Movement Disorders Unit, Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts, USA
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